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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");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return Vf.nextTensorId++}nextVariableId(){return Vf.nextVariableId++}clone(e){let t=L.runKernel(La,{x:e}),n={x:e},s=a=>({x:()=>{let i="float32",o={x:a},u={dtype:i};return L.runKernel(Ca,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Mf(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=wf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(wf(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Mf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:T,dtype:C}=x;return this.makeTensorFromDataId(k,T,C)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:c,attrs:l}=e,d=wf(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(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(u,c,t,d,n,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=Rv(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Of(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof od||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Of(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of 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a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(O(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));O(r instanceof tt,()=>"The result y returned by f() must be a tensor.");let a=g$(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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rp=B({depthwiseConv2d_:F_});function O_(e){let n={x:E(e,"x","diag")};return L.runKernel(tg,n)}var dde=B({diag_:O_});function P_(e,t,n,s,r=[1,1],a="NHWC"){let i=E(e,"x","dilation2d"),o=E(t,"filter","dilation2d");O(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),O(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),O(a==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);let u=i,c=!1;i.rank===3&&(u=G(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);let l={x:u,filter:o},d={strides:n,pad:s,dilations:r},p=L.runKernel(Vd,l,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var z_=B({dilation2d_:P_});function M_(e,t){let n=E(e,"a","equal","string_or_numeric"),s=E(t,"b","equal","string_or_numeric");[n,s]=vt(n,s),ot(n.shape,s.shape);let r={a:n,b:s};return L.runKernel(lo,r)}var Yn=B({equal_:M_});function L_(e,t,n){let 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m=oe(V(Se(h,oe(rn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),H(()=>{this.accBeta1.assign(da(this.beta1,this.iterations_+1)),this.accBeta2.assign(da(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};tb.className="Adam";Ar(tb);var nb=class extends _r{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],H(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),s==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=be(1,this.accBeta1),s=Se(-this.learningRate,oe(V(this.iteration,this.decay),1));t.forEach((r,a)=>{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)}};nb.className="Adamax";Ar(nb);var fp=class extends _r{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=L.registeredVariables[n];H(()=>{let i=oe(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=jt(Ce(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},sL=0,Ge=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=sL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Vs(n)+"_"+Sp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),c=r.axes[o],l=u>=0?i[u]:i[i.length+u];if(c!=null&&[c,null].indexOf(l)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${c} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof Ns)){s=!1;break}let r=!0;for(let a of n)if(a instanceof Ns){r=!1;break}if(s===r)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return Jr(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Re("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=rL(e),i=this.computeOutputShape(a),o,u=aL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((c,l)=>new Ns(u,c,this,dt(e),t,this.name,l)):o=new Ns(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Re("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Bs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Bs(`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 cs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return gd(this.weights)}build(e){this.built=!0}getWeights(e=!1){return am(e?this.trainableWeights:this.weights)}setWeights(e){H(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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_=0;_<R.length;++_){h.hasKey(R[_])||h.add(R[_],k[_],Array.isArray(T)?T[0]:T);let P=o.indexOf(R[_].name);P!==-1&&(u[P]=k[_])}r||Ee(v)}return h.disposeMasks(),a?u:u[0]}function zL(e,t){w.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){let r=dx(e[0],t);n=r.sorted,s=r.recipientMap}else{let r=new Set;for(let a of e){let{sorted:i,recipientMap:o}=dx(a,t);for(let u of i)r.has(u.name)||(n.push(u),r.add(u.name));for(let u in o)s[u]==null&&(s[u]=new Set),o[u].forEach(c=>s[u].add(c))}}return{sorted:n,recipientCounts:ML(s)}}function ML(e){let t={};for(let n in e)t[n]=e[n].size;return t}function dx(e,t){let n=new Set,s=[],r={};for(let o of t.names())n.add(o);let a=[],i=[];for(a.push(e);a.length>0;){let o=a[a.length-1];if(n.has(o.name)){a.pop();continue}let u=i[i.length-1]===a.length-1;if(o.inputs.length===0||u)a.pop(),s.push(o),n.add(o.name),u&&i.pop();else{i.push(a.length-1);for(let c of o.inputs)r[c.name]==null&&(r[c.name]=new 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All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);hr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Is(v===0,"input layer has >1 nodes"),Is(x===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Ko))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,T)=>{(x==null||k==null||T==null)&&(x=b.sourceLayer,k=b.nodeIndex,T=b.tensorIndex);let C=x.inboundNodes[k];if(v.indexOf(C)!==-1)throw new cs(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(C)!==-1)return;this.containerNodes.add(ws.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(C)===-1&&v.push(C);let R=C.inboundLayers.length;for(let _=0;_<R;_++){let P=C.inputTensors[_],D=C.inboundLayers[_],F=C.nodeIndices[_],$=C.tensorIndices[_];o(P,y,v,D,F,$)}for(y.push(C);v.indexOf(C)>=0;)v.splice(v.indexOf(C),1);i.push(C)},u=[],c=[];for(let b of this.outputs)o(b,u,c);let l=i.slice().reverse();for(let b of l){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],T=b.nodeIndices[x],C=k.inboundNodes[T],R=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(y+1,R),n[C.id]=C}}let d={};for(let b in t){let y=t[b];y in d||(d[y]=[]),d[y].push(n[b])}let p={};for(let b in s){let y=s[b];y in p||(p[y]=[]),p[y].push(r[b])}let h=Object.keys(p).map(b=>parseInt(b,10)).sort(Fc);this.layers=[];for(let b of h){let y=p[b];y.sort((v,x)=>{let k=a[v.id],T=a[x.id];return k<T?-1:k>T?1:0});for(let v of y)v instanceof ws&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=p,h=Object.keys(d).map(b=>parseInt(b,10)).sort(Fc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of d[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new cs(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=d;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new cs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Cp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}wb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${DI}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=om(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return H(()=>{e=dt(e);let n=new Yr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return $u(this.outputs,n,t)})}computeMask(e,t){return H(()=>{e=dt(e);let n;return t==null?n=ha(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=md(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],c=o.name+"_0_0";n[c]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Fc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let c=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let l=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];l.push(v)}let d=c.computeOutputShape(bn(l)),p=md(d),h=c.inboundNodes.indexOf(u);for(let f=0;f<p.length;f++){let m=`${c.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],l=`${o.name}_${u}_${c}`;a.push(l)}for(let i=0;i<a.length;i++){let o=a[i];Is(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=ha(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],c=e[o],l=t[o];n[u.id]=[c,l]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Fc);for(let o of s){let u=this.nodesByDepth[o];for(let c of u){let l=c.outboundLayer,d=c.inputTensors,p=c.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,b,y;if(c.callArgs!=null&&(f=c.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(l.call(v,f)),y=dt(l.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(l.call(m,f)),y=dt(l.computeMask(m,g));if(l.activityRegularizer)throw new Re("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<p.length;++v){let x=p[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Is(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,c]=n[o.id];i.push(u.shape),r.push(u),a.push(c)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof ws?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=ws.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return H(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=ws.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let l=0;l<a.inboundNodes.length;l++){let d=a.inboundNodes[l],p=ws.nodeKey(a,l),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],b=d.nodeIndices[m],y=d.tensorIndices[m],v=ws.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let c={};c.name=a.name,c.className=i,c.config=o,c.inboundNodes=u,n.push(c)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=ws.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let l=this.inputLayersTensorIndices[a];s.push([i.name,c,l])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=ws.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let l=this.outputLayersTensorIndices[a];r.push([i.name,c,l])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let C=r[x];if(C.inboundNodes.length<=k){i(m,g);return}let R=C.inboundNodes[k];b.push(R.outputTensors[T])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=ps(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let c=t.name,l=t.layers;for(let m of l)u(m);for(;!cM(a);)for(let m of l){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Is(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Is(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}return new e({inputs:d,outputs:p,name:c})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){H(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function BL(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function FI(e,t){return BL(e,t,"classWeight")}async function OI(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=H(()=>{if(e.shape.length===1)return pr(e);if(e.shape.length===2){if(e.shape[1]>1)return Gu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Ee(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Jt(i,"float32")}else return null}function VL(e,t){return V(e,t)}var WL=32;function PI(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let a=px("input",e.inputNames,n),i=px("output",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function px(e,t,n){if(n instanceof tt)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function UL(e){if(e.length===3)throw new Re("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function GL(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,i;if(r)if(hx(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=UL(n.validationData);a=g.xs,i=g.ys}let o=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),c;r?c=u.slice().concat(u.map(g=>"val_"+g)):c=u.slice();let l=CI(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=NI(l,d,n.epochs,null,null,HL(t,n),null,r,c);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await p.onEpochBegin(f);let b=0,y=0;for(s||(m=await t.iterator());s?b<n.batchesPerEpoch:!0;){let v=await m.next();if(s&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===yd?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=mx(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=mx(t,this.feedOutputNames,r,!1,"target"),QL(e,t,null),ZL(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[i,o]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let u=null;if(s!=null){let c=FI(s,this.outputNames);u=[];for(let l=0;l<c.length;++l)u.push(await OI(o[l],null,c[l]))}return[i,o,u]}testLoop(e,t,n,s=0,r){return H(()=>{let a=this.checkNumSamples(t,n,r,"steps"),i=[];if(s>0)throw new Re("Verbose mode is not implemented yet.");if(r!=null)throw new Re("steps mode in testLoop() is not implemented yet");{let o=lm(a,n),u=Jt(gs(0,a));for(let c=0;c<o.length;++c){let l=o[c][0],d=o[c][1],p=ea(u,l,d-l),h=Tb(t,p),f=e(h);if(c===0)for(let m=0;m<f.length;++m)i.push(Ce(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=oe(i[m],V(d-l,g))}}for(let c=0;c<i.length;++c)i[c]=Se(i[c],a)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;ex(e,s)>1&&(r+=`_${ex(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let l=[];for(let f=0;f<this.inputs.length;++f)l.push({key:this.inputs[f],value:n[f]});let d=new Yr(l),p=$u(this.outputs,d,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],p[f]);r[f]!=null&&(g=VL(g,r[f]));let b=St(g);t.push(b),f===0?h=g:h=oe(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],b=this.metricsTensors[f][1];m=St(g(s[b],p[b]))}jt(m),a.push(m)}return h=St(h),this.calculateLosses().forEach(f=>{h=oe(h,f)}),h},o=this.collectedTrainableWeights.map(l=>l.read()),u=!0;return[this.optimizer_.minimize(i,u,o)].concat(a)}}makeTestFunction(){this.testFunction=e=>H(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Yr(a),o=$u(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let c=this.lossFunctions[u],l=St(c(r[u],o[u]));u===0?n=l:n=oe(n,l),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let c=this.metricsTensors[u][0],l=this.metricsTensors[u][1],d=St(c(r[l],o[l]));t.push(d)}return t})}async fit(e,t,n={}){return XL(this,e,t,n)}async fitDataset(e,t){return GL(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let c=await u.data();o.push(c[0])}return Ee(i),os(n[0],e),os(n[1],t),bn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Qf().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Qf().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Vs(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Vs(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=Vs(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Vs(zc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Vs(zc(e)));{let e={};for(let t in this.metrics)e[t]=Vs(zc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Qu(e.optimizer_config),n=ps(t),s;if(typeof e.loss=="string")s=jr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>jr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=jr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>jr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=jr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=An.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await An.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:eB,generatedBy:`TensorFlow.js tfjs-layers v${DI}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:c,specs:l}=await An.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...l),n.data=An.concatenateArrayBuffers([n.data,c])}return this.userDefinedMetadata!=null&&(lx(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){lx(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};mr.className="Model";ae.registerClass(mr);var MI=class extends mr{};MI.className="Functional";ae.registerClass(MI);async function tB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Qu(n),r=ps(s,t);if(e.weightsManifest!=null){let a=await An.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Ee(a)}return r}async function nB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=An.getLoadHandlers(e,t);if(n.length===0)n.push(An.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return sB(e,void 0,t)}async function sB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=ps(Qu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:l}=rB(s.weightData,s.weightSpecs);o.loadWeights(c,a),o.optimizer!=null&&l.length>0&&await o.optimizer.setWeights(l),Ee(c),Ee(l.map(d=>d.tensor))}return o}function rB(e,t){let n=An.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var dm=class extends mr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Sp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof dm||e instanceof mr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=II({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=kI(this.outputs[0])}this.inboundNodes=[],new Cp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:ha(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new mr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new cs("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new cs("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new cs("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new cs("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof dm))throw new Re(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let c=ps(o,void 0,s);s&&c.setFastWeightInitDuringBuild(!0),i.add(c)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},$b=dm;$b.className="Sequential";ae.registerClass($b);function Gde(e){return new mr(e)}function Hde(e){return new $b(e)}function qde(e,t){return t==null&&(t={}),nB(e,t)}function aB(e){return II(e)}function jde(e,t){kb.registerCallbackConstructor(e,t)}var kn=class extends ae.Serializable{getConfig(){return{}}},LI=class extends kn{apply(e,t=1){return AM(e,t)}};LI.className="elu";ae.registerClass(LI);var BI=class extends kn{apply(e){return Wk(e)}};BI.className="selu";ae.registerClass(BI);var VI=class extends kn{apply(e){return Ks(e)}};VI.className="relu";ae.registerClass(VI);var WI=class extends kn{apply(e){return H(()=>up(6,Ks(e)))}};WI.className="relu6";ae.registerClass(WI);var UI=class extends kn{apply(e){return e}};UI.className="linear";ae.registerClass(UI);var GI=class extends kn{apply(e){return Hs(e)}};GI.className="sigmoid";ae.registerClass(GI);var HI=class extends kn{apply(e){return _M(e)}};HI.className="hardSigmoid";ae.registerClass(HI);var qI=class extends kn{apply(e){return Ol(e)}};qI.className="softplus";ae.registerClass(qI);var jI=class extends kn{apply(e){return EM(e)}};jI.className="softsign";ae.registerClass(jI);var KI=class extends kn{apply(e){return Hu(e)}};KI.className="tanh";ae.registerClass(KI);var Ab=class extends kn{apply(e,t=-1){return jg(e,t)}};Ab.className="softmax";ae.registerClass(Ab);var XI=class extends kn{apply(e,t=-1){return Rk(e,t)}};XI.className="logSoftmax";ae.registerClass(XI);var YI=class extends kn{apply(e,t=1){return H(()=>V(Hs(V(e,t)),e))}};YI.className="swish";ae.registerClass(YI);var QI=class extends kn{apply(e){return H(()=>V(e,Hu(Ol(e))))}};QI.className="mish";ae.registerClass(QI);function xr(e){return e.getClassName()}function Ef(e,t={}){return zl(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function wr(e){if(e==null){let t={};return t.className="linear",t.config={},Ef(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ef(t)}else return e instanceof kn?e:Ef(e)}function Eb(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var ZI=class extends ae.Serializable{},Wl=class extends ZI{constructor(e){super();Eb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return H(()=>{let t=Tt([1]);return this.hasL1&&(t=oe(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=oe(t,ve(V(this.l2,Bl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Wl.className="L1L2";ae.registerClass(Wl);function iB(e){return Eb(e),new Wl({l1:e!=null?e.l1:null,l2:0})}function oB(e){return Eb(e),new Wl({l2:e!=null?e.l2:null,l1:0})}var bx={l1l2:"L1L2"};function it(e){return ib(e)}function yx(e,t={}){return zl(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function gt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in bx?bx[e]:e,config:{}};return yx(n)}else return e instanceof ZI?e:yx(e)}var _b=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=De(e);let n=Ks(e);return this.maxValue!=null&&(n=Vn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};_b.className="ReLU";ae.registerClass(_b);var Rb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=De(e);return Fg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Rb.className="LeakyReLU";ae.registerClass(Rb);var Db=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=mt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=gt(e.alphaRegularizer),this.alphaConstraint=Ot(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Rt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=De(e),Wg(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:it(this.alphaRegularizer),alphaConstraint:Ft(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Db.className="PReLU";ae.registerClass(Db);var Fb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Re(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=De(e);return ap(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Fb.className="ELU";ae.registerClass(Fb);var Ob=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=De(e);return V(n,pe(Un(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Ob.className="ThresholdedReLU";ae.registerClass(Ob);var Pb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Ab().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=De(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Pb.className="Softmax";ae.registerClass(Pb);function qi(e,t,n){if(typeof e=="number")return ha(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!CM(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function hs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ss(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+vr([n-t,0]);else if(s==="same")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function zb(e,t){return H(()=>(Ct(t),t==="channelsFirst"?qe(e,[0,2,3,1]):e))}function JI(e,t){return H(()=>(Ct(t),t==="channelsFirst"?qe(e,[0,2,3,4,1]):e))}function uB(e,t,n,s=1,r="valid",a,i=1){return H(()=>{if(a==null&&(a=ms()),Ct(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=qe(e,[0,2,1])),r==="causal")throw new Re("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Sk(e,t,s,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=vs(o,n)),o})}function vx(e,t,n,s=[1,1],r="valid",a,i,o=null){return H(()=>{if(a==null&&(a=ms()),Ct(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=zb(e,a);if(r==="causal")throw new Re("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=pa.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),a==="channelsFirst"&&(u=qe(u,[0,3,1,2])),u})}function lB(e,t,n,s=[1,1,1],r="valid",a,i){return H(()=>{if(a==null&&(a=ms()),Ct(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=JI(e,a);if(r==="causal")throw new Re("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Nk(o,t,s,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=vs(o,n)),a==="channelsFirst"&&(o=qe(o,[0,4,1,2,3])),o})}var Mb=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Mb.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Re(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=qi(t.kernelSize,e,"kernelSize"),this.strides=qi(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Gn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=wr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=mt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ot(t.biasConstraint),this.biasRegularizer=gt(t.biasRegularizer),this.activityRegularizer=gt(t.activityRegularizer),this.dilationRate=qi(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Is("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!ob(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:xr(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ft(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Ul=class extends Mb{constructor(e,t){super(e,t);this.kernel=null,Ul.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=mt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ot(t.kernelConstraint),this.kernelRegularizer=gt(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return H(()=>{e=De(e);let n,s=this.bias==null?null:this.bias.read(),r=fI(this.activation.getClassName());if(r!=null&&this.rank===2)n=vx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=uB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=vx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=lB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Re("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=hs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ft(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},e0=class extends Ul{constructor(e){super(2,e);e0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!ob(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},$p=e0;$p.className="Conv2D";ae.registerClass($p);var t0=class extends Ul{constructor(e){super(3,e);t0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Ap=t0;Ap.className="Conv3D";ae.registerClass(Ap);var Lb=class extends $p{constructor(e){super(e);if(this.inputSpec=[new Rt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Rt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=De(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],c=this.kernelSize[0],l=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=Ss(o,d,c,this.padding),f=Ss(u,p,l,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,1]));let g=Ck(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=vs(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ss(t[s],o,a,this.padding),t[r]=Ss(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Lb.className="Conv2DTranspose";ae.registerClass(Lb);var Bb=class extends Ap{constructor(e){super(e);if(this.inputSpec=[new Rt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Rt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=De(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat==="channelsFirst"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],c=s[a],l=s[i],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ss(u,f,d,this.padding),y=Ss(c,m,p,this.padding),v=Ss(l,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,4,1]));let k=N_(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=vs(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],c=this.strides[0],l=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=Ss(t[s],c,i,this.padding),t[r]=Ss(t[r],l,o,this.padding),t[a]=Ss(t[a],d,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Bb.className="Conv3DTranspose";ae.registerClass(Bb);var n0=class extends Ul{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=gt(t.depthwiseRegularizer),this.depthwiseConstraint=Ot(t.depthwiseConstraint),this.pointwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=gt(t.pointwiseRegularizer),this.pointwiseConstraint=Ot(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Rt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{e=De(e);let n;if(this.rank===1)throw new Re("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=qe(e,[0,2,3,1])),n=DD(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=vs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ft(this.depthwiseConstraint),e.pointwiseConstraint=Ft(this.pointwiseConstraint),e}};n0.className="SeparableConv";var Vb=class extends n0{constructor(e){super(2,e)}};Vb.className="SeparableConv2D";ae.registerClass(Vb);var s0=class extends Ul{constructor(e){super(1,e);s0.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"&&!ob(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},Wb=s0;Wb.className="Conv1D";ae.registerClass(Wb);var Ub=class extends Ge{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 H(()=>{if(e=De(e),this.dataFormat==="channelsLast"){let n=Oc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Oc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Oc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Oc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ub.className="Cropping2D";ae.registerClass(Ub);var Gb=class extends Ge{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,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,kM(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return H(()=>{let n=De(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation==="nearest"?ls.resizeNearestNeighbor(n,[r,a]):ls.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?ls.resizeNearestNeighbor(n,[r,a]):ls.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Gb.className="UpSampling2D";ae.registerClass(Gb);function cB(e,t,n=[1,1],s="valid",r,a){return H(()=>{r==null&&(r=ms()),Ct(r);let i=zb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=rp(i,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}var Hb=class extends Mb{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=mt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ot(e.depthwiseConstraint),this.depthwiseRegularizer=gt(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return H(()=>{e=De(e);let n=cB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=vs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=hs(t,this.kernelSize[0],this.padding,this.strides[0]),a=hs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ft(this.depthwiseRegularizer),e}};Hb.className="DepthwiseConv2D";ae.registerClass(Hb);function r0(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function a0(e,t,n,s=!1,r,a,i=!1,o=!1){return H(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let c=[1,0].concat(gs(2,u));if(t=qe(t,c),a!=null)throw new Re("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=pe(pe(r,"bool"),"float32"),r.rank===u-1&&(r=Pn(r,-1)),r=qe(r,c)),s&&(t=es(t,0),r!=null&&(r=es(r,0)));let l=[],d,p=n,h=t.shape[0],f=Rs(t),m;r!