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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 bg.nextTensorId++}nextVariableId(){return bg.nextVariableId++}clone(e){let t=B.runKernel(mi,{x:e}),r={x:e},n=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return B.runKernel(ei,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,w0(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 '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=sg(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(sg(e)){let{kernelName:c,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=w0(c,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(n){let b=this.getTensorsForGradient(c,m,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,m=f=>{!n||(r=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:d}=e,h=sg(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=Ag(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 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r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*yg(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 Vp||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*yg(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 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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=s$(this.state.activeTape,t,a);if(!n&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[a.id]=r==null?y$(a.shape):r,i$(i,s,l=>this.tidy(l),A$);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(Fs(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(Fs(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 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t=!1;this.accumulatedGrads=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Yf.className="Adagrad";ji(Yf);var Jf=class extends ns{constructor(e,t,r,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],X(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(r).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);X(()=>{let r=ce(1,this.accBeta1),n=ce(1,this.accBeta2);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)}};Qf.className="Adamax";ji(Qf);var Nh=class extends ns{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];X(()=>{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=gr(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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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=Tt(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 Tt(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=Tt(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=uB(e),i=this.computeOutputShape(s),o,l=dB(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,Tt(e),t,this.name,d)):o=new pa(l,i,this,Tt(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 Ga(`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 Ga(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new da(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return $0(this.weights)}build(e){this.built=!0}getWeights(e=!1){return zg(e?this.trainableWeights:this.weights)}setWeights(e){X(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=ko(e.metrics[s])}this.compile({loss:n,metrics:a,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Cr.getSaveHandlers(e);if(i.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new q(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Cr.encodeWeights(this.getNamedWeights(t)),n=!1,a=null,s={modelTopology:this.toJSON(a,n),format:TU,generatedBy:`TensorFlow.js tfjs-layers v${jA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Cr.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Cr.concatenateArrayBuffers([r.data,o])}return 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};QA.className="ThresholdedReLU";ue.registerClass(QA);var ex=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new qA().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}};ex.className="Softmax";ue.registerClass(ex);function vu(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(!XL(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+Us([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function tx(e,t){return X(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function q6(e,t){return X(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function DU(e,t,r,n=1,a="valid",s,i=1){return X(()=>{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=jy(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=va(o,r)),o})}function k4(e,t,r,n=[1,1],a="valid",s,i,o=null){return X(()=>{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=tx(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Vs.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 LU(e,t,r,n=[1,1,1],a="valid",s,i){return X(()=>{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 ${e.rank}.`);let o=q6(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Xy(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=va(o,r)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var rx=class extends st{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",rx.verifyArgs(t),this.rank=e,yr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=vu(t.kernelSize,e,"kernelSize"),this.strides=vu(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=js(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=$t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ur(t.biasConstraint),this.biasRegularizer=Mt(t.biasRegularizer),this.activityRegularizer=Mt(t.activityRegularizer),this.dilationRate=vu(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(Na("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!SA(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 ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Gs(this.activation),useBias:this.useBias,biasInitializer:zt(this.biasInitializer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),biasConstraint:lr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},_h=class extends rx{constructor(e,t){super(e,t),this.kernel=null,_h.verifyArgs(t),this.filters=t.filters,yr(this.filters,"filters"),this.kernelInitializer=$t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ur(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 X(()=>{e=je(e);let r,n=this.bias==null?null:this.bias.read(),a=Qw(this.activation.getClassName());if(a!=null&&this.rank===2)r=k4(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=DU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=k4(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=LU(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:lr(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)}`)}},X6=class extends _h{constructor(e){super(2,e),X6.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!SA(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)}.`)}},gm=X6;gm.className="Conv2D";ue.registerClass(gm);var K6=class extends _h{constructor(e){super(3,e),K6.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)}.`)}},ym=K6;ym.className="Conv3D";ue.registerClass(ym);var nx=class extends gm{constructor(e){if(super(e),this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return X(()=>{let r=je(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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),m=Ea(l,p,d,this.padding),f=[a,c,m,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,1]));let g=qy(r,this.kernel.read(),f,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}};nx.className="Conv2DTranspose";ue.registerClass(nx);var ax=class extends ym{constructor(e){if(super(e),this.inputSpec=[new Zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return X(()=>{let r=je(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=Ea(l,m,h,this.padding),A=Ea(u,f,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 w=ew(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=tt(w,[0,4,1,2,3])),this.bias!==null&&(w=va(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}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}};ax.className="Conv3DTranspose";ue.registerClass(ax);var Z6=class extends _h{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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Mt(t.depthwiseRegularizer),this.depthwiseConstraint=ur(t.depthwiseConstraint),this.pointwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Mt(t.pointwiseRegularizer),this.pointwiseConstraint=ur(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${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 Zt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return X(()=>{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=Iw(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=lr(this.depthwiseConstraint),e.pointwiseConstraint=lr(this.pointwiseConstraint),e}};Z6.className="SeparableConv";var sx=class extends Z6{constructor(e){super(2,e)}};sx.className="SeparableConv2D";ue.registerClass(sx);var Y6=class extends _h{constructor(e){super(1,e),Y6.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"&&!SA(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)}.`)}},ix=Y6;ix.className="Conv1D";ue.registerClass(ix);var ox=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 X(()=>{if(e=je(e),this.dataFormat==="channelsLast"){let r=Yc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Yc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=Yc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Yc(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}};ox.className="Cropping2D";ue.registerClass(ox);var lx=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,jL(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 X(()=>{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,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};lx.className="UpSampling2D";ue.registerClass(lx);function BU(e,t,r=[1,1],n="valid",a,s){return X(()=>{a==null&&(a=Aa()),jt(a);let i=tx(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 ${t.rank}-D`);return i=kh(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var ux=class extends rx{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=$t(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ur(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 X(()=>{e=je(e);let r=BU(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=lr(this.depthwiseRegularizer),e}};ux.className="DepthwiseConv2D";ue.registerClass(ux);function J6(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 Q6(e,t,r,n=!1,a,s,i=!1,o=!1){return X(()=>{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],m=nn(t),f;a!=null&&(f=nn(a));for(let y=0;y<c;++y){let A=m[y],x=X(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=X(()=>{let w=f[y],I=ce(Pn(w),w),N=le(L(x[0],w),L(p[0],I)),E=p.map((R,_)=>le(L(x[1][_],w),L(R,I)));return{output:N,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=dr(d,1)),[h,g,p]})}var ek=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 bm({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 Zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ya(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){_g(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 X(()=>{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.");_g(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Zt({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(!v.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 Zt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){X(()=>{if(!this.stateful)throw new Ga("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)te(this.states_),this.keptStates!=null&&(te(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()):te(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(!v.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=>gr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=J6(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 Zt({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 X(()=>{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 ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=Q6((p,c)=>{let m=this.cell.call([p].concat(c),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return X(()=>{let t=_t(e.shape);return t=ke(t,[1,2]),t=Rh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?Fg(t,[1,r]):t):this.cell.stateSize>1?[Fg(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===ek.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=fa(n,r);return new e(Object.assign(t,{cell:a}))}},as=ek;as.className="RNN";ue.registerClass(as);var zh=class extends st{},Am=class extends zh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,yr(this.units,"units"),this.activation=js(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=ur(e.kernelConstraint),this.recurrentConstraint=ur(e.recurrentConstraint),this.biasConstraint=ur(e.biasConstraint),this.dropout=Ru([1,Us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ru([1,Us([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 X(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Hs({ones:()=>Pn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Hs({ones:()=>Pn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=$a(L(e,s),this.kernel.read()):a=$a(e,this.kernel.read()),this.bias!=null&&(a=va(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,$a(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:Gs(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:lr(this.kernelConstraint),recurrentConstraint:lr(this.recurrentConstraint),biasConstraint:lr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Am.className="SimpleRNNCell";ue.registerClass(Am);var dx=class extends as{constructor(e){e.cell=new Am(e),super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let 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)}};dx.className="SimpleRNN";ue.registerClass(dx);var xm=class extends zh{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,yr(this.units,"units"),this.activation=js(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=js(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=ur(e.kernelConstraint),this.recurrentConstraint=ur(e.recurrentConstraint),this.biasConstraint=ur(e.biasConstraint),this.dropout=Ru([1,Us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ru([1,Us([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 X(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Hs({ones:()=>Pn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Hs({ones:()=>Pn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=$a(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]=Yt(d,[2*this.units,this.units],d.rank-1),c=$a(n,h),[m,f,g]=Yt(u,3,u.rank-1),[y,A]=Yt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(m,y)),o=this.recurrentActivation.apply(le(f,A));let x=$a(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:Gs(this.activation),recurrentActivation:Gs(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:lr(this.kernelConstraint),recurrentConstraint:lr(this.recurrentConstraint),biasConstraint:lr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};xm.className="GRUCell";ue.registerClass(xm);var px=class extends as{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 xm(e),super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let 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)}};px.className="GRU";ue.registerClass(px);var Oh=class extends zh{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,yr(this.units,"units"),this.activation=js(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=js(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(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=ur(e.kernelConstraint),this.recurrentConstraint=ur(e.recurrentConstraint),this.biasConstraint=ur(e.biasConstraint),this.dropout=Ru([1,Us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ru([1,Us([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 sm().apply([s]),d=a.apply([s*2]);return t4(t4(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 X(()=>{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 ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Hs({ones:()=>Pn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Hs({ones:()=>Pn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=$a(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,$a(n,this.recurrentKernel.read())),this.useBias&&(h=va(h,this.bias.read()));let[p,c,m,f]=Yt(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(m))),d=this.recurrentActivation.apply(f);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Gs(this.activation),recurrentActivation:Gs(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:lr(this.kernelConstraint),recurrentConstraint:lr(this.recurrentConstraint),biasConstraint:lr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Oh.className="LSTMCell";ue.registerClass(Oh);var hx=class extends as{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 Oh(e),super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let 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)}};hx.className="LSTM";ue.registerClass(hx);var bm=class extends zh{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 X(()=>{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){_g(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 zg(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]])}PA(t)}};bm.className="StackedRNNCells";ue.registerClass(bm);function Hs(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):i6(t(),r),o=()=>Mh(i,t,n);return!a||a<=1?gr(o().clone()):Array(a).fill(void 0).map(o).map(l=>gr(l.clone()))}var tk=class extends as{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 Zt({ndim:5})]}call(e,t){return X(()=>{if(this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let 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 X(()=>{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){X(()=>{if(!this.stateful)throw new Ga("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)te(this.states_),this.keptStates!=null&&(te(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()):te(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.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=>gr(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]]}};tk.className="ConvRNN2D";var vm=class extends Oh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t}),this.filters=t,yr(this.filters,"filters"),this.kernelSize=vu(r,2,"kernelSize"),this.kernelSize.forEach(o=>yr(o,"kernelSize")),this.strides=vu(n||1,2,"strides"),this.strides.forEach(o=>yr(o,"strides")),this.padding=a||"valid",Ln(this.padding),this.dataFormat=s||"channelsLast",jt(this.dataFormat),this.dilationRate=vu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>yr(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=hn([u]),m=l.apply([u*2]);return CA([p,c,m])}},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 X(()=>{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=Hs({ones:()=>Pn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,re,Y)=>!re||!re[Y]?V:L(re[Y],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=Hs({ones:()=>Pn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,m=l(a,c,0),f=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,w,I]=Yt(this.kernel.read(),i,A),[N,E,R,_]=this.useBias?Yt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,N,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,w,R,this.padding),p=this.inputConv(p,I,_,this.padding);let[M,S,z,O]=Yt(this.recurrentKernel.read(),i,A);m=this.recurrentConv(m,M),f=this.recurrentConv(f,S),g=this.recurrentConv(g,z),y=this.recurrentConv(y,O);let H=this.recurrentActivation.apply(le(u,m)),K=this.recurrentActivation.apply(le(d,f)),D=le(L(K,s),L(H,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=Ls(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 Ls(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};vm.className="ConvLSTM2DCell";ue.registerClass(vm);var cx=class extends tk{constructor(e){let t=new vm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};cx.className="ConvLSTM2D";ue.registerClass(cx);var wm=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 X(()=>{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 Mh(()=>i6(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()}};wm.className="Dropout";ue.registerClass(wm);var fx=class extends wm{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};fx.className="SpatialDropout1D";ue.registerClass(fx);var mx=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,yr(this.units,"units"),this.activation=js(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ur(e.kernelConstraint),this.biasConstraint=ur(e.biasConstraint),this.kernelRegularizer=Mt(e.kernelRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.activityRegularizer=Mt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=mt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=mt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e),n=Qw(this.activation.getClassName()),a;return n!=null?a=$a(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=$a(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:Gs(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:lr(this.kernelConstraint),biasConstraint:lr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};mx.className="Dense";ue.registerClass(mx);var gx=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 X(()=>{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 YL(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};gx.className="Flatten";ue.registerClass(gx);var yx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.activation=js(e.activation)}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e);return this.activation.apply(r)})}getConfig(){let e={activation:Gs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};yx.className="Activation";ue.registerClass(yx);var Ax=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 X(()=>(e=je(e),KL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="RepeatVector";ue.registerClass(Ax);var xx=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 X(()=>{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 U(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};xx.className="Reshape";ue.registerClass(xx);var bx=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(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Zt({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}};bx.className="Permute";ue.registerClass(bx);var vx=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 T0(Nu(r,this.maskValue),n)}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e),n=-1,a=!0,s=T0(Nu(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};vx.className="Masking";ue.registerClass(vx);var wx=class extends st{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let 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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=Rs(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Nx.className="Concatenate";ue.registerClass(Nx);function bp(e,t){for(;e<0;)e+=t;return e}function WU(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(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof 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 X(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=U(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=U(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=Ze(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=Qe(o,u)}return o.shape.length===1&&(o=Kt(o,1)),o})}var Ex=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){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],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)=>bp(a,e[s].shape.length)):n=[bp(this.axes,t.shape.length),bp(this.axes,r.shape.length)],this.normalize&&(t=P0(t,n[0]),r=P0(r,n[1])),WU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[bp(this.axes,e.length),bp(this.axes,t.length)],r}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),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}};Ex.className="Dot";ue.registerClass(Ex);var Rx=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 X(()=>{this.invokeCallHook(e,t);let r=je(e);return Mh(()=>le(am(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};Rx.className="GaussianNoise";ue.registerClass(Rx);var $x=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let r=je(e);return this.rate>0&&this.rate<1?Mh(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,am(r.shape,1,n))},()=>r,t.training||!1):r})}};$x.className="GaussianDropout";ue.registerClass($x);var Mx=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 X(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return Mh(()=>{let n=je(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Ml(Ad(r),this.rate);o=nm(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})}};Mx.className="AlphaDropout";ue.registerClass(Mx);function Kp(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=j7(e,t,r,n,a,s);else if(e.rank===3)i=H7(e,t,r,n,a,s);else if(e.rank===4)i=q7(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function VU(e,t,r,n,a=.001){return X(()=>{let s=Lf(e,n),i=s.mean,o=s.variance;return[Kp(e,i,o,r,t,a),i,o]})}function UU(e,t,r,n,a=.001){return X(()=>{let s=Lf(e,n),i=s.mean,o=s.variance,l=[];for(let c of ya(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=U(i,l),d=U(o,l),h=t==null?null:U(t,l),p=r==null?null:U(r,l);return[Kp(e,u,d,p,h,a),i,o]})}function GU(e,t,r,n,a=.001){return v.arraysEqual(n.slice().sort(),ya(0,e.rank-1))?VU(e,t,r,n,a):UU(e,t,r,n,a)}var Fx=class extends st{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(e.gammaInitializer||"ones"),this.movingMeanInitializer=$t(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=$t(e.movingVarianceInitializer||"ones"),this.betaConstraint=ur(e.betaConstraint),this.gammaConstraint=ur(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 Zt({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 X(()=>{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=!v.arraysEqual(u,ya(0,s).slice(0,s-1)),h=()=>{if(d){let g=U(this.movingMean.read(),l),y=U(this.movingVariance.read(),l),A=this.center?U(this.beta.read(),l):null,x=this.scale?U(this.gamma.read(),l):null;return Kp(n,g,y,A,x,this.epsilon)}else return Kp(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,m]=GU(n,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,A)=>{X(()=>{let x=1-A,b=g.read(),w=L(ce(b,y),x);g.write(ce(b,w))})};return f(this.movingMean,c,this.momentum),f(this.movingVariance,m,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:lr(this.betaConstraint),gammaConstraint:lr(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Fx.className="BatchNormalization";ue.registerClass(Fx);var Px=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 ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(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!==Rs(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 X(()=>{let{mean:s,variance:i}=Lf(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?U(c,o):c,u=this.scale?l(this.gamma.read()):null,d=this.center?l(this.beta.read()):null,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!=null&&(u=Gn(u,p)),d!=null&&(d=Gn(d,p)),Kp(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}};Px.className="LayerNormalization";ue.registerClass(Px);function jU(e,t,r){return X(()=>{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: ${r}. 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length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new Zt({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 X(()=>jU(je(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};_x.className="ZeroPadding2D";ue.registerClass(_x);function km(e,t,r,n,a,s){return X(()=>{jt(a),t6(s),Ln(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=Aa()),s==null&&(s="max"),e=tx(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=Df(e,t,r,o):i=Ef(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}function rk(e,t,r,n,a,s){return X(()=>{jt(a),t6(s),Ln(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=Aa()),s==null&&(s="max"),e=q6(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=aA(e,t,r,o):i=Uy(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var nk=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(yr(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)}`);yr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ln(this.padding),this.inputSpec=[new Zt({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 X(()=>{this.invokeCallHook(e,t),e=Rh(je(e),2);let r=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Qe(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},zx=class extends nk{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),km(e,t,r,n,a,"max")}};zx.className="MaxPooling1D";ue.registerClass(zx);var Ox=class extends nk{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),km(e,t,r,n,a,"avg")}};Ox.className="AveragePooling1D";ue.registerClass(Ox);var ak=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];yr(this.poolSize,"poolSize"),yr(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 Zt({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 X(()=>(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}},Dx=class extends ak{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),km(e,t,r,n,a,"max")}};Dx.className="MaxPooling2D";ue.registerClass(Dx);var Lx=class extends ak{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),km(e,t,r,n,a,"avg")}};Lx.className="AveragePooling2D";ue.registerClass(Lx);var sk=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];yr(this.poolSize,"poolSize"),yr(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 Zt({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 X(()=>(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}},Bx=class extends sk{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),rk(e,t,r,n,a,"max")}};Bx.className="MaxPooling3D";ue.registerClass(Bx);var Wx=class extends sk{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),rk(e,t,r,n,a,"avg")}};Wx.className="AveragePooling3D";ue.registerClass(Wx);var ik=class extends st{constructor(e){super(e),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},Vx=class extends ik{constructor(e){super(e||{})}call(e,t){return X(()=>{let r=je(e);return Vt(r,1)})}};Vx.className="GlobalAveragePooling1D";ue.registerClass(Vx);var Ux=class extends ik{constructor(e){super(e||{})}call(e,t){return X(()=>{let r=je(e);return Ar(r,1)})}};Ux.className="GlobalMaxPooling1D";ue.registerClass(Ux);var ok=class extends st{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Gx=class extends ok{call(e,t){return X(()=>{let r=je(e);return this.dataFormat==="channelsLast"?Vt(r,[1,2]):Vt(r,[2,3])})}};Gx.className="GlobalAveragePooling2D";ue.registerClass(Gx);var jx=class extends ok{call(e,t){return X(()=>{let r=je(e);return this.dataFormat==="channelsLast"?Ar(r,[1,2]):Ar(r,[2,3])})}};jx.className="GlobalMaxPooling2D";ue.registerClass(jx);var lk=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)}},Hx=class extends lk{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 X(()=>(e=je(e),Q6((r,n)=>[je(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Hx.className="TimeDistributed";ue.registerClass(Hx);function HU(e){_l(GL,"BidirectionalMergeMode",e)}var qU="concat",qx=class extends lk{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?qU:e.mergeMode,HU(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=J6(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 Zt({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 X(()=>{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=CA([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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t=Cr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Cr.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}.${t.versions.minConsumer}`;let n=Cr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},F4=class extends br{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${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}}},e5=class extends br{constructor(){super(),this.outputQueue=new Xk,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}}},ZH=class extends e5{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}},Zk=class extends br{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}},Yk=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(Yk||{}),YH=class extends br{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 br?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await jk(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 ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Jk=class extends br{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new Hk(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},JH=class extends Jk{constructor(e,t,r){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=MH.alea(r||v.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}}},kd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),un(async()=>(await r.iterator()).columnMajorBatch(e,t,tq),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,un(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,un(async()=>(await t.iterator()).filter(n=>X(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return un(async()=>(await t.iterator()).map(r=>X(()=>e(r))),this.size)}mapAsync(e){let t=this;return un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,un(async()=>{let n=Qx(async()=>({value:await t.iterator(),done:!1}));return LH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,un(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=$H.alea(t||v.now().toString());return un(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,un(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()}};kd.MAX_BUFFER_SIZE=1e4;function un(e,t=null){return new class extends kd{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function QH(e){return un(async()=>Kk(e),e.length)}function eq(e){if(!Fu(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 un(async()=>{let r=await jk(e,n=>{if(n instanceof kd)return{value:n.iterator(),recurse:!1};if(Fu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return BH(r,1)},t)}function tq(e){if(e===null)return null;let t=e[0];return _H(t)?{value:rq(e),recurse:!1}:{value:null,recurse:!0}}function rq(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof nt?dr(e):ft(e)}var Qk=class extends kd{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))}},e0='"',wp=Symbol("out"),P4=Symbol("field"),t0=Symbol("quote"),fg=Symbol("quoteafterquote"),_4=Symbol("quoteinquote"),e8=class extends kd{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 Qk(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.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&&v.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(v.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=wp;for(let i=0;i<a;i++)switch(s){case wp:switch(e.charAt(i)){case e0:n=i+1,s=t0;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=wp;break;default:s=P4,n=i;break}break;case P4:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=wp,n=i+1;break;default:}break;case t0:switch(e.charAt(i)){case e0:s=fg;break;default:}break;case fg:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=wp,n=i+1;break;case e0:s=t0;break;default:s=_4;break}break;case _4:switch(e.charAt(i)){case e0:s=t0;break;default:}break;default:}if(s===fg?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}},t8=class extends br{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!Z().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new t8(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(v.sizeFromShape(t));return r.set(e,r.length-e.length),ft(r,t)}},r8=class extends br{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=Nt([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(!Z().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 r8(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return X(()=>{let t=Kt(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return U(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},n8=class{},a8=class extends br{split(e){return new nq(this,e)}},nq=class extends a8{constructor(e,t){super(),this.upstream=e,this.impl=new aq(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},aq=class extends e5{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},sq=class extends br{decodeUTF8(){return new iq(this)}},iq=class extends a8{constructor(e){super(),this.upstream=e,this.impl=new oq(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},oq=class extends e5{constructor(e){if(super(),this.upstream=e,Z().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Cv();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return Z().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},s8=class extends sq{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Z().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},n.onabort=s=>t(new Error("Aborted")),n.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,r);n.readAsArrayBuffer(a)}this.offset=r}),done:!1}}};async function lq(e,t={},r){let n,a;typeof e=="string"?n=e:(n=e.url,a=uq(e));let s=await(r||v.fetch)(n,a);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new s8(i,t)}else throw new Error(s.statusText)}var uq=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function i8(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var o8=class extends n8{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(i8(this.input)&&Z().get("IS_NODE")){let e=yy();this.input=e.readFileSync(this.input.slice(7))}return new s8(this.input,this.options)}},l8=class extends n8{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return i8(this.url)?new o8(this.url,this.fileOptions).iterator():lq(this.url,this.fileOptions)}};function dq(e,t={}){return new e8(new l8(e),t)}function pq(e){let t=Qx(e);return un(async()=>t)}function hq(e){return un(async()=>{let t=await e();return Qx(()=>t.next())})}async function cq(e,t){return r8.create(e,t)}async function fq(e){return t8.create(e)}var mq="0.0.0";function Ce(e,t){Array.isArray(e)||(e=[e]),e.forEach(r=>{r!=null&&v.assert(r.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var gq=Kn.whereImpl,u8=class extends Du{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Jp(this,Xt())}nextDataId(){return u8.nextDataId++}write(e,t,r){this.firstUse&&(this.firstUse=!1,Z().get("IS_NODE")&&C.warn(`
============================
Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details.