=null&&(m=Rs(r));for(let b=0;b<h;++b){let y=f[b],v=H(()=>e(y,p));if(r==null)d=v[0],p=v[1];else{let x=H(()=>{let k=m[b],T=be(Jn(k),k),C=oe(V(v[0],k),V(p[0],T)),R=p.map((_,P)=>oe(V(v[1][P],k),V(_,T)));return{output:C,newStates:R}});d=x.output,p=x.newStates}o&&l.push(d)}let g;return o&&(g=ts(l,1)),[d,g,p]})}var i0=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Rp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Rt({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 gs(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){rm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return H(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Re("Constants support is not implemented in RNN yet.");rm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Rt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Rt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Tt([n,s])):this.states_=[Tt([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Tt([n,s])):this.states_[0]=Tt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>jt(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=r0(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Rt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof Ns){let u=[e].concat(a),c=this.inputSpec.concat(i),l=this.inputSpec;this.inputSpec=c;let d=super.apply(u,t);return this.inputSpec=l,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=De(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},u=a0((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=u[0],l=u[1],d=u[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?l:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return H(()=>{let t=Tt(e.shape);return t=ve(t,[1,2]),t=Ll(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?nm(t,[1,n]):t):this.cell.stateSize>1?[nm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===i0.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=ps(s,n);return new e(Object.assign(t,{cell:r}))}},Rr=i0;Rr.className="RNN";ae.registerClass(Rr);var Gl=class extends Ge{},Ep=class extends Gl{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,Bt(this.units,"units"),this.activation=wr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Xi([1,vr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xi([1,vr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(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 H(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=kr({ones:()=>Jn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=kr({ones:()=>Jn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=As(V(e,a),this.kernel.read()):r=As(e,this.kernel.read()),this.bias!=null&&(r=vs(r,this.bias.read())),i!=null&&(n=V(n,i));let o=oe(r,As(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:xr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Ep.className="SimpleRNNCell";ae.registerClass(Ep);var qb=class extends Rr{constructor(e){e.cell=new Ep(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};qb.className="SimpleRNN";ae.registerClass(qb);var _p=class extends Gl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=wr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=wr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Xi([1,vr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xi([1,vr([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=nt(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 H(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=kr({ones:()=>Jn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=kr({ones:()=>Jn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let c=As(e,this.kernel.read());this.useBias&&(c=vs(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let l=this.recurrentKernel.read(),[d,p]=Bn(l,[2*this.units,this.units],l.rank-1),h=As(s,d),[f,m,g]=Bn(c,3,c.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(oe(f,b)),o=this.recurrentActivation.apply(oe(m,y));let v=As(V(o,s),p);u=this.activation.apply(oe(g,v));let x=oe(V(i,s),V(oe(1,It(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:xr(this.activation),recurrentActivation:xr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};_p.className="GRUCell";ae.registerClass(_p);var jb=class extends Rr{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 _p(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};jb.className="GRU";ae.registerClass(jb);var Hl=class extends Gl{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,Bt(this.units,"units"),this.activation=wr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=wr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Xi([1,vr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xi([1,vr([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=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends rs{apply(i,o){let u=r.apply([a]),c=new bp().apply([a]),l=r.apply([a*2]);return rx(rx(u,c),l)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=kr({ones:()=>Jn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=kr({ones:()=>Jn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,c,l;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let d=As(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),d=oe(d,As(s,this.recurrentKernel.read())),this.useBias&&(d=vs(d,this.bias.read()));let[p,h,f,m]=Bn(d,4,d.rank-1);o=this.recurrentActivation.apply(p),u=this.recurrentActivation.apply(h),c=oe(V(u,r),V(o,this.activation.apply(f))),l=this.recurrentActivation.apply(m);let g=V(l,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:xr(this.activation),recurrentActivation:xr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Hl.className="LSTMCell";ae.registerClass(Hl);var Kb=class extends Rr{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 Hl(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Kb.className="LSTM";ae.registerClass(Kb);var Rp=class extends Gl{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 H(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){rm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Jr(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(ps(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return am(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}wb(t)}};Rp.className="StackedRNNCells";ae.registerClass(Rp);function kr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):xI(t(),n),o=()=>Vl(i,t,s);return!r||r<=1?jt(o().clone()):Array(r).fill(void 0).map(o).map(c=>jt(c.clone()))}var o0=class extends Rr{constructor(e){if(e.unroll)throw new Re("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Re("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Rt({ndim:5})]}call(e,t){return H(()=>{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return H(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Tt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Tt(r)):this.states_=[Tt(r)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Tt(r)):this.states_[0]=Tt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>jt(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],c=e[o?4:3],l=hs(u,s[0],r,a[0],i[0]),d=hs(c,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,l,d]:[l,d,n]]}};o0.className="ConvRNN2D";var Dp=class extends Hl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t});this.filters=t,Bt(this.filters,"filters"),this.kernelSize=qi(n,2,"kernelSize"),this.kernelSize.forEach(o=>Bt(o,"kernelSize")),this.strides=qi(s||1,2,"strides"),this.strides.forEach(o=>Bt(o,"strides")),this.padding=r||"valid",Gn(this.padding),this.dataFormat=a||"channelsLast",Ct(this.dataFormat),this.dilationRate=qi(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Bt(o,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;o=new(t=class extends rs{apply(l,d){let p=u.apply([c]),h=Mn([c]),f=u.apply([c*2]);return hb([p,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return H(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=kr({ones:()=>Jn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Q,J,ne)=>!J||!J[ne]?Q:V(J[ne],Q),c=u(s,o,0),l=u(s,o,1),d=u(s,o,2),p=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=kr({ones:()=>Jn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,T]=Bn(this.kernel.read(),i,y),[C,R,_,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,v,C,this.padding),l=this.inputConv(l,x,R,this.padding),d=this.inputConv(d,k,_,this.padding),p=this.inputConv(p,T,P,this.padding);let[D,F,$,z]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,D),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(oe(c,f)),q=this.recurrentActivation.apply(oe(l,m)),K=oe(V(q,a),V(W,this.activation.apply(oe(d,g)))),j=V(this.recurrentActivation.apply(oe(p,b)),this.activation.apply(K));return[j,j,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=la(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?vs(r,n,this.dataFormat):r}recurrentConv(e,t){return la(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Dp.className="ConvLSTM2DCell";ae.registerClass(Dp);var Xb=class extends o0{constructor(e){let t=new Dp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};Xb.className="ConvLSTM2D";ae.registerClass(Xb);var Fp=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=De(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Vl(()=>xI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Fp.className="Dropout";ae.registerClass(Fp);var Yb=class extends Fp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Yb.className="SpatialDropout1D";ae.registerClass(Yb);var Qb=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=wr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ot(e.kernelConstraint),this.biasConstraint=Ot(e.biasConstraint),this.kernelRegularizer=gt(e.kernelRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(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=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=De(e),s=fI(this.activation.getClassName()),r;return s!=null?r=As(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=As(n,this.kernel.read()),this.bias!=null&&(r=vs(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:xr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),biasConstraint:Ft(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Qb.className="Dense";ae.registerClass(Qb);var Zb=class extends Ge{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],fr(e,1)]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=De(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return $M(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Zb.className="Flatten";ae.registerClass(Zb);var Jb=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=wr(e.activation)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=De(e);return this.activation.apply(n)})}getConfig(){let e={activation:xr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Jb.className="Activation";ae.registerClass(Jb);var ey=class extends Ge{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 H(()=>(e=De(e),NM(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};ey.className="RepeatVector";ae.registerClass(ey);var ty=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=fr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=De(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};ty.className="Reshape";ae.registerClass(ty);var ny=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=gs(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Rt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(De(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};ny.className="Permute";ae.registerClass(ny);var sy=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=De(e),s=-1;return Zf(Ku(n,this.maskValue),s)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=De(e),s=-1,r=!0,a=Zf(Ku(n,this.maskValue),s,r);return V(n,pe(a,n.dtype))})}};sy.className="Masking";ae.registerClass(sy);var ry=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=mt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=gt(e.embeddingsRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.embeddingsConstraint=Ot(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return H(()=>this.maskZero?(e=De(e),Ku(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=De(e);n.dtype!=="int32"&&(n=mp(n,"int32"));let s=vI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ft(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};ry.className="Embedding";ae.registerClass(ry);var gi=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Re}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=hr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&hr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return H(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=vr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Ll(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let c=o.shape,l=c[0],d=c.slice(1).concat([l]),p=G(o,[l].concat(fr(c.slice(1))));p=qe(p,[1,0]),p=G(p,d),n.push(p),r=!0}else if(u>1){let c=gs(1,u).concat([0]);n.push(qe(o,c)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,c=o[u-1],l=[c].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,c]),[1,0]),l)}else if(i>1){let o=[i-1].concat(gs(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=hr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return H(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=_s(n,t[s]);return n})}},ay=class extends gi{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return t})}};ay.className="Add";ae.registerClass(ay);var iy=class extends gi{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};iy.className="Multiply";ae.registerClass(iy);var oy=class extends gi{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return V(1/e.length,t)})}};oy.className="Average";ae.registerClass(oy);var uy=class extends gi{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Er(t,e[n]);return t})}};uy.className="Maximum";ae.registerClass(uy);var ly=class extends gi{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=up(t,e[n]);return t})}};ly.className="Minimum";ae.registerClass(ly);var cy=class extends gi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return H(()=>hb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return H(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(pe(Jn(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Pn(t[a],-1)):s.push(t[a]);let r=Dt(s,this.axis);return yk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};cy.className="Concatenate";ae.registerClass(cy);function Iu(e,t){for(;e<0;)e+=t;return e}function dB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Re("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Re("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return H(()=>{let i;if(s>r){i=s-r;let u=[];for(let c=0;c<i;++c)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let c=0;c<i;++c)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,c=a[1]===t.shape.length-1;o=Me(e,t,u,c)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let c=[];for(let l=u;l<u+i;++l)c.push(l);o=yr(o,c)}return o.shape.length===1&&(o=Pn(o,1)),o})}var dy=class extends gi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Re("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Iu(r,e[a].shape.length)):s=[Iu(this.axes,t.shape.length),Iu(this.axes,n.shape.length)],this.normalize&&(t=bd(t,s[0]),n=bd(n,s[1])),dB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Iu(this.axes,e.length),Iu(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Re("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};dy.className="Dot";ae.registerClass(dy);var py=class extends Ge{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 H(()=>{this.invokeCallHook(e,t);let n=De(e);return Vl(()=>oe(gp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};py.className="GaussianNoise";ae.registerClass(py);var hy=class extends Ge{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 H(()=>{this.invokeCallHook(e,t);let n=De(e);return this.rate>0&&this.rate<1?Vl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,gp(n.shape,1,r))},()=>n,t.training||!1):n})}};hy.className="GaussianDropout";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||De(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 H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Vl(()=>{let r=De(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=Ho(Pl(n),this.rate);u=mp(u,"float32");let c=((1-this.rate)*(1+this.rate*o**2))**-.5,l=-c*o*this.rate,d=oe(V(r,u),V(oe(u,-1),o));return oe(V(d,c),l)},()=>De(e),t.training||!1)}return e})}};fy.className="AlphaDropout";ae.registerClass(fy);function Zu(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=JE(e,t,n,s,r,a);else if(e.rank===3)i=t_(e,t,n,s,r,a);else if(e.rank===4)i=s_(e,t,n,s,r,a);else throw new Re(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function pB(e,t,n,s,r=.001){return H(()=>{let a=Bg(e,s),i=a.mean,o=a.variance;return[Zu(e,i,o,n,t,r),i,o]})}function hB(e,t,n,s,r=.001){return H(()=>{let a=Bg(e,s),i=a.mean,o=a.variance,u=[];for(let f of gs(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let c=G(i,u),l=G(o,u),d=t==null?null:G(t,u),p=n==null?null:G(n,u);return[Zu(e,c,l,p,d,r),i,o]})}function fB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),gs(0,e.rank-1))?pB(e,t,n,s,r):hB(e,t,n,s,r)}var my=class extends Ge{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=mt(e.betaInitializer||"zeros"),this.gammaInitializer=mt(e.gammaInitializer||"ones"),this.movingMeanInitializer=mt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=mt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ot(e.betaConstraint),this.gammaConstraint=Ot(e.gammaConstraint),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Rt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training,s=De(e),r=s.shape,a=r.length,i=gs(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=ha(1,a);u[o]=r[o];let c=i.slice();c.sort();let l=!w.arraysEqual(c,gs(0,a).slice(0,a-1)),d=()=>{if(l){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Zu(s,b,y,v,x,this.epsilon)}else return Zu(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[p,h,f]=fB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{H(()=>{let x=1-v,k=b.read(),T=V(be(k,y),x);b.write(be(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ft(this.betaConstraint),gammaConstraint:Ft(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};my.className="BatchNormalization";ae.registerClass(my);var gy=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=mt(e.betaInitializer||"zeros"),this.gammaInitializer=mt(e.gammaInitializer||"ones"),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==hr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=De(e),s=n.shape,r=s.length;return H(()=>{let{mean:i,variance:o}=Bg(n,this.axis,!0),u=ha(1,r);for(let f of this.axis)u[f]=s[f];let c=f=>f!=null&&f.shape.length!==r?G(f,u):f,l=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return i=us(i,p),o=us(o,p),l=us(l,h),d=us(d,h),Zu(n,i,o,d,l,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};gy.className="LayerNormalization";ae.registerClass(gy);function mB(e,t,n){return H(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ms()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],hi(e,s)})}var by=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?ms():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Rt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return H(()=>mB(De(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};by.className="ZeroPadding2D";ae.registerClass(by);function Op(e,t,n,s,r,a){return H(()=>{Ct(r),mI(a),Gn(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=ms()),a==null&&(a="max"),e=zb(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=Lg(e,t,n,o):i=Ag(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}function u0(e,t,n,s,r,a){return H(()=>{Ct(r),mI(a),Gn(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=ms()),a==null&&(a="max"),e=JI(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=zk(e,t,n,o):i=kk(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,4,1,2,3])),i})}var l0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Gn(this.padding),this.inputSpec=[new Rt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=hs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return H(()=>{this.invokeCallHook(e,t),e=Ll(De(e),2);let n=this.poolingFunction(De(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return yr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},yy=class extends l0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Op(e,t,n,s,r,"max")}};yy.className="MaxPooling1D";ae.registerClass(yy);var vy=class extends l0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Op(e,t,n,s,r,"avg")}};vy.className="AveragePooling1D";ae.registerClass(vy);var c0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Rt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=hs(t,this.poolSize[0],this.padding,this.strides[0]),n=hs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(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}},xy=class extends c0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Op(e,t,n,s,r,"max")}};xy.className="MaxPooling2D";ae.registerClass(xy);var wy=class extends c0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),Op(e,t,n,s,r,"avg")}};wy.className="AveragePooling2D";ae.registerClass(wy);var d0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Rt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=hs(t,this.poolSize[0],this.padding,this.strides[0]),n=hs(n,this.poolSize[1],this.padding,this.strides[1]),s=hs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(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}},ky=class extends d0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),u0(e,t,n,s,r,"max")}};ky.className="MaxPooling3D";ae.registerClass(ky);var Iy=class extends d0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),u0(e,t,n,s,r,"avg")}};Iy.className="AveragePooling3D";ae.registerClass(Iy);var p0=class extends Ge{constructor(e){super(e);this.inputSpec=[new Rt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Re}},Sy=class extends p0{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=De(e);return St(n,1)})}};Sy.className="GlobalAveragePooling1D";ae.registerClass(Sy);var Cy=class extends p0{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=De(e);return $s(n,1)})}};Cy.className="GlobalMaxPooling1D";ae.registerClass(Cy);var h0=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Rt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Re}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ny=class extends h0{call(e,t){return H(()=>{let n=De(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};Ny.className="GlobalAveragePooling2D";ae.registerClass(Ny);var Ty=class extends h0{call(e,t){return H(()=>{let n=De(e);return this.dataFormat==="channelsLast"?$s(n,[1,2]):$s(n,[2,3])})}};Ty.className="GlobalMaxPooling2D";ae.registerClass(Ty);var f0=class extends Ge{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=ps(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},$y=class extends f0{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return H(()=>(e=De(e),a0((a,i)=>[De(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};$y.className="TimeDistributed";ae.registerClass($y);function gB(e){fi(wM,"BidirectionalMergeMode",e)}var bB="concat",Ay=class extends f0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=ps(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=ps(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?bB:e.mergeMode,gB(this.mergeMode),e.weights)throw new Re("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=r0(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(l=>new Rt({shape:l.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(s!=null)throw new Re("Support for constants in Bidirectional layers is not implemented yet.");let o=a[0]instanceof Ns;for(let u of a)if(u instanceof Ns!==o)throw new U("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let u=[e].concat(a),c=this.inputSpec.concat(i),l=this.inputSpec;this.inputSpec=c;let d=super.apply(u,t);return this.inputSpec=l,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=es(r,1));let i;return this.mergeMode==="concat"?i=hb([s,r]):this.mergeMode==="sum"?i=oe(s,r):this.mergeMode==="ave"?i=V(.5,oe(s,r)):this.mergeMode==="mul"?i=V(s,r):this.mergeMode==null&&(i=[s,r]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Jr(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Jr(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=ps(t.layer);if(delete t.layer,t.numConstants!=null)throw new Re("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};Ay.className="Bidirectional";ae.registerClass(Ay);function yB(e){return new Ko(e)}function vB(e){return new Fb(e)}function xB(e){return new _b(e)}function wB(e){return new Rb(e)}function kB(e){return new Db(e)}function IB(e){return new Pb(e)}function SB(e){return new Ob(e)}function CB(e){return new Wb(e)}function NB(e){return new $p(e)}function TB(e){return new Lb(e)}function 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implemented`)}},WV=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Dc.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,i]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Dc.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Dc.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Dc.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},UV=(e,t,n)=>{switch(e.op){case"FFT":return[Kg(I("x",e,t,n))];case"IFFT":return[fd(I("x",e,t,n))];case"RFFT":return[Xg(I("x",e,t,n))];case"IRFFT":return[qk(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},GV=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Sf.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=Sf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Sf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},HV=(e,t,n)=>{switch(e.op){case"Cast":return[pe(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[Pn(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[yr(I("x",e,t,n),s)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[UR(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[hi(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[Vg(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Eg(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[D_(I("x",e,t,n),s,r)]}case"BroadcastTo":return[jc(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[i_(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Nx(e,t,n,s){let r=((a,i,o)=>{switch(a.category){case"arithmetic":return H(()=>wV(a,i,o));case"basic_math":return H(()=>kV(a,i,o));case"control":return $V(a,i,o);case"convolution":return H(()=>AV(a,i,o));case"creation":return H(()=>EV(a,i,o));case"dynamic":return _V(a,i,o);case"evaluation":return H(()=>RV(a,i,o));case"image":return H(()=>PV(a,i,o));case"graph":return H(()=>DV(a,i,o));case"logical":return H(()=>zV(a,i,o));case"matrices":return H(()=>MV(a,i,o));case"normalization":return H(()=>LV(a,i,o));case"reduction":return H(()=>BV(a,i,o));case"slice_join":return H(()=>VV(a,i,o));case"sparse":return H(()=>WV(a,i,o));case"spectral":return H(()=>UV(a,i,o));case"string":return H(()=>GV(a,i,o));case"transformation":return H(()=>HV(a,i,o));case"hash_table":return OV(a,i,o,s);case"custom":let u=x0(a.op);if(u&&u.customExecutor)return u.customExecutor(new xV(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Tx=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function $x(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,c=Object.keys(e).map(p=>$n(p)[0]),l=[];s!=null&&(l=s.map(p=>$n(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((W0(p)||YV(p)||QV(p))&&i==null&&(i=p,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&l.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function qV(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(l=>$n(l)[0]).map(l=>e.nodes[l]),o=e.initNodes;i.forEach(l=>{s.has(l.name)&&a.push(l)}),e.weights.forEach(l=>{s.has(l.name)&&a.push(l)}),o!=null&&o.forEach(l=>{s.has(l.name)&&a.push(l)});let u=new Set,c=[];for(;a.length>0;){let l=a.pop();u.add(l.name),t[l.name]||c.push(l),l.children.forEach(d=>{!u.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>u.has(p.name))&&a.push(d)})}return c}var jV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],KV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],XV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function W0(e){return jV.indexOf(e.op)>=0}function YV(e){return KV.indexOf(e.op)>=0}function QV(e){return XV.indexOf(e.op)>=0}var Im=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Im(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=$x(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`)}return qV(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(l=>this.graph.nodes[$n(l)[0]]),r=t.map(l=>$n(l)[0]),a=r.map(l=>this.graph.nodes[l]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},c={};return H(()=>{let l=new Tx(this.weightMap,u,c,this.functionExecutorMap),d={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=$n(f),b=[];b[g]=e[f],d[m]=b});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!d[m.name]){let g=Nx(m,d,l,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,l,p,r,h)}}return this.parent==null&&l.dispose(p),t.map(f=>nn(f,d,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,i){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let u=J4(o.name,n,s);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!r.has(c.id)){let l=i[c.id];if(l===1){if(!this.keepTensorForDebug)c.dispose();else{let[d,p]=Cs(t.name,s);this.intermediateTensors[d]?this.intermediateTensors[d][p]=c:(this.intermediateTensors[d]=[],this.intermediateTensors[d][p]=c)}delete i[c.id]}else l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=X().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){console.warn(c.message)}this.resetIntermediateTensors();let a=new Tx(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let i=t.map(c=>nn(c,this.tensorsMap,a)),o=i.map(c=>c.id),u=Object.keys(e).map(c=>e[c].id);return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,i)=>(r[this.inputs[i].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[$n(y)[0]]),i=n.map(y=>$n(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:d}=$x(e,o,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=$n(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let y=this.processStack(a,p,t,h,g,m,i,f,u);await Promise.all(y)}l==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=o.filter(y=>!W0(y)&&!nn(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw l!