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i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=sn({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[d,h]=C.computeOutAndReduceShapes(l.shape,i),p=v.sizeFromShape(d),c=v.makeZerosTypedArray(p,"int32"),m=v.sizeFromShape(h),f=r.data.get(l.dataId).values;for(let g=0;g<c.length;++g){let y=g*m,A=f[y],x=0;for(let b=0;b<m;++b){let w=f[y+b];w<A&&(A=w,x=b)}c[g]=x}return u.forEach(g=>r.disposeIntermediateTensorInfo(g)),r.makeTensorInfo(d,"int32",c)}var 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d=C.computePool3DInfo(s.shape,i,o,1,l,u),h=d.strideDepth,p=d.strideHeight,c=d.strideWidth,m=d.filterDepth,f=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,b=d.effectiveFilterDepth,w=d.effectiveFilterHeight,I=d.effectiveFilterWidth,N=b-1-d.padInfo.front,E=I-1-d.padInfo.left,R=w-1-d.padInfo.top,_=Le(s.shape,"float32"),M=1/(m*f*g),S=r.bufferSync(a);for(let z=0;z<d.batchSize;++z)for(let O=0;O<d.inChannels;++O)for(let H=0;H<d.inDepth;++H)for(let K=0;K<d.inHeight;++K)for(let D=0;D<d.inWidth;++D){let Q=H-N,V=K-R,re=D-E,Y=0;for(let ie=0;ie<b;ie+=y){let J=(Q+ie)/h;if(!(J<0||J>=d.outDepth||Math.floor(J)!==J))for(let ae=0;ae<w;ae+=A){let de=(V+ae)/p;if(!(de<0||de>=d.outHeight||Math.floor(de)!==de))for(let xe=0;xe<I;xe+=x){let be=(re+xe)/c;be<0||be>=d.outWidth||Math.floor(be)!==be||(Y+=S.get(z,J,de,be,O))}}}_.set(Y*M,z,H,K,D,O)}return r.makeTensorInfo(_.shape,_.dtype,_.values)}var aK={kernelName:tf,backendName:"cpu",kernelFunc:nK};function sK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Ce([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=C.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,w=Le(i.shape,"float32"),I=1/(c*m),N=r.data.get(a.dataId).values,E=Le(a.shape,"float32",N);for(let R=0;R<d.batchSize;++R)for(let _=0;_<d.inChannels;++_)for(let M=0;M<d.inHeight;++M)for(let S=0;S<d.inWidth;++S){let z=M-b,O=S-x,H=0;for(let K=0;K<y;K+=f){let D=(z+K)/h;if(!(D<0||D>=d.outHeight||Math.floor(D)!==D))for(let Q=0;Q<A;Q+=g){let V=(O+Q)/p;V<0||V>=d.outWidth||Math.floor(V)!==V||(H+=E.get(R,D,V,_))}}w.set(H*I,R,M,S,_)}return r.makeTensorInfo(w.shape,w.dtype,w.values)}var iK={kernelName:ef,backendName:"cpu",kernelFunc:sK};function 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r.makeTensorInfo(a.shape,a.dtype,f)}var lK={kernelName:ci,backendName:"cpu",kernelFunc:oK};function uK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Ce([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=Ct({inputs:{x:a},backend:r,attrs:{shape:l}}),m=sn({inputs:{x:c},backend:r,attrs:{perm:u}}),f=Ct({inputs:{x:m},backend:r,attrs:{shape:d}}),g=Lo({inputs:{x:f},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(f),g}var dK={kernelName:Ho,backendName:"cpu",kernelFunc:uK};function pK(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=n5(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var hK={kernelName:rf,backendName:"cpu",kernelFunc:pK};function cK(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.data.get(n.dataId).values,i=r.data.get(a.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var fK={kernelName:nf,backendName:"cpu",kernelFunc:cK},mK=gt(Qa,(e,t)=>{let r=t;return e>r.clipValueMax?r.clipValueMax:e<r.clipValueMin?r.clipValueMin:e}),gK={kernelName:Qa,backendName:"cpu",kernelFunc:mK},yK=e=>{let{x:t}=e.inputs,r=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),a=r.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=r.data.get(s.dataId).values,l=r.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let d=o[u],h=l[u];n[u]=Math.hypot(d,h)}return r.makeOutput(n,t.shape,"float32")},AK={kernelName:th,backendName:"cpu",kernelFunc:yK};function 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f.forEach(b=>r.disposeIntermediateTensorInfo(b)),g.forEach(b=>r.disposeIntermediateTensorInfo(b)),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(A),x}let u=o.map(f=>{let g=v.sizeFromShape(f.shape.slice(s));return Ct({inputs:{x:f},backend:r,attrs:{shape:[-1,g]}})}),d=u.map(f=>({vals:r.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(u.map(f=>f.shape),1);let h=u[0].shape[0]===1,p=a5(d,i,t[0].dtype,h),c=C.computeOutShape(o.map(f=>f.shape),s),m=r.makeTensorInfo(c,t[0].dtype,p);return u.forEach(f=>r.disposeIntermediateTensorInfo(f)),m}var bK={kernelName:qo,backendName:"cpu",kernelFunc:zu};function rI(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;Ce([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,A=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new or(p.outShape,a.dtype),w=v.computeStrides(a.shape),I=v.computeStrides(s.shape),N=w[0],E=x?w[1]:w[2],R=x?w[2]:1,_=x?1:w[1],M=b.strides[0],S=x?b.strides[1]:b.strides[2],z=x?b.strides[2]:1,O=x?1:b.strides[1],H=r.data.get(a.dataId).values,K=r.data.get(s.dataId).values,D=b.values;for(let Q=0;Q<p.batchSize;++Q){let V=Q*N,re=Q*M;for(let Y=0;Y<p.outHeight;++Y){let ie=re+Y*S,J=Y*p.strideHeight-A;for(let ae=0;ae<c;++ae){let de=J+ae*f;if(de<0||de>=p.inHeight)continue;let xe=ae*I[0],be=V+de*E;for(let Ee=0;Ee<p.outWidth;++Ee){let $e=ie+Ee*z,De=Ee*p.strideWidth-y;for(let We=0;We<m;++We){let Xe=De+We*g;if(Xe<0||Xe>=p.inWidth)continue;let ot=xe+We*I[1],pt=be+Xe*R,ht=ot;for(let Fe=0;Fe<p.inChannels;++Fe){let wt=H[pt+Fe*_];for(let At=0;At<p.outChannels;++At)D[$e+At*O]+=wt*K[ht+At];ht+=p.outChannels}}}}}}return r.makeTensorInfo(b.shape,b.dtype,D)}var vK={kernelName:ri,backendName:"cpu",kernelFunc:rI};function wK(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;Ce([a,s],"conv2dBackpropFilter");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),{strideHeight:c,strideWidth:m,filterHeight:f,filterWidth:g}=p,y=p.dataFormat==="channelsLast",A=new or(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,w=r.data.get(a.dataId).values,I=r.data.get(s.dataId).values,N=new or(a.shape,a.dtype,w),E=new or(s.shape,s.dtype,I);for(let R=0;R<f;++R){let _=Math.max(0,Math.ceil((b-R)/c)),M=Math.min(p.outHeight,(p.inHeight+b-R)/c);for(let S=0;S<g;++S){let z=Math.max(0,Math.ceil((x-S)/m)),O=Math.min(p.outWidth,(p.inWidth+x-S)/m);for(let H=0;H<p.inChannels;++H)for(let K=0;K<p.outChannels;++K){let D=0;for(let Q=0;Q<p.batchSize;++Q)for(let V=_;V<M;++V){let re=R+V*c-b;for(let Y=z;Y<O;++Y){let ie=S+Y*m-x;y?D+=N.get(Q,re,ie,H)*E.get(Q,V,Y,K):D+=N.get(Q,H,re,ie)*E.get(Q,K,V,Y)}}A.set(D,R,S,H,K)}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var kK={kernelName:af,backendName:"cpu",kernelFunc:wK};function IK(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;Ce([a,s],"conv2dBackpropInput");let h=v.computeStrides(s.shape),p=v.computeStrides(a.shape),c=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),f=new or(m.inShape,"float32"),g=f.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,w]=h,{batchSize:I,filterHeight:N,filterWidth:E,inChannels:R,inHeight:_,inWidth:M,outChannels:S,outHeight:z,outWidth:O,strideHeight:H,strideWidth:K}=m;c=m.dataFormat;let D=N-1-m.padInfo.top,Q=E-1-m.padInfo.left,V=c==="channelsLast",re=f.strides[0],Y=V?f.strides[1]:f.strides[2],ie=V?f.strides[2]:1,J=V?1:f.strides[1],ae=p[0],de=V?p[1]:p[2],xe=V?p[2]:1,be=V?1:p[1];for(let Ee=0;Ee<I;++Ee)for(let $e=0;$e<R;++$e)for(let De=0;De<_;++De){let We=De-D,Xe=Math.max(0,Math.ceil(We/H)),ot=Math.min(z,(N+We)/H);for(let pt=0;pt<M;++pt){let ht=pt-Q,Fe=Math.max(0,Math.ceil(ht/K)),wt=Math.min(O,(E+ht)/K),At=0;for(let cr=Xe;cr<ot;++cr){let Jr=cr*H-We;for(let nr=Fe;nr<wt;++nr){let fr=nr*K-ht,ta=ae*Ee+de*cr+xe*nr,Qr=x*(N-1-Jr)+b*(E-1-fr)+w*$e;for(let ar=0;ar<S;++ar){let kn=y[ta+be*ar],In=A[Qr+ar];At+=kn*In}}}let Pr=re*Ee+Y*De+ie*pt+J*$e;g[Pr]=At}}return r.makeTensorInfo(f.shape,f.dtype,f.values)}var SK={kernelName:ni,backendName:"cpu",kernelFunc:IK};function CK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n;Ce([a,s],"conv3d");let u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:h,filterWidth:p,dilationDepth:c,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,A=g.left,x=g.top,b=new or(u.outShape,a.dtype),w=r.data.get(a.dataId).values,I=r.data.get(s.dataId).values,N=b.values,E=v.computeStrides(a.shape),R=v.computeStrides(s.shape);for(let _=0;_<u.batchSize;++_){let M=_*E[0],S=_*b.strides[0];for(let z=0;z<u.outDepth;++z){let O=S+z*b.strides[1],H=z*u.strideDepth-y;for(let K=0;K<d;++K){let D=H+K*c;if(D<0||D>=u.inDepth)continue;let Q=K*R[0],V=M+D*E[1];for(let re=0;re<u.outHeight;++re){let Y=O+re*b.strides[2],ie=re*u.strideHeight-x;for(let J=0;J<h;++J){let ae=ie+J*m;if(ae<0||ae>=u.inHeight)continue;let de=Q+J*R[1],xe=V+ae*E[2];for(let be=0;be<u.outWidth;++be){let Ee=Y+be*u.outChannels,$e=be*u.strideWidth-A;for(let De=0;De<p;++De){let We=$e+De*f;if(We<0||We>=u.inWidth)continue;let Xe=de+De*R[2],ot=xe+We*u.inChannels,pt=Xe;for(let ht=0;ht<u.inChannels;++ht){let Fe=w[ot+ht];for(let wt=0;wt<u.outChannels;++wt)N[Ee+wt]+=Fe*I[pt+wt];pt+=u.outChannels}}}}}}}}return r.makeTensorInfo(b.shape,b.dtype,b.values)}var TK={kernelName:rh,backendName:"cpu",kernelFunc:CK};function NK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n;Ce([a,s],"conv3dBackpropFilterV2");let u=v.computeStrides(a.shape),d=v.computeStrides(s.shape),h=C.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,c=h.strideHeight,m=h.strideWidth,f=h.filterDepth,g=h.filterHeight,y=h.filterWidth,A=new or(h.filterShape,"float32"),x=A.values,[b,w,I,N]=A.strides,E=r.data.get(s.dataId).values,[R,_,M,S]=d,z=r.data.get(a.dataId).values,[O,H,K,D]=u,Q=h.padInfo.front,V=h.padInfo.left,re=h.padInfo.top;for(let Y=0;Y<f;++Y){let ie=Math.max(0,Math.ceil((Q-Y)/p)),J=Math.min(h.outDepth,(h.inDepth+Q-Y)/p),ae=Y*b;for(let de=0;de<g;++de){let xe=Math.max(0,Math.ceil((re-de)/c)),be=Math.min(h.outHeight,(h.inHeight+re-de)/c),Ee=de*w+ae;for(let $e=0;$e<y;++$e){let De=Math.max(0,Math.ceil((V-$e)/m)),We=Math.min(h.outWidth,(h.inWidth+V-$e)/m),Xe=$e*I+Ee;for(let ot=0;ot<h.inChannels;++ot){let pt=ot*N+Xe;for(let ht=0;ht<h.outChannels;++ht){let Fe=0;for(let wt=0;wt<h.batchSize;++wt){let At=wt*O,Pr=wt*R;for(let cr=ie;cr<J;++cr){let Jr=(Y+cr*p-Q)*H+At,nr=cr*_+Pr;for(let fr=xe;fr<be;++fr){let ta=(de+fr*c-re)*K+Jr,Qr=fr*M+nr;for(let ar=De;ar<We;++ar){let kn=($e+ar*m-V)*D+ta,In=ar*S+Qr;Fe+=z[kn+ot]*E[In+ht]}}}}x[pt+ht]=Fe}}}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var EK={kernelName:sf,backendName:"cpu",kernelFunc:NK};function RK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n;Ce([a],"conv3dBackpropInputV2");let u=v.computeStrides(a.shape),d=v.computeStrides(s.shape),h=C.computeConv3DInfo(l,s.shape,o,1,i),p=new or(h.inShape,"float32"),c=p.values,[m,f,g,y]=p.strides,A=r.data.get(a.dataId).values,[x,b,w,I]=u,N=r.data.get(s.dataId).values,[E,R,_,M]=d,{batchSize:S,filterDepth:z,filterHeight:O,filterWidth:H,inChannels:K,inDepth:D,inHeight:Q,inWidth:V,outChannels:re,outDepth:Y,outHeight:ie,outWidth:J,strideDepth:ae,strideHeight:de,strideWidth:xe}=h,be=z-1-h.padInfo.front,Ee=O-1-h.padInfo.top,$e=H-1-h.padInfo.left;for(let De=0;De<S;++De)for(let We=0;We<K;++We)for(let Xe=0;Xe<D;++Xe){let ot=Xe-be,pt=Math.max(0,Math.ceil(ot/ae)),ht=Math.min(Y,(z+ot)/ae);for(let Fe=0;Fe<Q;++Fe){let wt=Fe-Ee,At=Math.max(0,Math.ceil(wt/de)),Pr=Math.min(ie,(O+wt)/de);for(let cr=0;cr<V;++cr){let Jr=cr-$e,nr=Math.max(0,Math.ceil(Jr/xe)),fr=Math.min(J,(H+Jr)/xe),ta=0;for(let Qr=pt;Qr<ht;++Qr){let ar=Qr*ae-ot;for(let kn=At;kn<Pr;++kn){let In=kn*de-wt;for(let gs=nr;gs<fr;++gs){let lo=gs*xe-Jr,dc=x*De+b*Qr+w*kn+I*gs,ys=E*(z-1-ar)+R*(O-1-In)+_*(H-1-lo)+M*We;for(let Ua=0;Ua<re;++Ua){let ap=A[dc+Ua],Zl=N[ys+Ua];ta+=ap*Zl}}}}c[m*De+f*Xe+g*Fe+y*cr+We]=ta}}}return r.makeTensorInfo(p.shape,p.dtype,p.values)}var $K={kernelName:of,backendName:"cpu",kernelFunc:RK},MK=gt(ai,e=>Math.cos(e)),FK={kernelName:ai,backendName:"cpu",kernelFunc:MK},PK=gt(si,e=>Math.cosh(e)),_K={kernelName:si,backendName:"cpu",kernelFunc:PK};function zK(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[d,h,p,c]=a.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,c],"float32"),A=r.data.get(s.dataId).values,x=r.data.get(i.dataId).values,b=r.data.get(a.dataId).values,w=v.computeStrides(a.shape),I=v.computeStrides(y.shape);for(let N=0;N<m;N++){let E=N*4,R=A[E],_=A[E+1],M=A[E+2],S=A[E+3],z=x[N];if(z>=d)continue;let O=f>1?(M-R)*(h-1)/(f-1):0,H=g>1?(S-_)*(p-1)/(g-1):0;for(let K=0;K<f;K++){let D=f>1?R*(h-1)+K*O:.5*(R+M)*(h-1);if(D<0||D>h-1){for(let Q=0;Q<g;Q++)for(let V=0;V<c;V++){let re=V+Q*I[2]+K*I[1]+N*I[0];y.values[re]=u}continue}if(l==="bilinear"){let Q=Math.floor(D),V=Math.ceil(D),re=D-Q;for(let Y=0;Y<g;Y++){let ie=g>1?_*(p-1)+Y*H:.5*(_+S)*(p-1);if(ie<0||ie>p-1){for(let xe=0;xe<c;xe++){let be=xe+Y*I[2]+K*I[1]+N*I[0];y.values[be]=u}continue}let J=Math.floor(ie),ae=Math.ceil(ie),de=ie-J;for(let xe=0;xe<c;xe++){let be=xe+J*w[2]+Q*w[1]+z*w[0],Ee=b[be];be=xe+ae*w[2]+Q*w[1]+z*w[0];let $e=b[be];be=xe+J*w[2]+V*w[1]+z*w[0];let De=b[be];be=xe+ae*w[2]+V*w[1]+z*w[0];let We=b[be],Xe=Ee+($e-Ee)*de,ot=De+(We-De)*de;be=xe+Y*I[2]+K*I[1]+N*I[0],y.values[be]=Xe+(ot-Xe)*re}}}else for(let Q=0;Q<g;++Q){let V=g>1?_*(p-1)+Q*H:.5*(_+S)*(p-1);if(V<0||V>p-1){for(let ie=0;ie<c;ie++){let J=ie+Q*I[2]+K*I[1]+N*I[0];y.values[J]=u}continue}let re=Math.round(V),Y=Math.round(D);for(let ie=0;ie<c;ie++){let J=ie+re*w[2]+Y*w[1]+z*w[0],ae=ie+Q*I[2]+K*I[1]+N*I[0];y.values[ae]=b[J]}}}}return r.makeTensorInfo(y.shape,y.dtype,y.values)}var OK={kernelName:Ko,backendName:"cpu",kernelFunc:zK};function DK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Ce(a,"cumprod");let l=C.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=sn({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=C.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Nr(u.dtype,"int32"),p=v.makeOnesTypedArray(v.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)p[x]=i?1:c[x];else{let b=f(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=C.getUndoAxesPermutation(l),A=sn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var LK={kernelName:Xo,backendName:"cpu",kernelFunc:DK};function BK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Ce(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=sn({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=C.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=Nr(u.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)p[x]=i?0:c[x];else{let b=f(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=C.getUndoAxesPermutation(l),A=sn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var WK={kernelName:ii,backendName:"cpu",kernelFunc:BK};function VK(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=n5(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=h8(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 UK={kernelName:lf,backendName:"cpu",kernelFunc:VK};function GK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n;v.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=a.shape[0],l=a.shape[1],u=a.shape[2],d=a.shape[3],h=l*s,p=u*s,c=d/(s*s),m=r.data.get(a.dataId).values,f=new Float32Array(o*h*p*c),g=0;for(let y=0;y<o;++y)for(let A=0;A<h;++A){let x=Math.floor(A/s),b=A%s;for(let w=0;w<p;++w){let I=Math.floor(w/s),N=w%s,E=(b*s+N)*c;for(let R=0;R<c;++R){let _=R+E+d*(I+u*(x+l*y));f[g++]=m[_]}}}return r.makeTensorInfo([o,h,p,c],a.dtype,f)}var jK={kernelName:Zo,backendName:"cpu",kernelFunc:GK};function nI(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n;Ce([a,s],"depthwiseConv2DNative");let d=v.computeStrides(a.shape),h=v.computeStrides(s.shape),p=l;p==null&&(p=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=C.computeConv2DInfo(a.shape,s.shape,i,p,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:A}=c,x=A.left,b=A.top,w=c.outChannels/c.inChannels,I=new or(c.outShape,a.dtype),N=r.data.get(a.dataId).values,E=r.data.get(s.dataId).values,R=I.values;for(let _=0;_<c.batchSize;++_){let M=_*d[0],S=_*I.strides[0];for(let z=0;z<c.outHeight;++z){let O=S+z*I.strides[1],H=z*c.strideHeight-b;for(let K=0;K<m;++K){let D=H+K*g;if(D<0||D>=c.inHeight)continue;let Q=K*h[0],V=M+D*d[1];for(let re=0;re<c.outWidth;++re){let Y=O+re*I.strides[2],ie=re*c.strideWidth-x;for(let J=0;J<f;++J){let ae=ie+J*y;if(ae<0||ae>=c.inWidth)continue;let de=Q+J*h[1],xe=V+ae*c.inChannels,be=Y,Ee=de;for(let $e=0;$e<c.inChannels;++$e){let De=N[xe+$e];for(let We=0;We<w;++We)R[be+We]+=De*E[Ee+We];be+=w,Ee+=w}}}}}}return r.makeTensorInfo(I.shape,I.dtype,I.values)}var HK={kernelName:oi,backendName:"cpu",kernelFunc:nI};function qK(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;Ce([a,s],"depthwiseConv2dNativeBackpropFilter");let h=C.computeConv2DInfo(a.shape,d,i,o,l,u,!0),{strideHeight:p,strideWidth:c,filterHeight:m,filterWidth:f}=h,g=new or(h.filterShape,"float32"),y=h.padInfo.left,A=h.padInfo.top,x=h.outChannels/h.inChannels,b=r.data.get(a.dataId).values,w=new or(a.shape,a.dtype,b),I=r.data.get(s.dataId).values,N=new or(s.shape,s.dtype,I);for(let E=0;E<m;++E){let R=Math.max(0,Math.ceil((A-E)/p)),_=Math.min(h.outHeight,(h.inHeight+A-E)/p);for(let M=0;M<f;++M){let S=Math.max(0,Math.ceil((y-M)/c)),z=Math.min(h.outWidth,(h.inWidth+y-M)/c);for(let O=0;O<h.outChannels;++O){let H=Math.trunc(O/x),K=O%x,D=0;for(let Q=0;Q<h.batchSize;++Q)for(let V=R;V<_;++V){let re=E+V*p-A;for(let Y=S;Y<z;++Y){let ie=M+Y*c-y;D+=w.get(Q,re,ie,H)*N.get(Q,V,Y,O)}}g.set(D,E,M,H,K)}}}return r.makeTensorInfo(g.shape,g.dtype,g.values)}var 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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 g5(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var _I=`
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:zI}=C;function UQ(e,t,r){let n=[];if(e.forEach(p=>{let c=v.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:m}=y5(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(m.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?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
`),s=e.map(p=>GQ(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
`),i=t.texShape,o=Hr(),l=qQ(o),u,d,h=ZQ(o);return t.isPacked?(u=jQ(t.logicalShape,i,r.enableShapeUniforms),d=KQ(o)):(u=HQ(t.logicalShape,i,r.enableShapeUniforms),d=XQ(o)),r.packedInputs&&(h+=eee),[h,l,d,a,u,s,r.userCode].join(`
`)}function Td(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return hee(e,t);case 1:return fee(e,t);case 2:return gee(e,t);case 3:return Aee(e,t);case 4:return bee(e,t);case 5:return vee(e);case 6:return wee(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function OI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return pee(e);case 1:return cee(e,t);case 2:return mee(e,t);case 3:return yee(e,t);default:return xee(e,t)}}function GQ(e,t,r=!1,n){let a="";r?a+=OI(e,n):a+=Td(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=kee(e,t):a+=Iee(e,t)),a}function jQ(e,t,r){switch(e.length){case 0:return DI();case 1:return tee(e,t,r);case 2:return uee(e,t,r);case 3:return nee(e,t,r);default:return see(e,t,r)}}function HQ(e,t,r){switch(e.length){case 0:return DI();case 1:return ree(e,t,r);case 2:return dee(e,t,r);case 3:return aee(e,t,r);case 4:return iee(e,t,r);case 5:return oee(e,t);case 6:return lee(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function qQ(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function XQ(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function KQ(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);
}
${YQ}
${JQ}
${QQ}
`}var YQ=`
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);
}
`,JQ=`
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);
}
`,QQ=`
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);
}
`,eee=`
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 DI(){return`
int getOutputCoords() {
return 0;
}
`}function tee(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 ree(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 * ${t[1]} + resTexRC.y;
}
`}function nee(e,t,r){if(r)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),s=a*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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 aee(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;
${Nm(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let n=Dl(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function see(e,t,r){if(r)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),s=a*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u<e.length-1;u++)i*=e[e.length-u-1],o=`
int b${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 iee(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;
${Nm(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let n=Dl(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function oee(e,t){let r=Dl(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function lee(e,t){let r=Dl(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function uee(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return r?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${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, ${a}) * 2;
return ivec2(r, c);
}
`}function dee(e,t,r){return v.arraysEqual(e,t)?r?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${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 pee(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 hee(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 cee(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 fee(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${Nd(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 mee(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&&v.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 gee(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&&v.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}=v.squeezeShape(r),l=i;if(l.length<r.length){let p=Ed(e,l),c=["row","col"];return`
${Td(p,t)}
float ${a}(int row, int col) {
return ${a}(${Rd(c,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${r[1]}, 1)));
${Nd(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 yee(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],m=Ed(e,p),f=["b","row","col"];return`
${OI(m,t)}
vec4 ${a}(int b, int row, int col) {
return ${a}(${Rd(f,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}=v.squeezeShape(r),u=o;if(u.length<r.length){let f=Ed(e,u),g=["row","col","depth"];return`
${Td(f,t)}
float ${a}(int row, int col, int depth) {
return ${a}(${Rd(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)));
${Nd(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 m=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 + ${m};
vec2 uv = uvFromFlat(${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 + ${m};
vec2 uv = uvFromFlat(${h}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function xee(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",m=`b * ${p} + (row / 2) * ${h} + (col / 2)`;for(let f=2;f<i-1;f++)c=`int b${f}, `+c,p*=s[i-f-1],m=`b${f} * ${p} + `+m;return`
vec4 ${n}(${c}) {
int index = ${m};
int texR = index / ${d};
int texC = index - texR * ${d};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});
return ${a.texture2D}(${r}, uv);
}
`}function bee(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}=v.squeezeShape(r);if(l.length<r.length){let A=Ed(e,l),x=["row","col","depth","depth2"];return`
${Td(A,t)}
float ${a}(int row, int col, int depth, int depth2) {
return ${a}(${Rd(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)));
${Nd(e)}
}
`;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1],m=`int stride2 = ${n}Shape[3];`,f=`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) {
${m}
${f}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${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.
${m}
${f}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${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 vee(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}=v.squeezeShape(t);if(l.length<t.length){let f=Ed(e,l),g=["row","col","depth","depth2","depth3"];return`
${Td(f)}
float ${n}(int row, int col, int depth, int depth2, int depth3) {
return ${n}(${Rd(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;
${Nd(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 m=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 + ${m};
vec2 uv = uvFromFlat(${p}, ${c}, index);
return sampleTexture(${r}, uv);
}
`}function wee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let g=Ed(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
${Td(g)}
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${n}(${Rd(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)));
${Nd(e)}
}
`;let h=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],m=p[1];if(m===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}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${c}.0);
return sampleTexture(${r}, uv);
}
`;if(m===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]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${c}.0);
return sampleTexture(${r}, uv);
}
`;let f=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 + ${f};
vec2 uv = uvFromFlat(${c}, ${m}, index);
return sampleTexture(${r}, uv);
}
`}function Nd(e){let t=e.name,r=v.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
for (int i = 0; i < ${r}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function kee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=zI(e.shapeInfo.logicalShape,t.logicalShape),l=vt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${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;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)c=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!f)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 Iee(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&&v.arraysEqual(i,s))return`
float ${a}() {
return sampleTexture(${r}, resultUV);
}
`;let u=vt(l),d=zI(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(f=>`coords.${c[f+h]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${c[g+h]}`).join(", "),`
float ${a}() {
${u} coords = getOutputCoords();
${p}
return get${n}(${m});
}
`}function vt(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 ${e} is not yet supported`)}function y5(e,t,r){let{newShape:n,keptDims:a}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function Ed(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function Rd(e,t){return t.map(r=>e[r]).join(", ")}function See(e,t,r,n){let a=r.map((d,h)=>{let p={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(p.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[h],shapeInfo:p}}),s=a.map(d=>d.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=UQ(a,i,t),l=fI(e.gl,o),u=e.createProgram(l);return Z().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,...LI(e,t,u)}}function LI(e,t,r){let n={},a={},s={},i=[],o,l,u,d=null,h=null;h=e.getUniformLocation(r,"NAN",!1),Z().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(r,"INFINITY",!1));let p=!1;for(let c=0;c<t.variableNames.length;c++){let m=t.variableNames[c];n[m]=e.getUniformLocation(r,m,p),n[`offset${m}`]=e.getUniformLocation(r,`offset${m}`,p),t.enableShapeUniforms&&(a[`${m}Shape`]=e.getUniformLocation(r,`${m}Shape`,p),s[`${m}TexShape`]=e.getUniformLocation(r,`${m}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,m)=>{i[m]=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 D4(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(!v.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(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function Cee(e,t,r,n,a){t.program.enableShapeUniforms||(D4(t.inShapeInfos,r),D4([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Z().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],m=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:f}=y5(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(c,new Int32Array(f));break;case 2:e.gl.uniform2iv(c,new Int32Array(f));break;case 3:e.gl.uniform3iv(c,new Int32Array(f));break;case 4:e.gl.uniform4iv(c,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(h,f)}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=v.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 ${l.type} is not supported yet.`)}),e.executeProgram()}function Tee(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=y5(e.packedInputs,i.shape,l),p="",c="",m="";if(d.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let w=v.computeStrides(d);m=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=d.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,A=C.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&f===r.shape.length&&v.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${f}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${m}_${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+`${Z().getNumber("WEBGL_VERSION")}`,s}function ln(e){return Z().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var Nee=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=ln(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Nm(["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;
}
`}},Eee=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=ln(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Nm(["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;
}
`}},Ree=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Hr();this.outputShape=e,this.userCode=`
${_I}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},$ee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Hr();this.outputShape=e,this.userCode=`
${_I}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},Mee=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Hr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?g5():m5(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.);
}
`}},Fee=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=ln(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?g5():m5(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;
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${t.varyingVs} vec2 resultUV;
void main() {
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resultUV = uv;
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vte=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=ln(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let t=Br("rc",this.rank),r=vt(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]})`}},o9=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ln(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}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${n>0?"}":""}
`}this.userCode=`
${wte(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?g5():m5(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 wte(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?VQ(["r","c","d"],"inputShape"):Dl(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var kte=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=B4(t,r),a=W4(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=L4(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=B4(r,n),s=W4(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=L4(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=Z().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 Ite(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 L4(e,t,r,n,a){let s=Ste(t,n),i;if(a){let[l,u]=Sd(e[0],e[1]);i=l*u}else{let[l,u]=Lh(e[0],e[1]);i=l*u}let o=Ite(r,s);return i*o}function Ste(e,t){switch(e){case 3:return v5(t);case 4:return w5(t);case 1:return A5(t);case 0:return x5(t);case 2:return b5(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Cte(e){return Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function B4(e,t){if(e===1)return 3;if(e===0||e==null)return Cte(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function W4(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Xa=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},Yn="if (isnan(x)) return x;",Tte="return x;",V4="return abs(x);",Nte="return (x >= 0.0) ? x : (exp(x) - 1.0);",Ete=Yn+`
return (x < 0.0) ? 0.0 : x;
`,Rte=Yn+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,du="return x;",$te="return 1.0 / (1.0 + exp(-1.0 * x));",Mte="return x;",Fte=`
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;
`,Pte=`
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;
`,_te=`
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;
`,zte="return 1.0 / (1.0 + exp(-1.0 * x));",To=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},Ote=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let t=e.length,r=Br("rc",t),n=vt(t),a=bte(t,r),s=r.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${n} rc = getOutputCoords();
vec4 packedInput = getA(${a});
setOutput(getChannel(packedInput, ${i}));
}
`}},Dte=Kn.whereImpl,Lte=1e-7,Bte=1e-4,gg={};function Wte(e){return e in gg||(gg[e]={}),gg[e]}var Vte=Z().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Ute=600;function Gte(){return Z().global.screen==null?1024:Z().global.screen.height*Z().global.screen.width*window.devicePixelRatio*Ute/1024/1024}var l9=class extends Du{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,!Z().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof wu)t=e;else{let r=xa(Z().getNumber("WEBGL_VERSION"),e);t=new wu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=xa(Z().getNumber("WEBGL_VERSION"));t=new wu(r),this.binaryCache=Wte(Z().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new kte(this.gpgpu),this.numMBBeforeWarning=Gte(),this.texData=new Jp(this,Xt())}nextDataId(){return l9.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Z().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Z().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(Z().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 To(i,du):h=new Xa(i,du);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=v.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=C.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(m=>c.push(m))}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 To(n,du):c=new Xa(n,du);let m=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(r!=null)return this.convertAndCacheOnCPU(e);if(Z().getBool("DEBUG")&&!Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Z().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"&&Z().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...r0(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)]),m=c[0],f=c[1];d=C.mergeRealAndImagArrays(m,f)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=v.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)&&Xt().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 To(a,du):p=new Xa(a,du);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),m=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),m}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=Xt().makeTensorFromTensorInfo(u),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let r=t.map(n=>v.decodeString(n));return Le(e.shape,e.dtype,r)}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!pI(r))throw Z().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=v.sizeFromShape(t);if(Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...r0(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=Z().getBool("WEBGL_PACK")&&n===!0,i=s?p0(t):t,o=s?new $ee(i):new Ree(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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 Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos: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=Vte){return Z().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return Dte(e.shape,t)}packedUnaryOp(e,t,r){let n=new To(e.shape,t),a=this.compileAndRun(n,[e],r);return Xt().makeTensorFromTensorInfo(a)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=a9(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Z().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,V4,e.dtype);let t=new Xa(e.shape,V4),r=this.compileAndRun(t,[e]);return Xt().makeTensorFromTensorInfo(r)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&v.isString(r[0])){let a=r.map(s=>v.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){return Xt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,r),this)}unpackTensor(e){let t=new Ote(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vte(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 o9(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=v.sizeFromShape(a),p=t[0]*t[1]*4;v.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=p0(a),o;n?o=new Eee(i):o=new Nee(i);let l=!0,u=[t!=null?t:r0(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:r0(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.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&&v.sizeFromShape(g.shape)<=Z().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&&!Zp(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=Tee(e,u,d),p=this.getAndSaveBinary(h,()=>See(this.gpgpu,e,u,d)),c=this.activeTimers!=null,m;c&&(m=this.startTimer()),Z().get("ENGINE_COMPILE_ONLY")||Cee(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=Z().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Z().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||(Z().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=X(()=>{if(!Z().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Z().getBool("DEBUG");Z().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Z().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Lte:Bte}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=v.now());let 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if (isnan(a)) return a;
if (isnan(b)) return b;
`,Ou=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Em=`
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;
`,Vh=class{constructor(e,t,r,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,r);let a=this.outputShape.length;this.enableShapeUniforms=ln(a);let s="";if(n)if(a===0||v.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${vt(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);
${s}
setOutput(result);
}
`}};function fn(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Xte={kernelName:mi,backendName:"webgl",kernelFunc:fn};function qi(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=fn({inputs:{x:n},backend:r}),l=fn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Kte={kernelName:eh,backendName:"webgl",kernelFunc:qi},p9="return (a < 0.) ? b * a : a;",h9=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Zte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vh(h9,a.shape,i.shape):new Ou(p9,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var Yte={kernelName:gi,backendName:"webgl",kernelFunc:Zte},c9="return (a < 0.) ? b * a : a;",f9=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Jte(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vh(f9,n.shape,a.shape):new Ou(c9,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var Qte={kernelName:Ni,backendName:"webgl",kernelFunc:Jte},$d="if (isnan(x)) return x;",ere=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,tre=`
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=Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new To(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 m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[b,w]=x,I={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new Ou(e,l.shape,u.shape);return d.runWebGLProgram(E,[I,N],Nr(b.dtype,w.dtype))}),A=qi({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Nr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let m=d.texData.get(l.dataId).values,f=d.texData.get(u.dataId).values,g=l.dtype==="string"?C.fromUint8ToStringArray(m):m,y=l.dtype==="string"?C.fromUint8ToStringArray(f):f,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),w=d.texData.get(b.dataId);return w.values=A,b}let p=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Vh(t,l.shape,u.shape,r):c=new Ou(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function Rm(e,t=!1){if(e==="linear")return t?Mte:Tte;if(e==="relu")return t?Pte:Ete;if(e==="elu")return t?Fte:Nte;if(e==="relu6")return t?_te:Rte;if(e==="prelu")return t?f9:c9;if(e==="leakyrelu")return t?h9:p9;if(e==="sigmoid")return t?zte:$te;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var m9=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=ln(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"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:l?f=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:f=`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=`
${f}
// 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]} * ${m[0]});
result += (${c[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},U4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},G4=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},j4="return a * b;";function I5(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=C.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new G4(U4.REAL,n.shape,a.shape),d=new G4(U4.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),m=qi({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=ete(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Vh(j4,n.shape,a.shape):i=new Ou(j4,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var rre={kernelName:Si,backendName:"webgl",kernelFunc:I5};function nre(e,t,r){let n=[Bo(e.shape),...Wo(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Bo(t),...Wo(t)],i=new o9(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${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&&!Zp(a.shape,l)&&!(d.texture!==null&&Zp(d.shape,l))?nre(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var are={kernelName:fl,backendName:"webgl",kernelFunc:ve},H4=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 * ${v.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);
${l}
}
setOutput(sumValue);
}
`}},sre=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 ire(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=C.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Bl(e,t,r,n){let a=ire(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 H4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new H4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new sre({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 ore=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=vt(this.rank),a=lre(t);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function lre(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 ure=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 ${this.rank} is not yet supported.`);let n=vt(this.rank),a=i9("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${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 $m(e,t,r){let n=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ure(e.shape,t):new ore(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function dre(e,t,r,n){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=$m(e,l,n),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=C.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=C.expandShapeToKeepDim(h,i));let m=v.sizeFromShape(p),f=v.sizeFromShape(e.shape)/m,g=ve({inputs:{x:d},attrs:{shape:[f,m]},backend:n}),y=Ah(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 Mm(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return dre(a,s,i,r)}var pre={kernelName:Oi,backendName:"webgl",kernelFunc:Mm};function Jt(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=k5(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=$m(a,s,i);return u}var hre={kernelName:Vi,backendName:"webgl",kernelFunc:Jt},g9=1e3;function H0({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],m=n?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),A=v.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,m]);v.assert(h===p,()=>`Error in matMul: inner shapes (${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],w=n?[A,m,p]:[A,p,m],I=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),N=ve({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[I,N],R=Math.max(y,A),_=r?I.shape[1]:I.shape[2],M=s!=null,S=i!=null,z=l==="leakyrelu",O=l!=null?Rm(l,!0):null,H=M||S||z||O!=null,K;if((c===1||m===1)&&_>g9&&H===!1){let Q=I,V=N;r&&(Q=Jt({inputs:{x:I},backend:a,attrs:{perm:[0,2,1]}}),E.push(Q)),n&&(V=Jt({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let re=m!==1,Y=m===1,ie=Q;re&&(ie=ve({inputs:{x:Q},backend:a,attrs:{shape:[R,_,1]}}),E.push(ie));let J=m===1?2:1,ae=V;Y&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,_]}}),E.push(ae));let de=I5({inputs:{a:ie,b:ae},backend:a});K=Mm({inputs:{x:de},backend:a,attrs:{axis:J,keepDims:!0}}),E.push(de)}else{let Q=Nr(e.dtype,t.dtype),V=new m9(b,w,[R,c,m],r,n,M,O,S,z),re=[I,N];if(s!=null&&re.push(s),S&&re.push(i),z){let Y=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));re.push(Y),E.push(Y)}K=a.runWebGLProgram(V,re,Q)}let D=ve({inputs:{x:K},backend:a,attrs:{shape:x}});E.push(K);for(let Q of E)a.disposeIntermediateTensorInfo(Q);return D}function cre(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 H0({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var fre={kernelName:Ps,backendName:"webgl",kernelFunc:cre},q4="return abs(x);";function mre(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=a9(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new To(n.shape,q4):a=new Xa(n.shape,q4),r.runWebGLProgram(a,[n],n.dtype)}var gre={kernelName:jo,backendName:"webgl",kernelFunc:mre},yre=Yn+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,Are=it({opSnippet:yre}),xre={kernelName:Bu,backendName:"webgl",kernelFunc:Are},bre=Yn+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,vre=it({opSnippet:bre}),wre={kernelName:Wu,backendName:"webgl",kernelFunc:vre},X4="return a + b;",kre=wr({opSnippet:X4,packedOpSnippet:X4,supportsComplex:!0,cpuKernelImpl:_ee}),Ire={kernelName:Ja,backendName:"webgl",kernelFunc:kre},Sre=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 = ${n};
setOutput(result);
}
`}},Cre=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${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 f0(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return fn({inputs:{x:n[0]},backend:r});if(n.length>Z().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=f0({inputs:n.slice(0,o),backend:r}),u=f0({inputs:n.slice(o),backend:r});return f0({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Nr(o,l)),s=n.map(o=>o.shape),i=Z().getBool("WEBGL_PACK")?new Cre(n[0].shape,s):new Sre(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var Tre={kernelName:Zs,backendName:"webgl",kernelFunc:f0};function Nre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Jt({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(c),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Bl(f,f.dtype,"all",r),y;if(i){let A=C.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(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Ere={kernelName:Vu,backendName:"webgl",kernelFunc:Nre};function Rre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Jt({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(c),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Bl(f,f.dtype,"any",r),y;if(i){let A=C.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(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var $re={kernelName:Uu,backendName:"webgl",kernelFunc:Rre},Mre=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 ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},Fre=class{constructor(e,t,r,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${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=vt(o),u=Br("coords",o),d,h;if(s===1){h=o+1;let N=vt(h);d=`
${N} sourceLocR = ${N}(${u.join()}, 0);
++${u[o-1]};
${N} sourceLocG = ${N}(${u.join()}, 0);
++${u[o-2]};
${N} sourceLocA = ${N}(${u.join()}, 0);
--${u[o-1]};
${N} sourceLocB = ${N}(${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],m=p.map(N=>"int "+N),f=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(${f.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${A.join()})));`,w=`vec4(
getAChannel(${f.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,I=n?"":`
float getBestIndicesAChannel(${m.join()}) {
return getChannel(getBestIndicesA(${p.join()}),
vec2(${p.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${p.join()}),
vec2(${p.slice(-2).join()}));
}
${I}
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 = ${w};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${w};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function y9(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=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new Mre(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=y9(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function A9(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new Fre(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=A9(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function x9(e,t,r,n){let a=[r];if(C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!Z().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]=C.computeOutAndReduceShapes(l.shape,a),h=v.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=y9(e,p,n);s.push(c);let m=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return A9(e,t,n)}function Pre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Jt({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=x9(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var _re={kernelName:Ys,backendName:"webgl",kernelFunc:Pre};function zre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Jt({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=x9(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Ore={kernelName:Gu,backendName:"webgl",kernelFunc:zre},Dre=Yn+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,Lre=it({opSnippet:Dre}),Bre={kernelName:ju,backendName:"webgl",kernelFunc:Lre},Wre=Yn+"return log(x + sqrt(x * x + 1.0));",Vre=it({opSnippet:Wre}),Ure={kernelName:Hu,backendName:"webgl",kernelFunc:Vre},Gre=Yn+`
return atan(x);
`,jre=it({opSnippet:Gre}),Hre={kernelName:qu,backendName:"webgl",kernelFunc:jre},qre=ere+`
return atan(a, b);
`,Xre=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+tre+`
return result;
`,Kre=wr({opSnippet:qre,packedOpSnippet:Xre}),Zre={kernelName:Ku,backendName:"webgl",kernelFunc:Kre},Yre=Yn+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Jre=it({opSnippet:Yre}),Qre={kernelName:Xu,backendName:"webgl",kernelFunc:Jre},Yp=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 m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),r){let N=">=";this.userCode=`
const ivec2 strides = ivec2(${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 ${N} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${n?a?f: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,w=s%4,I=`
if (${m}) {
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)
);
${I}
}
int xC = xCCorner + ${b};
if (${w===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${I}
} else if (${w===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
initializationValue,
initializationValue
);
${I}
} else if (${w===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
initializationValue
);
${I}
}
}
setOutput(${x});
}
`}},S5=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,m=e.effectiveFilterWidth,f=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}, ${l});
const ivec3 pads = ivec3(${f}, ${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 < ${m};
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} * ${m} +
wR * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let I=Math.floor(s/4)*4,N=s%4,E=`
if (${A}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${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 < ${I}; 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 + ${I};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${E}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
initializationValue,
initializationValue
);
${E}
} else if (${N===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
);
${E}
}
}
setOutput(${w});
}
}
`}};function ene(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Cd(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return fn({inputs:{x:a},backend:r});let h=new Yp(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var tne={kernelName:Js,backendName:"webgl",kernelFunc:ene};function rne(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=C.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new S5(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var nne={kernelName:Qp,backendName:"webgl",kernelFunc:rne},ane=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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},sne=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=d-1-e.padInfo.front,m=h-1-e.padInfo.top,f=p-1-e.padInfo.left,g=1/(t*r*n);this.userCode=`
const ivec3 pads = ivec3(${c}, ${m}, ${f});
const float avgMultiplier = float(${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 ine(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=C.computePool3DInfo(i.shape,o,l,h,u,d),c=new sne(p);return r.runWebGLProgram(c,[a],i.dtype)}var one={kernelName:tf,backendName:"webgl",kernelFunc:ine};function lne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Cd([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=C.computePool2DInfo(i.shape,o,l,1,u),h=new ane(d);return r.runWebGLProgram(h,[a],i.dtype)}var une={kernelName:ef,backendName:"webgl",kernelFunc:lne};function dne(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return H0({a,b:s,transposeA:i,transposeB:o,backend:r})}var pne={kernelName:Qs,backendName:"webgl",kernelFunc:dne},hne=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.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)));
}
`}},cne=class{constructor(e,t,r,n,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r);let i="vec4(0.0)";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.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);
}
`}},fne=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.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=Z().getBool("WEBGL_PACK_NORMALIZATION")?new cne(n.shape,a.shape,s.shape,d,h,l):new hne(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},mne={kernelName:ci,backendName:"webgl",kernelFunc:fne},gne=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=vt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=yne(this.rank),n,a=e.map((s,i)=>`sourceLoc.${oy[i]} = start[${i}] + coords.${oy[i]};`);n=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${a.join(`
`)}
`,this.userCode=`
void main() {
${n}
setOutput(getSource(${r}));
}
`}},oy=["x","y","z","w","u","v"];function yne(e){if(e===1)return"sourceLoc";if(e<=6)return oy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Ane=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=vt(this.rank),r=Br("coords",this.rank),n=Br("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${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 xne(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,v.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 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),v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=lte(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=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ane(l):new gne(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),xne(a,o,l,r)}var bne={kernelName:xl,backendName:"webgl",kernelFunc:Md},vne=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.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=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=[],m=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Jt({inputs:{x:m},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:f},backend:r,attrs:{shape:d}}),y=Md({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(m),c.push(f),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},wne={kernelName:Ho,backendName:"webgl",kernelFunc:vne};function kne(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=n9(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var Ine={kernelName:rf,backendName:"webgl",kernelFunc:kne};function Sne(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Cne={kernelName:nf,backendName:"webgl",kernelFunc:Sne},Tne="return float(a != b);",b9=wr({opSnippet:Tne,cpuKernelImpl:rte,dtype:"bool"}),Nne={kernelName:ll,backendName:"webgl",kernelFunc:b9};function Uh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return fn({inputs:{x:a.complexTensorInfos.real},backend:r})}var Ene={kernelName:uh,backendName:"webgl",kernelFunc:Uh},Rne="return float(int(x));";function $ne(e,t){let r=new Xa(e.shape,Rne),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function ly(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return fn({inputs:{x:a},backend:r});let i=_t(a.shape),o=ly({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=qi({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Uh({inputs:{input:a},backend:r}),o=ly({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=fn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return $ne(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=b9({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 Mne={kernelName:ei,backendName:"webgl",kernelFunc:ly},K4="return ceil(x);",Fne=it({opSnippet:K4,packedOpSnippet:K4,cpuKernelImpl:Oee}),Pne={kernelName:ti,backendName:"webgl",kernelFunc:Fne},_ne=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));
}
`}},zne=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 One(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o;Z().getBool("WEBGL_PACK_CLIP")?o=new zne(a.shape):o=new _ne(a.shape);let l=[[s],[i]];return r.runWebGLProgram(o,[a],a.dtype,l)}var Dne={kernelName:Qa,backendName:"webgl",kernelFunc:One},Lne=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 Z4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Bne(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new Lne(n.shape),i=[Z4(n,a.complexTensorInfos.real),Z4(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var Wne={kernelName:th,backendName:"webgl",kernelFunc:Bne},Vne=class{constructor(e){this.outputShape=[],this.outputShape=C.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;
${r.join(`
`)}
}
`}},Une=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let r=this.outputShape,n=r.length,a=vt(n),s=Br("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],u=i.slice(-2),d=i.join(),h=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${d}), vec2(${u.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];h+=`
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
return getChannel(
getT${m}(${a0(i,l,f)}),
vec2(${a0(u,l,f)}));
}`}let p=o.length,c=o[o.length-1];h+=`
return getChannel(
getT${p}(${a0(i,l,c)}),
vec2(${a0(u,l,c)}));`,this.userCode=`
float getValue(${i.map(m=>"int "+m)}) {
${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 a0(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function Fm(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return fn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var Gne={kernelName:sh,backendName:"webgl",kernelFunc:Fm};function gu(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(f=>Uh({inputs:{input:f},backend:r})),h=e.map(f=>Fm({inputs:{input:f},backend:r})),p=gu(d,t,r),c=gu(h,t,r),m=qi({inputs:{real:p,imag:c},backend:r});return d.forEach(f=>r.disposeIntermediateTensorInfo(f)),h.forEach(f=>r.disposeIntermediateTensorInfo(f)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=v.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=C.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,m=Dee(h,p,n,c),f=C.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(f,n,m);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Z().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=gu(e.slice(0,d),t,r),p=gu(e.slice(d),t,r),c=gu([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new Une(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=jne(e,t,r),o=new Vne(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 jne(e,t,r){let n=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function v9(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return fn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),gu(o,s,r)}var Hne={kernelName:qo,backendName:"webgl",kernelFunc:v9},w9=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,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,A=f?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 w=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 < ${c}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${f}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${m===1}) {