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${y}`)}return h}processStack(e,t,n,s,r,a,i,o,u){let c=[];for(;t.length>0;){let l=t.pop();n.currentContext=l.contexts;let d="";if(l.node.op==="Enter"&&I("isConstant",l.node,s,n)&&([d]=Cs(l.node.name,n)),s[l.node.name]==null){let p=Nx(l.node,s,n,this._resourceManager);d||([d]=Cs(l.node.name,n));let h=n.currentContext;w.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,l.node,s,n,a,i,o),this.processChildNodes(l.node,t,n,s,r,u),f))):(s[d]=p,this.checkTensorForDisposal(d,l.node,s,n,a,i,o),this.processChildNodes(l.node,t,n,s,r,u))}else this.processChildNodes(l.node,t,n,s,r,u)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(i=>{let[o]=Cs(i.name,n);r[o]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!nn(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!nn(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=$n(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((o,u)=>a[u]===-1||a[u]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=$n(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=$n(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},ZV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},JV="?tfjs-format=file",eW="model.json",tW=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new ZV}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=An.browserHTTPRequest(e,this.loadOptions);else{let t=An.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(An.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=An.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Im(kx.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=kx.Instance.transformGraph(e.modelInitializer);this.initializer=new Im(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=An.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof tt)&&!Array.isArray(e))return 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},tS=class extends Ut{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(X().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new tS(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),ds(n,t)}},nS=class extends Ut{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Jt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Hi([a,r,o,i],[1,4])}else this.cropBox=Hi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(X().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new nS(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Zw.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 H(()=>{let t=Pn(pe(e,"float32"),0),n;n=ls.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},sS=class{},rS=class extends Ut{split(e){return new AW(this,e)}},AW=class extends 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============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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XU={kernelName:Sa,backendName:"cpu",kernelFunc:e1};function YU(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:c,activation:l,leakyreluAlpha:d}=s,p,h,f,m=[];p=e1({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:c},backend:n}),i&&(h=jl({inputs:{a:p,b:i},backend:n}),m.push(p),p=h),l&&(f=qy(n,p,l,o,d),m.push(p),p=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return p}var QU={kernelName:sa,backendName:"cpu",kernelFunc:YU},ZU=st(nl,e=>Math.acos(e)),JU={kernelName:nl,backendName:"cpu",kernelFunc:ZU},eG=st(sl,e=>Math.acosh(e)),tG={kernelName:sl,backendName:"cpu",kernelFunc:eG};function nG(e){let{inputs:t,backend:n}=e,s=t;xe(t,"addN");let r=s.map(o=>n.data.get(o.dataId).values),a=_e(s[0].shape,s[0].dtype),i=a.values;for(let o=0;o<s.length;o++){let u=r[o];for(let c=0;c<i.length;c++)i[c]+=u[c]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var sG={kernelName:wa,backendName:"cpu",kernelFunc:nG};function 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dG={kernelName:il,backendName:"cpu",kernelFunc:cG},pG=st(ol,e=>Math.asin(e)),hG={kernelName:ol,backendName:"cpu",kernelFunc:pG},fG=st(ul,e=>Math.asinh(e)),mG={kernelName:ul,backendName:"cpu",kernelFunc:fG},gG=st(ll,e=>Math.atan(e)),bG={kernelName:ll,backendName:"cpu",kernelFunc:gG},yG=At((e,t)=>Math.atan2(e,t)),vG=Gt(dl,yG),xG={kernelName:dl,backendName:"cpu",kernelFunc:vG},wG=st(cl,e=>Math.atanh(e)),kG={kernelName:cl,backendName:"cpu",kernelFunc:wG};function jy(e,t,n,s,r,a){let i=r.strideHeight,o=r.strideWidth,u=r.dilationHeight,c=r.dilationWidth,l=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=_e(r.outShape,n),g=m.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let k=x*b,T=x*s[0];for(let C=0;C<r.inChannels;++C)for(let R=0;R<r.outHeight;++R){let 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l=N.computePool3DInfo(a.shape,i,o,1,u,c),d=l.strideDepth,p=l.strideHeight,h=l.strideWidth,f=l.filterDepth,m=l.filterHeight,g=l.filterWidth,b=l.dilationDepth,y=l.dilationHeight,v=l.dilationWidth,x=l.effectiveFilterDepth,k=l.effectiveFilterHeight,T=l.effectiveFilterWidth,C=x-1-l.padInfo.front,R=T-1-l.padInfo.left,_=k-1-l.padInfo.top,P=_e(a.shape,"float32"),D=1/(f*m*g),F=n.bufferSync(r);for(let $=0;$<l.batchSize;++$)for(let z=0;z<l.inChannels;++z)for(let W=0;W<l.inDepth;++W)for(let q=0;q<l.inHeight;++q)for(let K=0;K<l.inWidth;++K){let j=W-C,Q=q-_,J=K-R,ne=0;for(let se=0;se<x;se+=b){let te=(j+se)/d;if(!(te<0||te>=l.outDepth||Math.floor(te)!==te))for(let ie=0;ie<k;ie+=y){let ee=(Q+ie)/p;if(!(ee<0||ee>=l.outHeight||Math.floor(ee)!==ee))for(let ce=0;ce<T;ce+=v){let me=(J+ce)/h;if(me<0||me>=l.outWidth||Math.floor(me)!==me)continue;ne+=F.get($,te,ee,me,z)}}}P.set(ne*D,$,W,q,K,z)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var AG={kernelName:qm,backendName:"cpu",kernelFunc:$G};function EG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;xe([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:c}=s,l=N.computePool2DInfo(i.shape,o,u,1,c),d=l.strideHeight,p=l.strideWidth,h=l.filterHeight,f=l.filterWidth,m=l.dilationHeight,g=l.dilationWidth,b=l.effectiveFilterHeight,y=l.effectiveFilterWidth,v=y-1-l.padInfo.left,x=b-1-l.padInfo.top,k=_e(i.shape,"float32"),T=1/(h*f),C=n.data.get(r.dataId).values,R=_e(r.shape,"float32",C);for(let _=0;_<l.batchSize;++_)for(let P=0;P<l.inChannels;++P)for(let D=0;D<l.inHeight;++D)for(let F=0;F<l.inWidth;++F){let $=D-x,z=F-v,W=0;for(let q=0;q<b;q+=m){let K=($+q)/d;if(!(K<0||K>=l.outHeight||Math.floor(K)!==K))for(let j=0;j<y;j+=g){let Q=(z+j)/p;if(Q<0||Q>=l.outWidth||Math.floor(Q)!==Q)continue;W+=R.get(_,K,Q,P)}}k.set(W*T,_,D,F,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var _G={kernelName:Hm,backendName:"cpu",kernelFunc:EG};function RG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:i,mean:o,variance:u}=t;w.assert(o.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),xe([r,o,u,a,i],"batchNorm");let{varianceEpsilon:c}=s;c==null&&(c=.001);let l=n.data.get(r.dataId).values,d=n.data.get(o.dataId).values,p=n.data.get(u.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(l.length),g=f.length,b=h.length,y=p.length,v=d.length,x=0,k=0,T=0,C=0;for(let R=0;R<l.length;++R)m[R]=f[x++]+(l[R]-d[k++])*h[T++]/Math.sqrt(p[C++]+c),x>=g&&(x=0),k>=v&&(k=0),T>=b&&(T=0),C>=y&&(C=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var DG={kernelName:za,backendName:"cpu",kernelFunc:RG};function FG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;xe([r],"batchToSpaceND");let o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),c=N.getPermuted(u.length,a.length),l=N.getReshapedPermuted(r.shape,a,o),d=N.getSliceBeginCoords(i,a.length),p=N.getSliceSize(l,i,a.length),h=bt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=Wn({inputs:{x:h},backend:n,attrs:{perm:c}}),m=bt({inputs:{x:f},backend:n,attrs:{shape:l}}),g=ma({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var OG={kernelName:ro,backendName:"cpu",kernelFunc:FG};function PG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=Ly(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}var zG={kernelName:jm,backendName:"cpu",kernelFunc:PG};function MG(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var LG={kernelName:Km,backendName:"cpu",kernelFunc:MG},BG=st(Nr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),VG={kernelName:Nr,backendName:"cpu",kernelFunc:BG},WG=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(a.dataId).values,u=n.data.get(i.dataId).values;for(let c=0;c<o.length;c++){let l=o[c],d=u[c];s[c]=Math.hypot(l,d)}return n.makeOutput(s,t.shape,"float32")},UG={kernelName:Ld,backendName:"cpu",kernelFunc:WG};function Zi(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var GG={kernelName:Ud,backendName:"cpu",kernelFunc:Zi};function Ji(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>w.sizeFromShape(m.shape)>0);if(o.length===1)return Ds({inputs:{x:o[0]},backend:n});let u=o.map(m=>m.shape);if(N.assertParamsConsistent(u,a),o[0].dtype==="complex64"){let m=o.map(x=>fa({inputs:{input:x},backend:n})),g=o.map(x=>Zi({inputs:{input:x},backend:n})),b=Ji({inputs:m,backend:n,attrs:{axis:a}}),y=Ji({inputs:g,backend:n,attrs:{axis:a}}),v=En({inputs:{real:b,imag:y},backend:n});return 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jG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:c,filterShape:l}=s;xe([r,a],"conv2dBackpropFilter");let d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(r.shape,l,i,1,o,c,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,b=p.dataFormat==="channelsLast",y=new Vt(p.filterShape,"float32"),v=p.padInfo.left,x=p.padInfo.top,k=n.data.get(r.dataId).values,T=n.data.get(a.dataId).values,C=new Vt(r.shape,r.dtype,k),R=new Vt(a.shape,a.dtype,T);for(let _=0;_<m;++_){let P=Math.max(0,Math.ceil((x-_)/h)),D=Math.min(p.outHeight,(p.inHeight+x-_)/h);for(let F=0;F<g;++F){let $=Math.max(0,Math.ceil((v-F)/f)),z=Math.min(p.outWidth,(p.inWidth+v-F)/f);for(let W=0;W<p.inChannels;++W)for(let q=0;q<p.outChannels;++q){let K=0;for(let j=0;j<p.batchSize;++j)for(let Q=P;Q<D;++Q){let J=_+Q*h-x;for(let ne=$;ne<z;++ne){let se=F+ne*f-v;b?K+=C.get(j,J,se,W)*R.get(j,Q,ne,q):K+=C.get(j,W,J,se)*R.get(j,q,Q,ne)}}y.set(K,_,F,W,q)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var KG={kernelName:Xm,backendName:"cpu",kernelFunc:jG};function XG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:c,dimRoundingMode:l}=s;xe([r,a],"conv2dBackpropInput");let d=w.computeStrides(a.shape),p=w.computeStrides(r.shape),h=N.convertConv2DDataFormat(c),f=N.computeConv2DInfo(i,a.shape,o,1,u,l,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=d,{batchSize:T,filterHeight:C,filterWidth:R,inChannels:_,inHeight:P,inWidth:D,outChannels:F,outHeight:$,outWidth:z,strideHeight:W,strideWidth:q}=f;h=f.dataFormat;let K=C-1-f.padInfo.top,j=R-1-f.padInfo.left,Q=h==="channelsLast",J=m.strides[0],ne=Q?m.strides[1]:m.strides[2],se=Q?m.strides[2]:1,te=Q?1:m.strides[1],ie=p[0],ee=Q?p[1]:p[2],ce=Q?p[2]:1,me=Q?1:p[1];for(let Ie=0;Ie<T;++Ie)for(let we=0;we<_;++we)for(let Te=0;Te<P;++Te){let Fe=Te-K,Xe=Math.max(0,Math.ceil(Fe/W)),We=Math.min($,(C+Fe)/W);for(let Be=0;Be<D;++Be){let rt=Be-j,Ye=Math.max(0,Math.ceil(rt/q)),at=Math.min(z,(R+rt)/q),ht=0;for(let Qe=Xe;Qe<We;++Qe){let Sn=Qe*W-Fe;for(let Ht=Ye;Ht<at;++Ht){let Hn=Ht*q-rt,hn=ie*Ie+ee*Qe+ce*Ht,Cn=v*(C-1-Sn)+x*(R-1-Hn)+k*we;for(let Rn=0;Rn<F;++Rn){let qn=b[hn+me*Rn],Dn=y[Cn+Rn];ht+=qn*Dn}}}let pn=J*Ie+ne*Te+se*Be+te*we;g[pn]=ht}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var YG={kernelName:$a,backendName:"cpu",kernelFunc:XG};function QG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s;xe([r,a],"conv3d");let c=N.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:l,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=c,b=g.front,y=g.left,v=g.top,x=new Vt(c.outShape,r.dtype),k=n.data.get(r.dataId).values,T=n.data.get(a.dataId).values,C=x.values,R=w.computeStrides(r.shape),_=w.computeStrides(a.shape);for(let P=0;P<c.batchSize;++P){let 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c=w.computeStrides(r.shape),l=w.computeStrides(a.shape),d=N.computeConv3DInfo(r.shape,u,i,1,o),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,b=d.filterWidth,y=new Vt(d.filterShape,"float32"),v=y.values,[x,k,T,C]=y.strides,R=n.data.get(a.dataId).values,[_,P,D,F]=l,$=n.data.get(r.dataId).values,[z,W,q,K]=c,j=d.padInfo.front,Q=d.padInfo.left,J=d.padInfo.top;for(let ne=0;ne<m;++ne){let se=Math.max(0,Math.ceil((j-ne)/p)),te=Math.min(d.outDepth,(d.inDepth+j-ne)/p),ie=ne*x;for(let ee=0;ee<g;++ee){let ce=Math.max(0,Math.ceil((J-ee)/h)),me=Math.min(d.outHeight,(d.inHeight+J-ee)/h),Ie=ee*k+ie;for(let we=0;we<b;++we){let Te=Math.max(0,Math.ceil((Q-we)/f)),Fe=Math.min(d.outWidth,(d.inWidth+Q-we)/f),Xe=we*T+Ie;for(let We=0;We<d.inChannels;++We){let Be=We*C+Xe;for(let rt=0;rt<d.outChannels;++rt){let Ye=0;for(let at=0;at<d.batchSize;++at){let ht=at*z,pn=at*_;for(let Qe=se;Qe<te;++Qe){let Ht=(ne+Qe*p-j)*W+ht,Hn=Qe*P+pn;for(let hn=ce;hn<me;++hn){let Rn=(ee+hn*h-J)*q+Ht,qn=hn*D+Hn;for(let Dn=Te;Dn<Fe;++Dn){let Zs=(we+Dn*f-Q)*K+Rn,Qt=Dn*F+qn;Ye+=$[Zs+We]*R[Qt+rt]}}}}v[Be+rt]=Ye}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var eH={kernelName:Ym,backendName:"cpu",kernelFunc:JG};function tH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s;xe([r],"conv3dBackpropInputV2");let c=w.computeStrides(r.shape),l=w.computeStrides(a.shape),d=N.computeConv3DInfo(u,a.shape,o,1,i),p=new Vt(d.inShape,"float32"),h=p.values,[f,m,g,b]=p.strides,y=n.data.get(r.dataId).values,[v,x,k,T]=c,C=n.data.get(a.dataId).values,[R,_,P,D]=l,{batchSize:F,filterDepth:$,filterHeight:z,filterWidth:W,inChannels:q,inDepth:K,inHeight:j,inWidth:Q,outChannels:J,outDepth:ne,outHeight:se,outWidth:te,strideDepth:ie,strideHeight:ee,strideWidth:ce}=d,me=$-1-d.padInfo.front,Ie=z-1-d.padInfo.top,we=W-1-d.padInfo.left;for(let Te=0;Te<F;++Te)for(let Fe=0;Fe<q;++Fe)for(let Xe=0;Xe<K;++Xe){let 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oH(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:c}=s,[l,d,p,h]=r.shape,f=a.shape[0],[m,g]=o,b=_e([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),T=w.computeStrides(b.shape);for(let C=0;C<f;C++){let R=C*4,_=y[R],P=y[R+1],D=y[R+2],F=y[R+3],$=v[C];if($>=l)continue;let z=m>1?(D-_)*(d-1)/(m-1):0,W=g>1?(F-P)*(p-1)/(g-1):0;for(let q=0;q<m;q++){let K=m>1?_*(d-1)+q*z:.5*(_+D)*(d-1);if(K<0||K>d-1){for(let j=0;j<g;j++)for(let Q=0;Q<h;Q++){let J=Q+j*T[2]+q*T[1]+C*T[0];b.values[J]=c}continue}if(u==="bilinear"){let j=Math.floor(K),Q=Math.ceil(K),J=K-j;for(let ne=0;ne<g;ne++){let se=g>1?P*(p-1)+ne*W:.5*(P+F)*(p-1);if(se<0||se>p-1){for(let ce=0;ce<h;ce++){let me=ce+ne*T[2]+q*T[1]+C*T[0];b.values[me]=c}continue}let te=Math.floor(se),ie=Math.ceil(se),ee=se-te;for(let ce=0;ce<h;ce++){let me=ce+te*k[2]+j*k[1]+$*k[0],Ie=x[me];me=ce+ie*k[2]+j*k[1]+$*k[0];let we=x[me];me=ce+te*k[2]+Q*k[1]+$*k[0];let Te=x[me];me=ce+ie*k[2]+Q*k[1]+$*k[0];let Fe=x[me],Xe=Ie+(we-Ie)*ee,We=Te+(Fe-Te)*ee;me=ce+ne*T[2]+q*T[1]+C*T[0],b.values[me]=Xe+(We-Xe)*J}}}else for(let j=0;j<g;++j){let Q=g>1?P*(p-1)+j*W:.5*(P+F)*(p-1);if(Q<0||Q>p-1){for(let se=0;se<h;se++){let te=se+j*T[2]+q*T[1]+C*T[0];b.values[te]=c}continue}let J=Math.round(Q),ne=Math.round(K);for(let se=0;se<h;se++){let te=se+J*k[2]+ne*k[1]+$*k[0],ie=se+j*T[2]+q*T[1]+C*T[0];b.values[ie]=x[te]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var uH={kernelName:oo,backendName:"cpu",kernelFunc:oH};function lH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;xe(r,"cumsum");let u=N.getAxesPermutation([a],r.shape.length),c=r;u!=null&&(c=Wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let l=N.getInnerMostAxes(1,r.shape.length)[0];if(l!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${l}`);let d=vn(c.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(c.shape),d),h=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)p[v]=i?0:h[v];else{let x=m(b,y-1);p[v]=i?h[x]+p[x]:h[v]+p[x]}}let g=n.makeTensorInfo(c.shape,d,p);if(u!=null){let b=N.getUndoAxesPermutation(u),y=Wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),y}return g}var cH={kernelName:io,backendName:"cpu",kernelFunc:lH};function dH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,l=Ly(u,c,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}else if(r.shape.length===2){let u=n.bufferSync(r),c=n.bufferSync(a),l=hS(u,c,i,o);return n.makeTensorInfo(l.shape,a.dtype,l.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var pH={kernelName:Zm,backendName:"cpu",kernelFunc:dH};function hH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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gH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:c,filterShape:l}=s;xe([r,a],"depthwiseConv2dNativeBackpropFilter");let d=N.computeConv2DInfo(r.shape,l,i,o,u,c,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new Vt(d.filterShape,"float32"),b=d.padInfo.left,y=d.padInfo.top,v=d.outChannels/d.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),T=n.data.get(a.dataId).values,C=new Vt(a.shape,a.dtype,T);for(let R=0;R<f;++R){let _=Math.max(0,Math.ceil((y-R)/p)),P=Math.min(d.outHeight,(d.inHeight+y-R)/p);for(let D=0;D<m;++D){let F=Math.max(0,Math.ceil((b-D)/h)),$=Math.min(d.outWidth,(d.inWidth+b-D)/h);for(let z=0;z<d.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let j=0;j<d.batchSize;++j)for(let Q=_;Q<P;++Q){let J=R+Q*p-y;for(let ne=F;ne<$;++ne){let se=D+ne*h-b;K+=k.get(j,J,se,W)*C.get(j,Q,ne,z)}}g.set(K,R,D,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var bH={kernelName:Jm,backendName:"cpu",kernelFunc:gH};function yH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:c,inputShape:l}=s;xe([r,a],"depthwiseConv2DNativeBackpropInput");let d=w.computeStrides(r.shape),p=w.computeStrides(a.shape),h=N.computeConv2DInfo(l,a.shape,i,o,u,c,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,T]=d,C=n.data.get(a.dataId).values,[R,_,P]=p,{batchSize:D,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:j,outWidth:Q,strideHeight:J,strideWidth:ne}=h,se=F-1-h.padInfo.top,te=$-1-h.padInfo.left,ie=K/z;for(let ee=0;ee<D;++ee)for(let ce=0;ce<z;++ce)for(let me=0;me<W;++me){let Ie=me-se,we=Math.max(0,Math.ceil(Ie/J)),Te=Math.min(j,(F+Ie)/J);for(let Fe=0;Fe<q;++Fe){let Xe=Fe-te,We=Math.max(0,Math.ceil(Xe/ne)),Be=Math.min(Q,($+Xe)/ne),rt=0;for(let Ye=we;Ye<Te;++Ye){let at=Ye*J-Ie;for(let ht=We;ht<Be;++ht){let 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jH={kernelName:ho,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,r=n,a=w.getTypedArrayFromDType(s.dtype,w.sizeFromShape(s.shape)),[i,o,u,c]=s.shape,l=r.data.get(s.dataId).values;for(let p=0;p<i;p++){let h=p*u*o*c;for(let f=0;f<o;f++){let m=f*(u*c);for(let g=0;g<u;g++){let b=g*c;for(let y=0;y<c;y++){let v=Math.round(u-g-1),x=h+m+b+y,k=l[x];if(v>=0&&v<u){let T=v*c,C=h+m+T+y;k=l[C]}a[x]=k}}}}return{dataId:r.write(a,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},KH=At((e,t)=>Math.floor(e/t)),XH=Gt(Pa,KH,null,"int32"),YH={kernelName:Pa,backendName:"cpu",kernelFunc:XH};function QH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:c,dataFormat:l,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=s1({inputs:{x:r,filter:a},backend:n,attrs:{strides:u,pad:c,dataFormat:l,dilations:d,dimRoundingMode:p}});if(i){let g=m;m=jl({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let 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s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),i}function o5(e,t){let n=Yy(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(i),o}function O1(e){return e!==2?!1:bs(e).fenceSync!=null}function Jo(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=X();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>$m(2)?2:$m(1)?1:0);Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ne.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ne.get("WEBGL_VERSION")===2);Ne.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ne.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ne.registerFlag("WEBGL_PACK",()=>Ne.getBool("HAS_WEBGL"));Ne.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_CLIP",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_REDUCE",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_CONV_IM2COL",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>E1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>_1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:R1(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!np.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>D1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ne.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>F1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>O1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ne.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>np.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Ne.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Ne.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Ne.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Ne.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function ln(){let e,t,n,s,r,a,i,o,u,c;return X().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",i="out vec4 outputColor;",o=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,u="",c=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",i="",o=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,u=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,c=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:c}}function bi(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join("")}function zp(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join("")}function u5(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function l5(e,t,n="index"){let s=e.map((a,i)=>i),r=u5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join("")}function Qy(e){let t=w.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function Zy(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var P1=`
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:z1}=N;function c5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Jy(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
`),a=e.map(h=>d5(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),i=t.texShape,o=ln(),u=f5(o),c,l,d=b5(o);return t.isPacked?(c=p5(t.logicalShape,i,n.enableShapeUniforms),l=g5(o)):(c=h5(t.logicalShape,i,n.enableShapeUniforms),l=m5(o)),n.packedInputs&&(d+=w5),[d,u,l,r,c,a,n.userCode].join(`
`)}function eu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return D5(e,t);case 1:return O5(e,t);case 2:return z5(e,t);case 3:return L5(e,t);case 4:return V5(e,t);case 5:return W5(e);case 6:return U5(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function M1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return R5(e);case 1:return F5(e,t);case 2:return P5(e,t);case 3:return M5(e,t);default:return B5(e,t)}}function d5(e,t,n=!1,s){let r="";n?r+=M1(e,s):r+=eu(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=G5(e,t):r+=H5(e,t)),r}function p5(e,t,n){switch(e.length){case 0:return L1();case 1:return k5(e,t,n);case 2:return E5(e,t,n);case 3:return S5(e,t,n);default:return N5(e,t,n)}}function h5(e,t,n){switch(e.length){case 0:return L1();case 1:return I5(e,t,n);case 2:return _5(e,t,n);case 3:return C5(e,t,n);case 4:return T5(e,t,n);case 5:return $5(e,t);case 6:return A5(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function f5(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function m5(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function g5(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function b5(e){return`${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);
}
${y5}
${v5}
${x5}
`}var y5=`
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);
}
`,v5=`
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);
}
`,x5=`
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);
}
`,w5=`
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 L1(){return`
int getOutputCoords() {
return 0;
}
`}function k5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${s[1]}.0);
}
`:s[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${s[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
}
`}function I5(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function S5(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function C5(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${zp(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let s=bi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec3(r, c, d);
}
`}function N5(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o="",u="b, r, c";for(let c=2;c<e.length-1;c++)i*=e[e.length-c-1],o=`
int b${c} = index / ${i};
index -= b${c} * ${i};
`+o,u=`b${c}, `+u;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
${o}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${u});
}
`}function T5(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${zp(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let s=bi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec4(r, c, d, d2);
}
`}function $5(e,t){let n=bi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function A5(e,t){let n=bi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function E5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
}
`;let r=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function _5(e,t,n){return w.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function yi(e){return`offset${e}`}function R5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=ln();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function D5(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
float ${s}() {
return sampleTexture(${n}, halfCR);
}
`;let i=yi(n);if(t)return`
float ${s}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
return sampleTexture(${n}, uv);
}
`;let[o,u]=e.shapeInfo.texShape;return`
float ${s}() {
vec2 uv = uvFromFlat(${o}, ${u}, ${i});
return sampleTexture(${n}, uv);
}
`}function F5(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=ln();if(t)return`
vec4 ${s}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
vec4 ${s}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function O5(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${s}(int index) {
${tu(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`
float ${s}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let o=yi(n);return i===1?t?`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${s}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});
return sampleTexture(${n}, uv);
}
`}function P5(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=ln();if(a!=null&&w.arraysEqual(n,a))return t?`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return ${u.texture2D}(${s}, uv);
}
`:`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
return ${u.texture2D}(${s}, uv);
}
`;if(t)return`
vec4 ${r}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${u.texture2D}(${s}, uv);
}
`;let c=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=Math.ceil(n[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${l}, ${c[0]}, ${c[1]}, row, col);
return ${u.texture2D}(${s}, uv);
}
`}function z5(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`;let p=a[0],h=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let p=nu(e,u),h=["row","col"];return`
${eu(p,t)}
float ${r}(int row, int col) {
return ${r}(${su(h,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${tu(e)}
}
`;let c=a[0],l=a[1],d=yi(s);return l===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
return sampleTexture(${s}, uv);
}
`:c===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${l}.0, 0.5);
return sampleTexture(${s}, uv);
}
`:t?`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s}Shape[1] + col + ${d};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${d};
vec2 uv = uvFromFlat(${c}, ${l}, index);
return sampleTexture(${s}, uv);
}
`}function M5(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=nu(e,p),m=["b","row","col"];return`
${M1(f,t)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${su(m,h)});
}
`}let o=ln();if(t)return`
vec4 ${r}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${o.texture2D}(${s}, uv);
}
`;let u=i[0],c=i[1],l=Math.ceil(n[2]/2),d=l*Math.ceil(n[1]/2);return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${u}, ${c}, ${d}, ${l}, b, row, col);
return ${o.texture2D}(${s}, uv);
}
`}function L5(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),c=o;if(c.length<n.length){let m=nu(e,c),g=["row","col","depth"];return`
${eu(m,t)}
float ${r}(int row, int col, int depth) {
return ${r}(${su(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${i}, 1)));
${tu(e)}
}
`;let l=e.shapeInfo.texShape,d=l[0],p=l[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
float ${r}(int row, int col, int depth) {
int stride1 = ${s}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;if(p===i&&h==null)return t?`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;let f=yi(s);return t?`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${s}Shape[1] * ${s}Shape[2];
int stride1 = ${s}Shape[2];
int index = row * ${a} + col * ${i} + depth + ${f};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${i} + depth + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${s}, uv);
}
`}function B5(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=ln();if(t)return`
vec4 ${s}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],c=u[0],l=u[1],d=Math.ceil(a[i-1]/2),p=d*Math.ceil(a[i-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,p*=a[i-m-1],f=`b${m} * ${p} + `+f;return`
vec4 ${s}(${h}) {
int index = ${f};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${c});
return ${r.texture2D}(${n}, uv);
}
`}function V5(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:c}=w.squeezeShape(n);if(u.length<n.length){let y=nu(e,u),v=["row","col","depth","depth2"];return`
${eu(y,t)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${su(v,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${a}, 1)));
${tu(e)}
}
`;let l=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&l==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${i}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;if(h===a&&l==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;let b=yi(s);return t?`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${p}, ${h}, index + ${b});