if (${f}) {
dotProd +=
getX(batch, xR, xC, ${c}) *
getW(wR, wC, ${c}, d2);
} else {
dotProd +=
getX(batch, ${c}, xR, xC) *
getW(wR, wC, ${c}, d2);
}
} else if (${m===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${c}, d2),
getW(wR, wC, ${c} + 1, d2)
);
if (${f}) {
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, ${c} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${m===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${c}, d2),
getW(wR, wC, ${c} + 1, d2),
getW(wR, wC, ${c} + 2, d2)
);
if (${f}) {
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;
${w}
${b}
setOutput(result);
}
`}},qne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,r=e.padInfo.top,n=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${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 < ${c}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${m===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${c}) *
getW(wF, wR, wC, ${c}, d2);
} else if (${m===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, ${c} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${m===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);
}
`}},Xne=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=ln(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 k9({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",m=!1,f=!1,g,y=[];if(s!=null&&!c&&s.shape.length===3){let A=Jt({inputs:{x:s},backend:n,attrs:{perm:[1,2,0]}});y.push(A),s=A}if(!((h===1||p===1)&&d>g9)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&v.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]++,v.assert(Zp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let w=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(w);let I=H0({a:x,b:w,backend:n,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),N=n.texData.get(I.dataId);v.assert(N.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,N.shape=r.outShape,g=fn({inputs:{x:I},backend:n}),g.shape=r.outShape,y.push(I)}else{let A=c?e:Jt({inputs:{x:e},backend:n,attrs:{perm:[0,2,3,1]}}),x=A.shape,b=x[0]*x[1]*x[2],w=ve({inputs:{x:A},backend:n,attrs:{shape:[1,b,r.inChannels]}}),I=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),N=H0({a:w,b:I,transposeA:m,transposeB:f,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=[r.batchSize,r.outHeight,r.outWidth,r.outChannels],R=ve({inputs:{x:N},backend:n,attrs:{shape:E}});g=c?R:Jt({inputs:{x:R},backend:n,attrs:{perm:[0,3,1,2]}}),c||(y.push(A),y.push(R)),y.push(w),y.push(I),y.push(N)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function I9({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,m=c==="channelsLast",f=l*u*d,g=p*h,y=[f,g],A=!0,x=!1,b=[];if(s!=null&&!m&&s.shape.length===3){let Y=Jt({inputs:{x:s},backend:n,attrs:{perm:[1,2,0]}});b.push(Y),s=Y}let w=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),I=ve({inputs:{x:t},backend:n,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w),b.push(I);let N=new Xne(y,r),E=[w.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(N,[w],"float32",E),_=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(_);let M=a!=null,S=s!=null,z=o==="leakyrelu",O=o?Rm(o,!0):null,H=new m9(_.shape,I.shape,[1,g,r.outChannels],A,x,M,O,S,z),K=[_,I];if(a&&K.push(a),S&&K.push(s),z){let Y=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));K.push(Y),b.push(Y)}let D=n.runWebGLProgram(H,K,"float32"),Q=[1,p,h,r.outChannels],V=ve({inputs:{x:D},backend:n,attrs:{shape:Q}}),re=m?V:Jt({inputs:{x:V},backend:n,attrs:{perm:[0,3,1,2]}});m||b.push(V),b.push(D);for(let Y of b)n.disposeIntermediateTensorInfo(Y);return re}function Kne(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=C.convertConv2DDataFormat(l),p=C.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=k9({x:a,filter:s,convInfo:p,backend:r});else if(Z().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=I9({x:a,filter:s,convInfo:p,backend:r});else{let f=new w9(p);c=r.runWebGLProgram(f,[a,s],"float32")}let m=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),m}var Zne={kernelName:ri,backendName:"webgl",kernelFunc:Kne},Yne=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);
}
`}},Jne=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);
}
`}},Qne=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},eae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=r-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${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 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function tae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new Yne(p);return r.runWebGLProgram(c,[a,s],"float32")}var rae={kernelName:af,backendName:"webgl",kernelFunc:tae};function nae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(u),p=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new Jne(p);return r.runWebGLProgram(c,[a,s],"float32")}var aae={kernelName:ni,backendName:"webgl",kernelFunc:nae};function sae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new qne(u);return r.runWebGLProgram(d,[a,s],"float32")}var iae={kernelName:rh,backendName:"webgl",kernelFunc:sae};function oae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(a.shape,l,i,1,o),d=new Qne(u);return r.runWebGLProgram(d,[a,s],"float32")}var lae={kernelName:sf,backendName:"webgl",kernelFunc:oae};function uae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,o,1,i),d=new eae(u);return r.runWebGLProgram(d,[a,s],"float32")}var dae={kernelName:of,backendName:"webgl",kernelFunc:uae},pae=$d+`
return cos(x);
`,hae=it({opSnippet:pae}),cae={kernelName:ai,backendName:"webgl",kernelFunc:hae},fae=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,mae=it({opSnippet:fae}),gae={kernelName:si,backendName:"webgl",kernelFunc:mae},yae=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,m]=[`${i-1}.0`,`${o-1}.0`],[f,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*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
const float height_ratio = float(${f});
const float width_ratio = float(${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 > ${m} ) {
setOutput(float(${a}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},Aae=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new yae(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},xae={kernelName:Ko,backendName:"webgl",kernelFunc:Aae},Y4=class{constructor(e,t,r,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.op=e,this.outputShape=t;let a=t.length,s=this.op==="*"?"1.0":"0.0",i=r?s:`getX(${J4(a,"coords",this.op)})`,o=t[t.length-1],l="",u="";r?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${vt(a)} coords = getOutputCoords();
int end = ${Q4(a,"coords",this.op)};
float val = ${i};
int pow2 = int(pow(2.0, index));
if (${l}) {
int idx = ${u};
${Q4(a,"coords",this.op)} = idx;
val ${this.op}= getX(${J4(a,"coords",this.op)});
}
setOutput(val);
}
`}};function J4(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 ${e} is not yet supported`)}function Q4(e,t,r){if(e===1)return`${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 ${e} is not yet supported`)}function S9(e,t,r,n,a,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Jt({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=fn({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new Y4(e,l.shape,!1,s),m=[[p]],f=h;h=r.runWebGLProgram(c,[h],h.dtype,m),r.disposeIntermediateTensorInfo(f)}if(a){let p=new Y4(e,l.shape,a,s),c=h;h=r.runWebGLProgram(p,[h],h.dtype),r.disposeIntermediateTensorInfo(c)}if(o!=null){let p=C.getUndoAxesPermutation(o),c=Jt({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(l),c}return h}function bae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return S9("*",a,r,s,i,o)}var vae={kernelName:Xo,backendName:"webgl",kernelFunc:bae};function wae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return S9("+",a,r,s,i,o)}var kae={kernelName:ii,backendName:"webgl",kernelFunc:wae};function Iae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=n9(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=zee(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Sae={kernelName:lf,backendName:"webgl",kernelFunc:Iae},Cae=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 Tae(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),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=new Cae(m,s,i);return r.runWebGLProgram(f,[a],a.dtype)}var Nae={kernelName:Zo,backendName:"webgl",kernelFunc:Tae},C9=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=ln(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}
${u}
setOutput(result);
}
`}},T9=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ln(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<d;g++)p+=`
vec4 xTexelC${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?v.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="",m="";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}
}`,m="result = activation(result);");let f=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);
${f}
${m}
setOutput(result);
}
`}};function Eae(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]),v.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new T9(h):p=new C9(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 Rae={kernelName:oi,backendName:"webgl",kernelFunc:Eae},$ae=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},Mae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=r-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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 Fae(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=C.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new $ae(h);return r.runWebGLProgram(p,[a,s],"float32")}var Pae={kernelName:uf,backendName:"webgl",kernelFunc:Fae};function _ae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=C.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new Mae(h);return r.runWebGLProgram(p,[a,s],"float32")}var zae={kernelName:df,backendName:"webgl",kernelFunc:_ae},Oae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function Dae(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new Oae(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var Lae={kernelName:pf,backendName:"webgl",kernelFunc:Dae},Bae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
const ivec2 strides = ivec2(${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 Wae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new Bae(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 Vae={kernelName:nh,backendName:"webgl",kernelFunc:Wae};function Uae(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=C.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,m=[];for(let f=0;f<h;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=C.getEinsumPermutation(c,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Jt({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),m.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),m.push(x)),p===null?p=x:(p=I5({inputs:{a:x,b:p},backend:r}),m.push(p))}f<h-1&&(u[f]>=0&&(p=Mm({inputs:{x:p},backend:r,attrs:{axis:u[f]-(i.length-c),keepDims:!1}}),m.push(p)),c--)}for(let f of m)f!==p&&r.disposeIntermediateTensorInfo(f);return p}var Gae={kernelName:ah,backendName:"webgl",kernelFunc:Uae},jae="return (x >= 0.0) ? x : (exp(x) - 1.0);",Hae=`
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;
`,qae=it({opSnippet:jae,packedOpSnippet:Hae}),Xae={kernelName:ui,backendName:"webgl",kernelFunc:qae},Kae="return (b >= 1.0) ? a : a * (b + 1.0);",Zae=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Yae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vh(Zae,n.shape,a.shape):new Ou(Kae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},Jae={kernelName:hf,backendName:"webgl",kernelFunc:Yae},Qae=`
return vec4(equal(a, b));
`,ese="return float(a == b);",tse=wr({opSnippet:ese,packedOpSnippet:Qae,dtype:"bool",cpuKernelImpl:Lee}),rse={kernelName:Yo,backendName:"webgl",kernelFunc:tse},nse=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${C.ERF_P};
float a1 = ${C.ERF_A1};
float a2 = ${C.ERF_A2};
float a3 = ${C.ERF_A3};
float a4 = ${C.ERF_A4};
float a5 = ${C.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,ase=it({opSnippet:nse}),sse={kernelName:Zu,backendName:"webgl",kernelFunc:ase},ise=$d+`
return exp(x);
`,ose=`
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;
`,N9=it({opSnippet:ise,packedOpSnippet:ose,cpuKernelImpl:Bee,dtype:"float32"}),lse={kernelName:di,backendName:"webgl",kernelFunc:N9};function uy(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&&(v.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 use={kernelName:Jo,backendName:"webgl",kernelFunc:uy},ev="return exp(x) - 1.0;",dse=it({opSnippet:ev,packedOpSnippet:ev,cpuKernelImpl:Wee}),pse={kernelName:Qo,backendName:"webgl",kernelFunc:dse},tv=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 E9(e,t,r){let n=r.texData.get(e.dataId),a=v.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 tv("real",l,t),d=new tv("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"),m=qi({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let f=ve({inputs:{x:m},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(m),f}function hse(e){let{inputs:t,backend:r}=e,{input:n}=t;return E9(n,!1,r)}var cse={kernelName:cf,backendName:"webgl",kernelFunc:hse},fse=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 Gh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new fse(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var mse={kernelName:Yu,backendName:"webgl",kernelFunc:Gh},gse=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},yse={kernelName:el,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new gse(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},rv="return floor(x);",Ase=it({opSnippet:rv,packedOpSnippet:rv,cpuKernelImpl:Vee}),xse={kernelName:pi,backendName:"webgl",kernelFunc:Ase},bse=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,vse=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,wse=wr({opSnippet:bse,packedOpSnippet:vse,dtype:"int32"}),kse={kernelName:hi,backendName:"webgl",kernelFunc:wse},Ise=class{constructor(e){this.variableNames=["A"];let t=Hr(),[r,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${r}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},Sse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Hr(),[r,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${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;
}
`}},Cse={kernelName:Lp,backendName:"webgl",kernelFunc:Tse},pu;function Tse(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)&&(pu==null&&(pu=document.createElement("canvas").getContext("2d")),pu.canvas.width=l,pu.canvas.height=u,pu.drawImage(a,0,0,l,u),a=pu.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=Z().getBool("WEBGL_PACK")?new Sse(h):new Ise(h),m=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),m}function Nse(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:m}=n,f=C.convertConv2DDataFormat(d),g=C.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,f),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=k9({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else if(Z().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=I9({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else{let b=i!=null,w=o!=null,I=c==="leakyrelu",N=c?Rm(c,!1):null,E=new w9(g,b,N,w,I),R=[a,s];if(i&&R.push(i),o&&R.push(o),I){let _=r.makeTensorInfo([],"float32",v.createScalarValue(m,"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 Ese={kernelName:_s,backendName:"webgl",kernelFunc:Nse};function Rse(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,m=[],f=d;f==null&&(f=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=C.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),y=Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?Rm(p,y):null,x=[a,s],b=i!=null,w=o!=null,I=p==="leakyrelu";if(b&&x.push(i),w&&x.push(o),I){let _=r.makeTensorInfo([],"float32",v.createScalarValue(c,"float32"));x.push(_),m.push(_)}let N;y?N=new T9(g,b,A,w,I):N=new C9(g,b,A,w,I);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(N,x,"float32",E);return m.forEach(_=>r.disposeIntermediateTensorInfo(_)),R}var $se={kernelName:zs,backendName:"webgl",kernelFunc:Rse},Mse=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=vt(t.length),a=vt(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 Fse(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=C.prepareAndValidate(n,a),p=ve({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=ve({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=Uee(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let m=new Mse(i,h,[u,d]),f=r.runWebGLProgram(m,[c,p],c.dtype),g=ve({inputs:{x:f},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),g}var Pse={kernelName:rl,backendName:"webgl",kernelFunc:Fse},_se=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=vt(this.rank),n=zse(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 zse(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 R9(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0];if(Z().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let w=A[b];v.assert(w<=x-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${x-1}]`)}}let u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.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 m=[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=Gee(x,A,m);return h.forEach(w=>r.disposeIntermediateTensorInfo(w)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let f=new _se(p.shape,m),g=r.runWebGLProgram(f,[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 Ose={kernelName:tl,backendName:"webgl",kernelFunc:R9},Dse="return float(a > b);",Lse=`
return vec4(greaterThan(a, b));
`,Bse=wr({opSnippet:Dse,packedOpSnippet:Lse,cpuKernelImpl:jee,dtype:"bool"}),Wse={kernelName:nl,backendName:"webgl",kernelFunc:Bse},Vse="return float(a >= b);",Use=`
return vec4(greaterThanEqual(a, b));
`,Gse=wr({opSnippet:Vse,packedOpSnippet:Use,dtype:"bool",cpuKernelImpl:Hee}),jse={kernelName:fi,backendName:"webgl",kernelFunc:Gse};function Hse(e){let{inputs:t,backend:r}=e,{input:n}=t;return E9(n,!0,r)}var qse={kernelName:ff,backendName:"webgl",kernelFunc:Hse},Xse="return float(!isnan(x) && !isinf(x));",Kse=it({opSnippet:Xse,dtype:"bool"}),Zse={kernelName:Ju,backendName:"webgl",kernelFunc:Kse},Yse="return float(isinf(x));",Jse=it({opSnippet:Yse,dtype:"bool"}),Qse={kernelName:Qu,backendName:"webgl",kernelFunc:Jse},eie="return float(isnan(x));",tie=it({opSnippet:eie,dtype:"bool"}),rie={kernelName:ed,backendName:"webgl",kernelFunc:tie},nie="return float(a < b);",aie=`
return vec4(lessThan(a, b));
`,sie=wr({opSnippet:nie,packedOpSnippet:aie,cpuKernelImpl:qee,dtype:"bool"}),iie={kernelName:al,backendName:"webgl",kernelFunc:sie},oie="return float(a <= b);",lie=`
return vec4(lessThanEqual(a, b));
`,uie=wr({opSnippet:oie,packedOpSnippet:lie,cpuKernelImpl:Xee,dtype:"bool"}),die={kernelName:sl,backendName:"webgl",kernelFunc:uie};function pie(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=Kee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var hie={kernelName:mf,backendName:"webgl",kernelFunc:pie},cie=$d+`
return x < 0.0 ? 0./0. : log(x);
`,fie=`
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;
`,mie=it({opSnippet:cie,packedOpSnippet:fie,cpuKernelImpl:Zee}),gie={kernelName:yi,backendName:"webgl",kernelFunc:mie},yie=$d+`
return log(1.0 + x);
`,Aie=it({opSnippet:yie}),xie={kernelName:td,backendName:"webgl",kernelFunc:Aie},bie="return float(a >= 1.0 && b >= 1.0);",vie=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,wie=wr({opSnippet:bie,packedOpSnippet:vie,dtype:"bool"}),kie={kernelName:il,backendName:"webgl",kernelFunc:wie},Iie="return float(!(x >= 1.0));",Sie=it({opSnippet:Iie}),Cie={kernelName:rd,backendName:"webgl",kernelFunc:Sie},Tie="return float(a >= 1.0 || b >= 1.0);",Nie=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Eie=wr({opSnippet:Tie,packedOpSnippet:Nie,dtype:"bool"}),Rie={kernelName:ih,backendName:"webgl",kernelFunc:Eie},$ie=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 * ${o};
setOutput(val);
}
`}},Mie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
}
`}},Fie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Mie(a.shape,s,i,o,l):new $ie(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},Pie={kernelName:oh,backendName:"webgl",kernelFunc:Fie},_ie=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);
}
`}},zie=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 _ie(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},Oie={kernelName:gf,backendName:"webgl",kernelFunc:zie};function Die(e,t,r,n){let a=v.sizeFromShape(t),s=v.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 $9(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let I=0;I<x.length;I++)x[I]=a.shape[d[I]];let b=k5(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let w=r.texData.get(c.dataId);w.values=b}else c=$m(a,d,r);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(c.shape,u),g=m;i&&(g=C.expandShapeToKeepDim(m,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=Yee(A,v.sizeFromShape(f),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=Die(c,f,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var Lie={kernelName:Ai,backendName:"webgl",kernelFunc:$9},Bie=d9+`
return max(a, b);
`,Wie=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Em+`
return result;
`,Vie=wr({opSnippet:Bie,packedOpSnippet:Wie,cpuKernelImpl:Jee}),Uie={kernelName:xi,backendName:"webgl",kernelFunc:Vie};function Gie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Cd(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return fn({inputs:{x:a},backend:r});let h=new Yp(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var jie={kernelName:bi,backendName:"webgl",kernelFunc:Gie};function Hie(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=C.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new S5(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var qie={kernelName:lh,backendName:"webgl",kernelFunc:Hie},Xie=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 * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Kie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`
const ivec3 pads = ivec3(${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 Zie(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=C.computePool3DInfo(i.shape,o,l,h,u,d),c=new S5(p,"max",!0),m=r.runWebGLProgram(c,[i],i.dtype),f=new Kie(p),g=r.runWebGLProgram(f,[a,m],i.dtype);return r.disposeIntermediateTensorInfo(m),g}var Yie={kernelName:Af,backendName:"webgl",kernelFunc:Zie};function Jie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;Cd([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=C.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,m=new Yp(p,"max",c),f=r.runWebGLProgram(m,[o],o.dtype),g=new Xie(p),y=r.runWebGLProgram(g,[a,f],o.dtype);return r.disposeIntermediateTensorInfo(f),y}var Qie={kernelName:yf,backendName:"webgl",kernelFunc:Jie};function eoe(e,t,r,n){let a=new Yp(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new Yp(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var toe={kernelName:xf,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=C.computePool2DInfo(n.shape,a,s,u,i),[h,p]=eoe(n,o,d,l);return[h,p]}};function roe(e,t,r,n){let a=v.sizeFromShape(t),s=v.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 noe={kernelName:vi,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=v.parseAxisParam(s,n.shape),u=l,d=C.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],m=n;if(h){if(p){let x=i.texData.get(m.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=n.shape[d[N]];let w=k5(x,n.shape,n.dtype,d,b);m=i.makeTensorInfo(b,n.dtype);let I=i.texData.get(m.dataId);I.values=w}else m=$m(n,d,i);c.push(m),u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=C.computeOutAndReduceShapes(m.shape,u),y=f;a&&(y=C.expandShapeToKeepDim(f,l));let A=roe(m,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function aoe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Jt({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=v.sizeFromShape(c),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Bl(f,f.dtype,"min",r),y;if(i){let A=C.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(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var soe={kernelName:wi,backendName:"webgl",kernelFunc:aoe},ioe=d9+`
return min(a, b);
`,ooe=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Em+`
return result;
`,loe=wr({opSnippet:ioe,packedOpSnippet:ooe,cpuKernelImpl:Qee}),uoe={kernelName:ki,backendName:"webgl",kernelFunc:loe},doe=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=vt(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}));
}
`}},poe=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,m)=>c[0]+e[m]+c[1]);let n=e.length,a=vt(n),s=t.map(c=>c[0]).join(","),i=t.map((c,m)=>c[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=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);
}
`}},hoe=({inputs:e,backend:t,attrs:r})=>{let{x:n}=e,{paddings:a,mode:s}=r,i=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new poe(n.shape,a,s):new doe(n.shape,a,s);return t.runWebGLProgram(i,[n],n.dtype)},coe={kernelName:Ii,backendName:"webgl",kernelFunc:hoe},foe=`if (b == 0.0) return NAN;
return mod(a, b);`,moe=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Em+`
return result;
`,goe=wr({opSnippet:foe,packedOpSnippet:moe}),yoe={kernelName:nd,backendName:"webgl",kernelFunc:goe},Aoe=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}));
}
`}},xoe=`
if (a == b) {
return 1.0;
};
return a / b;`,boe=`
// 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;
`,M9=wr({opSnippet:xoe,packedOpSnippet:boe,checkOutOfBounds:!0}),voe={kernelName:li,backendName:"webgl",kernelFunc:M9},nv="return a - b;",F9=wr({opSnippet:nv,packedOpSnippet:nv,supportsComplex:!0,cpuKernelImpl:gte}),woe={kernelName:Bi,backendName:"webgl",kernelFunc:F9};function P9(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=$9({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=F9({inputs:{a,b:u},backend:r}),h=N9({inputs:{x:d},backend:r}),p=Mm({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),m=M9({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),m}var koe={kernelName:Di,backendName:"webgl",kernelFunc:P9};function Ioe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:P9({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new Aoe(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var Soe={kernelName:bf,backendName:"webgl",kernelFunc:Ioe},Coe=Yn+`
return -x;
`,Toe=`
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 Noe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=tte(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new To(n.shape,Toe):a=new Xa(n.shape,Coe),r.runWebGLProgram(a,[n],n.dtype)}var Eoe={kernelName:ol,backendName:"webgl",kernelFunc:Noe},Roe=Kn.nonMaxSuppressionV3Impl;function $oe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Roe(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Moe={kernelName:ul,backendName:"webgl",kernelFunc:$oe},Foe=Kn.nonMaxSuppressionV4Impl;function Poe(e){C.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}=Foe(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var _oe={kernelName:ad,backendName:"webgl",kernelFunc:Poe},zoe=Kn.nonMaxSuppressionV5Impl;function Ooe(e){C.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,m=l,f=u,{selectedIndices:g,selectedScores:y}=zoe(d,h,p,c,m,f);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Doe={kernelName:dl,backendName:"webgl",kernelFunc:Ooe},Loe=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)));
}
`}},Boe=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=v.sizeFromShape(a.shape),u=new Loe(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},Woe={kernelName:hl,backendName:"webgl",kernelFunc:Boe};function q0(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Uh({inputs:{input:n},backend:r}),s=q0({inputs:{x:a},backend:r}),i=Fm({inputs:{input:n},backend:r}),o=q0({inputs:{x:i},backend:r}),l=qi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Gh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Voe={kernelName:Nl,backendName:"webgl",kernelFunc:q0};function _9(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=Uh({inputs:{input:n},backend:r}),s=_9({inputs:{x:a},backend:r}),i=Fm({inputs:{input:n},backend:r}),o=q0({inputs:{x:i},backend:r}),l=qi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Gh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var Uoe={kernelName:pl,backendName:"webgl",kernelFunc:_9};function Goe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return uy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=uy({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.disposeIntermediateTensorInfo(d)),u}var joe={kernelName:cl,backendName:"webgl",kernelFunc:Goe},Hoe=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=vt(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}));
}
}
`}},qoe=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let n=e.length,a=vt(n),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[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=[`${a} rc = outputLoc;`,`${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 m=0,f=n===1?2:4;m<f;m++)c+=`
${h[m]}
if (${p}) {
result[${m}] = float(value);
} else {
${a} source = rc - start;
result[${m}] = 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);
}
`}},z9=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Gh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qoe(a.shape,s,i):new Hoe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},Xoe={kernelName:Ci,backendName:"webgl",kernelFunc:z9},Koe=`
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);
`,Zoe=`
// 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));
`+Em+`
return result;
`,Yoe=wr({opSnippet:Koe,packedOpSnippet:Zoe}),Joe={kernelName:Ti,backendName:"webgl",kernelFunc:Yoe};function Qoe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),d=u,h=C.getAxesPermutation(d,o),p=a;h!=null&&(p=Jt({inputs:{x:a},backend:r,attrs:{perm:h}}),d=C.getInnerMostAxes(d.length,o),l.push(p)),C.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let m=r.texData.get(p.dataId).values,{outVals:f,outShape:g,outDtype:y}=nte(p.shape,p.dtype,m,d);c=r.makeTensorInfo(g,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(p.shape,d),g=v.sizeFromShape(f),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=Ah(a.dtype),x=Bl(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(c);let m=C.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:m}})}return l.forEach(m=>r.disposeIntermediateTensorInfo(m)),c}var ele={kernelName:Ei,backendName:"webgl",kernelFunc:Qoe},O9=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=ate(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},tle={kernelName:sd,backendName:"webgl",kernelFunc:O9},rle="return 1.0 / x;",nle=it({opSnippet:rle}),ale={kernelName:id,backendName:"webgl",kernelFunc:nle},sle=Yn+`
return (x < 0.0) ? 0.0 : x;
`,ile=`
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;
`,ole=it({opSnippet:sle,packedOpSnippet:ile}),lle={kernelName:Ri,backendName:"webgl",kernelFunc:ole},ule=Yn+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,dle=`
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;
`,ple=it({opSnippet:ule,packedOpSnippet:dle}),hle={kernelName:Mi,backendName:"webgl",kernelFunc:ple},cle=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);
}
`}},fle=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 < ${r-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function mle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new fle(a.shape,l,u,s,i):new cle(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],"float32")}var gle={kernelName:$i,backendName:"webgl",kernelFunc:mle},yle=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,m=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(${c});
const int winWidth = int(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${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 Ale(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new yle(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var xle={kernelName:wf,backendName:"webgl",kernelFunc:Ale},ble=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);
}
`}},vle=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 < ${r-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function wle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new vle(a.shape,l,u,s,i):new ble(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var kle={kernelName:od,backendName:"webgl",kernelFunc:wle},Ile=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,m=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${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(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${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),
${r} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Sle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Ile(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Cle={kernelName:vf,backendName:"webgl",kernelFunc:Sle},Tle=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${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=vt(r);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}},Nle=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 < ${this.outputShape[r-2]}`,i=vt(r);r===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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 = ${d(n.slice())};
}
}
setOutput(result);
}
`;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let m=e.map((y,A)=>p(A,c)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function p(c,m){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${m[c]} - 1`:`${m[c]}`}}};function Ele(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return fn({inputs:{x:a},backend:r});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Nle(a.shape,o):new Tle(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var Rle={kernelName:ml,backendName:"webgl",kernelFunc:Ele},$le=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);
}
`}},Mle={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 $le(n.shape,s),[u,d]=C.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},Fle=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,Ple=it({opSnippet:Fle}),_le={kernelName:gl,backendName:"webgl",kernelFunc:Ple},zle="return inversesqrt(x);",Ole=it({opSnippet:zle,cpuKernelImpl:ste}),Dle={kernelName:Fi,backendName:"webgl",kernelFunc:Ole},D9=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=vt(a.length),l=vt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${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 Lle(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}=C.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]}}),m=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),f=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new D9(l,o,c.shape.length,m.shape.length,d,p),y=r.runWebGLProgram(g,[m,c,f],m.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(f),A}var Ble={kernelName:yl,backendName:"webgl",kernelFunc:Lle},Wle=class{constructor(e,t,r,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,r];let a="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=Z().getNumber("WEBGL_VERSION")===2?a:s,o=n==="left"?"<":"<=";this.userCode=`