return sampleTexture(${s}, uv);
}
`}function W5(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:c}=w.squeezeShape(t);if(u.length<t.length){let m=nu(e,u),g=["row","col","depth","depth2","depth3"];return`
${eu(m)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${su(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${a}, ${r})) +
depth3;
${tu(e)}
}
`;let l=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===o&&l==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${a}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&l==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let f=yi(n);return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${a} +
depth2 * ${r} + depth3 + ${f};
vec2 uv = uvFromFlat(${p}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function U5(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=nu(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
${eu(g)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${su(b,a)});
}
`}let i=t[5],o=t[4]*i,u=t[3]*o,c=t[2]*u,l=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${l}, ${c}, ${u}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${tu(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===l&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${u}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===i&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=yi(n);return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${l} + col * ${c} + depth * ${u} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function tu(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function G5(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=z1(e.shapeInfo.logicalShape,t.logicalShape),u=ut(i),c=i-a,l,d=["x","y","z","w","u","v"];a===0?l="":i<2&&o.length>=1?l="coords = 0;":l=o.map(y=>`coords.${d[y+c]} = 0;`).join(`
`);let p="";i<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((y,v)=>`coords.${d[v+c]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!b)i===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h="return vec4(outputValue.x);":o.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${u} coords = getOutputCoords();
${l}
vec4 outputValue = get${s}(${p});
${h}
}
`}function H5(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let c=ut(u),l=z1(e.shapeInfo.logicalShape,t.logicalShape),d=u-o,p,h=["x","y","z","w","u","v"];o===0?p="":u<2&&l.length>=1?p="coords = 0;":p=l.map(m=>`coords.${h[m+d]} = 0;`).join(`
`);let f="";return u<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
float ${r}() {
${c} coords = getOutputCoords();
${p}
return get${s}(${f});
}
`}function ut(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 ${e} is not yet supported`)}function Jy(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function nu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function su(e,t){return t.map(n=>e[n]).join(", ")}function q5(e,t,n,s){let r=n.map((x,k)=>{let T={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(T.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[k],shapeInfo:T}}),a=r.map(x=>x.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=c5(r,i,t),u=m1(e.gl,o),c=e.createProgram(u),l=null,d=e.getUniformLocation(c,"NAN",!1);X().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(c,"INFINITY",!1));let p=!1,h={},f={},m={};for(let x=0;x<t.variableNames.length;x++){let k=t.variableNames[x];h[k]=e.getUniformLocation(c,k,p),h[`offset${k}`]=e.getUniformLocation(c,`offset${k}`,p),t.enableShapeUniforms&&(f[`${k}Shape`]=e.getUniformLocation(c,`${k}Shape`,p),m[`${k}TexShape`]=e.getUniformLocation(c,`${k}TexShape`,p))}let g,b,y;t.enableShapeUniforms&&(g=e.getUniformLocation(c,"outShape",p),y=e.getUniformLocation(c,"outShapeStrides",p),b=e.getUniformLocation(c,"outTexShape",p));let v=[];return t.customUniforms&&t.customUniforms.forEach((x,k)=>{v[k]=e.getUniformLocation(c,x.name,p)}),{program:t,fragmentShader:u,source:o,webGLProgram:c,uniformLocations:h,customUniformLocations:v,inShapeInfos:a,outShapeInfo:i,infLoc:l,nanLoc:d,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:y,outTexShapeLocation:b}}function Fx(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function j5(e,t,n,s,r){t.program.enableShapeUniforms||(Fx(t.inShapeInfos,n),Fx([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,c)=>{let l=t.program.variableNames[c],d=t.uniformLocations[l],p=t.uniformLocations[`offset${l}`],h=t.inShapesLocations[`${l}Shape`],f=t.inTexShapesLocations[`${l}TexShape`];if(h){let{uniformShape:m}=Jy(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),d!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(d,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}u.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,d,c)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,c)=>{let l=t.customUniformLocations[c],d=r[c];if(u.type==="float")e.gl.uniform1fv(l,d);else if(u.type==="vec2")e.gl.uniform2fv(l,d);else if(u.type==="vec3")e.gl.uniform3fv(l,d);else if(u.type==="vec4")e.gl.uniform4fv(l,d);else if(u.type==="int")e.gl.uniform1iv(l,d);else if(u.type==="ivec2")e.gl.uniform2iv(l,d);else if(u.type==="ivec3")e.gl.uniform3iv(l,d);else if(u.type==="ivec4")e.gl.uniform4iv(l,d);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function K5(e,t,n){let s="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:c,uniformShape:l,keptDims:d}=Jy(e.packedInputs,i.shape,u),p="",h="",f="";if(l.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];p=`${k[0]>1}_${k[1]>1}`}else if(l.length===2&&!e.packedInputs)h=`${l[0]>1}_${l[1]>1}`;else if(l.length>2&&!e.packedInputs){let k=w.computeStrides(l);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=l.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||l.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${c?d:""}_${l.length}_${b}_${y}_${g}_${p}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?"uniform":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,a}function In(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var X5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=ln();this.outputShape=e,this.enableShapeUniforms=In(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?zp(["r","c","d"],e):bi(["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;
}
`}},Y5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=ln();this.outputShape=e,this.enableShapeUniforms=In(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?zp(["r","c","d"],e):bi(["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;
}
`}},Q5=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=ln();this.outputShape=e,this.userCode=`
${P1}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},Z5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=ln();this.outputShape=e,this.userCode=`
${P1}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},J5=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=ln();this.outputShape=e,this.enableShapeUniforms=In(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?Zy():Qy(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${s}, 0., 0., 0.);
}
`}},eK=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=ln();this.outputShape=e,this.enableShapeUniforms=In(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`
localCoords = coords;
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${i};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${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?Zy():Qy(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${s}
${n.output} = ${r};
}
`}},tK={};Ae(tK,{bindVertexProgramAttributeStreams:()=>K1,createBufferFromOutputTexture:()=>Q1,createFloat16MatrixTexture:()=>G1,createFloat16PackedMatrixTexture:()=>j1,createFloat32MatrixTexture:()=>U1,createIndexBuffer:()=>W1,createPackedMatrixTexture:()=>q1,createUnsignedBytesMatrixTexture:()=>H1,createVertexBuffer:()=>V1,createVertexShader:()=>B1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>J1,downloadFloat32MatrixFromBuffer:()=>Z1,downloadMatrixFromPackedOutputTexture:()=>tC,downloadPackedMatrixFromBuffer:()=>eC,getInternalFormatForFloat16MatrixTexture:()=>tv,getInternalFormatForFloat16PackedMatrixTexture:()=>rv,getInternalFormatForFloat32MatrixTexture:()=>ev,getInternalFormatForPackedMatrixTexture:()=>sv,getInternalFormatForUnsignedBytesMatrixTexture:()=>nv,uploadDenseMatrixToTexture:()=>X1,uploadPixelDataToTexture:()=>Y1});function B1(e){let t=ln(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return f1(e,n)}function V1(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return y1(e,t)}function W1(e){let t=new Uint16Array([0,1,2,2,1,3]);return v1(e,t)}function Yl(e,t,n,s,r,a){w1(t,n);let i=x1(e),o=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(o,i)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),X().getNumber("WEBGL_VERSION")===1?fe(e,()=>e.texImage2D(o,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(o,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function ev(e){return e.internalFormatFloat}function U1(e,t,n,s){let[r,a]=Xl(t,n);return Yl(e,r,a,ev(s),s.textureFormatFloat,e.FLOAT)}function tv(e){return e.internalFormatHalfFloat}function G1(e,t,n,s){let[r,a]=Xl(t,n);return Yl(e,r,a,tv(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function nv(e){return e.downloadTextureFormat}function H1(e,t,n,s){let[r,a]=Xl(t,n);return Yl(e,r,a,nv(s),e.RGBA,e.UNSIGNED_BYTE)}function sv(e){return e.internalFormatPackedFloat}function q1(e,t,n,s){let[r,a]=Zo(t,n);return Yl(e,r,a,sv(s),e.RGBA,e.FLOAT)}function rv(e){return e.internalFormatPackedHalfFloat}function j1(e,t,n,s){let[r,a]=Zo(t,n);return Yl(e,r,a,rv(s),e.RGBA,s.textureTypeHalfFloat)}function K1(e,t,n){return fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Nm(e,t,"clipSpacePos",n,3,20,0)&&Nm(e,t,"uv",n,2,20,12)}function X1(e,t,n,s,r,a){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,u;r instanceof Uint8Array?(i=new Uint8Array(n*s*4),o=e.UNSIGNED_BYTE,u=e.RGBA):(i=new Float32Array(n*s*4),o=e.FLOAT,u=a.internalFormatPackedFloat),i.set(r),X().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,o,i)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,s,0,e.RGBA,o,i)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Y1(e,t,n){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?X().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):X().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Q1(e,t,n,s){let r=e.createBuffer();fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let o=4*4*t*n;return fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,o,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function Z1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function J1(e,t,n,s){let[r,a]=Xl(t,n),i=4,o=new Uint8Array(Xj(t*n,i));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function eC(e,t,n,s,r,a,i,o){let u=e,c=new Float32Array(Yj(a,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,c),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),c}function tC(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Ff=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=X().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,qj(t,e)):this.gl=bs(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(X().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Ru(this.gl,r),Ln(this.gl,a))this.textureHalfFloatExtension=Ru(this.gl,a);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ln(this.gl,s))this.colorBufferHalfFloatExtension=Ru(this.gl,s);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Ln(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ln(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=V1(this.gl),this.indexBuffer=W1(this.gl),this.framebuffer=k1(this.gl),this.textureConfig=Yy(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),U1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),G1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),H1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Y1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),X1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),j1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),q1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Tm(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>J1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return eC(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Z1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=Q1(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(X().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>tC(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=B1(t));let n=g1(t);return fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),b1(t,n),this.debug&&Xc(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=K1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Xc(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?S1(this.gl,e,t):C1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),N1(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=Zo(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Xc(this.gl,this.program),Du(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Ru(this.gl,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=nK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Yc(this.gl,e,this.framebuffer),this.debug&&Du(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Yc(this.gl,this.outputTexture,this.framebuffer),this.debug&&Du(this.gl)):Tm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;Yc(s,e,this.framebuffer),this.debug&&Du(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function nK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:sK,bincountImpl:nC,bincountReduceImpl:rK,ceilImpl:aK,concatImpl:iK,equalImpl:oK,expImpl:uK,expm1Impl:lK,floorImpl:cK,gatherNdImpl:dK,gatherV2Impl:pK,greaterImpl:hK,greaterEqualImpl:fK,lessImpl:mK,lessEqualImpl:gK,linSpaceImpl:bK,logImpl:yK,maxImpl:vK,maximumImpl:xK,minimumImpl:wK,multiplyImpl:kK,negImpl:IK,notEqualImpl:SK,prodImpl:CK,rangeImpl:NK,rsqrtImpl:TK,sigmoidImpl:$K,simpleAbsImpl:sC,sliceImpl:AK,sparseFillEmptyRowsImpl:EK,sparseReshapeImpl:_K,sparseSegmentReductionImpl:rC,sqrtImpl:RK,stridedSliceImpl:DK,stringNGramsImpl:FK,stringSplitImpl:OK,stringToHashBucketFastImpl:PK,subImpl:zK,tileImpl:MK,topKImpl:LK,transposeImpl:av,uniqueImpl:BK}=zy;function aC(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function sn(e,t){return t===1?[e]:aC(e,t)}function VK(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var WK=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=In(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let t=sn("rc",this.rank),n=ut(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`
void main() {
${n} rc = getOutputCoords();
if(${s}) {
setOutput(vec4(0));
} else {
${r}
setOutput(vec4(${a}));
}
}
`}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?"r":"rp1"}, ${s===0?"c":"cp1"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${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 >= ${n};
bool rEdge = rp1 >= ${s};
`}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]})`}},iC=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=In(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
${r}
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${s}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${s>0?"}":""}
`}this.userCode=`
${UK(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?Zy():Qy(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function UK(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?l5(["r","c","d"],"inputShape"):bi(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var GK=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=Px(t,n),r=zx(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=Ox(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=Px(n,s),a=zx(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=Ox(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.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 HK(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Ox(e,t,n,s,r){let a=qK(t,s),i;if(r){let[u,c]=Zo(e[0],e[1]);i=u*c}else{let[u,c]=Xl(e[0],e[1]);i=u*c}let o=HK(n,a);return i*o}function qK(e,t){switch(e){case 3:return sv(t);case 4:return rv(t);case 1:return ev(t);case 0:return tv(t);case 2:return nv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function jK(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function Px(e,t){if(e===1)return 3;if(e===0||e==null)return jK(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function zx(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Gs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=In(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},as="if (isnan(x)) return x;",KK="return x;",Mx="return abs(x);",XK="return (x >= 0.0) ? x : (exp(x) - 1.0);",YK=as+`
return (x < 0.0) ? 0.0 : x;
`,QK=as+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Di="return x;",ZK="return 1.0 / (1.0 + exp(-1.0 * x));",JK="return x;",eX=`
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;
`,tX=`
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;
`,nX=`
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;
`,sX="return 1.0 / (1.0 + exp(-1.0 * x));",Qr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=In(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},rX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=In(this.outputShape.length);let t=e.length,n=sn("rc",t),s=ut(t),r=VK(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${i}));
}
`}},aX=ys.whereImpl,iX=1e-7,oX=1e-4,Uc={};function uX(e){return e in Uc||(Uc[e]={}),Uc[e]}var lX=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),cX=600;function dX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*cX/1024/1024}var oC=class extends tl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Ff)t=e;else{let n=bs(X().getNumber("WEBGL_VERSION"),e);t=new Ff(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=bs(X().getNumber("WEBGL_VERSION"));t=new Ff(n),this.binaryCache=uX(X().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new GK(this.gpgpu),this.numMBBeforeWarning=dX(),this.texData=new Fd(this,ks())}nextDataId(){return oC.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let d;o?d=new Qr(i,Di):d=new Gs(i,Di);let p=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,c;u&&(c=w.now());let l;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);l=N.mergeRealAndImagArrays(d,p)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Qr(s,Di):h=new Gs(s,Di);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Vc(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];l=N.mergeRealAndImagArrays(f,m)}else if(u==null)l=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let d=this.convertAndCacheOnCPU(e,l),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ks().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let p;o?p=new Qr(r,Di):p=new Gs(r,Di);let h=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(e,t.customTexShape),l=ks().makeTensorFromDataId(c.dataId,c.shape,c.dtype),d=this.texData.get(c.dataId);return{tensorRef:l,...d.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!p1(n))throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Vc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=X().getBool("WEBGL_PACK")&&s===!0,i=a?Qc(t):t,o=a?new Z5(i):new Q5(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(u.dataId),l=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),l}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=lX){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return aX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Qr(e.shape,t),r=this.compileAndRun(s,[e],n);return ks().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=sC(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Mx,e.dtype);let t=new Gs(e.shape,Mx),n=this.compileAndRun(t,[e]);return ks().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return ks().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new rX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new WK(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ga(e.shape),...ba(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ga(t),...ba(t)],a=new iC(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let d=w.sizeFromShape(r),p=t[0]*t[1]*4;w.assert(d<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Qc(r),o;s?o=new Y5(i):o=new X5(i);let u=!0,c=[t!=null?t:Vc(i)],l=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Vc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!Ju(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:o,isUniform:!1},d=K5(e,c,l),p=this.getAndSaveBinary(d,()=>q5(this.gpgpu,e,c,l)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),j5(this.gpgpu,p,c,l,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=H(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(Ce(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?iX:oX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=w.now());let l=t.texShape;if(l==null&&(l=A1(n,o),t.texShape=l),r!=null){let d=Qc(n),p,h=l[1],f=l[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=Zo(l[0],l[1])),o?p=new eK(d,m):p=new J5(d,m);let g=m?[f,h]:l,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(p,[b],s,v,x),T=this.texData.get(k.dataId);t.texture=T.texture,t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,this.disposeIntermediateTensorInfo(b),this.texData.delete(k.dataId),t.values=null,u&&(this.uploadWaitMs+=w.now()-c)}else{let d=this.acquireTexture(l,i,s,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=pX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},uC=oC;uC.nextDataId=0;function pX(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var epe="0.0.0";function hX(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}np.isBrowser()&&sp("webgl",()=>new uC,2);var tpe={forceHalfFloat:hX},lC=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,eo=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=In(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Mp=`
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;
`,Ql=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=In(r);let a="";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${ut(r)} coords = getOutputCoords();
`,r===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let o=sn("coords",r);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${o[r-2]} + 1) >= outShape[${r} - 2];
bool nextColOutOfBounds =
(${o[r-1]} + 1) >= outShape[${r} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${o[r-2]} + 1) >= ${this.outputShape[r-2]};
bool nextColOutOfBounds =
(${o[r-1]} + 1) >= ${this.outputShape[r-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function _n(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var fX={kernelName:La,backendName:"webgl",kernelFunc:_n};function Fr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(a.dataId),o=_n({inputs:{x:s},backend:n}),u=_n({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var mX={kernelName:Md,backendName:"webgl",kernelFunc:Fr},cC="return (a < 0.) ? b * a : a;",dC=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function gX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ql(dC,r.shape,i.shape):new eo(cC,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),u}var bX={kernelName:Ba,backendName:"webgl",kernelFunc:gX},pC="return (a < 0.) ? b * a : a;",hC=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function yX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ql(hC,s.shape,r.shape):new eo(pC,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var vX={kernelName:Za,backendName:"webgl",kernelFunc:yX},ru="if (isnan(x)) return x;",xX=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,wX=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),p=n(d.values,u);return o.makeTensorInfo(i.shape,u,p)}let c=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,l;return c?l=new Qr(i.shape,t):l=new Gs(i.shape,e),o.runWebGLProgram(l,[i],u)}}function Kt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:c}=i,l=o;if(s&&u.dtype==="complex64"){let f=l.texData.get(u.dataId),m=l.texData.get(c.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},C={dataId:k.dataId,dtype:k.dtype,shape:c.shape},R=new eo(e,u.shape,c.shape);return l.runWebGLProgram(R,[T,C],vn(x.dtype,k.dtype))}),y=Fr({inputs:{real:g,imag:b},backend:l});return l.disposeIntermediateTensorInfo(g),l.disposeIntermediateTensorInfo(b),y}let d=a||vn(u.dtype,c.dtype);if((u.dtype==="string"||c.dtype==="string"||l.shouldExecuteOnCPU([u,c]))&&r!=null){let f=l.texData.get(u.dataId).values,m=l.texData.get(c.dataId).values,g=u.dtype==="string"?N.fromUint8ToStringArray(f):f,b=u.dtype==="string"?N.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,c.shape,g,b,d),x=l.makeTensorInfo(v,d),k=l.texData.get(x.dataId);return k.values=y,x}let p=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new Ql(t,u.shape,c.shape,n):h=new eo(e,u.shape,c.shape),l.runWebGLProgram(h,[u,c],d)}}function Lp(e,t=!1){if(e==="linear")return t?JK:KK;if(e==="relu")return t?tX:YK;if(e==="elu")return t?eX:XK;if(e==="relu6")return t?nX:QK;if(e==="prelu")return t?hC:pC;if(e==="leakyrelu")return t?dC:cC;if(e==="sigmoid")return t?sX:ZK;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var fC=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=In(this.outputShape.length);let c=s?e[1]:e[2],l=Math.ceil(c/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:u?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:m=`vec4 activation(vec4 x) {
${i}
}`,g="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let y="rc.x",v="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${l}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${l}; i++) {
int batchA = ${y};
int batchB = ${v};
vec4 a = getMatrixA(batchA, ${d});
vec4 b = getMatrixB(batchB, ${p});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${f[0]});
result += (${h[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${b}
${g}
setOutput(result);
}
`}},Lx={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Bx=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},Vx="return a * b;";function iv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),c=new Bx(Lx.REAL,s.shape,r.shape),l=new Bx(Lx.IMAG,s.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(l,d,"float32"),f=Fr({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[c,l]=kK(s.shape,r.shape,o.values,u.values,a),d=n.makeTensorInfo(l,a),p=n.texData.get(d.dataId);return p.values=c,d}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ql(Vx,s.shape,r.shape):i=new eo(Vx,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var kX={kernelName:Xa,backendName:"webgl",kernelFunc:iv};function IX(e,t,n){let s=[ga(e.shape),...ba(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ga(t),...ba(t)],i=new iC(a,s),o=!0,u=[s],c=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),c=w.sizeFromShape(u);w.assert(o===c,()=>`The new shape (${u}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let l=i.texData.get(r.dataId);return l.isPacked&&!Ju(r.shape,u)&&!(l.texture!==null&&Ju(l.shape,u))?IX(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var SX={kernelName:$o,backendName:"webgl",kernelFunc:he},Wx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${w.isInt(l)?l.toPrecision(2):l}, ones);`}let c="";r%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${r}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${u}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`}},CX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(n/4)*4,l=n%4,d=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
if (${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",d=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(i="0.0",d=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${d}
}
int inIdx = inOffset + ${c};
if (${l===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${d}
} else if (${l===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${d}
} else if (${l===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${d}
}
setOutput(${u});
}
`}};function NX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function vi(e,t,n,s){let r=NX(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:c}=r[i],l,d;n==="mean"?l=i===0?new Wx({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:c},o):new Wx({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:c}):l=new CX({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:c},n),d=a,a=s.runWebGLProgram(l,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var TX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ut(this.rank),r=$X(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function $X(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var AX=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=ut(this.rank),r=aC("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${o}) {
result[1] = ${u};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${u};
if(${o}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}};function Bp(e,t,n){let s=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AX(e.shape,t):new TX(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function EX(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=N.getAxesPermutation(o,a),c=u!=null,l=e;c&&(l=Bp(e,u,s),o=N.getInnerMostAxes(o.length,a)),N.assertAxesAreInnerMostDims("sum",o,a);let[d,p]=N.computeOutAndReduceShapes(l.shape,o),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let f=w.sizeFromShape(p),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:l},attrs:{shape:[g,f]},backend:s}),y=tp(e.dtype),v=vi(b,y,"sum",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),c&&s.disposeIntermediateTensorInfo(l),x}function Vp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return EX(r,a,i,n)}var _X={kernelName:ii,backendName:"webgl",kernelFunc:Vp};function an(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let l=0;l<u.length;l++)u[l]=r.shape[a[l]];let c;if(i.shouldExecuteOnCPU([r])){let d=i.texData.get(r.dataId).values,p=av(d,r.shape,r.dtype,a,u);c=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(c.dataId);h.values=p}else c=Bp(r,a,i);return c}var RX={kernelName:di,backendName:"webgl",kernelFunc:an},mC=1e3;function $d({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let c=e.shape.length,l=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[l-1]:t.shape[l-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[l-2]:t.shape[l-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Go.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,d,h]:[b,h,d],T=s?[y,f,p]:[y,p,f],C=he({inputs:{x:e},backend:r,attrs:{shape:k}}),R=he({inputs:{x:t},backend:r,attrs:{shape:T}}),_=[C,R],P=Math.max(b,y),D=n?C.shape[1]:C.shape[2],F=a!=null,$=i!=null,z=u==="leakyrelu",W=u!=null?Lp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&D>mC&&q===!1){let Q=C,J=R;n&&(Q=an({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Q)),s&&(J=an({inputs:{x:R},backend:r,attrs:{perm:[0,2,1]}}),_.push(J));let ne=f!==1,se=f===1,te=Q;ne&&(te=he({inputs:{x:Q},backend:r,attrs:{shape:[P,D,1]}}),_.push(te));let ie=f===1?2:1,ee=J;se&&(ee=he({inputs:{x:J},backend:r,attrs:{shape:[P,1,D]}}),_.push(ee));let ce=iv({inputs:{a:te,b:ee},backend:r});K=Vp({inputs:{x:ce},backend:r,attrs:{axis:ie,keepDims:!0}}),_.push(ce)}else{let Q=vn(e.dtype,t.dtype),J=new fC(k,T,[P,h,f],n,s,F,W,$,z),ne=[C,R];if(a!=null&&ne.push(a),$&&ne.push(i),z){let se=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ne.push(se),_.push(se)}K=r.runWebGLProgram(J,ne,Q)}let j=he({inputs:{x:K},backend:r,attrs:{shape:x}});_.push(K);for(let Q of _)r.disposeIntermediateTensorInfo(Q);return j}function DX(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:c,activation:l,leakyreluAlpha:d}=s;return $d({a:r,b:a,transposeA:u,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:l})}var FX={kernelName:sa,backendName:"webgl",kernelFunc:DX},Ux="return abs(x);";function OX(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),i=sC(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Qr(s.shape,Ux):r=new Gs(s.shape,Ux),n.runWebGLProgram(r,[s],s.dtype)}var PX={kernelName:so,backendName:"webgl",kernelFunc:OX},zX=as+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,MX=Ke({opSnippet:zX}),LX={kernelName:nl,backendName:"webgl",kernelFunc:MX},BX=as+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,VX=Ke({opSnippet:BX}),WX={kernelName:sl,backendName:"webgl",kernelFunc:VX},Gx="return a + b;",UX=Kt({opSnippet:Gx,packedOpSnippet:Gx,supportsComplex:!0,cpuKernelImpl:sK}),GX={kernelName:Cr,backendName:"webgl",kernelFunc:UX},HX=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${s};
setOutput(result);
}
`}},qX=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${s};
setOutput(result);
}
`}};function ed(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return _n({inputs:{x:s[0]},backend:n});if(s.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),c=ed({inputs:s.slice(0,u),backend:n}),l=ed({inputs:s.slice(u),backend:n});return ed({inputs:[c,l],backend:n})}let r=s.map(u=>u.dtype).reduce((u,c)=>vn(u,c)),a=s.map(u=>u.shape),o=X().getBool("WEBGL_PACK")?new qX(s[0].shape,a):new HX(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var jX={kernelName:wa,backendName:"webgl",kernelFunc:ed};function KX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),c=u,l=N.getAxesPermutation(c,o),d=r;l!=null&&(d=an({inputs:{x:r},backend:n,attrs:{perm:l}}),c=N.getInnerMostAxes(c.length,o)),N.assertAxesAreInnerMostDims("all",c,o);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=w.sizeFromShape(h),m=he({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=vi(m,m.dtype,"all",n),b;if(i){let y=N.expandShapeToKeepDim(p,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),l!=null&&n.disposeIntermediateTensorInfo(d),b}var XX={kernelName:rl,backendName:"webgl",kernelFunc:KX};function YX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),c=u,l=N.getAxesPermutation(c,o),d=r;l!=null&&(d=an({inputs:{x:r},backend:n,attrs:{perm:l}}),c=N.getInnerMostAxes(c.length,o)),N.assertAxesAreInnerMostDims("any",c,o);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=w.sizeFromShape(h),m=he({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=vi(m,m.dtype,"any",n),b;if(i){let y=N.expandShapeToKeepDim(p,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),l!=null&&n.disposeIntermediateTensorInfo(d),b}var QX={kernelName:al,backendName:"webgl",kernelFunc:YX},ZX=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${s};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${s}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},JX=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,u=ut(o),c=sn("coords",o),l,d;if(a===1){d=o+1;let C=ut(d);l=`
${C} sourceLocR = ${C}(${c.join()}, 0);
++${c[o-1]};
${C} sourceLocG = ${C}(${c.join()}, 0);
++${c[o-2]};
${C} sourceLocA = ${C}(${c.join()}, 0);
--${c[o-1]};
${C} sourceLocB = ${C}(${c.join()}, 0);
--${c[o-2]};`}else d=o,l=`
${u} sourceLocR = coords;
++${c[o-1]};
${u} sourceLocG = coords;
++${c[o-2]};
${u} sourceLocA = coords;
--${c[o-1]};
${u} sourceLocB = coords;
--${c[o-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(C=>"int "+C),m=sn("sourceLocR",d-1).concat("inIdx.r"),g=sn("sourceLocG",d-1).concat("inIdx.g"),b=sn("sourceLocB",d-1).concat("inIdx.b"),y=sn("sourceLocA",d-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",x=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${y.join()})));`,k=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,T=s?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${p.join()}),
vec2(${p.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${p.join()}),
vec2(${p.slice(-2).join()}));
}
${T}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
${l}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${k};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${x}
vec4 candidate = ${k};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function gC(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=N.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new ZX(o,n,s==null),c=[t];s!=null&&c.push(s);let l=e.runWebGLProgram(u,c,"int32");if(l.shape[1]===1)return l;let d=gC(e,t,n,l);return e.disposeIntermediateTensorInfo(l),d}function bC(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=N.computeOptimalWindowSize(a),o=new JX(r,i,n,s==null),u=s==null?[t]:[t,s],c=e.runWebGLProgram(o,u,"int32");if(c.shape.length===t.shape.length){let l=bC(e,t,n,c);return e.disposeIntermediateTensorInfo(c),l}return c}function yC(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[c,l]=N.computeOutAndReduceShapes(u.shape,r),d=w.sizeFromShape(l),p=he({inputs:{x:u},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=gC(e,p,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return bC(e,t,s)}function e8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,c=[];o!=null&&(u=an({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let l=yC(n,u,i[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),l}var t8={kernelName:ka,backendName:"webgl",kernelFunc:e8};function n8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,c=[];o!=null&&(u=an({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let l=yC(n,u,i[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),l}var s8={kernelName:il,backendName:"webgl",kernelFunc:n8},r8=as+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,a8=Ke({opSnippet:r8}),i8={kernelName:ol,backendName:"webgl",kernelFunc:a8},o8=as+"return log(x + sqrt(x * x + 1.0));",u8=Ke({opSnippet:o8}),l8={kernelName:ul,backendName:"webgl",kernelFunc:u8},c8=as+`
return atan(x);
`,d8=Ke({opSnippet:c8}),p8={kernelName:ll,backendName:"webgl",kernelFunc:d8},h8=xX+`
return atan(a, b);
`,f8=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+wX+`
return result;
`,m8=Kt({opSnippet:h8,packedOpSnippet:f8}),g8={kernelName:dl,backendName:"webgl",kernelFunc:m8},b8=as+`
if ((x < -1.0) || (x > 1.0)) return NAN;
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const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${p}, ${h});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${l};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${C} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let y="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let x=Math.floor(a/4)*4,k=a%4,T=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${y}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${p}, ${h});
const float initializationValue = ${b};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${b});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${l};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${T}
}
int xC = xCCorner + ${x};
if (${k===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${T}
} else if (${k===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${T}
} else if (${k===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${T}
}
}
setOutput(${v});
}
`}},ov=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let _=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${u});
const ivec3 pads = ivec3(${m}, ${g}, ${b});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${p};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${d}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${_} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / count");let T=Math.floor(a/4)*4,C=a%4,R=`
if (${y}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${u});
const ivec3 pads = ivec3(${m}, ${g}, ${b});
const float initializationValue = ${v};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${v});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${p};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${T}; wC += 4) {
int xC = xCCorner + wC * ${d};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
getValue(batch, xD, xR, xC + 3 * ${d}, ch)
);
${R}
}
int xC = xCCorner + ${T};
if (${C===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${R}
} else if (${C===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
initializationValue,
initializationValue
);
${R}
} else if (${C===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
initializationValue
);
${R}
}
}
setOutput(${k});
}
}
`}};function x8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Jo(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,c=1;w.assert(N.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let l=N.computePool2DInfo(r.shape,a,i,c,o,u);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return _n({inputs:{x:r},backend:n});let d=new el(l,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var w8={kernelName:Ia,backendName:"webgl",kernelFunc:x8};function k8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:c}=s,l=[1,1,1],d=N.computePool3DInfo(r.shape,a,i,l,o,u,c),p=new ov(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var I8={kernelName:zd,backendName:"webgl",kernelFunc:k8},S8=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.top,l=u-1-e.padInfo.left,d=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${l});
const float avgMultiplier = float(${d});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},C8=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=l-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${h}, ${f}, ${m});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${l};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${r}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${d};
wR += ${u}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${p};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${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 N8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:c,dimRoundingMode:l}=s,d=[1,1,1],p=N.computePool3DInfo(i.shape,o,u,d,c,l),h=new C8(p);return n.runWebGLProgram(h,[r],i.dtype)}var T8={kernelName:qm,backendName:"webgl",kernelFunc:N8};function $8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;Jo([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:c}=s,l=N.computePool2DInfo(i.shape,o,u,1,c),d=new S8(l);return n.runWebGLProgram(d,[r],i.dtype)}var A8={kernelName:Hm,backendName:"webgl",kernelFunc:$8};function E8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return $d({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var _8={kernelName:Sa,backendName:"webgl",kernelFunc:E8},R8=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},D8=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}},F8=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:i,scale:o}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let c=[s,r,a],l=null;i!=null&&(l=i.shape,c.push(i));let d=null;o!=null&&(d=o.shape,c.push(o));let p=X().getBool("WEBGL_PACK_NORMALIZATION")?new D8(s.shape,r.shape,a.shape,l,d,u):new R8(s.shape,r.shape,a.shape,l,d,u);return t.runWebGLProgram(p,c,c[0].dtype)},O8={kernelName:za,backendName:"webgl",kernelFunc:F8},P8=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=z8(this.rank),s,r=e.map((a,i)=>`sourceLoc.${Em[i]} = start[${i}] + coords.${Em[i]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${r.join(`
`)}
`,this.userCode=`
void main() {
${s}
setOutput(getSource(${n}));
}
`}},Em=["x","y","z","w","u","v"];function z8(e){if(e===1)return"sourceLoc";if(e<=6)return Em.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var M8=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ut(this.rank),n=sn("coords",this.rank),s=sn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,i=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.y = ${a};
--${s[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${s[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.w = ${a};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${s[l]} = ${n[l]} + start[${l}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}};function L8(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function au(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=kt.parseSliceParams(r,a,i);if(kt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=AK(d.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),l=kt.isSliceContinous(r.shape,o,u);if(c||!l){let d=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new M8(u):new P8(u),p=[o];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),L8(r,o,u,n)}var B8={kernelName:Do,backendName:"webgl",kernelFunc:au},V8=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),c=N.getPermuted(u.length,a.length),l=N.getReshapedPermuted(r.shape,a,o),d=N.getSliceBeginCoords(i,a.length),p=N.getSliceSize(l,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=an({inputs:{x:f},backend:n,attrs:{perm:c}}),g=he({inputs:{x:m},backend:n,attrs:{shape:l}}),b=au({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},W8={kernelName:ro,backendName:"webgl",kernelFunc:V8};function U8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),c=nC(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}var G8={kernelName:jm,backendName:"webgl",kernelFunc:U8};function H8(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var q8={kernelName:Km,backendName:"webgl",kernelFunc:H8},j8="return float(a != b);",vC=Kt({opSnippet:j8,cpuKernelImpl:SK,dtype:"bool"}),K8={kernelName:wo,backendName:"webgl",kernelFunc:vC};function Zl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return _n({inputs:{x:r.complexTensorInfos.real},backend:n})}var X8={kernelName:jd,backendName:"webgl",kernelFunc:Zl},Y8="return float(int(x));";function Q8(e,t){let n=new Gs(e.shape,Y8),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function _m(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return _n({inputs:{x:r},backend:n});let i=Tt(r.shape),o=_m({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Fr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=Zl({inputs:{input:r},backend:n}),o=_m({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=_n({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return Q8(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=vC({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Z8={kernelName:Ca,backendName:"webgl",kernelFunc:_m},Hx="return ceil(x);",J8=Ke({opSnippet:Hx,packedOpSnippet:Hx,cpuKernelImpl:aK}),e7={kernelName:Na,backendName:"webgl",kernelFunc:J8},t7=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));
}
`}},n7=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 s7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;X().getBool("WEBGL_PACK_CLIP")?o=new n7(r.shape):o=new t7(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var r7={kernelName:Nr,backendName:"webgl",kernelFunc:s7},a7=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 qx(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function i7(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new a7(s.shape),i=[qx(s,r.complexTensorInfos.real),qx(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var o7={kernelName:Ld,backendName:"webgl",kernelFunc:i7},u7=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},l7=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=ut(s),a=sn("coords",s),i=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],c=i.slice(-2),l=i.join(),d=`if (${u} < ${o[0]}) {
return getChannel(
getT0(${l}), vec2(${c.join()}));
}`;for(let f=1;f<o.length;f++){let m=o[f-1];d+=`
if (${u} < ${o[f]} && ${u} >= ${o[f-1]}) {
return getChannel(
getT${f}(${Gc(i,u,m)}),
vec2(${Gc(c,u,m)}));
}`}let p=o.length,h=o[o.length-1];d+=`
return getChannel(
getT${p}(${Gc(i,u,h)}),
vec2(${Gc(c,u,h)}));`,this.userCode=`
float getValue(${i.map(f=>"int "+f)}) {
${d}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[s-1]} = ${a[s-1]} + 1;
if (${a[s-1]} < ${n[s-1]}) {
result.g = getValue(${a});
}
${a[s-2]} = ${a[s-2]} + 1;
if (${a[s-2]} < ${n[s-2]}) {
result.a = getValue(${a});
}
${a[s-1]} = ${a[s-1]} - 1;
if (${a[s-2]} < ${n[s-2]} &&
${a[s-1]} < ${n[s-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function Gc(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function Wp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return _n({inputs:{x:r.complexTensorInfos.imag},backend:n})}var c7={kernelName:Ud,backendName:"webgl",kernelFunc:Wp};function Bi(e,t,n){let s=e[0].dtype;if(s==="complex64"){let l=e.map(m=>Zl({inputs:{input:m},backend:n})),d=e.map(m=>Wp({inputs:{input:m},backend:n})),p=Bi(l,t,n),h=Bi(d,t,n),f=Fr({inputs:{real:p,imag:h},backend:n});return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let l=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),d=l.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),p=N.computeOutShape(l.map(b=>b.shape),1),h=l[0].shape[0]===1,f=iK(d,p,s,h),m=N.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(e.length/2),d=Bi(e.slice(0,l),t,n),p=Bi(e.slice(l),t,n),h=Bi([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let l=new l7(e.map(d=>d.shape),t);return n.runWebGLProgram(l,e,s)}let{tensors2D:a,outShape:i}=d7(e,t,n),o=new u7(a.map(l=>l.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(l=>n.disposeIntermediateTensorInfo(l));let c=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),c}function d7(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function xC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(c=>c.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>w.sizeFromShape(c.shape)>0);if(o.length===1)return _n({inputs:{x:o[0]},backend:n});let u=o.map(c=>c.shape);return N.assertParamsConsistent(u,a),Bi(o,a,n)}var p7={kernelName:ao,backendName:"webgl",kernelFunc:xC},wC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(s?v=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?v=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:v=`
float activation(float x) {
${n}
}
`,x="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
const ivec2 strides = ivec2(${o}, ${u});
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${y}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${b}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${l};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${k}
${x}
setOutput(result);
}
`}},h7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${a}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${s});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${l}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},f7=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=In(this.outputShape.length);let{dataFormat:n}=t,s=ln(),r=n==="channelsLast",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u="";for(let c=0;c<=1;c++)for(let l=0;l<=1;l++)u+=`
blockIndex = rc.y + ${l};
pos = rc.x + ${c};
${o}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${i}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${r}) {
innerDims = vec2(d1, ch);
result[${c*2+l}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${c*2+l}] = 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;
${u}
${s.output} = result;
}
`}};function kC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,c=s.texData.get(e.dataId),l=n.inChannels,d=u[0]*u[1]*u[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((d===1||p===1)&&l>mC)&&c.isPacked&&h&&c.texture!=null&&u[2]%2!==0&&w.arraysEqual(c.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,w.assert(Ju(c.shape,k.shape),()=>`packed reshape ${c.shape} to ${k.shape} isn't free`);let C=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(C);let R=$d({a:k,b:C,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),_=s.texData.get(R.dataId);w.assert(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=T,_.shape=n.outShape,g=_n({inputs:{x:R},backend:s}),g.shape=n.outShape,b.push(R)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=$d({a:k,b:T,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:C},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(C)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function IC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:c,inChannels:l,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=u*c*l,g=p*d,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let C=new f7(b,n),R=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],_=s.runWebGLProgram(C,[k],"float32",R),P=he({inputs:{x:_},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(_),x.push(P);let D=r!=null,F=a!=null,$=o==="leakyrelu",z=o?Lp(o,!0):null,W=new fC(P.shape,T.shape,[1,g,n.outChannels],y,v,D,z,F,$),q=[P,T];if(r&&q.push(r),F&&q.push(a),$){let J=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));q.push(J),x.push(J)}let K=s.runWebGLProgram(W,q,"float32"),j=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],Q=he({inputs:{x:K},backend:s,attrs:{shape:j}});x.push(K);for(let J of x)s.disposeIntermediateTensorInfo(J);return Q}function m7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:c,dimRoundingMode:l}=s,d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=kC({x:r,filter:a,convInfo:p,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=IC({x:r,filter:a,convInfo:p,backend:n});else{let m=new wC(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=he({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var g7={kernelName:Ta,backendName:"webgl",kernelFunc:m7},b7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},y7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${l}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},v7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${r};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${s} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},x7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${u}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${r}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${s} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function w7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:c,filterShape:l}=s,d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(r.shape,l,i,1,o,c,!1,d),h=new b7(p);return n.runWebGLProgram(h,[r,a],"float32")}var k7={kernelName:Xm,backendName:"webgl",kernelFunc:w7};function I7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:c,dimRoundingMode:l}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(i,a.shape,o,1,u,l,!1,d),h=new y7(p);return n.runWebGLProgram(h,[r,a],"float32")}var S7={kernelName:$a,backendName:"webgl",kernelFunc:I7};function C7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,c=N.computeConv3DInfo(r.shape,a.shape,i,u,o),l=new h7(c);return n.runWebGLProgram(l,[r,a],"float32")}var N7={kernelName:Bd,backendName:"webgl",kernelFunc:C7};function T7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,c=N.computeConv3DInfo(r.shape,u,i,1,o),l=new v7(c);return n.runWebGLProgram(l,[r,a],"float32")}var $7={kernelName:Ym,backendName:"webgl",kernelFunc:T7};function A7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,c=N.computeConv3DInfo(u,a.shape,o,1,i),l=new x7(c);return n.runWebGLProgram(l,[r,a],"float32")}var E7={kernelName:Qm,backendName:"webgl",kernelFunc:A7},_7=ru+`
return cos(x);
`,R7=Ke({opSnippet:_7}),D7={kernelName:Aa,backendName:"webgl",kernelFunc:R7},F7=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,O7=Ke({opSnippet:F7}),P7={kernelName:Ea,backendName:"webgl",kernelFunc:O7},z7=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,o,u]=e,[c]=t,[l,d]=n;this.outputShape=[c,l,d,u];let p=s==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${y});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${g};
float width_scale = ${v};
float in_y = ${b};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${x};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},M7=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:c}=s,l=new z7(r.shape,a.shape,o,u,c);return n.runWebGLProgram(l,[r,a,i],"float32")},L7={kernelName:oo,backendName:"webgl",kernelFunc:M7},jx=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${Kx(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${ut(s)} coords = getOutputCoords();
int end = ${Xx(s,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${Xx(s,"coords")} = idx;
val += getX(${Kx(s,"coords")});
}
setOutput(val);
}
`}};function Kx(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Xx(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function B7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,c=N.getAxesPermutation([a],u),l=r;c!=null&&(l=an({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=N.getInnerMostAxes(1,u)[0];if(d!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=l.shape[d],h=_n({inputs:{x:l},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new jx(l.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new jx(l.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=N.getUndoAxesPermutation(c),m=an({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(l),m}return h}var V7={kernelName:io,backendName:"webgl",kernelFunc:B7};function W7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),c=n.readSync(a.dataId),l=nC(u,c,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}else if(r.shape.length===2){let u=n.bufferSync(r),c=n.bufferSync(a),l=rK(u,c,i,o);return n.makeTensorInfo(l.shape,a.dtype,l.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var U7={kernelName:Zm,backendName:"webgl",kernelFunc:W7},G7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function H7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],l=i==="NHWC"?r.shape[3]:r.shape[1],d=u*a,p=c*a,h=l/(a*a),f=i==="NHWC"?[o,d,p,h]:[o,h,d,p],m=new G7(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var q7={kernelName:uo,backendName:"webgl",kernelFunc:H7},SC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=In(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u="",c="";n&&(s?u=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?u=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:u=`
float activation(float x) {
${n}
}
`,c="result = activation(result);");let l=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${u}
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 < ${a}; 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;
${l}
${c}
setOutput(result);
}
`}},CC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=In(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,d=l,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<l;g++)p+=`
vec4 xTexelC${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 < ${c}; r++) {
`;for(let g=0;g<l;g++)p+=`
xTexelC${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<(d+1)/2;g++){let b=g*2;if(p+=`
xC = xCCorner + ${b*u};
`,o===1){if(b<l&&(i%2===1?(p+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
`,u===1&&b>0?p+=`
xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.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${b} = vec4(previous.zw, xTexelC${b}.xy);
} else {
xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);
}
`):p+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xC${b} = xTexelC${b};
`,b+1<l)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(p+=`
xCOffset = xC + imod(pads[1], 2) + ${y};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
`,u>1&&(p+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
xTexelC${b}Ready = 1;
}
`),p+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);
`):y===1?p+=`
xC${b+1} = xTexelC${b};
`:p+=`
xCOffset = xC + ${y};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
xC${b+1} = xTexelC${b+1};
`}}else b<l&&(i%2===1?(p+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`,b+1<l&&(p+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);
`)):(p+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.);
}
xTexelC${b+1}Ready = 1;
}
xC${b} = vec4(
xTexelC${b}.xy, xTexelC${b+1}.xy);
`,b+1<l&&(p+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`)));b<l&&(p+=`
wTexel = getW(r, ${b}, d1, q);
dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
`,b+1<l&&(p+=`
wTexel = getW(r, ${b+1}, d1, q);
dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
`))}p+=`
}
`,p+=`
}