int findBound(int batch, float value) {
int left = 0;
int right = numInputs;
int mid;
${i}
mid = (left + right) / 2;
if (getSortedSequence(batch, mid) ${o} value) {
left = mid + 1;
} else {
right = mid;
}
}
return right;
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int valueIndex = coords[1];
float value = getValues(batch, valueIndex);
setOutput(float(findBound(batch, value)));
}
`}};function Vle(e){let{inputs:t,backend:r,attrs:n}=e,{sortedSequence:a,values:s}=t,{side:i}=n,o=new Wle(a.shape[0],a.shape[1],s.shape[1],i),l=[[a.shape[1]]];return r.runWebGLProgram(o,[a,s],"int32",l)}var Ule={kernelName:kf,backendName:"webgl",kernelFunc:Vle},Gle=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=vt(r);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${n});
if (cVal >= 1.0) {
setOutput(getA(${a}));
} else {
setOutput(getB(${a}));
}
}
`}};function jle(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new Gle(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Nr(a.dtype,s.dtype))}var Hle={kernelName:Al,backendName:"webgl",kernelFunc:jle},qle=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${C.SELU_SCALEALPHA};
float scale = ${C.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Xle=it({opSnippet:qle}),Kle={kernelName:ld,backendName:"webgl",kernelFunc:Xle},Zle=$d+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,Yle=`
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;
`,Jle=it({opSnippet:Zle,packedOpSnippet:Yle,cpuKernelImpl:ote}),Qle={kernelName:_i,backendName:"webgl",kernelFunc:Jle},eue=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,tue=it({opSnippet:eue}),rue={kernelName:ud,backendName:"webgl",kernelFunc:tue},nue=$d+`
return sin(x);
`,aue=it({opSnippet:nue}),sue={kernelName:Pi,backendName:"webgl",kernelFunc:aue},iue=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,oue=it({opSnippet:iue}),lue={kernelName:bl,backendName:"webgl",kernelFunc:oue},uue=`
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;
`,due=it({opSnippet:uue}),pue={kernelName:dd,backendName:"webgl",kernelFunc:due},hue=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.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=z9({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(d.shape,s,o,!1),p=C.getPermuted(h.length,s.length,!1),c=C.getReshapedPermuted(d.shape,s,o,!1),m=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),f=Jt({inputs:{x:m},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:f},backend:r,attrs:{shape:c}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},cue={kernelName:vl,backendName:"webgl",kernelFunc:hue};function fue(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,m,f]=ute(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([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),r.makeTensorInfo([f.length],n.dtype,new Int32Array(f))]}var mue={kernelName:dh,backendName:"webgl",kernelFunc:fue};function gue(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]=dte(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var yue={kernelName:pd,backendName:"webgl",kernelFunc:gue};function Aue(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]=s9(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var xue={kernelName:ph,backendName:"webgl",kernelFunc:Aue};function bue(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
${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=s9(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var vue={kernelName:hh,backendName:"webgl",kernelFunc:bue};function wue(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=C.calculateShapes(s,a,o),c=!1;if(s.dtype==="string"){let y=r.bufferSync(a),A=r.bufferSync(s),x=v.decodeString(r.readSync(i.dataId)[0]),b=ite(y,A,o,p,d,u,l,h,x,c);return r.makeTensorInfo(o,b.dtype,b.values)}let m=new D9(u,l,a.shape.length,s.shape.length,h,[p,1],c),f=r.runWebGLProgram(m,[s,a,i],s.dtype),g=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),g}var kue={kernelName:ch,backendName:"webgl",kernelFunc:wue};function Iue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let m=Md({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,m})}var Sue={kernelName:wl,backendName:"webgl",kernelFunc:Iue},av="return sqrt(x);",Cue=it({opSnippet:av,packedOpSnippet:av,cpuKernelImpl:pte}),Tue={kernelName:zi,backendName:"webgl",kernelFunc:Cue},Nue="return x * x;",Eue=it({opSnippet:Nue}),Rue={kernelName:hd,backendName:"webgl",kernelFunc:Eue},sv="return (a - b) * (a - b);",$ue=wr({opSnippet:sv,packedOpSnippet:sv}),Mue={kernelName:Li,backendName:"webgl",kernelFunc:$ue};function Fue({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=Yn+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new Xa(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var Pue={kernelName:Ui,backendName:"webgl",kernelFunc:Fue},_ue=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=vt(r.length),s=vt(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 zue(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:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Ot.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(f)w=ve({inputs:{x:a},backend:r,attrs:{shape:m}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let N=Ot.computeOutShape(A,x,b),E=Md({inputs:{x:a},backend:r,attrs:{begin:A,size:N}});w=ve({inputs:{x:E},backend:r,attrs:{shape:m}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let N=r.readSync(a.dataId),E=Le(a.shape,a.dtype,N),R=hte(c,E,b,A);w=r.makeTensorInfo(m,a.dtype,R.values)}else{let N=new _ue(A,b,c);w=r.runWebGLProgram(N,[a],a.dtype)}let I=ve({inputs:{x:w},backend:r,attrs:{shape:m}});return r.disposeIntermediateTensorInfo(w),I}var Oue={kernelName:kl,backendName:"webgl",kernelFunc:zue};function Due(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),[m,f]=cte(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([m.length],"string",m),r.makeTensorInfo(h.shape,"int32",f)]}var Lue={kernelName:fh,backendName:"webgl",kernelFunc:Due};function Bue(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]=fte(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 Wue={kernelName:If,backendName:"webgl",kernelFunc:Bue};function Vue(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=mte(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var Uue={kernelName:Sf,backendName:"webgl",kernelFunc:Vue},Gue="return tan(x);",jue=it({opSnippet:Gue}),Hue={kernelName:Il,backendName:"webgl",kernelFunc:jue},que=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Xue=it({opSnippet:que}),Kue={kernelName:Wi,backendName:"webgl",kernelFunc:Xue},Zue=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=vt(this.rank),a=Yue(e);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function Yue(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 L9(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=>v.decodeString(h)):o,u=Le(a.shape,a.dtype,l),d=yte(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new Zue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var Jue={kernelName:es,backendName:"webgl",kernelFunc:L9},Que=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));
}
}
`}},ede=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 iv(e){let t=1;for(;t<e;)t*=2;return t}function tde(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=Z().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Z().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]=Ate(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,Gh({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,m=v.sizeFromShape(u)/d,f=ve({inputs:{x:c},attrs:{shape:[m,d]},backend:r});p&&yo(r,c);let g=iv(s),y=iv(d),A=null,x=()=>A===null?[f,f]:[f,A],b=(R,_,M)=>{let S=x(),z=new Que(M),O=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[_]],H=A;A=r.runWebGLProgram(z,S,"int32",O),yo(r,H)};for(let R=1;R<g;R*=2){let _=R*2;for(let M=R;M>=1;M/=2)b(_,M,[m,y])}for(let R=y;R>g;R/=2){let _=x(),M=new ede([m,R/2]),S=[[d],[A===null?1:0],[g]],z=A;A=r.runWebGLProgram(M,_,"int32",S),yo(r,z);let O=g/2,H=O*2;for(let K=O;K>=1;K/=2)b(H,K,A.shape)}let w=A;A=Md({inputs:{x:A},backend:r,attrs:{begin:0,size:[m,s]}}),yo(r,w);let I=R9({inputs:{x:f,indices:A},backend:r,attrs:{axis:1,batchDims:1}});yo(r,f);let N=u.slice(0,-1);N.push(s),w=A,A=ve({inputs:{x:A},attrs:{shape:N},backend:r}),yo(r,w);let E=I;return I=ve({inputs:{x:I},attrs:{shape:N},backend:r}),yo(r,E),[I,A]}var rde={kernelName:Sl,backendName:"webgl",kernelFunc:tde},nde=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 ade(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,[m,f]=u!=null?u:[h,p],g=[d,m,f,c],y=new nde(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var sde={kernelName:Cl,backendName:"webgl",kernelFunc:ade};function ide(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;Cd(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}=xte(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var ode={kernelName:Cf,backendName:"webgl",kernelFunc:ide};function lde(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 f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){p[s]=f;let g=Md({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});m[f]=y,h.push(g)}return h.forEach(f=>r.disposeIntermediateTensorInfo(f)),m}var ude={kernelName:Tl,backendName:"webgl",kernelFunc:lde},dde=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 pde(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=C.getAxesPermutation([u],o),h=a;d!=null&&(h=Jt({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=C.getInnerMostAxes(1,o)[0]);let p=C.segment_util.computeOutShape(h.shape,u,i),c=v.sizeFromShape([h.shape[u]]),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(m);let f=Ah(a.dtype),g=(b,w,I,N,E)=>{let R=b.shape[0],_=b.shape[1],M=C.segment_util.segOpComputeOptimalWindowSize(_,E),S={windowSize:M,inSize:_,batchSize:R,numSegments:E},z=new dde(S,w),O=r.compileAndRun(z,[b,I],N);if(l.push(O),O.shape[1]===E)return O;let H=O9({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),K=L9({inputs:{x:H},backend:r,attrs:{reps:[_/M]}});return l.push(H),l.push(K),g(O,w,K,N,E)},y=g(m,"unsortedSegmentSum",s,f,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=C.getUndoAxesPermutation(d);x=Jt({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var hde={kernelName:mh,backendName:"webgl",kernelFunc:pde},cde=[fre,gre,xre,wre,Ire,Tre,Ere,$re,_re,Ore,Bre,Ure,Hre,Zre,Qre,tne,nne,one,une,pne,mne,wne,Ine,Cne,Mne,Pne,Dne,Kte,Wne,Hne,Zne,rae,aae,iae,lae,dae,cae,gae,xae,vae,kae,Sae,Nae,Rae,Pae,zae,Lae,Vae,Gae,Xae,Jae,rse,sse,lse,use,pse,cse,mse,yse,xse,kse,Cse,Ese,$se,Pse,Ose,Wse,jse,Xte,qse,Gne,Zse,Qse,rie,Yte,iie,die,hie,gie,xie,kie,Cie,Rie,Pie,Oie,Lie,Uie,jie,qie,Yie,Qie,toe,noe,soe,uoe,coe,yoe,Soe,rre,Eoe,Moe,_oe,Doe,Nne,Woe,Uoe,joe,Xoe,Joe,Qte,ele,tle,Ene,voe,ale,lle,hle,are,gle,xle,kle,Cle,Rle,Mle,_le,Dle,Ble,Ule,Hle,Kle,Qle,rue,sue,lue,bne,koe,pue,cue,mue,yue,xue,vue,kue,Sue,Tue,Rue,Mue,Pue,Oue,Lue,Wue,Uue,woe,pre,Hue,Kue,Jue,rde,sde,hre,ode,ude,hde,Voe];for(let e of cde)qn(e);var Xi=Z();Xi.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Xi.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Xi.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Xi.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Xi.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Xi.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Xi.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Xi.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var fde="return a + b;",mde="return areal * breal - aimag * bimag;",gde="return areal * bimag + aimag * breal;",yde="return a / b;",Ade="return a * b;",xde="return (a - b) * (a - b);",bde="return a - b;",vde="return f32(a == b);",wde="return vec4<f32>(a == b);",kde="return f32(a > b);",Ide="return vec4<f32>(a > b);",Sde="return f32(a >= b);",Cde="return vec4<f32>(a >= b);",Tde="return f32(a < b);",Nde="return vec4<f32>(a < b);",Ede="return f32(a <= b);",Rde="return vec4<f32>(a <= b);",$de="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Mde=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,Fde=`
if (isnan(a)) { return a; }
if (isnan(b)) { return b; }
`,B9=`
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;
}
`,Pde=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,_de=`
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);
`,zde="return f32(a != b);",Ode="return vec4<f32>(a != b);",Dde=`
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);
`,Lde=`
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;
${B9}
return resultTemp;
`,Bde="if (a < 0.0) { return b * a; } return a;",Wde=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function ov(e,t){let r=t?B9:Fde;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = isnanVec4(a) | isnanVec4(b);
`+r+`
return resultTemp;
`:r+`
return ${e}(a, b);
`}function jh(e,t){switch(e){case 0:return Ade;case 1:return fde;case 2:return bde;case 3:return yde;case 4:return t?wde:vde;case 5:return t?Ide:kde;case 6:return t?Cde:Sde;case 7:return t?Nde:Tde;case 8:return t?Rde:Ede;case 9:return t?Mde:$de;case 10:return t?Ode:zde;case 11:return xde;case 12:return t?_de:Pde;case 14:return t?Wde:Bde;case 15:return ov("max",t);case 16:return ov("min",t);case 13:return t?Lde:Dde;case 17:return mde;case 18:return gde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Vde="return abs(a);",Ude="return ceil(a);",Gde="return cos(a);",jde=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Hde="return exp(a) - 1.0;",qde="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Xde=`
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;
`,Kde="return exp(a);",Zde="return floor(a);",Yde="return a;",Jde=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,Qde="return f32(!(a >= 1.0));",epe="return -a;",tpe="if (a < 0.0) { return uniforms.alpha * a; } return a;",rpe=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`,npe="return select(a, 0.0, a < 0.0);",ape="return clamp(a, 0.0, 6.0);",spe="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",ipe=`
return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
`,ope="return 1.0/sqrt(a);",lpe="return 1.0 / (1.0 + exp(-1.0 * a));",upe="return sin(a);",dpe=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,ppe="return sqrt(a);",hpe="return a * a;",cpe=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,fpe="return f32(i32((a)));";function bo(e,t){switch(e){case 0:return Vde;case 2:return Gde;case 3:return jde;case 1:return Ude;case 4:return t?Xde:qde;case 5:return Kde;case 6:return Hde;case 7:return Zde;case 8:return Yde;case 9:return Jde;case 10:return Qde;case 11:return epe;case 14:return t?rpe:tpe;case 12:return t?ipe:npe;case 13:return t?spe:ape;case 15:return ope;case 18:return lpe;case 16:return upe;case 17:return dpe;case 19:return ppe;case 20:return hpe;case 21:return cpe;case 22:return fpe;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Ki(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 jh(14,t);if(e==="sigmoid")return bo(18,t);if(e==="leakyrelu")return bo(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function mpe(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${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 xr(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Ms(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function m0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function C5(){return`
@stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`}function Wl(){return`
${C5()}
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 rt(){return`
${Wl()}
let index = getGlobalIndex();
`}function gpe(e,t,r,n=!1){let a=[];if(a.push(`
let workGroupSizeX = ${r.workGroupSize[0]}u;
let workGroupSizeY = ${r.workGroupSize[1]}u;
let workGroupSizeZ = ${r.workGroupSize[2]}u;
var<private> localId: vec3<u32>;
var<private> globalId: vec3<u32>;
var<private> numWorkgroups: vec3<u32>;
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex() -> i32 {
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
return i32(globalId.x);
}
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
workGroupID.y * numWorkgroups.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
}
`),n===!0)return a.push(`
struct Uniform {
size : i32,
numChannels : i32,
outShapeStrides : vec2<i32>,
dispatchSize : vec3<u32>,
};
@group(0) @binding(0) var<storage, write> result: array<${m0(t.dtype,r.isVec4)}>;
@group(0) @binding(2) var<uniform> uniforms: Uniform;
`),[lv,a.join(`
`),uv(t.shape),r.getUserCode()].join(`
`);let s=!1,i=!1,o="struct Uniforms { NAN : f32, ";r.variableNames.forEach((m,f)=>{let g=xr(e[f].shape.length);(g==="vec5"||g==="vec6")&&(i=!0),(s||i)&&(o+="@align(16) "),s=i,o+=`${m.charAt(0).toLowerCase()+m.slice(1)}Shape : ${g}, `});let l=xr(t.shape.length);i=l==="vec5"||l==="vec6",(s||i)&&(o+="@align(16) "),s=i,o+=`outShape : ${l}, `;let u=t.shape.length-1,d=xr(u);i=d==="vec5"||d==="vec6",(s||i)&&(o+="@align(16) "),s=i,o+=`
outShapeStrides: ${d}, `,r.size&&(s&&(o+="@align(16) "),s=!1,o+="size : i32, "),r.uniforms&&(s&&(o+="@align(16) "),o+=r.uniforms),o+="};",a.push(o),r.atomic?a.push(`
@group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
`):a.push(`
@group(0) @binding(0) var<storage, write> result: array<${m0(t.dtype,r.isVec4)}>;
`),r.variableNames.forEach((m,f)=>{a.push(`
@group(0) @binding(${1+f}) var<storage, read> ${m}: array<${m0(e[f].dtype,r.isVec4)}>;
`)}),o!==""&&a.push(`
@group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms: Uniforms;
`);let[h,p]=wpe(t.shape,r.dispatchLayout),c=[lv,a.join(`
`),uv(t.shape),h,ype(t.shape.length)];if(r.atomic||c.push(Ape(t.shape,t.dtype,r.isVec4)),p===t.shape.length){let m=e.map(f=>xpe(f,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
`);c.push(m)}return c.push(r.getUserCode()),c.join(`
`)}var lv=`
struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
// 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 getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {
let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);
return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;
}
fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {
let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);
return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;
}
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 ype(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;case 5:t+=`
fn getOutputIndexFromCoords(coords : vec5) -> i32 {
return coords.x * uniforms.outShapeStrides.x +
coords.y * uniforms.outShapeStrides.y +
coords.z * uniforms.outShapeStrides.z +
coords.w * uniforms.outShapeStrides.w +
coords.u;
}
`;break;case 6:t+=`
fn getOutputIndexFromCoords(coords : vec6) -> i32 {
return coords.x * uniforms.outShapeStrides.x +
coords.y * uniforms.outShapeStrides.y +
coords.z * uniforms.outShapeStrides.z +
coords.w * uniforms.outShapeStrides.w +
coords.u * uniforms.outShapeStrides.u +
coords.v;
}
`;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Ape(e,t,r){let n=e.length,a=m0(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] = ${a}(value);
}`,n>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=xr(n);r?s+=`
fn setOutputAtCoords(${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}(${i.join(", ")}));
setOutputAtIndexI32(flatIndex, value);
}
`}return s}function xpe(e,t,r,n){let a=bpe(e,r);return e.shape.length<=t.length&&(a+=vpe(e,t,r,n)),a}function bpe(e,t){let r=e.name,n=e.shape.length,a=xr(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(d=>`${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 vpe(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=xr(l);if(v.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=C.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.${Ms(g+h)} = 0;`).join(`
`);let c="";if(l<2&&o>0)c="coords";else if(l>1){let g=xr(o),y=e.shape.map((A,x)=>`coords.${Ms(x+h)}`).join(", ");c=`${g}(${y})`}else c="coords";let m=`uniforms.${a.charAt(0).toLowerCase()+a.slice(1)}Shape`,f=`${o}D`;return r?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
var coords = getCoordsFromIndex(globalIndex);
${p}
return ${a}[getIndexFromCoords${f}(${c}, ${m}) / 4];
}
fn ${i}Coords(coordsIn : ${u}) -> vec4<f32> {
var coords = coordsIn;
${p}
return ${a}[getIndexFromCoords${f}(${c}, ${m}) / 4];
}
`:`
fn ${i}Index(globalIndex : i32) -> f32 {
var coords = getCoordsFromIndex(globalIndex);
${p}
return f32(${a}[getIndexFromCoords${f}(${c}, ${m})]);
}
fn ${i}Coords(coordsIn : ${u}) -> f32 {
var coords = coordsIn;
${p}
return f32(${a}[getIndexFromCoords${f}(${c}, ${m})]);
}
`}function wpe(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${xr(s)}{
let globalIndex = getGlobalIndex();
return getCoordsFromIndex(globalIndex);
}
`,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let m=mpe(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let f=0;f<m.length;f++)i+=`let d${c[f]} = index${p} / ${m[f]};`,f===m.length-1?i+=`let d${c[f+1]} = index${p} - d${c[f]} * ${m[f]};`:i+=`index${p} = index${p} - d${c[f]} * ${m[f]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=xr(l),h=`fn getOutputCoords() -> ${d} {
${i}
`;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function uv(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=v.computeStrides(e),n=xr(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;return s="var index2 = index;"+r.map((i,o)=>{let l=`let ${a[o]} = index2 / uniforms.outShapeStrides.${Ms(o)}`,u=o===r.length-1?`let ${a[o+1]} = index2 - ${a[o]} * uniforms.outShapeStrides.${Ms(o)}`:`index2 = index2 - ${a[o]} * uniforms.outShapeStrides.${Ms(o)}`;return`${l}; ${u};`}).join(""),`
fn getCoordsFromIndex(index : i32) -> ${n} {
${s}
return ${n}(${a.join(",")});
}
`}var W9={};Be(W9,{ArrayBufferToTypedArray:()=>U9,GPUBytesPerElement:()=>g0,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>T5,computeWorkGroupSizeForMatMul:()=>V9,computeWorkPerThreadForConv2d:()=>N5,flatDispatchLayout:()=>Ye,isWebGPUSupported:()=>E5,tilesFitEvenlyIntoShape:()=>Za});var $o=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function Za(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function Oe(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil($o(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil($o(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil($o(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function T5(e,t){let r=$o(e.x.map(a=>t[a])),n=$o(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function V9(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function N5(e,t){let r=$o(e.x.map(a=>t[a])),n=$o(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Ye(e){return{x:e.map((t,r)=>r)}}function g0(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function U9(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 E5(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function G9(e,t,r,n){return v.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};
${Wl()}
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 kpe=class{constructor(e,t,r,n,a,s=null,i=null,o=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=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&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=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=n,this.batchBEqualOne=a,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`}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[Za(n,this.aShape.slice(1)),Za(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=Ki(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> {
${this.batchAEqualOne?`
let batchASize = 0;
let batch = 0;
`:`
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> {
${this.batchBEqualOne?`
let batchBSize = 0;
let batch = 0;
`:`
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);
}
}
${G9(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
`}};function R5(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}>;
${Wl()}
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]}>;
${Wl()}
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 Spe=class{constructor(e,t,r,n,a,s=!1,i=!1,o=null,l=null,u=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 d=s?e[1]:e[2];this.workGroupSize=V9(t[1],d,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let h=o!=null,p=u!=null;h&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=s,this.transposeB=i,this.addBias=h,this.activation=l,this.hasPreluActivationWeights=p,this.batchAEqualOne=n,this.batchBEqualOne=a;let c=this.outputShape[2],m=this.transposeB?[this.outputShape[0],c,d]:[this.outputShape[0],d,c];[this.fitA,this.fitB]=this.getShapeFit(m),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${i}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}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),v.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[Za(a,this.aShape.slice(1)),Za(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=Ki(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 {
${this.batchAEqualOne?`
let batch = 0;
let batchASize = 0;
`:`
let batch = i32(globalId.z);
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
`}
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${this.batchBEqualOne?`
let batch = 0;
let batchBSize = 0;
`:`
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?R5([this.workPerThread,this.workPerThread,1],this.workGroupSize):Ipe(this.workGroupSize)}
`}};function Cpe(){return`
var<workgroup> sumValues : array<f32, workGroupSizeX>;
${Wl()}
let coords = getOutputCoords();
let batch = coords[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
let dataA = mm_readA(batch, row, k);
let dataB = mm_readB(batch, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`}var Tpe=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=a,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=t,this.batchBEqualOne=r,this.shaderKey=`matMulReduce_${this.activation}_${n}_${a}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e="return f32(A[batch * batchASize + row * uniforms.dimInner + col]);":e="return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);";let t;this.transposeB===!1?t="return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);":t="return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);";let r="",n="";if(this.activation){let s=Ki(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(batchIn: i32, row : i32, col : i32) -> f32 {
${this.batchAEqualOne?`
let batchASize = 0;
let batch = 0;
`:`
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = batchIn;
`}
${e}
}
fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {
${this.batchBEqualOne?`
let batch = 0;
let batchBSize = 0;
`:`
let batch = batchIn;
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);
}
${Cpe()}
`}};function Npe(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.
${Wl()}
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 >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var Epe=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}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=Ki(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 {
${this.batchAEqualOne?`
let batch = 0;
let batchASize = 0;
`:`
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 {
${this.batchBEqualOne?`
let batch = 0;
let batchBSize = 0;
`:`
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);
}
}
${Npe(this.workGroupSize)}
`}};function et(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${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 Rpe={kernelName:fl,backendName:"webgpu",kernelFunc:et};function $5({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],m=n?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),A=v.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,m]);v.assert(h===p,()=>`Error in matMul: inner shapes (${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],w=n?[A,m,p]:[A,p,m],I=et({inputs:{x:e},backend:a,attrs:{shape:b}}),N=et({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[I,N],R=Math.max(y,A),_=y===1,M=A===1,S=h%4===0&&m%4===0&&!r&&!n,z;c*m<=32?z=new Tpe([R,c,m],_,M,r,n,s,l,i):!r&&!n&&(c<=16&&(m<=512||p>=2*m)||m<=16&&(c<=512||h>=2*c))?z=new Epe(b,w,[R,c,m],s,l,i):S?z=new kpe(b,[R,c,m],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),_,M,s,l,i):z=new Spe(b,[R,c,m],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),_,M,r,n,s,l,i);let O=[I,N];s&&O.push(s),i&&O.push(i);let H=[{type:"int32",data:[c]},{type:"int32",data:[m]},{type:"int32",data:[h]}];l==="leakyrelu"&&(H.push({type:"float32",data:[o]}),z.uniforms+=" alpha : f32,");let K=a.runWebGPUProgram(z,O,e.dtype,H),D=et({inputs:{x:K},backend:a,attrs:{shape:x}});E.push(K);for(let Q of E)a.disposeData(Q.dataId);return D}function $pe(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 $5({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Mpe={kernelName:Ps,backendName:"webgpu",kernelFunc:$pe},dv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(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 {
${jh(this.op,!1)}
}
${rt()}
if(index < uniforms.size) {
let areal = getARealByOutputIndex(index);
let aimag = getAImagByOutputIndex(index);
let breal = getBRealByOutputIndex(index);
let bimag = getBImagByOutputIndex(index);
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},Fpe=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ye(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${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 {
${jh(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${rt()}
// 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);
${t}
setOutputAtIndex(flatIndex, binaryOperation(a, b));
}
}
}
`}},Ppe=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${jh(this.op,this.isVec4)}
}
${rt()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}},j9=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${jh(this.op,!1)}
}
${rt()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}};function pv(e,t,r){if(v.arraysEqual(t,r)&&v.sizeFromShape(t)%4===0)return new Ppe(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new Fpe(e,t,r,a):new j9(e,t,r)}function zn(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var _pe={kernelName:mi,backendName:"webgpu",kernelFunc:zn};function Fd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=zn({inputs:{x:n},backend:r}),l=zn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var zpe={kernelName:eh,backendName:"webgpu",kernelFunc:Fd},Hh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${bo(this.op,!1)}
}
${rt()}
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 Hh(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,m;if(e!==0)[c,m]=[[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},w=pv(e,i.shape,o.shape);return l.runWebGPUProgram(w,[x,b],Nr(y.dtype,A.dtype))});else{let g=new dv(17,i.shape,o.shape),y=new dv(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"),m=l.runWebGPUProgram(y,A,"float32")}let f=Fd({inputs:{real:c,imag:m},backend:l});return l.disposeData(c.dataId),l.disposeData(m.dataId),f}let u=n||Nr(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"?C.fromUint8ToStringArray(h):h,m=i.dtype==="string"?C.fromUint8ToStringArray(p):p,[f,g]=t(i.shape,o.shape,c,m,u);return l.makeTensorInfo(g,u,f)}let d=pv(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:Ope,ceilImpl:Dpe,concatImpl:Lpe,equalImpl:Bpe,expImpl:Wpe,expm1Impl:Vpe,floorImpl:Upe,gatherNdImpl:Gpe,gatherV2Impl:jpe,greaterEqualImpl:Hpe,greaterImpl:qpe,lessEqualImpl:Xpe,lessImpl:Kpe,logImpl:Zpe,maxImpl:Ype,maximumImpl:Jpe,minimumImpl:Qpe,multiplyImpl:ehe,negImpl:the,notEqualImpl:rhe,prodImpl:nhe,rangeImpl:ahe,rsqrtImpl:she,scatterImpl:ihe,simpleAbsImpl:ohe,sliceImpl:lhe,stridedSliceImpl:uhe,stringNGramsImpl:dhe,subImpl:phe,tileImpl:hhe,topKImpl:che,transposeImpl:fhe,uniqueImpl:zxe}=Sm,mhe=kr({opType:0,cpuKernelImpl:ohe}),ghe={kernelName:jo,backendName:"webgpu",kernelFunc:mhe},yhe=qr({opSnippet:1,cpuKernelImpl:Ope,supportsComplex:!0}),Ahe={kernelName:Ja,backendName:"webgpu",kernelFunc:yhe},xhe=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=Ye(this.outputShape),this.dispatch=Oe(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`
${rt()}
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, ${t});
}
}
}
`}};function bhe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return zn({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Nr(o,l)),s=n.map(o=>o.shape),i=new xhe(s);return r.runWebGPUProgram(i,n,a)}var vhe={kernelName:Zs,backendName:"webgpu",kernelFunc:bhe},H9=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=C.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Ms(this.inputShape.length-1)}`,r=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let a=0;a<this.outputShape.length;a++)n+=`outputCoords.${Ms(a)},`;return n};return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${e}
${rt()}
let outputIndex = index / i32(workGroupSizeX);
let reduceLength = ${t()};
var bestIndex = i32(localId.x);
var bestValue = uniforms.infinityValue;
let outputCoords = getCoordsFromIndex(outputIndex);
for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;
k = k + i32(workGroupSizeX)) {
let candidate = getX(${r()} 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(reduceLength), 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]);
}
}
`}},whe=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=Oe(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]}>;
${C5()}
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
@builtin(workgroup_id) workgroupId : vec3<u32>) {
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] = A[y * width + x];
}
workgroupBarrier();
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputAtIndex((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},khe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=xr(this.outputShape.length),t=Ihe(this.newDim);return`
${rt()}
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 Ihe(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC.${Ms(n)}`;return r.join()}function Ya(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=fhe(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&v.arraysEqual(s,[1,0])){let d=new whe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new khe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var She={kernelName:Vi,backendName:"webgpu",kernelFunc:Ya};function Che(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ya({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new H9(l.shape,i[0],"max"),h=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var The={kernelName:Ys,backendName:"webgpu",kernelFunc:Che};function Nhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ya({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new H9(l.shape,i[0],"min"),h=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Ehe={kernelName:Gu,backendName:"webgpu",kernelFunc:Nhe},q9=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=Ye(this.outputShape),this.dispatch=Oe(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"),`
${rt()}
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});
}
}
`}},X9=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${rt()}
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 Rhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.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 X9(d):(h=new q9(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 $he={kernelName:Js,backendName:"webgpu",kernelFunc:Rhe};function Mhe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return $5({a,b:s,transposeA:i,transposeB:o,backend:r})}var Fhe={kernelName:Qs,backendName:"webgpu",kernelFunc:Mhe},Phe=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${xr(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=xr(this.rank),t=_he(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${dy[a]} = uniforms.start[${a}] + coords.${dy[a]};`),`
${rt()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getCoordsFromIndex(index);
${r.join(`
`)}
setOutputAtIndex(index, getSource(${t}));
}
}