`;let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${p}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function j7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:c}=s,l=u;l==null&&(l=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,l),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!0),p;X().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?p=new CC(d):p=new SC(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var K7={kernelName:_a,backendName:"webgl",kernelFunc:j7},X7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},Y7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${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) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function Q7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:c,filterShape:l}=s,d=N.computeConv2DInfo(r.shape,l,i,o,u,c,!0),p=new X7(d);return n.runWebGLProgram(p,[r,a],"float32")}var Z7={kernelName:Jm,backendName:"webgl",kernelFunc:Q7};function J7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:c,inputShape:l}=s,d=N.computeConv2DInfo(l,a.shape,i,o,u,c,!0),p=new Y7(d);return n.runWebGLProgram(p,[r,a],"float32")}var eY={kernelName:eg,backendName:"webgl",kernelFunc:J7},tY=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function nY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new tY(a),u=n.runWebGLProgram(o,[i],i.dtype),c=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),c}var sY={kernelName:tg,backendName:"webgl",kernelFunc:nY},rY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:c}=e,{top:l,left:d}=s;this.userCode=`
const ivec2 strides = ivec2(${r}, ${a});
const ivec2 pads = ivec2(${l}, ${d});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${u};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,c=N.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",u),l,d=new rY(c);l=n.runWebGLProgram(d,[r,a],"float32");let p=he({inputs:{x:l},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(l),p}var iY={kernelName:Vd,backendName:"webgl",kernelFunc:aY};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:c,steps:l}=N.getEinsumComputePath(o,u),d=l.length,p=null,h=i.length,f=[];for(let m=0;m<d;++m){for(let g of l[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=an({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),p===null?p=v:(p=iv({inputs:{a:v,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Vp({inputs:{x:p},backend:n,attrs:{axis:c[m]-(i.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var uY={kernelName:Wd,backendName:"webgl",kernelFunc:oY},lY="return (x >= 0.0) ? x : (exp(x) - 1.0);",cY=`
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;
`,dY=Ke({opSnippet:lY,packedOpSnippet:cY}),pY={kernelName:Da,backendName:"webgl",kernelFunc:dY},hY="return (b >= 1.0) ? a : a * (b + 1.0);",fY=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,mY=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ql(fY,s.shape,r.shape):new eo(hY,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},gY={kernelName:ng,backendName:"webgl",kernelFunc:mY},bY=`
return vec4(equal(a, b));
`,yY="return float(a == b);",vY=Kt({opSnippet:yY,packedOpSnippet:bY,dtype:"bool",cpuKernelImpl:oK}),xY={kernelName:lo,backendName:"webgl",kernelFunc:vY},wY=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${N.ERF_P};
float a1 = ${N.ERF_A1};
float a2 = ${N.ERF_A2};
float a3 = ${N.ERF_A3};
float a4 = ${N.ERF_A4};
float a5 = ${N.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,kY=Ke({opSnippet:wY}),IY={kernelName:pl,backendName:"webgl",kernelFunc:kY},SY=ru+`
return exp(x);
`,CY=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,NC=Ke({opSnippet:SY,packedOpSnippet:CY,cpuKernelImpl:uK,dtype:"float32"}),NY={kernelName:Fa,backendName:"webgl",kernelFunc:NC};function Rm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var TY={kernelName:co,backendName:"webgl",kernelFunc:Rm},Yx="return exp(x) - 1.0;",$Y=Ke({opSnippet:Yx,packedOpSnippet:Yx,cpuKernelImpl:lK}),AY={kernelName:po,backendName:"webgl",kernelFunc:$Y},Qx=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${s});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${s}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function TC(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,c=new Qx("real",u,t),l=new Qx("imag",u,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(l,d,"float32"),f=Fr({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=he({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function EY(e){let{inputs:t,backend:n}=e,{input:s}=t;return TC(s,!1,n)}var _Y={kernelName:sg,backendName:"webgl",kernelFunc:EY},RY=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 Jl(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new RY(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var DY={kernelName:hl,backendName:"webgl",kernelFunc:Jl},FY=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},OY={kernelName:ho,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new FY(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Zx="return floor(x);",PY=Ke({opSnippet:Zx,packedOpSnippet:Zx,cpuKernelImpl:cK}),zY={kernelName:Oa,backendName:"webgl",kernelFunc:PY},MY=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,LY=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,BY=Kt({opSnippet:MY,packedOpSnippet:LY,dtype:"int32"}),VY={kernelName:Pa,backendName:"webgl",kernelFunc:BY},WY=class{constructor(e){this.variableNames=["A"];let t=ln(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},UY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=ln(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},GY={kernelName:ad,backendName:"webgl",kernelFunc:HY},Fi;function HY(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],l=[c,u],d=[c,u,a];(o||i)&&(Fi==null&&(Fi=document.createElement("canvas").getContext("2d")),Fi.canvas.width=u,Fi.canvas.height=c,Fi.drawImage(r,0,0,u,c),r=Fi.canvas);let p=n.makeTensorInfo(l,"int32");n.texData.get(p.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=X().getBool("WEBGL_PACK")?new UY(d):new WY(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function qY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:c,dataFormat:l,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(l),g=N.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=kC({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)b=IC({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,T=h==="leakyrelu",C=h?Lp(h,!1):null,R=new wC(g,x,C,k,T),_=[r,a];if(i&&_.push(i),o&&_.push(o),T){let P=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));_.push(P),y.push(P)}b=n.runWebGLProgram(R,_,"float32")}let v=he({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var jY={kernelName:ra,backendName:"webgl",kernelFunc:qY};function KY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:c,dilations:l,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=l;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=N.computeConv2DInfo(r.shape,a.shape,u,m,c,d,!0),b=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=p?Lp(p,b):null,v=[r,a],x=i!=null,k=o!=null,T=p==="leakyrelu";if(x&&v.push(i),k&&v.push(o),T){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(P),f.push(P)}let C;b?C=new CC(g,x,y,k,T):C=new SC(g,x,y,k,T);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],_=n.runWebGLProgram(C,v,"float32",R);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),_}var XY={kernelName:aa,backendName:"webgl",kernelFunc:KY},YY=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=ut(t.length),r=ut(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${s} strides = ${s}(${this.strides});
void main() {
${r} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function QY(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,c,l,d]=N.prepareAndValidate(s,r),p=he({inputs:{x:r},backend:n,attrs:{shape:[c,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/l,l]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=dK(b,y,s.dtype,c,i,l,d,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new YY(i,d,[c,l]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var ZY={kernelName:mo,backendName:"webgl",kernelFunc:QY},JY=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),s=e9(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${s}));
}
`}};function e9(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function $C(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let c=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),l=w.sizeFromShape(a.shape),d=[],p=he({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,l/c.batchSize]}});d.push(p),d.push(h);let f=[c.batchSize,c.outerSize,l/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(p),x=pK(v,y,f);return d.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let m=new JY(p.shape,f),g=n.runWebGLProgram(m,[p,h],p.dtype);d.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var t9={kernelName:fo,backendName:"webgl",kernelFunc:$C},n9="return float(a > b);",s9=`
return vec4(greaterThan(a, b));
`,r9=Kt({opSnippet:n9,packedOpSnippet:s9,cpuKernelImpl:hK,dtype:"bool"}),a9={kernelName:go,backendName:"webgl",kernelFunc:r9},i9="return float(a >= b);",o9=`
return vec4(greaterThanEqual(a, b));
`,u9=Kt({opSnippet:i9,packedOpSnippet:o9,dtype:"bool",cpuKernelImpl:fK}),l9={kernelName:Ma,backendName:"webgl",kernelFunc:u9};function c9(e){let{inputs:t,backend:n}=e,{input:s}=t;return TC(s,!0,n)}var d9={kernelName:rg,backendName:"webgl",kernelFunc:c9},p9="return float(!isnan(x) && !isinf(x));",h9=Ke({opSnippet:p9,dtype:"bool"}),f9={kernelName:fl,backendName:"webgl",kernelFunc:h9},m9="return float(isinf(x));",g9=Ke({opSnippet:m9,dtype:"bool"}),b9={kernelName:ml,backendName:"webgl",kernelFunc:g9},y9="return float(isnan(x));",v9=Ke({opSnippet:y9,dtype:"bool"}),x9={kernelName:gl,backendName:"webgl",kernelFunc:v9},w9="return float(a < b);",k9=`
return vec4(lessThan(a, b));
`,I9=Kt({opSnippet:w9,packedOpSnippet:k9,cpuKernelImpl:mK,dtype:"bool"}),S9={kernelName:bo,backendName:"webgl",kernelFunc:I9},C9="return float(a <= b);",N9=`
return vec4(lessThanEqual(a, b));
`,T9=Kt({opSnippet:C9,packedOpSnippet:N9,cpuKernelImpl:gK,dtype:"bool"}),$9={kernelName:yo,backendName:"webgl",kernelFunc:T9};function A9(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=bK(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}var E9={kernelName:ag,backendName:"webgl",kernelFunc:A9},_9=ru+`
return x < 0.0 ? 0./0. : log(x);
`,R9=`
vec4 result = log(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
return result;
`,D9=Ke({opSnippet:_9,packedOpSnippet:R9,cpuKernelImpl:yK}),F9={kernelName:Va,backendName:"webgl",kernelFunc:D9},O9=ru+`
return log(1.0 + x);
`,P9=Ke({opSnippet:O9}),z9={kernelName:bl,backendName:"webgl",kernelFunc:P9},M9="return float(a >= 1.0 && b >= 1.0);",L9=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,B9=Kt({opSnippet:M9,packedOpSnippet:L9,dtype:"bool"}),V9={kernelName:vo,backendName:"webgl",kernelFunc:B9},W9="return float(!(x >= 1.0));",U9=Ke({opSnippet:W9}),G9={kernelName:yl,backendName:"webgl",kernelFunc:U9},H9="return float(a >= 1.0 || b >= 1.0);",q9=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,j9=Kt({opSnippet:H9,packedOpSnippet:q9,dtype:"bool"}),K9={kernelName:Gd,backendName:"webgl",kernelFunc:j9},X9=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},Y9=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${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);
}
`}},Q9=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,c=X().getBool("WEBGL_PACK_NORMALIZATION")?new Y9(r.shape,a,i,o,u):new X9(r.shape,a,i,o,u);return n.runWebGLProgram(c,[r],r.dtype)},Z9={kernelName:Hd,backendName:"webgl",kernelFunc:Q9},J9=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${s}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${s})
* float(${r})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},eQ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:c,beta:l}=s,d=new J9(r.shape,o,u,c,l);return n.runWebGLProgram(d,[r,a,i],r.dtype)},tQ={kernelName:ig,backendName:"webgl",kernelFunc:eQ};function nQ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=vi(o,e.dtype,"max",s),c=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),c}function AC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),c=u,l=N.getAxesPermutation(c,o),d=l!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=r.shape[l[C]];let k=av(v,r.shape,r.dtype,l,x);h=n.makeTensorInfo(x,r.dtype);let T=n.texData.get(h.dataId);T.values=k}else h=Bp(r,l,n);c=N.getInnerMostAxes(c.length,o)}N.assertAxesAreInnerMostDims("max",c,o);let[f,m]=N.computeOutAndReduceShapes(h.shape,c),g=f;i&&(g=N.expandShapeToKeepDim(f,u));let b;if(p){let v=n.texData.get(h.dataId).values,x=vK(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=nQ(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),b}var sQ={kernelName:Wa,backendName:"webgl",kernelFunc:AC},rQ=lC+`
return max(a, b);
`,aQ=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Mp+`
return result;
`,iQ=Kt({opSnippet:rQ,packedOpSnippet:aQ,cpuKernelImpl:xK}),oQ={kernelName:Ua,backendName:"webgl",kernelFunc:iQ};function uQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Jo(r,"maxPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,c=1;w.assert(N.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let l=N.computePool2DInfo(r.shape,a,i,c,o,u);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return _n({inputs:{x:r},backend:n});let d=new el(l,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var lQ={kernelName:Ga,backendName:"webgl",kernelFunc:uQ};function cQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:c}=s,l=[1,1,1],d=N.computePool3DInfo(r.shape,a,i,l,o,c,u),p=new ov(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var dQ={kernelName:qd,backendName:"webgl",kernelFunc:cQ},pQ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${r};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},hQ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=o-1-e.padInfo.front,d=u-1-e.padInfo.top,p=c-1-e.padInfo.left,h=o*u*c-1;this.userCode=`
const ivec3 pads = ivec3(${l}, ${d}, ${p});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${o};
wD += ${r}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${u};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${u} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function fQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:c,dimRoundingMode:l}=s,d=[1,1,1],p=N.computePool3DInfo(i.shape,o,u,d,c,l),h=new ov(p,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new hQ(p),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var mQ={kernelName:ug,backendName:"webgl",kernelFunc:fQ};function gQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;Jo([a,i],"maxPoolGrad");let{filterSize:u,strides:c,pad:l,dimRoundingMode:d}=s,p=N.computePool2DInfo(o.shape,u,c,1,l,d),h=!0,f=new el(p,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new pQ(p),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var bQ={kernelName:og,backendName:"webgl",kernelFunc:gQ};function yQ(e,t,n,s){let r=new el(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new el(n,"max",!0,!0,t);let i=s.runWebGLProgram(r,[e],"float32");return[a,i]}var vQ={kernelName:lg,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let c=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=N.computePool2DInfo(s.shape,r,a,c,i),[d,p]=yQ(s,o,l,u);return[d,p]}};function xQ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=vi(o,"float32","mean",s),c=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),c}var wQ={kernelName:Ha,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,u=w.parseAxisParam(a,s.shape),c=u,l=N.getAxesPermutation(c,o),d=l!=null,p=i.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let x=i.texData.get(f.dataId).values,k=new Array(o);for(let R=0;R<k.length;R++)k[R]=s.shape[l[R]];let T=av(x,s.shape,s.dtype,l,k);f=i.makeTensorInfo(k,s.dtype);let C=i.texData.get(f.dataId);C.values=T}else f=Bp(s,l,i);h.push(f),c=N.getInnerMostAxes(c.length,o)}N.assertAxesAreInnerMostDims("sum",c,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,c),b=m;r&&(b=N.expandShapeToKeepDim(m,u));let y=xQ(f,g,b,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return y}};function kQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),c=u,l=N.getAxesPermutation(c,o),d=r;l!=null&&(d=an({inputs:{x:r},backend:n,attrs:{perm:l}}),c=N.getInnerMostAxes(c.length,r.shape.length)),N.assertAxesAreInnerMostDims("min",c,o);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=w.sizeFromShape(h),m=he({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=vi(m,m.dtype,"min",n),b;if(i){let y=N.expandShapeToKeepDim(p,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),l!=null&&n.disposeIntermediateTensorInfo(d),b}var IQ={kernelName:qa,backendName:"webgl",kernelFunc:kQ},SQ=lC+`
return min(a, b);
`,CQ=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Mp+`
return result;
`,NQ=Kt({opSnippet:SQ,packedOpSnippet:CQ,cpuKernelImpl:wK}),TQ={kernelName:ja,backendName:"webgl",kernelFunc:NQ},$Q=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,l)=>c[0]+e[l]+c[1]);let s=e.length,r=ut(s),a=t.map(c=>c[0]).join(","),i=t.map((c,l)=>c[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),u=n==="reflect"?0:1;if(s===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${u};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${u};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${s}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${u};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
}
}
${r} coords = outC - start;
setOutput(getX(${o}));
}
`}},AQ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=ut(s),a=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=sn("rc",s),u=sn("source",s),c=`${o[s-1]} < ${this.outputShape[s-1]}`,l=s===1?"source":`vec2(${u.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${d};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${d};
}
source -= start;
`;p=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${u.join()}), ${l});
${o[s-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${u.join()}), ${l});
}
`}else{let h=`
${r} source = rc;
${r} lt = ${r}(lessThan(source, start));
${r} gte = ${r}(greaterThanEqual(source, end));
${r} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${d}) +
gte * ((end - 1) * 2 - source + ${d});
source -= start;
`;p=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${u.join()}), ${l});
${o[s-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${u.join()}), ${l});
}
rc = outputLoc;
${o[s-2]} += 1;
if(${o[s-2]} < ${this.outputShape[s-2]}) {
${h}
result[2] = getChannel(getX(${u.join()}), ${l});
${o[s-1]} += 1;
if(${c}) {
${h}
result[3] = getChannel(getX(${u.join()}), ${l});
}
}
`}this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},EQ=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AQ(s.shape,r,a):new $Q(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)},_Q={kernelName:Ka,backendName:"webgl",kernelFunc:EQ},RQ=`if (b == 0.0) return NAN;
return mod(a, b);`,DQ=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Mp+`
return result;
`,FQ=Kt({opSnippet:RQ,packedOpSnippet:DQ}),OQ={kernelName:vl,backendName:"webgl",kernelFunc:FQ},PQ=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}},zQ=`
if (a == b) {
return 1.0;
};
return a / b;`,MQ=`
// 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;
`,EC=Kt({opSnippet:zQ,packedOpSnippet:MQ,checkOutOfBounds:!0}),LQ={kernelName:Ra,backendName:"webgl",kernelFunc:EC},Jx="return a - b;",_C=Kt({opSnippet:Jx,packedOpSnippet:Jx,supportsComplex:!0,cpuKernelImpl:zK}),BQ={kernelName:li,backendName:"webgl",kernelFunc:_C};function RC(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=AC({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),c=he({inputs:{x:o},backend:n,attrs:{shape:u}}),l=_C({inputs:{a:r,b:c},backend:n}),d=NC({inputs:{x:l},backend:n}),p=Vp({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=he({inputs:{x:p},backend:n,attrs:{shape:u}}),f=EC({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var VQ={kernelName:oi,backendName:"webgl",kernelFunc:RC};function WQ(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:i,normalized:o}=s,u=o?r:RC({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=u.shape[0],l=u.shape[1],d=new PQ(c,l,a),p=[[i]],h=n.runWebGLProgram(d,[u],"int32",p);return o||n.disposeIntermediateTensorInfo(u),h}var UQ={kernelName:cg,backendName:"webgl",kernelFunc:WQ},GQ=as+`
return -x;
`,HQ=`
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 qQ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[i,o]=IK(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Qr(s.shape,HQ):r=new Gs(s.shape,GQ),n.runWebGLProgram(r,[s],s.dtype)}var jQ={kernelName:xo,backendName:"webgl",kernelFunc:qQ},KQ=ys.nonMaxSuppressionV3Impl;function XQ(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,c=n.readSync(r.dataId),l=n.readSync(a.dataId),{selectedIndices:d}=KQ(c,l,i,o,u);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var YQ={kernelName:ko,backendName:"webgl",kernelFunc:XQ},QQ=ys.nonMaxSuppressionV4Impl;function ZQ(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:c}=s,l=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=QQ(l,d,i,o,u,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var JQ={kernelName:xl,backendName:"webgl",kernelFunc:ZQ},eZ=ys.nonMaxSuppressionV5Impl;function tZ(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:c}=s,l=n.readSync(r.dataId),d=n.readSync(a.dataId),p=i,h=o,f=u,m=c,{selectedIndices:g,selectedScores:b}=eZ(l,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var nZ={kernelName:Io,backendName:"webgl",kernelFunc:tZ},sZ=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${s}), float(${n}),
float(index == coords.y)));
}
`}},rZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),c=new sZ(u,a,i,o),l=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),d=n.runWebGLProgram(c,[l],r.dtype);n.disposeIntermediateTensorInfo(l);let p=[...r.shape,a],h=he({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},aZ={kernelName:Co,backendName:"webgl",kernelFunc:rZ};function Ad(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Zl({inputs:{input:s},backend:n}),a=Ad({inputs:{x:r},backend:n}),i=Wp({inputs:{input:s},backend:n}),o=Ad({inputs:{x:i},backend:n}),u=Fr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Jl({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var iZ={kernelName:Wo,backendName:"webgl",kernelFunc:Ad};function DC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Zl({inputs:{input:s},backend:n}),a=DC({inputs:{x:r},backend:n}),i=Wp({inputs:{input:s},backend:n}),o=Ad({inputs:{x:i},backend:n}),u=Fr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Jl({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var oZ={kernelName:So,backendName:"webgl",kernelFunc:DC};function uZ(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Rm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(l=>{w.assertShapesMatch(a,l.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(l=>{let d=Rm({inputs:{input:l},backend:n,attrs:{dim:r}});return o.push(d),d}),c=xC({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(l=>n.disposeIntermediateTensorInfo(l)),c}var lZ={kernelName:No,backendName:"webgl",kernelFunc:uZ},cZ=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let s=e.length,r=ut(s),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},dZ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=ut(s),a=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=sn("rc",s),u=sn("source",s),c=`${o[s-1]} < ${this.outputShape[s-1]}`,l=s===1?"source":`vec2(${u.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;
if(${c}) {
`,s===1?"":`}
rc = outputLoc;
${o[s-2]} += 1;
if(${o[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${o[s-1]} += 1;
if(${c}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
${d[f]}
if (${p}) {
result[${f}] = float(value);
} else {
${r} source = rc - start;
result[${f}] = getChannel(getX(${u.join()}), ${l});
}
`;h+=s===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},FC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(w.sizeFromShape(r.shape)===0){let c=a.map((l,d)=>l[0]+r.shape[d]+l[1]);return Jl({backend:n,attrs:{shape:c,value:i,dtype:r.dtype}})}let o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dZ(r.shape,a,i):new cZ(r.shape,a,i),u=[[i]];return n.runWebGLProgram(o,[r],r.dtype,u)},pZ={kernelName:Ya,backendName:"webgl",kernelFunc:FC},hZ=`
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);
`,fZ=`
// 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));
`+Mp+`
return result;
`,mZ=Kt({opSnippet:hZ,packedOpSnippet:fZ}),gZ={kernelName:Qa,backendName:"webgl",kernelFunc:mZ};function bZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],c=w.parseAxisParam(a,r.shape),l=c,d=N.getAxesPermutation(l,o),p=r;d!=null&&(p=an({inputs:{x:r},backend:n,attrs:{perm:d}}),l=N.getInnerMostAxes(l.length,o),u.push(p)),N.assertAxesAreInnerMostDims("prod",l,o);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:b}=CK(p.shape,p.dtype,f,l);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,l),g=w.sizeFromShape(m),b=he({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),y=tp(r.dtype),v=vi(b,y,"prod",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=N.expandShapeToKeepDim(h.shape,c);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var yZ={kernelName:To,backendName:"webgl",kernelFunc:bZ},OC=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=NK(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},vZ={kernelName:wl,backendName:"webgl",kernelFunc:OC},xZ="return 1.0 / x;",wZ=Ke({opSnippet:xZ}),kZ={kernelName:kl,backendName:"webgl",kernelFunc:wZ},IZ=as+`
return (x < 0.0) ? 0.0 : x;
`,SZ=`
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;
`,CZ=Ke({opSnippet:IZ,packedOpSnippet:SZ}),NZ={kernelName:Ja,backendName:"webgl",kernelFunc:CZ},TZ=as+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,$Z=`
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;
`,AZ=Ke({opSnippet:TZ,packedOpSnippet:$Z}),EZ={kernelName:ti,backendName:"webgl",kernelFunc:AZ},_Z=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let c=[s&&t>1?i-1:i,s&&n>1?o-1:o],l=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/l[0]},
${c[1]/l[1]});
const vec2 inputShapeRC = vec2(${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 = ${d};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}},RZ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let c=[s&&t>1?i-1:i,s&&n>1?o-1:o],l=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/l[0]},
${c[1]/l[1]},
${c[1]/l[1]});
const vec3 inputShapeRC = vec3(${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 = ${d};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function DZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,c]=o,l=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new RZ(r.shape,u,c,a,i):new _Z(r.shape,u,c,a,i);return n.runWebGLProgram(l,[r],"float32")}var FZ={kernelName:ei,backendName:"webgl",kernelFunc:DZ},OZ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=o[0]/u[0],l=o[1]/u[1],d=1/c,p=1/l,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${l});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function PZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new OZ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var zZ={kernelName:pg,backendName:"webgl",kernelFunc:PZ},MZ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let c=[s&&t>1?i-1:i,s&&n>1?o-1:o],l=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/l[0]},
${c[1]/l[1]});
const vec2 inputShapeRC = vec2(${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 + ${d})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},LZ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let c=[s&&t>1?i-1:i,s&&n>1?o-1:o],l=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/l[0]},
${c[1]/l[1]},
${c[1]/l[1]});
const vec3 inputShapeRC = vec3(${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 + ${d})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function BZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,c]=o,l=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new LZ(r.shape,u,c,a,i):new MZ(r.shape,u,c,a,i);return n.runWebGLProgram(l,[r],r.dtype)}var VZ={kernelName:Il,backendName:"webgl",kernelFunc:BZ},WZ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=o[0]/u[0],l=o[1]/u[1],d=1/c,p=1/l,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${l});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function UZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new WZ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var GZ={kernelName:dg,backendName:"webgl",kernelFunc:UZ},HZ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let s=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>s(o)).join(","),a=ut(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},qZ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=sn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${r}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(s.slice())};
if(${r}){
result.g = ${u(s.slice())};
}
if(${a}) {
result.b = ${c(s.slice())};
if(${r}) {
result.a = ${l(s.slice())};
}
}
setOutput(result);
}
`;function o(h){return d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((b,y)=>p(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function jZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return _n({inputs:{x:r},backend:n});let u=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qZ(r.shape,o):new HZ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var KZ={kernelName:Ao,backendName:"webgl",kernelFunc:jZ},XZ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${r}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},YZ={kernelName:Uo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new XZ(s.shape,a),[c,l]=N.getImageCenter(i,s.shape[1],s.shape[2]),d=[[c,l,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,d)}},QZ=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,ZZ=Ke({opSnippet:QZ}),JZ={kernelName:Eo,backendName:"webgl",kernelFunc:ZZ},eJ="return inversesqrt(x);",tJ=Ke({opSnippet:eJ,cpuKernelImpl:TK}),nJ={kernelName:ni,backendName:"webgl",kernelFunc:tJ},PC=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let o=ut(r.length),u=ut(a.length),c="";n===1?c="i":n===2&&(c="i, j");let l=`getIndices(${c})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${r});
void main() {
${u} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${l});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${p};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function sJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:c,strides:l,outputSize:d}=N.calculateShapes(a,r,i),p=[d/c,c];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new PC(u,o,h.shape.length,f.shape.length,l,p),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var rJ={kernelName:_o,backendName:"webgl",kernelFunc:sJ},aJ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);s=o.join(),r=u.join()}let a=ut(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function iJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new aJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],vn(r.dtype,a.dtype))}var oJ={kernelName:Ro,backendName:"webgl",kernelFunc:iJ},uJ=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${N.SELU_SCALEALPHA};
float scale = ${N.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,lJ=Ke({opSnippet:uJ}),cJ={kernelName:Sl,backendName:"webgl",kernelFunc:lJ},dJ=ru+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,pJ=`
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,hJ=Ke({opSnippet:dJ,packedOpSnippet:pJ,cpuKernelImpl:$K}),fJ={kernelName:ri,backendName:"webgl",kernelFunc:hJ},mJ=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,gJ=Ke({opSnippet:mJ}),bJ={kernelName:Cl,backendName:"webgl",kernelFunc:gJ},yJ=ru+`
return sin(x);
`,vJ=Ke({opSnippet:yJ}),xJ={kernelName:si,backendName:"webgl",kernelFunc:vJ},wJ=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,kJ=Ke({opSnippet:wJ}),IJ={kernelName:Fo,backendName:"webgl",kernelFunc:kJ},SJ=`
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;
`,CJ=Ke({opSnippet:SJ}),NJ={kernelName:Nl,backendName:"webgl",kernelFunc:CJ},TJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let c=[],l=FC({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),d=N.getReshaped(l.shape,a,o,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(l.shape,a,o,!1),f=he({inputs:{x:l},backend:n,attrs:{shape:d}}),m=an({inputs:{x:f},backend:n,attrs:{perm:p}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(l),c.push(f),c.push(m),c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},$J={kernelName:Oo,backendName:"webgl",kernelFunc:TJ};function AJ(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),c=n.readSync(a.dataId),l=n.readSync(i.dataId)[0],[d,p,h,f,m]=EK(o,s.shape,s.dtype,u,r.dtype,c,l);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var EJ={kernelName:Kd,backendName:"webgl",kernelFunc:AJ};function _J(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[c,l,d]=_K(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(l,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var RJ={kernelName:Tl,backendName:"webgl",kernelFunc:_J};function DJ(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[c,l]=rC(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(l,s.dtype,c)}var FJ={kernelName:Xd,backendName:"webgl",kernelFunc:DJ};function OJ(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[c,l]=rC(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(l,s.dtype,c)}var PJ={kernelName:Yd,backendName:"webgl",kernelFunc:OJ};function zJ(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:c,strides:l,outputSize:d}=N.calculateShapes(a,r,o),p=!1,h=new PC(c,u,r.shape.length,a.shape.length,l,[d,1],p),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var MJ={kernelName:Qd,backendName:"webgl",kernelFunc:zJ};function LJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),c=r.shape.length,l=new Array(c).fill(0),d=r.shape.slice();return u.map(p=>{let h=[...d];h[o]=p;let f=au({inputs:{x:r},backend:n,attrs:{begin:l,size:h}});return l[o]+=p,f})}var BJ={kernelName:Po,backendName:"webgl",kernelFunc:LJ},ew="return sqrt(x);",VJ=Ke({opSnippet:ew,packedOpSnippet:ew,cpuKernelImpl:RK}),WJ={kernelName:ai,backendName:"webgl",kernelFunc:VJ},UJ="return x * x;",GJ=Ke({opSnippet:UJ}),HJ={kernelName:$l,backendName:"webgl",kernelFunc:GJ},tw="return (a - b) * (a - b);",qJ=Kt({opSnippet:tw,packedOpSnippet:tw}),jJ={kernelName:ui,backendName:"webgl",kernelFunc:qJ};function KJ({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=as+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new Gs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var XJ={kernelName:pi,backendName:"webgl",kernelFunc:KJ},YJ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=ut(n.length),a=ut(n.length),i="";if(s===1)i="coords * strides + begin";else{let o=0;i=n.map((u,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function QJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:c,ellipsisMask:l,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,i,o,u,c,l,d,p),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=kt.computeOutShape(y,v,x),R=au({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});k=he({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(R)}else if(n.shouldExecuteOnCPU([r])){let R=n.readSync(r.dataId),_=_e(r.shape,r.dtype,R),P=DK(h,_,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let R=new YJ(y,x,h);k=n.runWebGLProgram(R,[r],r.dtype)}let T=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),T}var ZJ={kernelName:zo,backendName:"webgl",kernelFunc:QJ};function JJ(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:c}=s,{data:l,dataSplits:d}=t,p=n.readSync(l.dataId),h=n.readSync(d.dataId),[f,m]=FK(p,h,r,a,i,o,u,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var eee={kernelName:Zd,backendName:"webgl",kernelFunc:JJ};function tee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[c,l,d]=OK(o,u,r),p=l.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",l),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var nee={kernelName:hg,backendName:"webgl",kernelFunc:tee};function see(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(a.dataId),o=PK(i,r);return n.makeTensorInfo(a.shape,"int32",o)}var ree={kernelName:fg,backendName:"webgl",kernelFunc:see},aee="return tan(x);",iee=Ke({opSnippet:aee}),oee={kernelName:Mo,backendName:"webgl",kernelFunc:iee},uee=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,lee=Ke({opSnippet:uee}),cee={kernelName:ci,backendName:"webgl",kernelFunc:lee},dee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=ut(this.rank),r=pee(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function pee(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function zC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let u=n.readSync(r.dataId),c=r.dtype==="string"?u.map(p=>w.decodeString(p)):u,l=_e(r.shape,r.dtype,c),d=MK(l,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new dee(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var hee={kernelName:Tr,backendName:"webgl",kernelFunc:zC},fee=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));
}
}
`}},mee=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 Ur(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function nw(e){let t=1;for(;t<e;)t*=2;return t}function gee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=r.shape,l=c[c.length-1];if(n.shouldExecuteOnCPU([r])||l<o||a>u){let P=n.readSync(r.dataId),[D,F]=LK(P,c,r.dtype,a,i);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(l===1)return[r,Jl({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=w.sizeFromShape(c)/l,g=he({inputs:{x:h},attrs:{shape:[m,l]},backend:n});p&&Ur(n,h);let b=nw(a),y=nw(l),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,D,F)=>{let $=x(),z=new fee(F),q=[[l],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[D]],K=v;v=n.runWebGLProgram(z,$,"int32",q),Ur(n,K)};for(let P=1;P<b;P*=2){let D=P*2;for(let F=P;F>=1;F/=2)k(D,F,[m,y])}for(let P=y;P>b;P/=2){let D=x(),F=new mee([m,P/2]),z=[[l],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,D,"int32",z),Ur(n,W);let q=b/2,K=q*2;for(let j=q;j>=1;j/=2)k(K,j,v.shape)}let T=v;v=au({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Ur(n,T);let C=$C({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Ur(n,g);let R=c.slice(0,-1);R.push(a),T=v,v=he({inputs:{x:v},attrs:{shape:R},backend:n}),Ur(n,T);let _=C;return C=he({inputs:{x:C},attrs:{shape:R},backend:n}),Ur(n,_),[C,v]}var bee={kernelName:Lo,backendName:"webgl",kernelFunc:gee},yee=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${o} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${r});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${r});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:c}=s,[l,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[l,f,m,h],b=new yee(d,p,i,o,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var xee={kernelName:Bo,backendName:"webgl",kernelFunc:vee};function wee(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;Jo(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:c}=BK(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([c.length],"int32",c)]}var kee={kernelName:mg,backendName:"webgl",kernelFunc:wee};function Iee(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],c=new Array(o-1),l=0;for(let m=0;m<o;m++)m!==a&&(c[l++]=i.shape[m]);let d=[],p=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){p[a]=m;let g=au({inputs:{x:i},backend:n,attrs:{begin:p,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=b,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var See={kernelName:Vo,backendName:"webgl",kernelFunc:Iee},Cee=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o="0.0",u="sumValue",c=Math.floor(n/4)*4,l=n%4,d=`
sumValue += dot(values, segFilter);
`,p="";r%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${d}
}
int inIdx = inOffset + ${c};
if (${l===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${d}
} else if (${l===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${d}
} else if (${l===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${d}
}
setOutput(${u});
}
`}};function Nee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],c=0,l=N.getAxesPermutation([c],o),d=r;l!=null&&(d=an({inputs:{x:r},backend:n,attrs:{perm:l}}),u.push(d),c=N.getInnerMostAxes(1,o)[0]);let p=N.segment_util.computeOutShape(d.shape,c,i),h=w.sizeFromShape([d.shape[c]]),f=he({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=tp(r.dtype),g=(x,k,T,C,R)=>{let _=x.shape[0],P=x.shape[1],D=N.segment_util.segOpComputeOptimalWindowSize(P,R),F={windowSize:D,inSize:P,batchSize:_,numSegments:R},$=new Cee(F,k),z=n.compileAndRun($,[x,T],C);if(u.push(z),z.shape[1]===R)return z;let W=OC({backend:n,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),q=zC({inputs:{x:W},backend:n,attrs:{reps:[P/D]}});return u.push(W),u.push(q),g(z,k,q,C,R)},b=g(f,"unsortedSegmentSum",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:p}}),v=y;if(l!=null){u.push(y);let x=N.getUndoAxesPermutation(l);v=an({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var Tee={kernelName:Jd,backendName:"webgl",kernelFunc:Nee},$ee=[FX,PX,LX,WX,GX,jX,XX,QX,t8,s8,i8,l8,p8,g8,v8,w8,I8,T8,A8,_8,O8,W8,G8,q8,Z8,e7,r7,mX,o7,p7,g7,k7,S7,N7,$7,E7,D7,P7,L7,V7,U7,q7,K7,Z7,eY,sY,iY,uY,pY,gY,xY,IY,NY,TY,AY,_Y,DY,OY,zY,VY,GY,jY,XY,ZY,t9,a9,l9,fX,d9,c7,f9,b9,x9,bX,S9,$9,E9,F9,z9,V9,G9,K9,Z9,tQ,sQ,oQ,lQ,dQ,mQ,bQ,vQ,wQ,IQ,TQ,_Q,OQ,UQ,kX,jQ,YQ,JQ,nZ,K8,aZ,oZ,lZ,pZ,gZ,vX,yZ,vZ,X8,LQ,kZ,NZ,EZ,SX,FZ,zZ,VZ,GZ,KZ,YZ,JZ,nJ,rJ,oJ,cJ,fJ,bJ,xJ,IJ,B8,VQ,NJ,$J,EJ,RJ,FJ,PJ,MJ,BJ,WJ,HJ,jJ,XJ,ZJ,eee,nee,ree,BQ,_X,oee,cee,hee,bee,xee,RX,kee,See,Tee,iZ];for(let e of $ee)Al(e);var Os=X();Os.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Os.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Os.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Os.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Os.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Os.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Os.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Os.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Os.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Os.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Aee(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function td(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function uv(){return`
@stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`}function Or(){return`
${uv()}
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 Le(){return`
${Or()}
let index = getGlobalIndex();
`}function Eee(e,t,n,s=!1){let r=[];if(r.push(`
let workGroupSizeX = ${n.workGroupSize[0]}u;
let workGroupSizeY = ${n.workGroupSize[1]}u;
let workGroupSizeZ = ${n.workGroupSize[2]}u;
var<private> localId: vec3<u32>;
var<private> globalId: vec3<u32>;
var<private> numWorkgroups: vec3<u32>;
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex() -> i32 {
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
return i32(globalId.x);
}
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
workGroupID.y * numWorkgroups.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
}
`),s===!0)return r.push(`
struct Matrix0 {
numbers: array<${td(t.dtype,n.isVec4)}>;
};
struct Uniform {
size : i32;
numChannels : i32;
outShapeStrides : vec2<i32>;
dispatchSize : vec3<u32>;
};
@group(0) @binding(0) var<storage, write> result : Matrix0;
@group(0) @binding(2) var<uniform> uniforms: Uniform;
`),[sw,r.join(`
`),rw(t.shape),n.getUserCode()].join(`
`);let a="struct Uniforms { NAN : f32; ";n.variableNames.forEach((d,p)=>{a+=`${d.charAt(0).toLowerCase()+d.slice(1)}Shape : ${Wt(e[p].shape.length)}; `}),a+=`outShape : ${Wt(t.shape.length)} ; `;let i=t.shape.length-1;a+=`
outShapeStrides: ${Wt(i)}; `,n.size&&(a+="size : i32; "),n.uniforms&&(a+=n.uniforms),a+="};",r.push(a),n.atomic?r.push(`
struct Matrix0 {
numbers: array<atomic<i32>>;
};
@group(0) @binding(0) var<storage, read_write> result : Matrix0;
`):r.push(`
struct Matrix0 {
numbers: array<${td(t.dtype,n.isVec4)}>;
};
@group(0) @binding(0) var<storage, write> result : Matrix0;
`),n.variableNames.forEach((d,p)=>{r.push(`
struct Matrix${1+p} {
numbers: array<${td(e[p].dtype,n.isVec4)}>;
};
@group(0) @binding(${1+p}) var<storage, read> ${d} : Matrix${1+p};
`)}),a!==""&&r.push(`
@group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms : Uniforms;
`);let[o,u]=Pee(t.shape,n.dispatchLayout),c=[sw,r.join(`
`),rw(t.shape),o,_ee(t.shape.length)];if(n.atomic||c.push(Ree(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let d=e.map(p=>Dee(p,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);c.push(d)}return c.push(n.getUserCode()),c.join(`
`)}var sw=`
// Checks whether coordinates lie within the bounds of the shape.
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
return all(coord >= vec2<i32>(0)) && all(coord < shape);
}
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
return all(coord >= vec3<i32>(0)) && all(coord < shape);
}
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
return all(coord >= vec4<i32>(0)) && all(coord < shape);
}
fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
return coord;
}
fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(shape.y, 1));
}
fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
}
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
var res: i32 = a / b;
let mod: i32 = a % b;
if (sign < 0. && mod != 0) {
res = res - 1;
}
return res;
}
fn isNanCustom(val : f32) -> bool {
if (val > 0.0) {
return false;
}
if (val < 0.0) {
return false;
}
if (val == 0.0) {
return false;
}
return true;
}
fn isNanCustomVec4(val : vec4<f32>) -> vec4<bool> {
return vec4<bool>(isNanCustom(val[0]), isNanCustom(val[1]), isNanCustom(val[2]), isNanCustom(val[3]));
}
`;function _ee(e){let t="";switch(e){case 0:case 1:t+=`
fn getOutputIndexFromCoords(coords : i32) -> i32 {
return coords;
}
`;break;case 2:t+=`
fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
}
`;break;case 3:t+=`
fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
}
`;break;case 4:t+=`
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
}
`;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Ree(e,t,n){let s=e.length,r=td(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
result.numbers[flatIndex] = ${r}(value);
}`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
result.numbers[flatIndex] = ${r}(value);
}`,s>=2){let i=["d0","d1","d2","d3"].slice(0,s),o=Wt(s);n?a+=`
fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndex(flatIndex / 4, value);
}
fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndexI32(flatIndex / 4, value);
}
`:a+=`
fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(", ")}, value : f32) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndex(flatIndex, value);
}
fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(", ")}, value : i32) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndexI32(flatIndex, value);
}
`}return a}function Dee(e,t,n,s){let r=Fee(e,n);return e.shape.length<=t.length&&(r+=Oee(e,t,n,s)),r}function Fee(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),i=["d0","d1","d2","d3"].slice(0,s),o=i.map(l=>`${l} : i32`).join(", ");if(s<1)return t?`
fn ${a}() -> vec4<f32> {
return vec4<f32>(${n}.numbers[0]);
}
`:`
fn ${a}() ->f32 {
return f32(${n}.numbers[0]);
}
`;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,c=`${s}D`;return s===0&&(c="1D"),t?`
fn ${a}(${o}) -> vec4<f32> {
return vec4<f32>(${n}.numbers[getIndexFromCoords${c}(${r}(${i.join(",")}),
${u}) / 4]);
}
`:`
fn ${a}(${o}) -> f32 {
return f32(${n}.numbers[getIndexFromCoords${c}(${r}(${i.join(",")}),
${u})]);
}
`}function Oee(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i="get"+a+"ByOutput",o=e.shape.length,u=t.length,c=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${r}.numbers[globalIndex]);
}
fn ${i}Coords(coords : ${c}) -> vec4<f32> {
return vec4<f32>(${r}.numbers[${u>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
}
`:`
fn ${i}Index(globalIndex : i32) -> f32 {
return f32(${r}.numbers[globalIndex]);
}
fn ${i}Coords(coords : ${c}) -> f32 {
return f32(${r}.numbers[${u>1?"getOutputIndexFromCoords(coords)":"coords"}]);
}
`;let l=N.getBroadcastDims(e.shape,t),d=u-o,p="";if(o===0)return n?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
return get${a}();
}
fn ${i}Coords(coords : ${c}) -> vec4<f32> {
return get${a}();
}
`:`
fn ${i}Index(globalIndex : i32) -> f32{
return get${a}();
}
fn ${i}Coords(coords : ${c}) -> f32{
return get${a}();
}
`;u<2&&l.length>=1?p="coords = 0;":p=l.map(g=>`coords[${g+d}] = 0;`).join(`
`);let h="";if(u<2&&o>0)h="coords";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords[${v+d}]`).join(", ");h=`${g}(${b})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
var coords = getCoordsFromIndex(globalIndex);
${p}
return ${r}.numbers[getIndexFromCoords${m}(${h}, ${f}) / 4];
}
fn ${i}Coords(coordsIn : ${c}) -> vec4<f32> {
var coords = coordsIn;
${p}
return ${r}.numbers[getIndexFromCoords${m}(${h}, ${f}) / 4];
}
`:`
fn ${i}Index(globalIndex : i32) -> f32 {
var coords = getCoordsFromIndex(globalIndex);
${p}
return f32(${r}.numbers[getIndexFromCoords${m}(${h}, ${f})]);
}
fn ${i}Coords(coordsIn : ${c}) -> f32 {
var coords = coordsIn;
${p}
return f32(${r}.numbers[getIndexFromCoords${m}(${h}, ${f})]);
}
`}function Pee(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{
let globalIndex = getGlobalIndex();
return getCoordsFromIndex(globalIndex);
}
`,a];let i="",o=[n,s,r],u=0;for(let p=0;p<o.length;p++){let h=o[p];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=Aee(h,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:i+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let c=[];for(let p=0;p<u;p++)c.push(`d${p}`);let l=Wt(u),d=`fn getOutputCoords() -> ${l} {
${i}
`;return c.length===0?d+=`return ${l}(0); }`:d+=`return ${l}(${c.join(",")}); }`,[d,u]}function rw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.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 a="var index2 = index;"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,c=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${c};`}).join("");return`
fn getCoordsFromIndex(index : i32) -> ${s} {
${a}
return ${s}(${r.join(",")});
}
`}var MC={};Ae(MC,{ArrayBufferToTypedArray:()=>BC,GPUBytesPerElement:()=>Dm,computeDispatch:()=>$e,computeWorkGroupSizeForConv2d:()=>lv,computeWorkGroupSizeForMatMul:()=>LC,computeWorkPerThreadForConv2d:()=>cv,flatDispatchLayout:()=>ze,isWebGPUSupported:()=>dv,tilesFitEvenlyIntoShape:()=>js});var Oi=65535,ta=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function js(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function $e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(ta(e.x.map(u=>t[u]))/(n[0]*s[0])),e.y?Math.ceil(ta(e.y.map(u=>t[u]))/(n[1]*s[1])):1,e.z?Math.ceil(ta(e.z.map(u=>t[u]))/(n[2]*s[2])):1];if(r<=Oi&&a<=Oi&&i<=Oi)return[r,a,i];w.assert(r>Oi&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let o=Math.ceil(Math.sqrt(r));return o>Oi?(o=Math.ceil(Math.cbrt(r)),w.assert(o<=Oi,()=>"Total dispatch size exceeds WebGPU maximum."),[o,o,o]):[o,o,1]}function lv(e,t){let n=ta(e.x.map(r=>t[r])),s=ta(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function LC(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function cv(e,t){let n=ta(e.x.map(r=>t[r])),s=ta(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function ze(e){return{x:e.map((t,n)=>n)}}function Dm(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function BC(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 dv(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var zee="return a + b;",Mee="return areal * breal - aimag * bimag;",Lee="return areal * bimag + aimag * breal;",Bee="return a / b;",Vee="return a * b;",Wee="return (a - b) * (a - b);",Uee="return a - b;",Gee="return f32(a == b);",Hee="return vec4<f32>(a == b);",qee="return f32(a > b);",jee="return vec4<f32>(a > b);",Kee="return f32(a >= b);",Xee="return vec4<f32>(a >= b);",Yee="return f32(a < b);",Qee="return vec4<f32>(a < b);",Zee="return f32(a <= b);",Jee="return vec4<f32>(a <= b);",ete="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",tte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,nte=`
if (isNanCustom(a)) { return a; }
if (isNanCustom(b)) { return b; }
`,VC=`
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;
}
`,ste=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,rte=`
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);
`,ate="return f32(a != b);",ite="return vec4<f32>(a != b);",ote=`
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);
`,ute=`
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;
${VC}
return resultTemp;
`,lte="if (a < 0.0) { return b * a; } return a;",cte=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function aw(e,t){let n=t?VC:nte;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = isNanCustomVec4(a) | isNanCustomVec4(b);
`+n+`
return resultTemp;
`:n+`
return ${e}(a, b);
`}function ec(e,t){switch(e){case 0:return Vee;case 1:return zee;case 2:return Uee;case 3:return Bee;case 4:return t?Hee:Gee;case 5:return t?jee:qee;case 6:return t?Xee:Kee;case 7:return t?Qee:Yee;case 8:return t?Jee:Zee;case 9:return t?tte:ete;case 10:return t?ite:ate;case 11:return Wee;case 12:return t?rte:ste;case 14:return t?cte:lte;case 15:return aw("max",t);case 16:return aw("min",t);case 13:return t?ute:ote;case 17:return Mee;case 18:return Lee;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var dte="return abs(a);",pte="return ceil(a);",hte="return cos(a);",fte=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,mte="return exp(a) - 1.0;",gte="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",bte=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,yte="return exp(a);",vte="return floor(a);",xte="return a;",wte=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,kte="return f32(!(a >= 1.0));",Ite="return -a;",Ste="return (a < 0.0) ? b * a : a;",Cte="if (a < 0.0) { return uniforms.alpha * a; } return a;",Nte="if(a < 0.0) { return 0.0; } return a;",Tte="return clamp(a, 0.0, 6.0);",$te="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Ate=`
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
let isNaN = isNanCustomVec4(a);
if (isNaN.r) {
resFloat.r = a.r;
}
if (isNaN.g) {
resFloat.g = a.g;
}
if (isNaN.b) {
resFloat.b = a.b;
}
if (isNaN.a) {
resFloat.a = a.a;
}
return resFloat;
`,Ete="return 1.0/sqrt(a);",_te="return 1.0 / (1.0 + exp(-1.0 * a));",Rte="return sin(a);",Dte=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,Fte="return sqrt(a);",Ote="return a * a;",Pte=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,zte="return f32(i32((a)));";function Vi(e,t){switch(e){case 0:return dte;case 2:return hte;case 3:return fte;case 1:return pte;case 4:return t?bte:gte;case 5:return yte;case 6:return mte;case 7:return vte;case 8:return xte;case 9:return wte;case 10:return kte;case 11:return Ite;case 12:return Ste;case 15:return Cte;case 13:return t?Ate:Nte;case 14:return t?$te:Tte;case 16:return Ete;case 19:return _te;case 17:return Rte;case 18:return Dte;case 20:return Fte;case 21:return Ote;case 22:return Pte;case 23:return zte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Qs(e,t=!1){if(e===null)return null;if(e==="linear")return Vi(8);if(e==="relu")return Vi(13,t);if(e==="elu")return Vi(4,t);if(e==="relu6")return Vi(14,t);if(e==="prelu")return ec(14,t);if(e==="sigmoid")return Vi(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function WC(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;
let RowPerThread = ${e[1]};
let ColPerThread = ${e[0]};
let TileInner = ${s};
${Or()}
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 Mte=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,8,1],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=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[js(s,this.aShape.slice(1)),js(r,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
}
return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0)`,n="",s="";if(this.activation){let i=Qs(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${i}
}`:n=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${i}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
let batch = i32(globalId.z);
${e};
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
let batch = i32(globalId.z);
${t};
}
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
{
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col * 4);
${r}
${s}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${WC(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
`}};function pv(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
${Or()}
let tileRow = i32(localId.y) * ${e[1]};
let tileCol = i32(localId.x) * ${e[0]};
let globalRow = i32(globalId.y) * ${e[1]};
let globalCol = i32(globalId.x) * ${e[0]};
let numTiles = (uniforms.dimInner - 1) / ${r} + 1;
var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
var ACached : f32;
var BCached : array<f32, ${e[0]}>;
// Without this initialization strange values show up in acc.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = 0.0;
}
}
let ColPerThreadA = ${r} / ${t[0]};
let tileColA = i32(localId.x) * ColPerThreadA;
let RowPerThreadB = ${r} / ${t[1]};
let tileRowB = i32(localId.y) * RowPerThreadB;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileColA + innerCol;
mm_Asub[inputRow][inputCol] = mm_readA(
globalRow + innerRow,
t * ${r} + inputCol, globalId);
}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol + innerCol;
mm_Bsub[inputRow][inputCol] = mm_readB(
t * ${r} + inputRow,
globalCol + innerCol, globalId);
}
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${r}; k = k + 1) {
for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
BCached[inner] = mm_Bsub[k][tileCol + inner];
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
ACached = mm_Asub[tileRow + innerRow][k];
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
if ((globalCol + innerCol) < uniforms.dimBOuter &&
(globalRow + innerRow) < uniforms.dimAOuter) {
mm_write(globalRow + innerRow,
globalCol + innerCol,
acc[innerRow][innerCol], globalId);
}
}
}
}
`}function Lte(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${Or()}
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
// Without this initialization strange values show up in acc.
var acc = 0.0;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * TileSize + tileCol * 4;
mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
mm_readA(globalRow, colA + 1, globalId),
mm_readA(globalRow, colA + 2, globalId),
mm_readA(globalRow, colA + 3, globalId));
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileSize / 4; k = k + 1) {
let rowB = t * TileSize + k * 4;
let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
mm_readB(rowB + 1, globalCol, globalId),
mm_readB(rowB + 2, globalCol, globalId),
mm_readB(rowB + 3, globalCol, globalId));
let ACached = mm_Asub[k];
acc = acc + dot(ACached, BCached);
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var UC=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=LC(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let c=a!=null,l=o!=null;c&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=c,this.activation=i,this.hasPreluActivationWeights=l;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,u]:[this.outputShape[0],u,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[js(r,this.aShape.slice(1)),js(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
}
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
}
return 0.0;`;let n="",s="";if(this.activation){let i=Qs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${i}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${i}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutputAtCoords(batch, row, col, value);
}
${this.outputShape[1]>1?pv([this.workPerThread,this.workPerThread,1],this.workGroupSize):Lte(this.workGroupSize)}
`}};function Bte(){return`
var<workgroup> sumValues : array<f32, workGroupSizeX>;
${Or()}
let coords = getOutputCoords();
let batch = coords[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
let dataA = mm_readA(batch, row, k);
let dataB = mm_readB(batch, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`}var Vte=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":e="return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let i=Qs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${i}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${i}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
${e}
}
fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
var value = valueIn;
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutputAtCoords(batch, row, col, value);
}
${Bte()}
`}};function Wte(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;
// If the output size is small for matrix multiplication, avoid to use vec4
// and handle some elements per thread to optimally utilize the ALU.