`}},dy=["x","y","z","w","u","v"];function _he(e){if(e===1)return"sourceLoc";if(e<=6)return dy.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Pd(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=lhe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new Phe(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var zhe={kernelName:xl,backendName:"webgpu",kernelFunc:Pd},Ohe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.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=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=[],m=et({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Ya({inputs:{x:m},backend:r,attrs:{perm:u}}),g=et({inputs:{x:f},backend:r,attrs:{shape:d}}),y=Pd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(m),c.push(f),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},Dhe={kernelName:Ho,backendName:"webgpu",kernelFunc:Ohe},K9=qr({opSnippet:10,dtype:"bool",cpuKernelImpl:rhe}),Lhe={kernelName:ll,backendName:"webgpu",kernelFunc:K9};function qh(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:uh,backendName:"webgpu",kernelFunc:qh};function Whe(e,t){let r=new Hh(e.shape,22),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function py(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=py({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Fd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=qh({inputs:{input:a},backend:r}),o=py({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!v.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 Whe(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=K9({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 Vhe={kernelName:ei,backendName:"webgpu",kernelFunc:py},Uhe=kr({opType:1,cpuKernelImpl:Dpe}),Ghe={kernelName:ti,backendName:"webgpu",kernelFunc:Uhe},jhe=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
${rt()}
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);
}
}
`}},Hhe=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
${rt()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
if (isnan(value)) {
setOutputAtIndex(index, value);
return;
}
setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function qhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(a.shape)%4===0?o=new jhe(a.shape):o=new Hhe(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var Xhe={kernelName:Qa,backendName:"webgpu",kernelFunc:qhe},Khe=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(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`
${rt()}
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 Pm(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 Zhe={kernelName:sh,backendName:"webgpu",kernelFunc:Pm};function hy(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>qh({inputs:{input:A},backend:r})),m=e.map(A=>Pm({inputs:{input:A},backend:r})),f=hy(c,t,r),g=hy(m,t,r),y=Fd({inputs:{real:f,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),m.forEach(A=>r.disposeData(A.dataId)),r.disposeData(f.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let w=v.sizeFromShape(b.shape.slice(t));return et({inputs:{x:b},backend:r,attrs:{shape:[-1,w]}})}),m=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),f=C.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=Lpe(m,f,n,g),A=C.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}=Yhe(e,t,r),o=s.map(c=>c.shape),l=new Khe(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=et({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function Yhe(e,t,r){let n=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>et({inputs:{x:a},backend:r,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function Z9(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return zn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),hy(o,s,r)}var Jhe={kernelName:qo,backendName:"webgpu",kernelFunc:Z9},Qhe=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.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),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.remainder=this.convInfo.inChannels%4===0,this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.remainder}`}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[Za(e,[r,a]),Za(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=G9(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.remainder?`// 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=Ki(this.activation,this.isVec4);this.hasPreluActivationWeights?a=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`: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);
}
}
${e}
`}},ece=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[1],y:[2,3],z:[0]},this.workGroupSize=T5(this.dispatchLayout,this.outputShape),this.elementsPerThread=N5(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}_${this.isChannelsLast}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;v.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.convInfo.outHeight*this.convInfo.outWidth,i=this.convInfo.outChannels,o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Za(n,[s,o]),Za(a,[o,i])]}getUserCode(){let e=this.isChannelsLast?`
let coord = vec4<i32>(batch, xRow, xCol, col % inChannels);
`:`
let coord = vec4<i32>(batch, col % inChannels, xRow, xCol);
`,t=this.isChannelsLast?`
let outCoord = vec4<i32>(
batch,
row / outWidth,
row % outWidth,
col);
`:`
let outCoord = vec4<i32>(
batch,
col,
row / outWidth,
row % outWidth);
`,r=R5(this.elementsPerThread,this.workGroupSize),n=`
let inChannels = uniforms.wShape[2];
let outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
let outRow = row / outWidth;
let outCol = row % outWidth;
let WRow = col / (uniforms.filterDims[1] * inChannels);
let WCol = col / inChannels % uniforms.filterDims[1];
let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
${e}
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x[getIndexFromCoords4D(coord, uniforms.xShape)];
}
return 0.0;`,a=this.fitA?`${n}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${n}
}
return 0.0;
`,s=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;
`,i="",o="";if(this.activation){let u=Ki(this.activation,!1);this.hasPreluActivationWeights?i=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${u}
}`:i=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${u}
}
`,o="value = activation(value, outCoord);"}let l=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${i}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
${a}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${s}
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
${t}
${l}
${o}
result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${r}
`}};function tce({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r.dataFormat==="channelsLast",u=!l,d=!1,h=l&&r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",p,c;if(h){let g=r.inHeight*r.inWidth*r.inChannels;p=et({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,g]}}),c=et({inputs:{x:t},backend:n,attrs:{shape:[1,g,r.outChannels]}})}else p=et({inputs:{x:e},backend:n,attrs:{shape:l?[r.batchSize,r.inHeight*r.inWidth,r.inChannels]:[r.batchSize,r.inChannels,r.inHeight*r.inWidth]}}),c=et({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});let m=$5({a:l?p:c,b:l?c:p,transposeA:u,transposeB:d,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),f=et({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(p.dataId),n.disposeData(c.dataId),n.disposeData(m.dataId),f}function Y9({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=r.dataFormat==="channelsLast",h;if(d&&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 tce({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let p=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0&&d,c=[r.padInfo.top,r.padInfo.left],m=[{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]}];p?h=new Qhe(r,l,o,u):h=new ece(r,l,o,u);let f=r.outHeight*r.outWidth,g=r.outChannels,y=r.filterHeight*r.filterWidth*r.inChannels;m.push({type:"int32",data:[f]},{type:"int32",data:[g]},{type:"int32",data:[y]});let A=[e,t];return l&&A.push(a),u&&A.push(s),o==="leakyrelu"&&(m.push({type:"float32",data:[i]}),h.uniforms+=" alpha : f32,"),n.runWebGPUProgram(h,A,e.dtype,m)}function rce(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=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return Y9({x:a,filter:s,convInfo:p,backend:n})}var nce={kernelName:ri,backendName:"webgpu",kernelFunc:rce},ace=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=T5(this.dispatchLayout,this.outputShape),this.elementsPerThread=N5(this.dispatchLayout,this.outputShape),this.dispatch=Oe(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;
}
${R5(this.elementsPerThread,this.workGroupSize)}
`}},sce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
${rt()} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[${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 (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`}};function ice(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(u),p=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],m;if(Z().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))m=new sce(p);else{m=new ace(p);let f=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[f]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(m,[a,s],"float32",c)}var oce={kernelName:ni,backendName:"webgpu",kernelFunc:ice},lce=kr({opType:2}),uce={kernelName:ai,backendName:"webgpu",kernelFunc:lce},dce=kr({opType:3}),pce={kernelName:si,backendName:"webgpu",kernelFunc:dce},hce=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${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`
${rt()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let height_ratio = f32(${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);
}
}
}
`}},cce=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 hce(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},fce={kernelName:Ko,backendName:"webgpu",kernelFunc:cce},hv=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=r,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",r=this.exclusive?t:`getX(${cv(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"),`
${rt()}
if (index < uniforms.size) {
var coords = getCoordsFromIndex(index);
let end = ${fv(e,"coords",this.op)};
var val = ${r};
let pow2 = i32(pow(2.0, uniforms.index));
if (${a}) {
let idx = ${s};
${fv(e,"coords",this.op)} = idx;
val ${this.op}= getX(${cv(e,"coords",this.op)});
}
setOutputAtIndex(index, val);
}
}
`}};function cv(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 ${e} is not yet supported`)}function fv(e,t,r){if(e===1)return`${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 ${e} is not yet supported`)}function J9(e,t,r,n,a,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Ya({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=zn({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new hv(e,l.shape,!1,s),m=h,f=[{type:"float32",data:[p]}];h=r.runWebGPUProgram(c,[h],h.dtype,f),r.disposeData(m.dataId)}if(a){let p=new hv(e,l.shape,a,s),c=h,m=[{type:"float32",data:[0]}];h=r.runWebGPUProgram(p,[h],h.dtype,m),r.disposeData(c.dataId)}if(o!=null){let p=C.getUndoAxesPermutation(o),c=Ya({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeData(h.dataId),r.disposeData(l.dataId),c}return h}function mce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return J9("*",a,r,s,i,o)}var gce={kernelName:Xo,backendName:"webgpu",kernelFunc:mce};function yce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return J9("+",a,r,s,i,o)}var Ace={kernelName:ii,backendName:"webgpu",kernelFunc:yce},xce=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
${rt()}
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 = ${this.getInputSamplingString()};
setOutputAtIndex(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function bce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=[{type:"int32",data:[s]}],g=new xce(m,i);return r.runWebGPUProgram(g,[a],a.dtype,f)}var vce={kernelName:Zo,backendName:"webgpu",kernelFunc:bce},Q9=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=Ki(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}
${C5()}
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}
${t}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},eS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=Ki(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);
}
}
${Wl()}
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}
${t}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function wce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.filterHeight===3&&h.inChannels%4===0?c=new Q9(h):(c=new eS(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var kce={kernelName:oi,backendName:"webgpu",kernelFunc:wce},tS=qr({opSnippet:0,cpuKernelImpl:ehe,supportsComplex:!0}),Ice={kernelName:Si,backendName:"webgpu",kernelFunc:tS},Sce=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${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;
}
${rt()}
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) {
${r}
}
}
`}};function Xh(e,t,r,n,a){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=C.getAxesPermutation(l,s),d=e;u!=null&&(d=Ya({inputs:{x:e},attrs:{perm:u},backend:a}),l=C.getInnerMostAxes(l.length,s),i.push(d)),C.assertAxesAreInnerMostDims(n,l,s);let[h,p]=C.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=C.expandShapeToKeepDim(h,o));let m;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let f=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=Ype(f,v.sizeFromShape(p),c,e.dtype);m=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=nhe(d.shape,d.dtype,f,l);m=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let f=v.sizeFromShape(p),g=v.sizeFromShape(d.shape)/f,y={windowSize:f,inSize:f,batchSize:g,outSize:1},A=n==="mean"?"float32":Ah(e.dtype),x=[{type:"int32",data:[f]}],b=new Sce(y,n),w=a.runWebGPUProgram(b,[d],A,x);i.push(w),m=et({inputs:{x:w},attrs:{shape:c},backend:a})}return i.forEach(f=>a.disposeData(f.dataId)),m}function M5(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Xh(a,s,i,"sum",r)}var Cce={kernelName:Oi,backendName:"webgpu",kernelFunc:M5};function Tce(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=C.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,m=[];for(let f=0;f<h;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=C.getEinsumPermutation(c,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Ya({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),m.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=et({inputs:{x},backend:r,attrs:{shape:b}}),m.push(x)),p===null?p=x:(p=tS({inputs:{a:x,b:p},backend:r}),m.push(p))}f<h-1&&(u[f]>=0&&(p=M5({inputs:{x:p},backend:r,attrs:{axis:u[f]-(i.length-c),keepDims:!1}}),m.push(p)),c--)}for(let f of m)f!==p&&r.disposeData(f.dataId);return p}var Nce={kernelName:ah,backendName:"webgpu",kernelFunc:Tce},Ece=kr({opType:4}),Rce={kernelName:ui,backendName:"webgpu",kernelFunc:Ece},$ce=qr({opSnippet:4,dtype:"bool",cpuKernelImpl:Bpe}),Mce={kernelName:Yo,backendName:"webgpu",kernelFunc:$ce},rS=kr({opType:5,cpuKernelImpl:Wpe,dtype:"float32"}),Fce={kernelName:di,backendName:"webgpu",kernelFunc:rS};function cy(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&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),et({inputs:{x:s},backend:n,attrs:{shape:o}})}var Pce={kernelName:Jo,backendName:"webgpu",kernelFunc:cy},_ce=kr({opType:6,cpuKernelImpl:Vpe}),zce={kernelName:Qo,backendName:"webgpu",kernelFunc:_ce},Oce=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
${rt()}
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.value);
}
}
`}};function _d(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Oce(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var Dce={kernelName:Yu,backendName:"webgpu",kernelFunc:_d},Lce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
${rt()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputAtIndex(index, outputValue);
}
}
`}},Bce={kernelName:el,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Lce(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},Wce=kr({opType:7,cpuKernelImpl:Upe}),Vce={kernelName:pi,backendName:"webgpu",kernelFunc:Wce},Uce=qr({opSnippet:12,dtype:"int32"}),Gce={kernelName:hi,backendName:"webgpu",kernelFunc:Uce},jce=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){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>"};
${rt()}
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]));
}
}
}
`}},Hce={kernelName:Lp,backendName:"webgpu",kernelFunc:qce},hu;function qce(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(Z().getBool("WEBGPU_USE_IMPORT")&&i)return mv({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(hu==null&&(hu=document.createElement("canvas").getContext("2d")),hu.canvas.width=d,hu.canvas.height=h,hu.drawImage(a,0,0,d,h),a=hu.canvas),u||l||i||o)return mv({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,m=c;if(s!=null&&s!==4){m=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(m[A++]=c[x])}let f=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(f.dataId);return g.values=new Int32Array(m),r.maybeReleaseBuffer(f.dataId),r.uploadToGPU(f.dataId),f}function mv(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=v.sizeFromShape(a),l=v.computeStrides(a),u=new jce(a,s),d=[{type:"uint32",data:[o]},{type:"uint32",data:[i]},{type:"uint32",data:[...l]},{type:"uint32",data:[...u.dispatch]}];return r.runFromPixelsProgram(u,a,d,s,t)}var Xce=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(C.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)"),`
${rt()}
if (index < uniforms.size)
{
let xValue = getXByOutputIndex(index);
let meanValue = getMeanByOutputIndex(index);
let varianValue = getVarianceByOutputIndex(index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
}
`}},Kce={kernelName:ci,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new Xce(n.shape,i.shape,o.shape,h,p),m=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,m)}};function Zce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=n;if(d!=="NHWC")throw new Error(`WebGPU backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let f=C.convertConv2DDataFormat(d),g=C.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,f);return Y9({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:m,activation:c})}var Yce={kernelName:_s,backendName:"webgpu",kernelFunc:Zce};function Jce(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,m=d;m==null&&(m=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let f=C.computeConv2DInfo(a.shape,s.shape,l,m,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:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],b;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.dilationHeight===1&&f.dilationWidth===1&&f.filterHeight===3&&f.inChannels%4===0?b=new Q9(f,y,p,A):(b=new eS(f,y,p,A),x.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),p==="leakyrelu"&&(x.push({type:"float32",data:[c]}),b.uniforms+=" alpha : f32,"),r.runWebGPUProgram(b,g,"float32",x)}var Qce={kernelName:zs,backendName:"webgpu",kernelFunc:Jce},e0e=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${xr(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${rt()}
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 t0e(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=C.prepareAndValidate(n,a),p=et({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=et({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=Gpe(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let m=new e0e(i,[u,d]),f=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(m,[c,p],c.dtype,f),y=et({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var r0e={kernelName:rl,backendName:"webgpu",kernelFunc:t0e},n0e=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=a0e(this.aShape);return`
${rt()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let indexZ = i32(getIndices(resRC.x, resRC.z));
let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
setOutputAtIndex(index, inBounds * getA(${e}));
}
}
`}};function a0e(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 nS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.sizeFromShape(s.shape),h=[],p=et({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=et({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=Le(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,w=Le(p.shape,p.dtype,b),I=jpe(w,x,m);return h.forEach(N=>r.disposeData(N.dataId)),r.makeTensorInfo(u.outputShape,I.dtype,I.values)}let f=new n0e(p.shape,m),g=r.runWebGPUProgram(f,[p,c],p.dtype);h.push(g);let y=et({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var s0e={kernelName:tl,backendName:"webgpu",kernelFunc:nS},i0e=qr({opSnippet:5,cpuKernelImpl:qpe,dtype:"bool"}),o0e={kernelName:nl,backendName:"webgpu",kernelFunc:i0e},l0e=qr({opSnippet:6,dtype:"bool",cpuKernelImpl:Hpe}),u0e={kernelName:fi,backendName:"webgpu",kernelFunc:l0e};function d0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Hh(a.shape,14);return o.uniforms="alpha : f32,",r.runWebGPUProgram(o,[a],"float32",i)}var p0e={kernelName:gi,backendName:"webgpu",kernelFunc:d0e},h0e=qr({opSnippet:7,dtype:"bool",cpuKernelImpl:Kpe}),c0e={kernelName:al,backendName:"webgpu",kernelFunc:h0e},f0e=qr({opSnippet:8,dtype:"bool",cpuKernelImpl:Xpe}),m0e={kernelName:sl,backendName:"webgpu",kernelFunc:f0e},g0e=kr({opType:9,cpuKernelImpl:Zpe}),y0e={kernelName:yi,backendName:"webgpu",kernelFunc:g0e},A0e=qr({opSnippet:9,dtype:"bool"}),x0e={kernelName:il,backendName:"webgpu",kernelFunc:A0e},b0e=kr({opType:10}),v0e={kernelName:rd,backendName:"webgpu",kernelFunc:b0e};function aS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return Xh(a,s,i,"max",r)}var w0e={kernelName:Ai,backendName:"webgpu",kernelFunc:aS},k0e=qr({opSnippet:15,cpuKernelImpl:Jpe}),I0e={kernelName:xi,backendName:"webgpu",kernelFunc:k0e};function S0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=C.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(v.arraysEqual(d.inShape,d.outShape))return zn({inputs:{x:a},backend:r});h=new X9(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new q9(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 C0e={kernelName:bi,backendName:"webgpu",kernelFunc:S0e};function T0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return Xh(a,i,s,"mean",r)}var N0e={kernelName:vi,backendName:"webgpu",kernelFunc:T0e};function E0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Xh(a,s,i,"min",r)}var R0e={kernelName:wi,backendName:"webgpu",kernelFunc:E0e},$0e=qr({opSnippet:16,cpuKernelImpl:Qpe}),M0e={kernelName:ki,backendName:"webgpu",kernelFunc:$0e},F0e=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${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=xr(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${rt()}
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}));
}
}
`}},P0e={kernelName:Ii,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 F0e(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function _0e(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=the(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new Hh(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var z0e={kernelName:ol,backendName:"webgpu",kernelFunc:_0e};function O0e(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}=Kn.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var D0e={kernelName:ul,backendName:"webgpu",kernelFunc:O0e};function L0e(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,m=l,f=u,{selectedIndices:g,selectedScores:y}=Kn.nonMaxSuppressionV5Impl(d,h,p,c,m,f);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var B0e={kernelName:dl,backendName:"webgpu",kernelFunc:L0e};function X0(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=qh({inputs:{input:n},backend:r}),s=X0({inputs:{x:a},backend:r}),i=Pm({inputs:{input:n},backend:r}),o=X0({inputs:{x:i},backend:r}),l=Fd({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 _d({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var W0e={kernelName:Nl,backendName:"webgpu",kernelFunc:X0};function sS(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=qh({inputs:{input:n},backend:r}),s=sS({inputs:{x:a},backend:r}),i=Pm({inputs:{input:n},backend:r}),o=X0({inputs:{x:i},backend:r}),l=Fd({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 _d({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var V0e={kernelName:pl,backendName:"webgpu",kernelFunc:sS};function U0e(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return cy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=cy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=Z9({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var G0e={kernelName:cl,backendName:"webgpu",kernelFunc:U0e},j0e=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=xr(e),r=this.xShape.map((u,d)=>`uniforms.pad${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`
${rt()}
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}));
}
}
}
`}},iS=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return zn({inputs:{x:a},backend:r});if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return _d({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 j0e(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},H0e={kernelName:Ci,backendName:"webgpu",kernelFunc:iS},q0e=qr({opSnippet:13}),X0e={kernelName:Ti,backendName:"webgpu",kernelFunc:q0e};function K0e(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new j9(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var Z0e={kernelName:Ni,backendName:"webgpu",kernelFunc:K0e};function Y0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Xh(a,s,i,"prod",r)}var J0e={kernelName:Ei,backendName:"webgpu",kernelFunc:Y0e},Q0e=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=ahe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},efe={kernelName:sd,backendName:"webgpu",kernelFunc:Q0e},oS=qr({opSnippet:3}),tfe={kernelName:li,backendName:"webgpu",kernelFunc:oS},rfe=kr({opType:12}),nfe={kernelName:Ri,backendName:"webgpu",kernelFunc:rfe},afe=kr({opType:13}),sfe={kernelName:Mi,backendName:"webgpu",kernelFunc:afe},ife=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
${rt()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC =
(vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutputAtIndex(index, newValue);
}
}
`}};function ofe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new ife(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var lfe={kernelName:$i,backendName:"webgpu",kernelFunc:ofe},ufe=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=Ye(this.outputShape),this.dispatch=Oe(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",`
${rt()}
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 dfe(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 ufe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var pfe={kernelName:od,backendName:"webgpu",kernelFunc:dfe},hfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(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`
${rt()}
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));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputAtIndex(index, outputValue);
}
}
`}},cfe={kernelName:El,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new hfe(n.shape,s),[u,d]=C.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},ffe=kr({opType:15,cpuKernelImpl:she}),mfe={kernelName:Fi,backendName:"webgpu",kernelFunc:ffe},gfe=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Ye(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=xr(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}
${rt()}
if (index < uniforms.size) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t}));
flattenedIndex = flattenedIndex + indexInside * ${r};
}
let updateValue = ${i};
let flatIndex = getOutputIndexFromCoords(${a});
${o}
}
}`}};function yfe(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}=C.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=et({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),m=et({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),f=m.dtype,g=_d({backend:r,attrs:{shape:p,value:0,dtype:f}}),y=v.sizeFromShape(m.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new gfe(m.shape,o,c.shape.length,m.shape.length,d,p,f),b=r.runWebGPUProgram(x,[m,c],f,A,g),w=et({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(m.dataId),r.disposeData(b.dataId),w}var Afe={kernelName:yl,backendName:"webgpu",kernelFunc:yfe},xfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(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`
${rt()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputAtIndex(index, getA(${t}));
} else {
setOutputAtIndex(index, getB(${t}));
}
}
}
`}};function bfe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new xfe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Nr(a.dtype,s.dtype))}var vfe={kernelName:Al,backendName:"webgpu",kernelFunc:bfe},wfe=kr({opType:18}),kfe={kernelName:_i,backendName:"webgpu",kernelFunc:wfe},Ife=kr({opType:16}),Sfe={kernelName:Pi,backendName:"webgpu",kernelFunc:Ife},Cfe=kr({opType:17}),Tfe={kernelName:bl,backendName:"webgpu",kernelFunc:Cfe},lS=qr({opSnippet:2,cpuKernelImpl:phe,supportsComplex:!0}),Nfe={kernelName:Bi,backendName:"webgpu",kernelFunc:lS};function Efe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=aS({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=et({inputs:{x:o},backend:r,attrs:{shape:l}}),d=lS({inputs:{a,b:u},backend:r}),h=rS({inputs:{x:d},backend:r}),p=M5({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=et({inputs:{x:p},backend:r,attrs:{shape:l}}),m=oS({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),m}var Rfe={kernelName:Di,backendName:"webgpu",kernelFunc:Efe},$fe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=iS({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(d.shape,s,o,!1),p=C.getPermuted(h.length,s.length,!1),c=C.getReshapedPermuted(d.shape,s,o,!1),m=et({inputs:{x:d},backend:r,attrs:{shape:h}}),f=Ya({inputs:{x:m},backend:r,attrs:{perm:p}}),g=et({inputs:{x:f},backend:r,attrs:{shape:c}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>r.disposeData(y.dataId)),g},Mfe={kernelName:vl,backendName:"webgpu",kernelFunc:$fe},Ffe=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=xr(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`
${rt()}
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 Pfe(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=C.calculateShapes(s,a,o),c=!1;if(s.dtype==="string"){let A=r.bufferSync(a),x=r.bufferSync(s),b=v.decodeString(r.readSync(i.dataId)[0]),w=ihe(A,x,o,p,d,u,l,h,b,c);return r.makeTensorInfo(o,w.dtype,w.values)}let m=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:h}],f=new Ffe(u,l,a.shape.length,s.shape.length,h,[p,1],c),g=r.runWebGPUProgram(f,[s,a,i],s.dtype,m),y=et({inputs:{x:g},backend:r,attrs:{shape:o}});return r.disposeData(g.dataId),y}var _fe={kernelName:ch,backendName:"webgpu",kernelFunc:Pfe};function zfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=C.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 m=Pd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,m})}var Ofe={kernelName:wl,backendName:"webgpu",kernelFunc:zfe},Dfe=kr({opType:19}),Lfe={kernelName:zi,backendName:"webgpu",kernelFunc:Dfe},Bfe={kernelName:hd,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new Hh(r.shape,20);return n.runWebGPUProgram(a,[r],r.dtype)}},Wfe=qr({opSnippet:11}),Vfe={kernelName:Li,backendName:"webgpu",kernelFunc:Wfe},Ufe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=xr(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`
${rt()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, getX(${t}));
}
}
`}};function Gfe(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:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Ot.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(f)w=et({inputs:{x:a},backend:r,attrs:{shape:m}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let I=Ot.computeOutShape(A,x,b),N=Pd({inputs:{x:a},backend:r,attrs:{begin:A,size:I}});w=et({inputs:{x:N},backend:r,attrs:{shape:m}}),r.disposeData(N.dataId)}else if(r.shouldExecuteOnCPU([a])){let I=r.readSync(a.dataId),N=Le(a.shape,a.dtype,I),E=uhe(c,N,b,A);w=r.makeTensorInfo(m,a.dtype,E.values)}else{let I=new Ufe(c),N=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(I,[a],a.dtype,N);w=et({inputs:{x:E},backend:r,attrs:{shape:m}}),r.disposeData(E.dataId)}return w}var jfe={kernelName:kl,backendName:"webgpu",kernelFunc:Gfe};function Hfe(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),[m,f]=dhe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([m.length],"string",m),r.makeTensorInfo(h.shape,"int32",f)]}var qfe={kernelName:fh,backendName:"webgpu",kernelFunc:Hfe},Xfe=kr({opType:21}),Kfe={kernelName:Wi,backendName:"webgpu",kernelFunc:Xfe},Zfe=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=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Yfe(this.rank,"uniforms.");return`
${rt()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`}};function Yfe(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 Jfe(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=>v.decodeString(h)):o,u=Le(a.shape,a.dtype,l),d=hhe(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new Zfe(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var Qfe={kernelName:es,backendName:"webgpu",kernelFunc:Jfe},eme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
${rt()}
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));
}
}
}
`}},tme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
${rt()}
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 cu(e,t){t!==null&&e.disposeData(t.dataId)}function gv(e){let t=1;for(;t<e;)t*=2;return t}function rme(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),[w,I]=che(b,o,a.dtype,s,i);return[r.makeTensorInfo(w.shape,w.dtype,w.values),r.makeTensorInfo(I.shape,I.dtype,I.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,_d({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=v.sizeFromShape(o)/l,d=et({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=gv(s),p=gv(l),c=null,m=()=>c===null?[d,d]:[d,c],f=(b,w,I)=>{let N=m(),E=new eme(I),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:[w]}],_=c;c=r.runWebGPUProgram(E,N,"int32",R),cu(r,_)};for(let b=1;b<h;b*=2){let w=b*2;for(let I=b;I>=1;I/=2)f(w,I,[u,p])}for(let b=p;b>h;b/=2){let w=m(),I=new tme([u,b/2]),N=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(I,w,"int32",N),cu(r,E);let R=h/2,_=R*2;for(let M=R;M>=1;M/=2)f(_,M,c.shape)}let g=c;c=Pd({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),cu(r,g);let y=nS({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});cu(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=et({inputs:{x:c},attrs:{shape:A},backend:r}),cu(r,g);let x=y;return y=et({inputs:{x:y},attrs:{shape:A},backend:r}),cu(r,x),[y,c]}var nme={kernelName:Sl,backendName:"webgpu",kernelFunc:rme},ame=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=Ye(this.outputShape),this.dispatch=Oe(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;
}
${rt()}
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 sme(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,[m,f]=u!=null?u:[h,p],g=[d,m,f,c],y=new ame(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 ime={kernelName:Cl,backendName:"webgpu",kernelFunc:sme};function ome(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 f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){p[s]=f;let g=Pd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=et({inputs:{x:g},backend:r,attrs:{shape:u}});m[f]=y,h.push(g)}return h.forEach(f=>r.disposeData(f.dataId)),m}var lme={kernelName:Tl,backendName:"webgpu",kernelFunc:ome},ume=[Mpe,ghe,Ahe,vhe,The,Ehe,$he,Fhe,Dhe,Vhe,Ghe,Xhe,zpe,Jhe,nce,oce,uce,pce,fce,gce,Ace,vce,kce,Nce,Rce,Mce,Fce,Pce,zce,Dce,Bce,Hce,Vce,Gce,Kce,Yce,Qce,r0e,s0e,o0e,u0e,_pe,Zhe,p0e,c0e,m0e,y0e,x0e,v0e,w0e,I0e,C0e,N0e,R0e,M0e,P0e,Ice,z0e,D0e,B0e,Lhe,V0e,G0e,H0e,X0e,Z0e,J0e,efe,Bhe,tfe,nfe,sfe,Rpe,lfe,pfe,cfe,mfe,Afe,vfe,kfe,Sfe,Tfe,zhe,jfe,qfe,Rfe,Mfe,_fe,Ofe,Lfe,Bfe,Vfe,Nfe,Cce,Kfe,Qfe,nme,ime,She,lme,W0e];for(let e of ume)qn(e);var dme=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=yv(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=yv(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 yv(e,t){return`${e}_${t}`}var pme=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,r,n){let a=xv(r),s=e*t*a,i=Av(e,t,r,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=s,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let l=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(l),l}this.numBytesAllocated+=s;let o=this.device.createTexture({size:[e,t],format:r,usage:n});return this.usedTextures.get(i).push(o),o}releaseTexture(e,t,r,n,a){if(this.freeTextures.size===0)return;let s=Av(t,r,n,a);this.freeTextures.has(s)||this.freeTextures.set(s,[]),this.freeTextures.get(s).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(s),o=i.indexOf(e);if(o<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(o,1);let l=xv(n),u=t*r*l;this.numBytesUsed-=u}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Av(e,t,r,n){return`${e}_${t}_${r}_${n}`}function xv(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}var hme=(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}))})},bv=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=gpe(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 vv(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}var cme=Z().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),wv=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;v.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])),v.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},uS=class extends Du{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!E5())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 dme(this.device),this.textureManager=new pme(this.device),this.tensorMap=new Jp(this,Xt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Z().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return uS.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.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}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}getTextureManager(){return this.textureManager}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=v.sizeFromShape(t)*g0(r);return this.tensorMap.set(n,{dtype:r,shape:t,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=v.sizeFromShape(r)*g0(n);this.tensorMap.set(e,{dtype:n,shape:r,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}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,t){let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,r,0,t),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=r.getMappedRange().slice(0);return r.unmap(),r!=null&&this.bufferManager.releaseBuffer(r,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Z().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let 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 ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=C.mergeRealAndImagArrays(s,i)}else{let a=t.values!=null?t.values:await this.getBufferData(t.bufferInfo.buffer,t.bufferInfo.byteSize);n=U9(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:r,dtype:n,shape:a,bufferInfo:s}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(s.buffer==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=v.sizeFromShape(a)*g0(n),o=this.acquireBuffer(i);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s.buffer,0,o,0,i),this.submitQueue();let l=this.makeTensorInfo(a,n),u=Xt().makeTensorFromTensorInfo(l),d=this.tensorMap.get(l.dataId);return d.bufferInfo.buffer=o,{tensorRef:u,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let r=t.map(n=>v.decodeString(n));return Le(e.shape,e.dtype,r)}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,t)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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&&v.isString(r[0])){let a=r.map(s=>v.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=0,n=[];e.forEach(o=>{o.data.length===0&&(o.data=[1]);let l;switch(o.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${o.data.length}D shape`)}(r===5||r===6)&&(l=16),t=Math.ceil(t/l)*l,r=o.data.length,n.push(t),t+=o.data.length*4});let a=new ArrayBuffer(t);e.forEach((o,l)=>{let u=n[l];o.type==="int32"?new Int32Array(a,u,o.data.length).set(o.data):o.type==="uint32"?new Uint32Array(a,u,o.data.length).set(o.data):new Float32Array(a,u,o.data.length).set(o.data)});let s=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(s,0,a,0,t);let i={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:s};return this.uniformDisposalQueue.push(i),{offset:0,size:t,buffer:s}}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),v.sizeFromShape(a.shape)===0){let I=this.tensorMap.get(a.dataId);return I.values=v.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=wv(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(I=>I.shape),o="int32";i.map(I=>{s.push({type:o,data:I})});let l=v.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let I=v.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?I/4:I]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((I,N)=>{if(I.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(I.dataId),{dtype:this.tensorMap.get(I.dataId).dtype,shape:I.shape,name:e.variableNames[N]}}),h=d.map(I=>I.dtype).concat(a.dtype),p=d.map(I=>C.getBroadcastDims(I.shape,a.shape)),c=d.map(I=>v.arraysEqual(I.shape,a.shape)).join("_"),m=p.map(I=>I.join("_")).join(";"),f=vv(e,i,h,m,c),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),A=this.getAndSavePipeline(f,()=>bv(this.device,e,y,d,a)),x=this.activeTimers!=null,b=hme(this.device,g,t.map(I=>this.tensorToBinding(I)),this.tensorToBinding(a),u);this.ensureCommandEncoderReady();let w=this.getComputePass();return x&&this.supportTimeQuery&&w.writeTimestamp(this.querySet,0),w.setPipeline(A),w.setBindGroup(0,b),w.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),x&&this.supportTimeQuery&&w.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(I=>{this.commandQueueOwnedIds.add(I.dataId)}),this.commandQueueOwnedIds.add(a.dataId),Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),x&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}copyExternalImageToTexture(e,t){let r=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,n="rgba8unorm",a=this.textureManager.acquireTexture(t[1],t[0],n,r),s=a.createView();this.queue.copyExternalImageToTexture({source:e},{texture:a},[t[1],t[0]]);let i={width:t[1],height:t[0],format:n,usage:r,texture:a};return this.textureDisposalQueue.push(i),s}runFromPixelsProgram(e,t,r,n,a){e.dispatch=wv(this.device,e);let s=this.makeTensorInfo(t,"int32");if(v.sizeFromShape(s.shape)===0){let f=this.tensorMap.get(s.dataId);return f.values=v.getTypedArrayFromDType(s.dtype,0),s}this.uploadToGPU(s.dataId);let i=vv(e,[s.shape]),o=this.getFromPixelTextureLayout(n),l=this.getAndSavePipeline(i,()=>bv(this.device,e,o.pipelineLayout,[],s,!0)),u;if(n){let f={source:a};u=this.device.importExternalTexture(f)}else u=this.copyExternalImageToTexture(a,s.shape);let d=this.tensorToBinding(s),h=this.makeUniforms(r),p=this.device.createBindGroup({layout:o.bindGroupLayout,entries:[{binding:0,resource:{buffer:d.buffer}},{binding:1,resource:u},{binding:2,resource:{buffer:h.buffer}}]});this.ensureCommandEncoderReady();let c=this.getComputePass(),m=this.activeTimers!=null;return m&&this.supportTimeQuery&&c.writeTimestamp(this.querySet,0),c.setPipeline(l),c.setBindGroup(0,p),c.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),m&&this.supportTimeQuery&&c.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(s.dataId),this.dispatchNumberInEncoder++,Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),m&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}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=cme){return Z().getBool("WEBGPU_CPU_FORWARD")&&e.every(r=>this.tensorMap.get(r.dataId).bufferInfo.buffer==null&&v.sizeFromShape(r.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}},F5=uS;F5.nextDataId=0;var dS={};Be(dS,{WebGPUBackend:()=>F5,webgpu_util:()=>W9});E5()&&$l("webgpu",async()=>{Z().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Z().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),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 F5(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||{}),_m=(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))(_m||{}),pS;function fme(e){pS=e.wasm.cwrap(Ps,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function mme(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,m=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 ${E.shape.length}.`);m=E.id}let f=o==null?0:r.dataIdMap.get(o.dataId).id,g=_m[d];if(g==null)throw new Error(`${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),w=r.dataIdMap.get(b.dataId).id,I=new Uint8Array(new Int32Array(a.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return pS(p,I,a.shape.length,c,N,s.shape.length,l,u,g,m,f,h||0,w),b}var gme={kernelName:Ps,backendName:"wasm",setupFunc:fme,kernelFunc:mme};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 v.sizeFromShape(u.shape)===0||r(l,Ut[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var yme=Ir(jo);function Xr(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,m=C.assertAndGetBroadcastShape(u.shape,d.shape),f=o.makeOutput(m,c);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(f.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Ut[u.dtype],A),f}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var Ame=!0,xme=Xr(Ja,Ame),hS;function bme(e){hS=e.wasm.cwrap(Zs,null,["array","number","number","number"])}function vme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(v.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 hS(s,a.length,Ut[n.dtype],i),n}var wme={kernelName:Zs,backendName:"wasm",setupFunc:bme,kernelFunc:vme};function zm(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 kme={kernelName:mi,backendName:"wasm",kernelFunc:zm},cS;function Ime(e){cS=e.wasm.cwrap(Vi,null,["number","array","number","number","number","array","number"])}function Xs(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=Cme(t.x.shape,n.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Sme(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let m=zm({inputs:t,backend:r});return m.shape=o,m}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 cS(d,c,l.shape.length,Ut[l.dtype],h,p,s.length),u}function Sme(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function Cme(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 Tme={kernelName:Vi,backendName:"wasm",kernelFunc:Xs,setupFunc:Ime};function Zi(e,t,r){let n=e.shape,a=e.shape.length,s=v.parseAxisParam(t,n),i=s,o=C.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=C.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 fS;function Nme(e){fS=e.wasm.cwrap(Vu,null,["number, number, number"])}function Eme(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;C.assertAxesAreInnerMostDims("all",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;fS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Rme={kernelName:Vu,backendName:"wasm",setupFunc:Nme,kernelFunc:Eme},mS;function $me(e){mS=e.wasm.cwrap(Uu,null,["number, number, number"])}function Mme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Zi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;C.assertAxesAreInnerMostDims("any",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;mS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Fme={kernelName:Uu,backendName:"wasm",setupFunc:$me,kernelFunc:Mme},gS;function Pme(e){gS=e.wasm.cwrap(Ys,null,["number","number","number","number","number"])}function _me(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"),m=t.dataIdMap.get(c.dataId).id,f=v.sizeFromShape(c.shape),g=l.shape[d[0]];return gS(o,Ut[l.dtype],f,g,m),h&&t.disposeData(u.dataId),c}var zme={kernelName:Ys,backendName:"wasm",kernelFunc:_me,setupFunc:Pme},yS;function Ome(e){yS=e.wasm.cwrap(Js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dme(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=C.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,m=d.padInfo.right,f=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"),w=n.dataIdMap.get(b.dataId).id;return yS(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,m,f,g,y,A,x,w),b}var Lme={kernelName:Js,backendName:"wasm",setupFunc:Ome,kernelFunc:Dme};function rn(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s);return v.assert(s===v.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Bme={kernelName:fl,backendName:"wasm",kernelFunc:rn},AS;function Wme(e){AS=e.wasm.cwrap(Qs,null,["number","array","number","number","array","number","number","number","number"])}function Vme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],m=a.shape.slice(0,-2),f=s.shape.slice(0,-2),g=v.sizeFromShape(m),y=v.sizeFromShape(f),A=Rl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);v.assert(d===h,()=>`Error in matMul: inner shapes (${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],w=rn({inputs:{x:a},backend:r,attrs:{shape:x}}),I=rn({inputs:{x:s},backend:r,attrs:{shape:b}}),N=r.dataIdMap.get(w.dataId).id,E=r.dataIdMap.get(I.dataId).id,R=i?w.shape[2]:w.shape[1],_=o?I.shape[1]:I.shape[2],M=Math.max(g,y),S=r.makeOutput([M,R,_],w.dtype),z=r.dataIdMap.get(S.dataId).id,O=new Uint8Array(new Int32Array(w.shape).buffer),H=new Uint8Array(new Int32Array(I.shape).buffer);return AS(N,O,w.shape.length,E,H,I.shape.length,i,o,z),r.disposeData(w.dataId),r.disposeData(I.dataId),S.shape=A,S}var Ume={kernelName:Qs,backendName:"wasm",setupFunc:Wme,kernelFunc:Vme};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=v.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let m=Ot.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(m,m+v.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(m,m+v.sizeFromShape(i))),u}if(t.dtype==="string"){let m=V0(l,s,i,t.shape,t.dtype);return h.stringBytes=m,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)Gme(l,d[0],p,s,i);else if(c===3)jme(l,d[0],d[1],p,s,i);else if(c===4)Hme(l,d[0],d[1],d[2],p,s,i);else{let m=V0(l,s,i,t.shape,t.dtype);p.set(m)}return u}function Gme(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 jme(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 m=p*t+c*r+u;n.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function Hme(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],m=s[3];for(let f=l;f<h;f++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=f*t+g*r+y*n+m;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var qme={kernelName:xl,backendName:"wasm",kernelFunc:Vo};function Xme(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=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=rn({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Xs({inputs:{x:c},backend:r,attrs:{perm:u}}),f=rn({inputs:{x:m},backend:r,attrs:{shape:d}}),g=Vo({inputs:{x:f},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(m.dataId),r.disposeData(c.dataId),g}var Kme={kernelName:Ho,backendName:"wasm",kernelFunc:Xme};function Kh(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 Zme={kernelName:ei,backendName:"wasm",kernelFunc:Kh},Yme=Ir(ti),xS;function Jme(e){xS=e.wasm.cwrap(Qa,null,["number","number","number","number"])}function Qme(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 xS(o,s,i,u),l}var e1e={kernelName:Qa,backendName:"wasm",setupFunc:Jme,kernelFunc:Qme};function bS(e){let{inputs:t,backend:r}=e,n=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=C.computeOutShape(t.map(c=>c.shape),n),s=t.filter(c=>v.sizeFromShape(c.shape)>0);if(s.length===1)return zm({inputs:{x:s[0]},backend:r});let i=r.makeOutput(a,t[0].dtype);if(v.sizeFromShape(a)===0)return i;let o=s.map(c=>c.shape);if(C.assertParamsConsistent(o,n),s[0].dtype==="string"){let c=s.map(x=>{let b=v.sizeFromShape(x.shape.slice(n));return rn({inputs:{x},backend:r,attrs:{shape:[-1,b]}})}),m=c.map(x=>({vals:r.readSync(x.dataId),shape:x.shape}));a=C.computeOutShape(c.map(x=>x.shape),1);let f=c[0].shape[0]===1,g=a5(m,a,t[0].dtype,f),y=C.computeOutShape(s.map(x=>x.shape),n);i.shape=y;let A=r.dataIdMap.get(i.dataId);return A.stringBytes=C.fromStringArrayToUint8(g),c.forEach(x=>r.disposeData(x.dataId)),i}let l=v.sizeFromShape(s[0].shape.slice(0,n)),u=0,d=s.map(c=>{let m=v.sizeFromShape(c.shape.slice(n));return u+=m,m}),h=s.map(c=>r.typedArrayFromHeap(c)),p=r.typedArrayFromHeap(i);for(let c=0;c<l;c++){let m=c*u;for(let f=0;f<h.length;f++){let g=d[f],y=c*g,A=h[f].subarray(y,y+g);p.set(A,m),m+=g}}return i}var t1e={kernelName:qo,backendName:"wasm",kernelFunc:bS},vS;function r1e(e){vS=e.wasm.cwrap(ri,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function n1e(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=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(a.shape,s.shape,l,u,d,h,!1,c),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,A=m.padInfo.right,x=m.padInfo.bottom,b=m.padInfo.left,w=m.dilationHeight,I=m.dilationWidth,N=m.strideHeight,E=m.strideWidth,R=m.inChannels,_=m.outChannels,M=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let S=n.makeOutput(m.outShape,"float32"),z=n.dataIdMap.get(S.dataId).id;return vS(i,a.shape[0],a.shape[1],a.shape[2],o,f,g,y,A,x,b,M,w,I,N,E,R,_,z),S}var a1e={kernelName:ri,backendName:"wasm",setupFunc:r1e,kernelFunc:n1e},wS;function s1e(e){wS=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function i1e(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=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(d,s.shape,i,h,o,u,!1,p),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:b,outHeight:w,outWidth:I,strideHeight:N,strideWidth:E}=c,R=f-1-c.padInfo.top,_=g-1-c.padInfo.left,M=c.dataFormat==="channelsLast",S=v.computeStrides(c.inShape),z=v.computeStrides(a.shape),[O,H,K]=v.computeStrides(s.shape),D=S[0],Q=M?S[1]:S[2],V=M?S[2]:1,re=M?1:S[1],Y=z[0],ie=M?z[1]:z[2],J=M?z[2]:1,ae=M?1:z[1],de=t.makeOutput(c.inShape,"float32"),xe=t.dataIdMap.get(de.dataId).id,be=t.dataIdMap.get(a.dataId).id,Ee=t.dataIdMap.get(s.dataId).id;return wS(be,Ee,m,f,g,A,x,y,w,I,b,N,E,R,_,O,H,K,D,Q,V,re,Y,ie,J,ae,xe),de}var o1e={kernelName:ni,backendName:"wasm",setupFunc:s1e,kernelFunc:i1e},l1e=Ir(ai),u1e=Ir(si),kS=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(kS||{}),IS;function d1e(e){IS=e.wasm.cwrap(Ko,null,["number","number","number","number","array","number","number","number","number","number"])}function p1e(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]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Kh({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.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,w=new Uint8Array(new Int32Array(o.shape).buffer);return IS(g,y,A,d,w,h,p,kS[a],s,b),f!=null&&t.disposeData(f.dataId),x}var h1e={kernelName:Ko,backendName:"wasm",setupFunc:d1e,kernelFunc:p1e},SS;function c1e(e){SS=e.wasm.cwrap(Xo,null,["number","number","number","number","number","number"])}function f1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;v.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumprod does not support ${a.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),d=a;u!==null&&(d=Xs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumprod",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],m=r.dataIdMap.get(d.dataId).id,f=r.dataIdMap.get(p.dataId).id;SS(m,i?1:0,o?1:0,c,f,Ut[a.dtype]);let g=p;if(u!==null){let y=C.getUndoAxesPermutation(u);g=Xs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var m1e={kernelName:Xo,backendName:"wasm",setupFunc:c1e,kernelFunc:f1e},CS;function g1e(e){CS=e.wasm.cwrap(ii,null,["number","number","number","number","number","number"])}function y1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;v.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),d=a;u!==null&&(d=Xs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],m=r.dataIdMap.get(d.dataId).id,f=r.dataIdMap.get(p.dataId).id;CS(m,i?1:0,o?1:0,c,f,Ut[a.dtype]);let g=p;if(u!==null){let y=C.getUndoAxesPermutation(u);g=Xs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var A1e={kernelName:ii,backendName:"wasm",setupFunc:g1e,kernelFunc:y1e},TS;function x1e(e){TS=e.wasm.cwrap(Zo,null,["number","number","number","array","number","array","array","number","number"])}function b1e(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),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(a.shape)).buffer),A=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),b=t.dataIdMap.get(f.dataId).id;return TS(g,s,i==="NHWC"?1:0,y,a.shape.length-1,A,x,m.length,b),f}var v1e={kernelName:Zo,backendName:"wasm",setupFunc:x1e,kernelFunc:b1e},NS;function w1e(e){NS=e.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function k1e(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=C.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),m=c.filterHeight,f=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,w=c.dilationWidth,I=c.strideHeight,N=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}'. Please use 'channelsLast'.`);let M=n.makeOutput(c.outShape,"float32"),S=n.dataIdMap.get(M.dataId).id;return NS(i,a.shape[0],a.shape[1],a.shape[2],o,m,f,g,y,A,x,_,b,w,I,N,E,R,S),M}var I1e={kernelName:oi,backendName:"wasm",setupFunc:w1e,kernelFunc:k1e},S1e=Ir(ui),C1e=!1,T1e=Xr(Yo,C1e,"bool"),N1e=Ir(di,"float32");function fy(e){let{inputs:t,attrs:r,backend:n}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),rn({inputs:{x:a},backend:n,attrs:{shape:o}})}var E1e={kernelName:Jo,backendName:"wasm",kernelFunc:fy};function ES(e){let{attrs:{shape:t,value:r,dtype:n},backend:a}=e,s=a.makeOutput(t,n);return a.typedArrayFromHeap(s).fill(r),s}var R1e={kernelName:Yu,backendName:"wasm",kernelFunc:ES},RS;function $1e(e){RS=e.wasm.cwrap(el,null,["number","number","number","number","number","number"])}function M1e(e){let{inputs:t,backend:r}=e,{image:n}=t,a=r.makeOutput(n.shape,n.dtype),s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,[o,l,u,d]=n.shape;return RS(s,o,l,u,d,i),a}var F1e={kernelName:el,backendName:"wasm",kernelFunc:M1e,setupFunc:$1e},P1e=Ir(pi),_1e=!1,z1e=Xr(hi,_1e),$S;function O1e(e){$S=e.wasm.cwrap(ci,null,["number","number","number","number","number","number","number"])}function D1e(e){let{backend:t,inputs:r,attrs:n}=e,{varianceEpsilon:a}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=r,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return $S(d,h,p,c,m,a,g),f}var L1e={kernelName:ci,backendName:"wasm",setupFunc:O1e,kernelFunc:D1e},MS;function B1e(e){MS=e.wasm.cwrap(_s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function W1e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=r,f=C.computeConv2DInfo(a.shape,s.shape,l,d,u,p),g=_m[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=f.outChannels,b=0;if(i!=null){let J=n.dataIdMap.get(i.dataId);if(J.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let w=f.filterHeight,I=f.filterWidth,N=f.padInfo.top,E=f.padInfo.right,R=f.padInfo.bottom,_=f.padInfo.left,M=f.dilationHeight,S=f.dilationWidth,z=f.strideHeight,O=f.strideWidth,H=f.inChannels,K=f.padInfo.type==="SAME"?1:0,D=f.batchSize,Q=f.inHeight,V=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let re=n.makeOutput(f.outShape,"float32"),Y=n.dataIdMap.get(re.dataId).id,ie=o==null?0:n.dataIdMap.get(o.dataId).id;return MS(y,D,Q,V,A,w,I,b,N,E,R,_,K,M,S,z,O,H,x,g,ie,m||0,Y),re}var V1e={kernelName:_s,backendName:"wasm",setupFunc:B1e,kernelFunc:W1e},FS;function U1e(e){FS=e.wasm.cwrap(zs,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 G1e(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:m}=r,f=C.computeConv2DInfo(a.shape,s.shape,l,d,u,p,!0),g=_m[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=f.outChannels,b=0;if(i!=null){let J=n.dataIdMap.get(i.dataId);if(J.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let w=f.filterHeight,I=f.filterWidth,N=f.padInfo.top,E=f.padInfo.right,R=f.padInfo.bottom,_=f.padInfo.left,M=f.dilationHeight,S=f.dilationWidth,z=f.strideHeight,O=f.strideWidth,H=f.inChannels,K=f.padInfo.type==="SAME"?1:0,D=f.batchSize,Q=f.inHeight,V=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let re=n.makeOutput(f.outShape,"float32"),Y=n.dataIdMap.get(re.dataId).id,ie=o==null?0:n.dataIdMap.get(o.dataId).id;return FS(y,D,Q,V,A,w,I,b,N,E,R,_,K,M,S,z,O,H,x,g,ie,m||0,Y),re}var j1e={kernelName:zs,backendName:"wasm",setupFunc:U1e,kernelFunc:G1e},PS;function H1e(e){PS=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","array","number"])}function q1e(e){let{backend:t,inputs:r}=e,{params:n,indices:a}=r,[s,i,o,l]=Ry.prepareAndValidate(n,a),u=t.makeOutput(s,n.dtype);if(i===0)return u;let d=a.shape,h=d[d.length-1],p=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(a.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return PS(p,Ut[n.dtype],c,i,h,o,m,f),u}var X1e={kernelName:rl,backendName:"wasm",setupFunc:H1e,kernelFunc:q1e},_S;function K1e(e){_S=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Z1e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a,indices:s}=r,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=t.readSync(s.dataId),d=a.shape[l];for(let N=0;N<u.length;++N){let E=u[N];v.assert(E<=d-1&&E>=0,()=>`GatherV2: the index value ${E} is not in [0, ${d-1}]`)}let h=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),p=rn({inputs:{x:a},attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]},backend:t}),c=v.sizeFromShape(s.shape),m=rn({inputs:{x:s},attrs:{shape:[h.batchSize,c/h.batchSize]},backend:t}),f=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],g=t.makeOutput(f,a.dtype);if(v.sizeFromShape(a.shape)===0)return g;let y=p.shape.length-1,A=t.dataIdMap.get(p.dataId).id,x=t.dataIdMap.get(m.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),I=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return _S(A,Ut[a.dtype],w,y,x,h.batchSize,I,b),t.disposeData(p.dataId),t.disposeData(m.dataId),g.shape=h.outputShape,g}var Y1e={kernelName:tl,backendName:"wasm",setupFunc:K1e,kernelFunc:Z1e},J1e=!1,Q1e=Xr(nl,J1e,"bool"),e2e=!1,t2e=Xr(fi,e2e,"bool"),zS;function r2e(e){zS=e.wasm.cwrap(gi,null,["number","number","number","number"])}function n2e(e){let{inputs:{x:t},attrs:{alpha:r},backend:n}=e,a=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;zS(a,Ut[t.dtype],r,i)}return s}var a2e={kernelName:gi,backendName:"wasm",setupFunc:r2e,kernelFunc:n2e},s2e=!1,i2e=Xr(al,s2e,"bool"),o2e=!1,l2e=Xr(sl,o2e,"bool"),u2e=Ir(yi),d2e=!1,p2e=Xr(il,d2e,"bool"),OS;function h2e(e){OS=e.wasm.cwrap(Ai,null,["number","number","number","number"])}function c2e(e){let{backend:t,inputs:r,attrs:n}=e,{reductionIndices: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;C.assertAxesAreInnerMostDims("max",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;OS(o,Ut[i.dtype],g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var f2e={kernelName:Ai,backendName:"wasm",setupFunc:h2e,kernelFunc:c2e},m2e=!1,g2e=Xr(xi,m2e),DS;function y2e(e){DS=e.wasm.cwrap(bi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function A2e(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id;v.assert(a.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${a.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=C.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,b=d.strideWidth,w=d.inChannels,I=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let N=n.makeOutput(d.outShape,"float32"),E=n.dataIdMap.get(N.dataId).id;return DS(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,m,f,g,y,A,x,b,w,I,E),N}var x2e={kernelName:bi,backendName:"wasm",setupFunc:y2e,kernelFunc:A2e},LS;function b2e(e){LS=e.wasm.cwrap(vi,null,["number, number, number"])}function v2e(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=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Zi(i,a,t),m=h;if(c){let b=t.dataIdMap.get(d.dataId).id;b!==o&&(u=d,l=b,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),A=u;u.dtype!=="float32"&&(A=Kh({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;LS(l,y,b)}if(c&&t.disposeData(d.dataId),s){let b=C.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var w2e={kernelName:vi,backendName:"wasm",setupFunc:b2e,kernelFunc:v2e},BS;function k2e(e){BS=e.wasm.cwrap(wi,null,["number","number","number","number"])}function I2e(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=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Zi(i,a,t);if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let m=u.shape.length;C.assertAxesAreInnerMostDims("min",h,m);let[f,g]=C.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(g),A=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;BS(l,Ut[i.dtype],y,x)}if(c&&t.disposeData(d.dataId),s){let x=C.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var S2e={kernelName:wi,backendName:"wasm",setupFunc:k2e,kernelFunc:I2e},C2e=!1,T2e=Xr(ki,C2e),WS=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(WS||{}),VS;function N2e(e){VS=e.wasm.cwrap(Ii,null,["number","array","number","number","array","array","number","number"])}function E2e(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[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(m=>m[0]),h=n.map(m=>m[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,WS[a],l),o}var R2e={kernelName:Ii,backendName:"wasm",kernelFunc:E2e,setupFunc:N2e},$2e=!0,M2e=Xr(Si,$2e),F2e=Ir(ol);function P5(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 US;function P2e(e){US=e.wasm.cwrap(ul,"number",["number","number","number","number","number"])}function _2e(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=US(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:m,pValidOutputs:f}=P5(t,h);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([c],"int32",p)}var z2e={kernelName:ul,backendName:"wasm",setupFunc:P2e,kernelFunc:_2e},GS;function O2e(e){GS=e.wasm.cwrap(ad,"number",["number","number","number","number","number","bool"])}function D2e(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=GS(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=P5(t,p);t.wasm._free(f);let 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Y2e={kernelName:cl,backendName:"wasm",kernelFunc:Z2e},qS;function J2e(e){qS=e.wasm.cwrap(Ci,null,["number","array","number","number","array","array","number","number"])}function Q2e(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]);if(v.sizeFromShape(t.shape)===0)return ES({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(m=>m[0]),h=n.map(m=>m[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return qS(i,u,t.shape.length,Ut[t.dtype],p,c,a,l),o}var XS={kernelName:Ci,backendName:"wasm",kernelFunc:Q2e,setupFunc:J2e},ege=!1,tge=Xr(Ti,ege),KS;function rge(e){KS=e.wasm.cwrap(Ni,null,["number","number","number"])}function 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vge(e){let{inputs:t,backend:r,attrs:n}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=n,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(a.dataId).id,d=r.dataIdMap.get(l.dataId).id,[h,p,c,m]=a.shape,[f,g]=C.getImageCenter(o,p,c),y=i===0,A=255,x=typeof i=="number"?[i,i,i,y?0:A]:[...i,A],b=new Uint8Array(new Int32Array(x).buffer);return QS(u,h,p,c,m,s,f,g,b,x.length,d),l}var wge={kernelName:El,backendName:"wasm",kernelFunc:vge,setupFunc:bge},kge=Ir(gl),Ige=Ir(Fi),eC;function Sge(e){eC=e.wasm.cwrap(yl,null,["number","number","number","number","number","number","array","number","number"])}function Cge(e){let{backend:t,inputs:r,attrs:n}=e,{indices:a,updates:s}=r,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=$y.calculateShapes(s,a,i),c=t.dataIdMap.get(a.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(h).buffer),g=t.dataIdMap.get(o.dataId).id;return eC(c,m,Ut[s.dtype],l,u,d,f,p,g),o}var Tge={kernelName:yl,backendName:"wasm",setupFunc:Sge,kernelFunc:Cge},tC;function Nge(e){tC=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Ege(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=r.dataIdMap.get(n.dataId).id,o=r.dataIdMap.get(a.dataId).id,l=r.dataIdMap.get(s.dataId).id,u=r.makeOutput(a.shape,a.dtype),d=r.dataIdMap.get(u.dataId).id,h=n.shape.length,p=a.shape.length,c=h===0||h>1||p===1?1:v.sizeFromShape(a.shape.slice(1));return tC(i,o,l,c,d),u}var Rge={kernelName:Al,backendName:"wasm",kernelFunc:Ege,setupFunc:Nge},rC;function $ge(e){rC=e.wasm.cwrap(_i,null,["number","number"])}function Mge(e){let{backend:t,inputs:{x:r}}=e,n=t.dataIdMap.get(r.dataId).id,a=t.makeOutput(r.shape,r.dtype),s=t.dataIdMap.get(a.dataId).id;return v.sizeFromShape(a.shape)===0||rC(n,s),a}var Fge={kernelName:"Sigmoid",backendName:"wasm",setupFunc:$ge,kernelFunc:Mge},Pge=Ir(Pi),nC;function _ge(e){nC=e.wasm.cwrap(Di,null,["number","number","number","number"])}function zge(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=v.sizeFromShape(r.shape)/o;return v.sizeFromShape(s.shape)===0||nC(a,i,o,l),s}var Oge={kernelName:Di,backendName:"wasm",setupFunc:_ge,kernelFunc:zge};function Dge(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n,o=v.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=XS.kernelFunc({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),d=C.getReshaped(u.shape,s,o,!1),h=C.getPermuted(d.length,s.length,!1),p=C.getReshapedPermuted(u.shape,s,o,!1),c=rn({inputs:{x:u},backend:r,attrs:{shape:d}}),m=Xs({inputs:{x:c},backend:r,attrs:{perm:h}}),f=rn({inputs:{x:m},backend:r,attrs:{shape:p}});return r.disposeData(u.dataId),r.disposeData(c.dataId),r.disposeData(m.dataId),f}var Lge={kernelName:vl,backendName:"wasm",kernelFunc:Dge},aC;function Bge(e){aC=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function Wge(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,m=t.makeOutput(d,n.dtype),f=t.dataIdMap.get(m.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),w=t.dataIdMap.get(b.dataId).id,I=t.makeOutput([4],"int32"),N=t.dataIdMap.get(I.dataId).id,E=aC(h,p,Ut[a.dtype],o,u,l,c,f,y,x,w,N),R=t.readSync(I.dataId),_;switch(R[0]){case 1:{_=C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 2:{_=C.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1],R[2]);break}case 3:_=C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1],R[2],R[3]);break;default:_=""}if(t.disposeData(I.dataId),_)throw t.disposeData(m.dataId),t.disposeData(g.dataId),t.disposeData(A.dataId),t.disposeData(b.dataId),new Error(_);let M=m,S=g;return E!==d[0]&&(M=Vo({inputs:{x:m},attrs:{begin:0,size:[E,l]},backend:t}),S=Vo({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(m.dataId),t.disposeData(g.dataId)),[M,S,A,b]}var Vge={kernelName:dh,backendName:"wasm",setupFunc:Bge,kernelFunc:Wge},sC;function Uge(e){sC=e.wasm.cwrap(pd,null,["number","number","number","number","number","number","number"])}function Gge(e){let{backend:t,inputs:r}=e,{inputIndices:n,inputShape:a,newShape:s}=r;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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x=Array.from(t.readSync(a.dataId)),b=Array.from(t.readSync(c.dataId));A=C.getSparseReshapeInputOutputMismatchErrorMessage(x,b);break}default:A=""}if(t.disposeData(f.dataId),A)throw t.disposeData(h.dataId),t.disposeData(c.dataId),new Error(A);return[h,c]}var jge={kernelName:pd,backendName:"wasm",setupFunc:Uge,kernelFunc:Gge},iC;function oC(e){iC=e.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function lC(e,t){let{backend:r,inputs:n}=e,{data:a,indices:s,segmentIds:i}=n,o=s.shape[0],l=r.readSync(i.dataId,o-1,o)[0],u=o>0?l+1:0;if(u<0)throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let d=a.shape.slice();d[0]=u;let h=r.dataIdMap.get(a.dataId).id,p=r.dataIdMap.get(s.dataId).id,c=r.dataIdMap.get(i.dataId).id,m=r.makeOutput(d,a.dtype),f=r.dataIdMap.get(m.dataId).id,g=r.makeOutput([4],"int32"),y=r.dataIdMap.get(g.dataId).id;iC(h,Ut[a.dtype],a.shape[0],p,c,f,y,t,0);let A=r.readSync(g.dataId),x;switch(A[0]){case 0:{x=C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{x=C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:x=C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(A[1],A[2]);break;case 3:x=C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(A[1],A[2],A[3]);break;default:x=""}if(r.disposeData(g.dataId),x)throw r.disposeData(m.dataId),new Error(x);return m}function Hge(e){return lC(e,!0)}var qge={kernelName:ph,backendName:"wasm",setupFunc:oC,kernelFunc:Hge};function Xge(e){return lC(e,!1)}var Kge={kernelName:hh,backendName:"wasm",setupFunc:oC,kernelFunc:Xge};function Zge(e){let{inputs:t,attrs:r,backend:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=new Array(a.shape.length).fill(0),d=a.shape.slice();return l.map(h=>{let p=[...d];p[o]=h;let c=Vo({inputs:{x:a},attrs:{begin:u,size:p},backend:n});return u[o]+=h,c})}var Yge={kernelName:wl,backendName:"wasm",kernelFunc:Zge},Jge=Ir(zi),Qge=Ir(hd),eye=!0,tye=Xr(Li,eye),uC;function rye(e){uC=e.wasm.cwrap(Ui,null,["number","number","number","number"])}function nye(e){let{backend:t,inputs:r,attrs:n}=e,{alpha:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return uC(i,a,Ut[s.dtype],l),o}var aye={kernelName:Ui,backendName:"wasm",setupFunc:rye,kernelFunc:nye},dC;function sye(e){dC=e.wasm.cwrap(kl,null,["number","array","number","array","array","array","array","array","number","number"])}function iye(e){let{backend:t,inputs:r,attrs:n}=e,{x:a}=r,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Ot.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(f)w=rn({inputs:{x:a},backend:t,attrs:{shape:m}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, 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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 AC=`
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];
}
`,xC=`
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;
}
`,bC=`
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);
}
`,vC=`
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;
}
`,wC=`
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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Kye=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Zye=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Yye=[33,133,362,263,1,78,308],Uwe=Kye.map(e=>Qh[e]),Gwe=Zye.map(e=>Qh[e]),jwe=Yye.map(e=>Qh[e]);function Yi(e){let t=e.map(r=>r[0]);return t.push(e[e.length-1][1]),t}var 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Dd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],Um=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],rb=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],nb=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],HC=(e,t)=>{let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:r,endPoint:n,landmarks:e.landmarks,confidence:e.confidence}},eb=(e,t,r)=>{let n=t.shape[1],a=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a],i=Ie.cropAndResize(t,[s],[0],r),o=pe(i,Je.tf255);return te(i),o},Gm=(e,t)=>{let r=Um(e),n=Dd(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},jm=e=>{let t=Um(e),r=Dd(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},qC=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},tb=[[1,0,0],[0,1,0],[0,0,1]],iAe=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),oAe=(e,t)=>iAe(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var GC=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ul=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},lAe=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},jC=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Ul(e[a],lAe(t,s)))}return r},XC=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=GC(t[0],t[1]),i=jC(s,a),o=GC(-t[0],-t[1]);return jC(i,o)},uAe=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Ul(t[0],r),-Ul(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},dAe=(e,t)=>[Ul(e,t[0]),Ul(e,t[1])];function KC(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function ZC(e,t,r,n,a){let s=Dd(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?XC(r,[0,0]):tb,u=o?i.map(c=>[...dAe(c,l),c[2]]):i,d=o?uAe(n):tb,h=Um(t),p=[Ul(h,d[0]),Ul(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function YC(e,t,r,n){let a=t.landmarks.length>=Y5.count?Y5.symmetryLine:Jh.symmetryLine,s=0,i=tb,o;if(e&&he.kernels.includes("rotatewithoffset"))if(s=oAe(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=Um(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=XC(-s,u),o=eb(t,h,[n,n]),te(h)}else o=eb(t,r,[n,n]);else o=eb(t,r,[n,n]);return[s,i,o]}var pAe=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]},JC=(e,t)=>{let r=pAe(e),n=Dd(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 QC=6,hAe=1.4,Da,eT=null,Ji=0,ec=null,Hm=()=>Ji;async function tT(e){var t;return he.initial&&(Da=null),Da?e.debug&&se("cached model:",Da.modelUrl):Da=await Ge((t=e.face.detector)==null?void 0:t.modelPath),Ji=Da.inputs[0].shape?Da.inputs[0].shape[2]:0,ec=Se(Ji,"int32"),eT=ca(KC(Ji)),Da}function cAe(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,eT),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,ec),t.centersNormalized=pe(t.centers,ec),t.halfBoxSize=pe(t.boxSizesNormalized,Je.tf2),t.starts=ce(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,ec),t.endNormalized=L(t.ends,ec);let r=fd([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>te(t[n])),r}async function rT(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,Je.tf127),r.normalized=ce(r.div,Je.tf05);let n=Da==null?void 0:Da.execute(r.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((p,c)=>p.size-c.size);r.concat384=St([h[0],h[2]],2),r.concat512=St([h[1],h[3]],2),r.concat=St([r.concat512,r.concat384],1),r.batch=Qe(r.concat,0)}else Array.isArray(n)?r.batch=Qe(n[0]):r.batch=Qe(n);te(n),r.boxes=cAe(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Tr(r.logits),r.scores=Qe(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],QC-1],[1,-1]),c.squeeze=Qe(c.slice),c.landmarks=U(c.squeeze,[QC,-1]);let m=await c.bbox.data(),f={startPoint:[m[0],m[1]],endPoint:[m[2],m[3]],landmarks:await c.landmarks.array(),confidence:p},g=HC(f,[(e.shape[2]||0)/Ji,(e.shape[1]||0)/Ji]),y=Gm(g,t.face.scale||hAe),A=jm(y);s.push(A),Object.keys(c).forEach(x=>te(c[x]))}}return Object.keys(r).forEach(h=>te(r[h])),s}var qm={};vs(qm,{connected:()=>ib,kpt:()=>sb});var sb=["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"],ib={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 aT=224,fAe,mAe=5,Xm=[8,16,32,32,32];async function sT(){let e=[],t=0;for(;t<mAe;){let r=0,n=t;for(;n<Xm.length&&Xm[n]===Xm[t];)r+=2,n++;let a=Xm[t],s=Math.ceil(aT/a),i=Math.ceil(aT/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}fAe={x:Nt(e.map(r=>r.x)),y:Nt(e.map(r=>r.y))}}function ls(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 iT(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 Km(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 uT={initial:!0},yn={detector:null,landmarks:null},Ld={detector:[224,224],landmarks:[256,256]},ob=Number.MAX_SAFE_INTEGER,yAe={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Ym=null,tc,Qi=[[0,0],[0,0],[0,0],[0,0]],oT=0,lT=e=>1-1/(1+Math.exp(e));async function dT(e){if(uT.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);Ld.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ld.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 sT(),yn.detector}async function pT(e){if(uT.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);Ld.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ld.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return yn.landmarks}async function AAe(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(tc&&(r.cropped=Ie.cropAndResize(e,[tc],[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=Xn(r.cropped||e,Qi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Je.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Je.tf255)):n=pe(r.cropped||e,Je.tf255);return Object.keys(r).forEach(a=>te(r[a])),n}function xAe(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(tc)for(let r of e)r.positionRaw=[r.positionRaw[0]+tc[1],r.positionRaw[1]+tc[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 bAe(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 vAe(e,t,r){var m;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(m=yn.landmarks)==null?void 0:m.execute(e,yAe.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(f=>te(n[f]));let o=[],l=5;for(let f=0;f<s.length/l;f++){let g=lT(s[l*f+3]),y=lT(s[l*f+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*f+0]/Ld.landmarks[0],s[l*f+1]/Ld.landmarks[1],s[l*f+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],w=[i[l*f+0],i[l*f+1],i[l*f+2]+0];o.push({part:sb[f],positionRaw:x,position:b,distance:w,score:A})}if(a<(t.body.minConfidence||0))return null;bAe(o);let u=xAe(o,r),d=u.map(f=>f.position),h=ls(d,[r[0],r[1]]),p={};for(let[f,g]of Object.entries(ib)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(w=>w.part===g[A]),b=u.find(w=>w.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[f]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function lb(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-oT,a=ob<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&Ym!==null)ob++;else{let s={};s.landmarks=await AAe(e,256),Ym=await vAe(s.landmarks,t,r),Object.keys(s).forEach(i=>te(s[i])),oT=oe(),ob=0}return Ym?[Ym]:[]}var Bd=[{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 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r=(t.body.skipTime||0)>oe()-gT,n=fb<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(Zr.keypoints).length>0?(fb++,[Zr]):(fb=0,new Promise(async a=>{var h;let s=X(()=>{if(!($r!=null&&$r.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[$r.inputs[0].shape[2],$r.inputs[0].shape[1]],!1),c=L(p,Je.tf2);return ce(c,Je.tf1)}),i;if(t.body.enabled&&(i=$r==null?void 0:$r.execute(s)),gT=oe(),te(s),i){Zr.keypoints.length=0;let p=i.squeeze();te(i);let c=p.unstack(2);te(p);for(let m=0;m<c.length;m++){let[f,g,y]=await kAe(c[m],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&Zr.keypoints.push({score:Math.round(100*y)/100,part:hb[m],positionRaw:[f/$r.inputs[0].shape[2],g/$r.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/$r.inputs[0].shape[2]),Math.round(e.shape[1]*g/$r.inputs[0].shape[1])]})}c.forEach(m=>te(m))}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(cb)){let m=[];for(let f=0;f<c.length-1;f++){let g=Zr.keypoints.find(A=>A.part===c[f]),y=Zr.keypoints.find(A=>A.part===c[f+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&m.push([g.position,y.position])}Zr.annotations[p]=m}a([Zr])}))}var IAe=["angry","disgust","fear","happy","sad","surprise","neutral"],Bn,Qm=[],xT=0,bT=0,gb=Number.MAX_SAFE_INTEGER;async function vT(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 yb(e,t,r,n){var i,o;if(!Bn)return[];let 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t,r,n;return he.initial&&(Ba=null),Ba?e.debug&&se("cached model:",Ba.modelUrl):(t=e.face.attention)!=null&&t.enabled?Ba=await Ge((r=e.face.attention)==null?void 0:r.modelPath):Ba=await Ge((n=e.face.mesh)==null?void 0:n.modelPath),Ud=Ba.inputs[0].shape?Ba.inputs[0].shape[2]:0,Ba}var OT=Vl,DT=Qh;var bn,t1=[],LT=0,BT=0,kb=Number.MAX_SAFE_INTEGER;async function WT(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 Ib(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,Je.tf255);return te(r),n}async function Sb(e,t,r,n){var i,o,l,u;if(!bn)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let a=kb<(((i=t.face.description)==null?void 0:i.skipFrames)||0),s=(((o=t.face.description)==null?void 0:o.skipTime)||0)>oe()-LT;return t.skipAllowed&&a&&s&&BT===n&&((l=t1[r])==null?void 0:l.age)&&((u=t1[r])==null?void 0:u.age)>0?(kb++,t1[r]):(kb=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 m=Ib(e),f=bn==null?void 0:bn.execute(m);LT=oe(),te(m);let y=await(await f.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(f.find(R=>R.shape[1]===100),1),b=(await x.data())[0];te(x);let I=await f.find(R=>R.shape[1]===100).data();h.age=Math.round(I[b-1]>I[b+1]?10*b-100*I[b-1]:10*b+100*I[b+1])/10;let N=f.find(R=>R.shape[1]===1024),E=N?await N.data():[];h.descriptor=Array.from(E),f.forEach(R=>te(R))}t1[r]=h,BT=n,d(h)}))}function r1(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=U(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=>te(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Je.tf127),n.image=ce(n.div,Je.tf1),n.batched=this.model.execute(n.image),n.predictions=Qe(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Tr(n.slice),n.scores=Qe(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=U(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]},m=jT(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(m),Object.keys(l).forEach(f=>te(l[f]))}return Object.keys(n).forEach(o=>te(n[o])),i}};var MAe=5,ZT=1.65,YT=[0,5,9,13,17,1,2],FAe=0,PAe=2,JT=0,i1=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=>Nb([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return n1(a1(a),MAe)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=n1(a1(r),ZT);n.palmLandmarks=[];for(let a=0;a<YT.length;a++)n.palmLandmarks.push(t[YT[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=r1(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=Tb(n,[0,0]),u=o.map(c=>[...Nb(c,l),c[2]]),d=qT(a),h=[...rc(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()-JT,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?HT(u.palmLandmarks[FAe],u.palmLandmarks[PAe]):0,h=rc(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(),m=Tb(-d,h),f=n?this.getBoxForPalmLandmarks(u.palmLandmarks,m):u,g=GT(f,c,[this.inputSize,this.inputSize]),y=pe(g,Je.tf255);te(g),te(c);let[A,x]=this.handPoseModel.execute(y);JT=oe(),te(y);let b=(await A.data())[0];if(te(A),b>=r.hand.minConfidence/4){let w=U(x,[-1,3]),I=await w.array();te(x),te(w);let N=this.transformRawCoords(I,f,d,m),E=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:N,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;te(x)}else{let d=n1(a1(u),ZT),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:ps,middle:hs,ring:jl,pinky:Hl}=Yr,{none:ka,half:zAe,full:Ia}=no,{verticalUp:Gd,verticalDown:_6e,horizontalLeft:Eb,horizontalRight:OAe,diagonalUpRight:DAe,diagonalUpLeft:jd,diagonalDownRight:z6e,diagonalDownLeft:O6e}=Bt,ao=new ro("thumbs up");ao.curl(wa,ka,1);ao.direction(wa,Gd,1);ao.direction(wa,jd,.25);ao.direction(wa,DAe,.25);for(let e of[Yr.index,Yr.middle,Yr.ring,Yr.pinky])ao.curl(e,Ia,1),ao.direction(e,Eb,1),ao.direction(e,OAe,1);var rr=new ro("victory");rr.curl(wa,zAe,.5);rr.curl(wa,ka,.5);rr.direction(wa,Gd,1);rr.direction(wa,jd,1);rr.curl(ps,ka,1);rr.direction(ps,Gd,.75);rr.direction(ps,jd,1);rr.curl(hs,ka,1);rr.direction(hs,Gd,1);rr.direction(hs,jd,.75);rr.curl(jl,Ia,1);rr.direction(jl,Gd,.2);rr.direction(jl,jd,1);rr.direction(jl,Eb,.2);rr.curl(Hl,Ia,1);rr.direction(Hl,Gd,.2);rr.direction(Hl,jd,1);rr.direction(Hl,Eb,.2);rr.weight(ps,2);rr.weight(hs,2);var so=new ro("point");so.curl(wa,Ia,1);so.curl(ps,ka,.5);so.curl(hs,Ia,.5);so.curl(jl,Ia,.5);so.curl(Hl,Ia,.5);so.weight(ps,2);so.weight(hs,2);var io=new ro("middle finger");io.curl(wa,ka,1);io.curl(ps,Ia,.5);io.curl(hs,Ia,.5);io.curl(jl,Ia,.5);io.curl(Hl,Ia,.5);io.weight(ps,2);io.weight(hs,2);var Hd=new ro("open palm");Hd.curl(wa,ka,.75);Hd.curl(ps,ka,.75);Hd.curl(hs,ka,.75);Hd.curl(jl,ka,.75);Hd.curl(Hl,ka,.75);var QT=[ao,rr,so,io,Hd];var LAe=.7,ql={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 eN(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 rN(e,t){if(!e||!t)return[0,0];let r=eN(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=eN(e[1],e[2],t[1],t[2]);return[r,n]}function tN(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 BAe(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),m=Math.sqrt(s*s+l*l+h*h),f=(m*m+p*p-c*c)/(2*m*p);f>1?f=1:f<-1&&(f=-1);let g=Math.acos(f);g=57.2958*g%180;let y;return g>ql.NO_CURL_START_LIMIT?y=no.none:g>ql.HALF_CURL_START_LIMIT?y=no.half:y=no.full,y}function nN(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 aN(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 WAe(e,t,r,n,a,s,i,o){let l,u=aN(e,t,r,n),d=nN(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 VAe(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,m=0,f=h/(d+1e-5);f>1.5?p+=ql.DISTANCE_VOTE_POWER:f>.66?c+=ql.DISTANCE_VOTE_POWER:m+=ql.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],w=e[1],I=r[0],N=r[1];x===g?(I=r[0],N=r[1]):x===A&&(b=t[0],w=t[1]);let _=rN([b,w],[I,N]),M=tN(_,ql.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],m+=M[2];for(let z of n){let O=tN(z,ql.SINGLE_ANGLE_VOTE_POWER);p+=O[0],c+=O[1],m+=O[2]}let S;return p===Math.max(p,c,m)?S=aN(l,o,u,h):m===Math.max(c,m)?S=nN(s,a,i,d):S=WAe(l,o,u,h,s,a,i,d),S}function sN(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=rN(d,h),c=p[0],m=p[1];o.push(c),l.push(m)}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=BAe(l,u,d),p=VAe(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function o1(e){if(!e||e.length===0)return null;let t=sN(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 iN(e){let t=[];if(!e||e.length===0)return t;let r=sN(e);for(let n of QT){let a=n.matchAgainst(r.curls,r.directions);a>=LAe&&t.push({name:n.name,confidence:a})}return t}var oN={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]},qd,Xd,lN;async function $b(e,t){let r=await lN.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(oN))s[d]=oN[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=o1(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 Mb(e){var r,n;he.initial&&(qd=null,Xd=null),!qd||!Xd?[qd,Xd]=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:",qd.modelUrl),e.debug&&se("cached model:",Xd.modelUrl));let t=new s1(qd);return lN=new i1(t,Xd),[qd,Xd]}var hr=[null,null],UAe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],oo=[[0,0],[0,0]],GAe=["hand","fist","pinch","point","face","tip","pinchtip"],dN=4,pN=1.6,jAe=512,HAe=1.4,l1=Number.MAX_SAFE_INTEGER,Fb=0,cs=[0,0],Ht={boxes:[],hands:[]},hN={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 cN(e){var t;if(he.initial&&(hr[0]=null),hr[0])e.debug&&se("cached model:",hr[0].modelUrl);else{u1(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),hr[0]=await Ge((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(hr[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 hr[0]}async function fN(e){var t;if(he.initial&&(hr[1]=null),hr[1])e.debug&&se("cached model:",hr[1].modelUrl);else{hr[1]=await Ge((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(hr[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 hr[1]}async function qAe(e,t){let r=[];if(!e||!hr[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,jAe),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 hr[0].executeAsync(n.cast,UAe),n.boxes=Qe(n.rawBoxes,[0,2]),n.scores=Qe(n.rawScores,[0]);let o=nn(n.scores,1);te(o[dN]),o.splice(dN,1),n.filtered=dr(o,1),te(o),n.max=Ar(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),m=await c.data();te(c);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],g=Km(f,HAe),y=[Math.trunc(f[0]*cs[0]),Math.trunc(f[1]*cs[1]),Math.trunc(f[2]*cs[0]),Math.trunc(f[3]*cs[1])],A=d[p],x=GAe[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>te(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 Pb(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&&hr[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,Je.tf255),[a.score,a.keypoints]=hr[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=U(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=>[cs[0]*(h[0]+t.boxRaw[0]),cs[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=o1(n.keypoints);for(let h of Object.keys(hN))n.annotations[h]=hN[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>te(a[l]))}return n}async function _b(e,t){var a,s;if(!hr[0]||!hr[1]||!((a=hr[0])!=null&&a.inputs[0].shape)||!((s=hr[1])!=null&&s.inputs[0].shape))return[];cs=[e.shape[2]||0,e.shape[1]||0],l1++;let r=(t.hand.skipTime||0)>oe()-Fb,n=l1<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Ht.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-Fb,l=l1<3*(t.hand.skipFrames||0);t.skipAllowed&&Ht.hands.length===t.hand.maxDetected?Ht.hands=await Promise.all(Ht.boxes.map(d=>Pb(e,d,t))):t.skipAllowed&&o&&l&&Ht.hands.length>0?Ht.hands=await Promise.all(Ht.boxes.map(d=>Pb(e,d,t))):(Ht.boxes=await qAe(e,t),Fb=oe(),Ht.hands=await Promise.all(Ht.boxes.map(d=>Pb(e,d,t))),l1=0);let u=[...Ht.boxes];if(Ht.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Ht.hands.length;d++){let h=iT(Ht.hands[d].keypoints,cs);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=Km(h.box,pN),c=Km(h.boxRaw,pN);Ht.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Ht.hands.length;d++){let h=ls(Ht.hands[d].keypoints,cs);Ht.hands[d].box=h.box,Ht.hands[d].boxRaw=h.boxRaw}i(Ht.hands)})}var Mr,d1=[],zb=Number.MAX_SAFE_INTEGER,gN=0,yN=0;async function AN(e){var t;return he.initial&&(Mr=null),Mr?e.debug&&se("cached model:",Mr.modelUrl):Mr=await Ge((t=e.face.liveness)==null?void 0:t.modelPath),Mr}async function Ob(e,t,r,n){var i,o;if(!Mr)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-yN,s=zb<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&gN===n&&d1[r]?(zb++,d1[r]):(zb=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[Mr!=null&&Mr.inputs[0].shape?Mr.inputs[0].shape[2]:0,Mr!=null&&Mr.inputs[0].shape?Mr.inputs[0].shape[1]:0],!1),d=Mr==null?void 0:Mr.execute(u),h=(await d.data())[0];d1[r]=Math.round(100*h)/100,gN=n,yN=oe(),te([u,d]),l(d1[r])}))}var nc={};vs(nc,{connected:()=>h1,horizontal:()=>Db,kpt:()=>p1,relative:()=>Bb,vertical:()=>Lb});var p1=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Db=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Lb=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Bb=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],h1={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var bN=.005,vn={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Wb(e){for(let t of Db){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 Lb){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 Bb){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 vN(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]<bN&&r[1]<bN?e[t]=vn.keypoints[t]:vn.keypoints[t]=e[t]}else vn.keypoints[t]=e[t];return e}function wN(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return 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p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:p1[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=ls(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(h1)){let c=[];for(let m=0;m<p.length-1;m++){let f=o.find(y=>y.part===p[m]),g=o.find(y=>y.part===p[m+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([f.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};Wb(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 Ub(e,t){if(!wn||!(wn!=null&&wn.inputs[0].shape))return[];t.skipAllowed||(Xl.boxes.length=0),Vb++;let r=(t.body.skipTime||0)>oe()-Xl.last,n=Vb<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?Xl.bodies:new Promise(async a=>{let s={};Vb=0,s.input=wN(e,c1),s.res=wn==null?void 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sc=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],JAe=sc.length,ac=sc.reduce((e,t,r)=>(e[t]=r,e),{}),QAe=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],oke=QAe.map(([e,t])=>[ac[e],ac[t]]),EN=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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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 cxe(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 fxe(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]];r3(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]];r3(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[o[0],o[1]],4)}}function mxe(e,t){if(dt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let r=0;r<Vl.length/3;r++){let n=[Vl[r*3+0],Vl[r*3+1],Vl[r*3+2]].map(a=>e.mesh[a]);t3(t,n,dt)}hxe(e,t)}}function gxe(e,t){if(dt.drawPoints&&e.mesh.length>=468)for(let r=0;r<e.mesh.length;r++)ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2],dt),dt.drawAttention&&(xn.lips.includes(r)&&ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]+127,dt),xn.eyeL.includes(r)&&ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt),xn.eyeR.includes(r)&&ms(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt))}function yxe(e,t){dt.drawBoxes&&Va(t,e.box[0],e.box[1],e.box[2],e.box[3],dt)}async function Yd(e,t,r){if(dt=Gt(Fr,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)yxe(a,n),pxe(a,n),a.mesh&&a.mesh.length>0&&(gxe(a,n),mxe(a,n),cxe(a,n),fxe(a,n))}}async function Jd(e,t,r){var s;let n=Gt(Fr,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&&(Va(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 ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=fs(t[i].keypoints[o].position[2],n),ms(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=fs(o.position[2],n),a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)WN(a,l,n)}}}async function Qd(e,t,r){let n=Gt(Fr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${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:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=fs(i[2],n),ms(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||-256;a.fillStyle=fs(u,n),a.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};a.font=n.font,i(s.annotations.index,"index"),i(s.annotations.middle,"middle"),i(s.annotations.ring,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palm,"palm")}if(n.drawPolygons&&s.annotations){let i=o=>{if(!(!o||o.length===0||!o[0]))for(let l=0;l<o.length;l++){a.beginPath();let u=o[l][2]||0;a.strokeStyle=fs(l*u,n),a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()}};a.lineWidth=n.lineWidth,i(s.annotations.index),i(s.annotations.middle),i(s.annotations.ring),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function ep(e,t,r){let n=Gt(Fr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let i=`${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]});rp&&rp>0&&(a=a.map(i=>({x:i.x>.5?i.x+rp:i.x-rp,y:i.y>.5?i.y+rp:i.y-rp})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)Axe(i/t,o/t,a)||(n.set(l3*n.get(0,o,i,0),0,o,i,0),n.set(l3*n.get(0,o,i,1),0,o,i,1),n.set(l3*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return te(n),s}var bxe=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}},UN=(e,t)=>{let r=f=>{let g=Math.sqrt(f[0]*f[0]+f[1]*f[1]+f[2]*f[2]);return f[0]/=g,f[1]/=g,f[2]/=g,f},n=(f,g)=>{let y=f[0]-g[0],A=f[1]-g[1],x=f[2]-g[2];return[y,A,x]},a=(f,g)=>{let y=f[1]*g[2]-f[2]*g[1],A=f[2]*g[0]-f[0]*g[2],x=f[0]*g[1]-f[1]*g[0];return[y,A,x]},s=f=>{let[g,y,A,x,b,w,I,N,E]=f,R,_,M;return x<1?x>-1?(M=Math.asin(x),_=Math.atan2(-I,g),R=Math.atan2(-w,b)):(M=-Math.PI/2,_=-Math.atan2(N,E),R=0):(M=Math.PI/2,_=Math.atan2(N,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(f=>[f[0]*t[0]/o,f[1]*t[1]/o,f[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),m=i.length===478?bxe(e):{bearing:0,strength:0};return{angle:c,matrix:p,gaze:m}};var u3=async(e,t)=>{var c,m,f,g,y,A,x,b,w,I,N,E,R,_,M,S,z,O,H,K,D,Q;let r=oe(),n,a,s,i,o,l,u,d,h=[];e.state="run:face";let p=await _T(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 VN(p[V]);te(p[V].tensor),p[V].tensor=ae}let re=p[V].mesh&&p[V].mesh.length>200?UN(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(m=e.config.face.emotion)!=null&&m.enabled?yb(p[V].tensor||ft([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=(f=e.config.face.emotion)!=null&&f.enabled?await yb(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?Z5(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 Z5(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?Ob(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 Ob(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?U5(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:gear",r=oe(),a=(w=e.config.face.gear)!=null&&w.enabled?await U5(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=(I=e.config.face.ssrnet)!=null&&I.enabled?j5(p[V].tensor||ft([]),e.config,V,p.length):null,s=(N=e.config.face.ssrnet)!=null&&N.enabled?X5(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 j5(p[V].tensor||ft([]),e.config,V,p.length):null,s=(R=e.config.face.ssrnet)!=null&&R.enabled?await X5(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?xb(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 xb(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=(S=e.config.face.description)!=null&&S.enabled?Sb(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 Sb(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}),((H=e.config.face.gear)==null?void 0:H.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((K=e.config.face.mobilefacenet)==null?void 0:K.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let Y=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?Qe(p[V].tensor):null;te(p[V].tensor),p[V].tensor&&delete p[V].tensor;let J={...p[V],id:V};d!=null&&d.age&&(J.age=d.age),d!=null&&d.gender&&(J.gender=d.gender),d!=null&&d.genderScore&&(J.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(J.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(J.race=d==null?void 0:d.race),i&&(J.emotion=i),l&&(J.real=l),u&&(J.live=u),Y&&Y!==0&&(J.iris=Math.trunc(500/Y/11.7)/100),re&&(J.rotation=re),ie&&(J.tensor=ie),h.push(J),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 GN=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},jN=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 ${l<0?"up":"down"}`})}return t},HN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],m=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(m<.01||c<.01||m>.022||c>.022)&&(u=!1),(m<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(m>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},qN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=iN(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ne={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},d3=0;function XN(e,t){var i,o,l,u,d,h,p,c,m,f,g,y,A,x,b,w,I,N,E,R,_,M,S,z,O,H,K;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ne.canvas=e.canvas),e.error&&(Ne.error=e.error),!Ne.body||e.body.length!==Ne.body.length)Ne.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let Q=e.body[D].box.map((J,ae)=>((a-1)*Ne.body[D].box[ae]+J)/a),V=e.body[D].boxRaw.map((J,ae)=>((a-1)*Ne.body[D].boxRaw[ae]+J)/a),re=e.body[D].keypoints.map((J,ae)=>{var de,xe,be,Ee,$e,De,We,Xe,ot;return{score:J.score,part:J.part,position:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[0]||0)+(J.position[0]||0))/a:J.position[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[1]||0)+(J.position[1]||0))/a:J.position[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[2]||0)+(J.position[2]||0))/a:J.position[2]],positionRaw:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[0]||0)+(J.positionRaw[0]||0))/a:J.positionRaw[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[1]||0)+(J.positionRaw[1]||0))/a:J.positionRaw[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[2]||0)+(J.positionRaw[2]||0))/a:J.positionRaw[2]],distance:[Ne.body[D].keypoints[ae]?((a-1)*(((de=Ne.body[D].keypoints[ae].distance)==null?void 0:de[0])||0)+(((xe=J.distance)==null?void 0:xe[0])||0))/a:(be=J.distance)==null?void 0:be[0],Ne.body[D].keypoints[ae]?((a-1)*(((Ee=Ne.body[D].keypoints[ae].distance)==null?void 0:Ee[1])||0)+((($e=J.distance)==null?void 0:$e[1])||0))/a:(De=J.distance)==null?void 0:De[1],Ne.body[D].keypoints[ae]?((a-1)*(((We=Ne.body[D].keypoints[ae].distance)==null?void 0:We[2])||0)+(((Xe=J.distance)==null?void 0:Xe[2])||0))/a:(ot=J.distance)==null?void 0:ot[2]]}}),Y={},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=qm:(h=(d=t.body)==null?void 0:d.modelPath)!=null&&h.includes("movenet")&&(ie=nc);for(let[J,ae]of Object.entries(ie.connected)){let de=[];for(let xe=0;xe<ae.length-1;xe++){let be=re.find($e=>$e.part===ae[xe]),Ee=re.find($e=>$e.part===ae[xe+1]);be&&Ee&&de.push([be.position,Ee.position])}Y[J]=de}Ne.body[D]={...e.body[D],box:Q,boxRaw:V,keypoints:re,annotations:Y}}if(!Ne.hand||e.hand.length!==Ne.hand.length)Ne.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,J)=>((a-1)*Ne.hand[D].box[J]+ie)/a),V=e.hand[D].boxRaw.map((ie,J)=>((a-1)*Ne.hand[D].boxRaw[J]+ie)/a);Ne.hand[D].keypoints.length!==e.hand[D].keypoints.length&&(Ne.hand[D].keypoints=e.hand[D].keypoints);let re=e.hand[D].keypoints&&e.hand[D].keypoints.length>0?e.hand[D].keypoints.map((ie,J)=>ie.map((ae,de)=>((a-1)*(Ne.hand[D].keypoints[J][de]||1)+(ae||0))/a)):[],Y={};if(Object.keys(Ne.hand[D].annotations).length!==Object.keys(e.hand[D].annotations).length)Ne.hand[D].annotations=e.hand[D].annotations,Y=Ne.hand[D].annotations;else if(e.hand[D].annotations)for(let ie of Object.keys(e.hand[D].annotations))Y[ie]=e.hand[D].annotations[ie]&&e.hand[D].annotations[ie][0]?e.hand[D].annotations[ie].map((J,ae)=>J.map((de,xe)=>((a-1)*Ne.hand[D].annotations[ie][ae][xe]+de)/a)):null;Ne.hand[D]={...e.hand[D],box:Q,boxRaw:V,keypoints:re,annotations:Y}}if(!Ne.face||e.face.length!==Ne.face.length)Ne.face=JSON.parse(JSON.stringify(e.face));else for(let D=0;D<e.face.length;D++){let Q=e.face[D].box.map((re,Y)=>((a-1)*Ne.face[D].box[Y]+re)/a),V=e.face[D].boxRaw.map((re,Y)=>((a-1)*Ne.face[D].boxRaw[Y]+re)/a);if(e.face[D].rotation){let re={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};re.matrix=(p=e.face[D].rotation)==null?void 0:p.matrix,re.angle={roll:((a-1)*(((m=(c=Ne.face[D].rotation)==null?void 0:c.angle)==null?void 0:m.roll)||0)+(((g=(f=e.face[D].rotation)==null?void 0:f.angle)==null?void 0:g.roll)||0))/a,yaw:((a-1)*(((A=(y=Ne.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)*(((I=(w=Ne.face[D].rotation)==null?void 0:w.angle)==null?void 0:I.pitch)||0)+(((E=(N=e.face[D].rotation)==null?void 0:N.angle)==null?void 0:E.pitch)||0))/a},re.gaze={bearing:((a-1)*(((_=(R=Ne.face[D].rotation)==null?void 0:R.gaze)==null?void 0:_.bearing)||0)+(((S=(M=e.face[D].rotation)==null?void 0:M.gaze)==null?void 0:S.bearing)||0))/a,strength:((a-1)*(((O=(z=Ne.face[D].rotation)==null?void 0:z.gaze)==null?void 0:O.strength)||0)+(((K=(H=e.face[D].rotation)==null?void 0:H.gaze)==null?void 0:K.strength)||0))/a},Ne.face[D]={...e.face[D],rotation:re,box:Q,boxRaw:V}}Ne.face[D]={...e.face[D],box:Q,boxRaw:V}}if(!Ne.object||e.object.length!==Ne.object.length)Ne.object=JSON.parse(JSON.stringify(e.object));else for(let D=0;D<e.object.length;D++){let Q=e.object[D].box.map((re,Y)=>((a-1)*Ne.object[D].box[Y]+re)/a),V=e.object[D].boxRaw.map((re,Y)=>((a-1)*Ne.object[D].boxRaw[Y]+re)/a);Ne.object[D]={...e.object[D],box:Q,boxRaw:V}}if(e.persons){let D=e.persons;if(!Ne.persons||D.length!==Ne.persons.length)Ne.persons=JSON.parse(JSON.stringify(D));else for(let Q=0;Q<D.length;Q++)Ne.persons[Q].box=D[Q].box.map((V,re)=>((a-1)*Ne.persons[Q].box[re]+V)/a)}e.gesture&&(Ne.gesture=e.gesture);let s=oe();return d3=he.perfadd?d3+Math.round(s-r):Math.round(s-r),e.performance&&(Ne.performance={...e.performance,interpolate:d3}),Ne}var c3={};vs(c3,{distance:()=>oc,match:()=>h3,similarity:()=>p3});function oc(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 KN=(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 p3(e,t,r={order:2,multiplier:25,min:.2,max:.8}){let n=oc(e,t,r);return KN(n,r.order||2,r.min||0,r.max||1)}function h3(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=oc(e,t[i],r);if(o<n&&(n=o,a=i),n<(r.threshold||0))break}let 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mb(o.tensor,p):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(u=this.config.body.enabled?await Ub(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?$b(o.tensor,c):[]:(M=(_=this.config.hand.detector)==null?void 0:_.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?_b(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(z=(S=this.config.hand.detector)==null?void 0:S.modelPath)!=null&&z.includes("handdetect")?d=this.config.hand.enabled?await $b(o.tensor,c):[]:(H=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&H.includes("handtrack")&&(d=this.config.hand.enabled?await _b(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?((K=this.config.object.modelPath)!=null&&K.includes("nanodet")?h=this.config.object.enabled?jb(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?pb(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 jb(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await pb(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 m=[];this.config.gesture.enabled&&(a=oe(),m=[...jN(l),...GN(u),...qN(d),...HN(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 f=((Y=(re=this.process)==null?void 0:re.tensor)==null?void 0:Y.shape)||[];this.result={face:l,body:u,hand:d,gesture:m,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return ZN(l,u,d,m,f)}},te(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};np=new WeakMap,lc=new WeakMap,uc=new WeakMap,I1=new WeakMap;return ZE(Mxe);})();
/**
* @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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