// Introduces two shared memory buffers, some logical threads could handle
// arithmetic operations and others handle IO operations between barrier api,
// makes ALUs and load/store units work simultaneously, could improves
// the performance.
${Or()}
let tileRow = i32(localId.y);
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y);
let globalCol = i32(globalId.x);
// uniforms.dimInner should be greater than 0.
let numTiles = (uniforms.dimInner - 1) / ${s} + 1;
var acc = 0.0;
var globalColA = tileCol;
var globalRowB = tileRow;
for (var t = 0; t < numTiles; t = t + 1) {
if (t == 0) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
}
} else {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
}
}
}
workgroupBarrier();
if (t != 0) {
t = t + 1;
}
if (t < numTiles) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub2[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
}
}
}
workgroupBarrier();
}
let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
if (tileRow >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var Ute=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push("bias");let o=a!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`,n="",s="";if(this.activation){let i=Qs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${i}
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${i}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
var value = valueIn;
${r}
${s}
setOutputAtCoords(batch, row, col, value);
}
}
${Wte(this.workGroupSize)}
`}};function Oe(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Gte={kernelName:$o,backendName:"webgpu",kernelFunc:Oe};function hv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let c=e.shape.length,l=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[l-1]:t.shape[l-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[l-2]:t.shape[l-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Go.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,d,h]:[b,h,d],T=s?[y,f,p]:[y,p,f],C=Oe({inputs:{x:e},backend:r,attrs:{shape:k}}),R=Oe({inputs:{x:t},backend:r,attrs:{shape:T}}),_=[C,R],P=Math.max(b,y),D=d%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new Vte([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?F=new Ute(k,T,[P,h,f],a,u,i):D?F=new Mte(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,i):F=new UC(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,u,i);let $=[C,R];a&&$.push(a),i&&$.push(i);let z=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],W=r.runWebGPUProgram(F,$,e.dtype,z),q=Oe({inputs:{x:W},backend:r,attrs:{shape:x}});_.push(W);for(let K of _)r.disposeData(K.dataId);return q}function Hte(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:c,activation:l,leakyreluAlpha:d}=s;return hv({a:r,b:a,transposeA:u,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:l})}var qte={kernelName:sa,backendName:"webgpu",kernelFunc:Hte},iw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${ec(this.op,!1)}
}
${Le()}
if(index < uniforms.size) {
let areal = getARealByOutputIndex(index);
let aimag = getAImagByOutputIndex(index);
let breal = getBRealByOutputIndex(index);
let bimag = getBImagByOutputIndex(index);
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},jte=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,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 {
${ec(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${Le()}
// Fill in the shared memory buffer. Here we need a loop to make sure
// that all data in A|B are uploaded when |sharedMemorySize| is larger
// than work group size.
for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
}
workgroupBarrier();
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
${t}
setOutputAtIndex(flatIndex, binaryOperation(a, b));
}
}
}
`}},Kte=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(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> {
${ec(this.op,this.isVec4)}
}
${Le()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}},GC=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${ec(this.op,!1)}
}
${Le()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}};function ow(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new Kte(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new jte(e,t,n,a):new GC(e,t,n)}function ns(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Xte={kernelName:La,backendName:"webgpu",kernelFunc:ns};function iu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.tensorMap.get(a.dataId),o=ns({inputs:{x:s},backend:n}),u=ns({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var Yte={kernelName:Md,backendName:"webgpu",kernelFunc:iu},tc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${Vi(this.op,!1)}
}
${Le()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
setOutputAtIndex(index, unaryOperation(a));
}
}
`}};function Xt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let c=i.tensorMap.get(a.dataId),l=t(c.values,o);return i.makeTensorInfo(a.shape,o,l)}let u=new tc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function cn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype==="complex64"){let d=u.tensorMap.get(i.dataId),p=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=ow(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],vn(b.dtype,y.dtype))});else{let g=new iw(17,i.shape,o.shape),b=new iw(18,i.shape,o.shape),y=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:i.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.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}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=iu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let c=s||vn(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let d=u.tensorMap.get(i.dataId).values,p=u.tensorMap.get(o.dataId).values,h=i.dtype==="string"?N.fromUint8ToStringArray(d):d,f=i.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(i.shape,o.shape,h,f,c);return u.makeTensorInfo(g,c,m)}let l=ow(e,i.shape,o.shape);return u.runWebGPUProgram(l,[i,o],c)}}var{addImpl:Qte,ceilImpl:Zte,concatImpl:Jte,equalImpl:ene,expImpl:tne,expm1Impl:nne,floorImpl:sne,gatherNdImpl:rne,gatherV2Impl:ane,greaterEqualImpl:ine,greaterImpl:one,lessEqualImpl:une,lessImpl:lne,logImpl:cne,maxImpl:dne,maximumImpl:pne,minimumImpl:hne,multiplyImpl:fne,negImpl:mne,notEqualImpl:gne,prodImpl:bne,rangeImpl:yne,rsqrtImpl:vne,simpleAbsImpl:xne,sliceImpl:wne,stridedSliceImpl:kne,stringNGramsImpl:Ine,subImpl:Sne,tileImpl:Cne,topKImpl:Nne,transposeImpl:Tne,uniqueImpl:npe}=zy,$ne=Xt({opType:0,cpuKernelImpl:xne}),Ane={kernelName:so,backendName:"webgpu",kernelFunc:$ne},Ene=cn({opSnippet:1,cpuKernelImpl:Qte,supportsComplex:!0}),_ne={kernelName:Cr,backendName:"webgpu",kernelFunc:Ene},Rne=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
${Le()}
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, ${t});
}
}
}
`}};function Dne(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return ns({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>vn(o,u)),a=s.map(o=>o.shape),i=new Rne(a);return n.runWebGPUProgram(i,s,r)}var Fne={kernelName:wa,backendName:"webgpu",kernelFunc:Dne},HC=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let s=[t];N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=N.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(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, ${this.workGroupSize[0]}>;
`,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${r}]`;return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${e}
// In order to get a flattened index into the input tensor, we need to
// add back the index along the reduced dimension to |outputCoords|.
// This function outputs the offset to the first value along
// |axis| and the stride to get the next value of the input along |axis|.
fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{
let outputCoords = getCoordsFromIndex(outputIndex);
var i = ${this.outputShape.length-1};
var stride = 1;
var inputStride = 1;
var offset = 0;
for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
let length = ${n(`${this.inputShape.length} - r`)};
if (${this.inputShape.length} - r == uniforms.axis) {
inputStride = stride;
} else {
offset = offset + ${t("outputCoords","i")} * stride;
i = i - 1;
}
stride = stride * length;
}
return vec2<i32>(offset, inputStride);
}
fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
return coordInfo[0] + coordInfo[1] * index;
}
${Le()}
let outputIndex = index / i32(workGroupSizeX);
let coordInfo = getInputCoordInfo(outputIndex);
let Length = ${n("uniforms.axis")};
var bestIndex = i32(localId.x);
var bestValue = uniforms.infinityValue;
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
k = k + i32(workGroupSizeX)) {
let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);
if (!isNanCustom(candidate) && candidate ${this.op} bestValue) {
bestValue = candidate;
bestIndex = k;
}
}
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = bestIndex;
workgroupBarrier();
var reduceSize = min(u32(Length), workGroupSizeX);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (localId.x < currentSize) {
let candidate = xBestValues[localId.x + interval];
if (candidate ${this.op} bestValue) {
bestValue = candidate;
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = xBestIndices[localId.x + interval];
}
}
reduceSize = interval;
workgroupBarrier();
}
if (localId.x == 0u && outputIndex < uniforms.size) {
setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
}
}
`}},One=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=$e(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]}>;
${uv()}
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
@builtin(workgroup_id) workgroupId : vec3<u32>) {
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] =
A.numbers[y * width + x];
}
workgroupBarrier();
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputAtIndex((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},Pne=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=zne(this.newDim);return`
${Le()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromIndex(flatIndex);
setOutputAtIndex(flatIndex, A.numbers[getIndexFromCoords${this.outputShape.length}D(
${e}(${t}), uniforms.aShape)]);
}
}
}
`}};function zne(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function xi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let l=0;l<u.length;l++)u[l]=r.shape[a[l]];if(n.shouldExecuteOnCPU([r])){let d=i.tensorMap.get(r.dataId).values,p=Tne(d,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,p)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let l=new One(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}let c=new Pne(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}var Mne={kernelName:di,backendName:"webgpu",kernelFunc:xi};function Lne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,c=[];o!=null&&(u=xi({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let l=new HC(u.shape,i[0],"max"),d=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=n.runWebGPUProgram(l,[u],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var Bne={kernelName:ka,backendName:"webgpu",kernelFunc:Lne};function Vne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,c=[];o!=null&&(u=xi({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let l=new HC(u.shape,i[0],"min"),d=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=n.runWebGPUProgram(l,[u],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var Wne={kernelName:il,backendName:"webgpu",kernelFunc:Vne},qC=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=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${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"),`
${Le()}
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, ${t});
}
}
`}},jC=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${Le()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d = coords[3];
let xRCCorner = coords.yz * uniforms.stride;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
let value = getX(batch, xRCorner, xCCorner, d);
setOutputAtIndex(index, value);
}
}
`}};function Une(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,c=1,l=N.computePool2DInfo(r.shape,a,i,c,o,u);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return ns({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[l.strideHeight,l.strideWidth]}];return l.filterHeight===1&&l.filterWidth===1?d=new jC(l):(d=new qC(l,"avg"),p.push({type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.dilationHeight,l.dilationWidth]},{type:"int32",data:[l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterHeight,l.effectiveFilterWidth]})),n.runWebGPUProgram(d,[r],r.dtype,p)}var Gne={kernelName:Ia,backendName:"webgpu",kernelFunc:Une};function Hne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return hv({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var qne={kernelName:Sa,backendName:"webgpu",kernelFunc:Hne},jne=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=Kne(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Fm[a]} = uniforms.start[${a}] + coords.${Fm[a]};`),`
${Le()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getCoordsFromIndex(index);
${n.join(`
`)}
setOutputAtIndex(index, getSource(${t}));
}
}
`}},Fm=["x","y","z","w","u","v"];function Kne(e){if(e===1)return"sourceLoc";if(e<=6)return Fm.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function ou(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=kt.parseSliceParams(r,a,i);if(kt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=wne(d.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,p)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let c=new jne(o,u),l=[{type:"int32",data:o}];return n.runWebGPUProgram(c,[r],r.dtype,l)}var Xne={kernelName:Do,backendName:"webgpu",kernelFunc:ou},Yne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),c=N.getPermuted(u.length,a.length),l=N.getReshapedPermuted(r.shape,a,o),d=N.getSliceBeginCoords(i,a.length),p=N.getSliceSize(l,i,a.length),h=[],f=Oe({inputs:{x:r},backend:n,attrs:{shape:u}}),m=xi({inputs:{x:f},backend:n,attrs:{perm:c}}),g=Oe({inputs:{x:m},backend:n,attrs:{shape:l}}),b=ou({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},Qne={kernelName:ro,backendName:"webgpu",kernelFunc:Yne},KC=cn({opSnippet:10,dtype:"bool",cpuKernelImpl:gne}),Zne={kernelName:wo,backendName:"webgpu",kernelFunc:KC};function nc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return ns({inputs:{x:r.complexTensorInfos.real},backend:n})}var Jne={kernelName:jd,backendName:"webgpu",kernelFunc:nc};function ese(e,t){let n=new tc(e.shape,23),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Om(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return ns({inputs:{x:r},backend:n});let i=Tt(r.shape),o=Om({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=iu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype==="complex64"){let i=nc({inputs:{input:r},backend:n}),o=Om({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=ns({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return ese(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=KC({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var tse={kernelName:Ca,backendName:"webgpu",kernelFunc:Om},nse=Xt({opType:1,cpuKernelImpl:Zte}),sse={kernelName:Na,backendName:"webgpu",kernelFunc:nse},rse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
${Le()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
var clampedValue : vec4<f32>;
for (var i = 0; i < 4; i = i + 1) {
if (isNanCustom(value[i])) {
clampedValue[i] = value[i];
} else {
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
}
}
setOutputAtIndex(index, clampedValue);
}
}
`}},ase=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
${Le()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
if (isNanCustom(value)) {
setOutputAtIndex(index, value);
return;
}
setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function ise(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:"float32",data:[a]},{type:"float32",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new rse(r.shape):o=new ase(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var ose={kernelName:Nr,backendName:"webgpu",kernelFunc:ise},use=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${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 r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
${Le()}
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 Up(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return ns({inputs:{x:r.complexTensorInfos.imag},backend:n})}var lse={kernelName:Ud,backendName:"webgpu",kernelFunc:Up};function Pm(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>nc({inputs:{input:y},backend:n})),f=e.map(y=>Up({inputs:{input:y},backend:n})),m=Pm(h,t,n),g=Pm(f,t,n),b=iu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Oe({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=N.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=Jte(f,m,s,g),y=N.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=cse(e,t,n),o=a.map(h=>h.shape),u=new use(o),c=[],l=new Array(o.length-1);if(l.length>0){l[0]=o[0][1],c.push({type:"int32",data:[l[0]]});for(let h=1;h<l.length;h++)l[h]=l[h-1]+o[h][1],c.push({type:"int32",data:[l[h]]})}let d=n.runWebGPUProgram(u,a,a[0].dtype,c);a.forEach(h=>n.disposeData(h.dataId));let p=Oe({inputs:{x:d},backend:n,attrs:{shape:i}});return n.disposeData(d.dataId),p}function cse(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Oe({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function XC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(c=>c.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>w.sizeFromShape(c.shape)>0);if(o.length===1)return ns({inputs:{x:o[0]},backend:n});let u=o.map(c=>c.shape);return N.assertParamsConsistent(u,a),Pm(o,a,n)}var dse={kernelName:ao,backendName:"webgpu",kernelFunc:XC},pse=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
${Le()}
for(var i = 0; i<${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
let rc = getCoordsFromIndex(flatIndex);
if(flatIndex < uniforms.size) {
let blockIndex = rc[0];
let pos = rc[1];
let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
var value = 0.0;
if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
uniforms.pad[0];
let d1 = offsetX + uniforms.dilation[0] * ((pos %
uniforms.itemsPerBlockRow) / uniforms.inChannels);
let ch = pos % uniforms.inChannels;
if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
value = getA(d0, d1, ch);
}
}
setOutputAtIndex(flatIndex, value);
}
}
}
`}},hse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[js(e,[n,r]),js(t,[r,s])]}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.numbers[divBy4Index${e}];
if (divBy4Remainder${e} == 0) {
temp = curData${e};
} else {
// TODO: This could end up being a redundant load with another one in
// the same shader invocation. Perhaps there's an opportunity for
// optimization
let nextData${e} = x.numbers[divBy4Index${e} + 1];
if (divBy4Remainder${e} == 1) {
temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
} 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=WC(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];
let outCol = r % uniforms.outShape[2];
let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
let inChCoord = c % uniforms.xShape[3];
var coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
inChCoord);
var resData = vec4<f32>(0.0);
${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
// the 'same' padding type.
if (coordsInBounds4D(coord, uniforms.xShape)) {
resData = x.numbers[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);
${this.getSampleAWithRemainder(2)}
if (inChCoord == 0) {
resData = vec4<f32>(resData.xyz, temp.x);
} else if (inChCoord == 1) {
resData = vec4<f32>(resData.xy, temp.xy);
} else {
resData = vec4<f32>(resData.x, temp.xyz);
}
}
`}
return resData;`,r=this.fitA?`${s}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
${s}
}
return vec4<f32>(0.0);
`,a=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0);
`,i="",o="";if(this.activation){let l=Qs(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${l}
}`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {
let b = getLeakyreluAlphaByOutputCoords(outCoord);
${l}
}`,new Error("Leakyrelu is not supported.");i=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${l}
}`}o="value = activation(value, outCoord);"}let u=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${i}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let r = row;
let c = col * 4;
var batch = i32(globalId.z);
${r}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${a}
}
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);
${u}
${o}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
value);
}
}
${e}
`}},fse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=lv(this.dispatchLayout,this.outputShape),this.elementsPerThread=cv(this.dispatchLayout,this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[js(s,[a,o]),js(r,[o,i])]}getUserCode(){let e=pv(this.elementsPerThread,this.workGroupSize),t=`
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
let coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
col % uniforms.xShape[3]);
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];
}
return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${t}
}
return 0.0;
`,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter + col];
}
return 0.0;
`,r="",a="";if(this.activation){let u=Qs(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${u}
}`:r=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${u}
}
`,a="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
${n}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${s}
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
${i}
${a}
result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${e}
`}},mse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Qs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${r}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
${r}
}
`,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${e}
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
let coord = vec4<i32>(batch, row, col, chan);
if(coordsInBounds4D(coord, uniforms.xShape)) {
return getX(batch, row, col, chan);
}
return 0.0;
}
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
let coord = vec4<i32>(row, col, xChannel, outChannel);
if(coordsInBounds4D(coord, uniforms.wShape)) {
return getW(row, col, xChannel, outChannel);
}
return 0.0;
}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
${n}
${t}
setOutputAtCoords(batch, row, col, chan, value);
}
}
${Or()}
let coords = getOutputCoords();
let batch = coords[0];
let outChannel = coords[3];
var acc = 0.0;
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
let v = readInp(batch, coordRow, coordCol, xChannel);
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, coords[1], coords[2], outChannel, acc);
}
`}};function gse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,c=n.dataFormat==="channelsLast",l=!1,d=!1,p=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",h,f;if(p){let b=n.inHeight*n.inWidth*n.inChannels;h=Oe({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Oe({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=c?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Oe({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Oe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=hv({a:h,b:f,transposeA:l,transposeB:d,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Oe({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function bse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:c,inChannels:l,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y==="channelsLast",x=u*c*l,k=m*f,T=[k,x],C=!1,R=!1,_=[],P=Oe({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),D=Oe({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});_.push(P),_.push(D);let F=new pse(T,v),$=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,b]},{type:"int32",data:[f]},{type:"int32",data:[l*u]},{type:"int32",data:[l]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Oe({inputs:{x:z},backend:s,attrs:{shape:[1,T[0],T[1]]}});_.push(z),_.push(W);let q=[1,T[0],T[1]],K=new UC(q,[1,k,n.outChannels],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),C,R,r,o,a),j=q[1],Q=q[2],J=n.outChannels,ne=[{type:"int32",data:[j]},{type:"int32",data:[J]},{type:"int32",data:[Q]}],se=[W,D];r&&se.push(r),a&&se.push(a);let te=s.runWebGPUProgram(K,se,W.dtype,ne),ie=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],ee=Oe({inputs:{x:te},backend:s,attrs:{shape:ie}});_.push(te);for(let ce of _)s.disposeData(ce.dataId);return ee}function YC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,c=a!=null,l;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return gse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return bse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let p=X().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type==="VALID")&&n.outChannels%4===0&&n.outChannels>=32,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];if(p)l=new mse(n,u,o,c);else{h?l=new hse(n,u,o,c):l=new fse(n,u,o,c);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}let g=[e,t];return u&&g.push(r),c&&g.push(a),s.runWebGPUProgram(l,g,e.dtype,m)}function yse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:c,dimRoundingMode:l}=n,d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!1,d);return YC({x:r,filter:a,convInfo:p,backend:s})}var vse={kernelName:Ta,backendName:"webgpu",kernelFunc:yse},xse=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=lv(this.dispatchLayout,this.outputShape),this.elementsPerThread=cv(this.dispatchLayout,this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
return 0.0;
}
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
return 0.0;
}
let coord = vec4<i32>(
batch,
i32(xR),
i32(xC),
col % uniforms.outBackprop[3]);
return x.numbers[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.numbers[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.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${pv(this.elementsPerThread,this.workGroupSize)}
`}},wse=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
${Le()} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[${n}];
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
let dyRCorner = dyCorner.x;
let dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR = dyR;
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC = dyC;
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
if (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`}};function kse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:c,dimRoundingMode:l}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(i,a.shape,o,1,u,l,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new wse(p);else{f=new xse(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],b=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Ise={kernelName:$a,backendName:"webgpu",kernelFunc:kse},Sse=Xt({opType:2}),Cse={kernelName:Aa,backendName:"webgpu",kernelFunc:Sse},Nse=Xt({opType:3}),Tse={kernelName:Ea,backendName:"webgpu",kernelFunc:Nse},$se=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${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`
${Le()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let height_ratio = f32(${n});
let width_ratio = f32(${a});
let b = coords[0];
let y = coords[1];
let x = coords[2];
let d = coords[3];
// get box vals
let y1 = getBoxes(b, 0);
let x1 = getBoxes(b, 1);
let y2 = getBoxes(b, 2);
let x2 = getBoxes(b, 3);
// get image in batch index
let bInd = i32(round(getBoxInd(b)));
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
return;
}
let height_scale = ${s};
let width_scale = ${i};
let in_y = ${r};
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);
}
}
}
`}},Ase=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:c}=s,l=new $se(r.shape[3],a.shape,o,u),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(l,[r,a,i],"float32",d)},Ese={kernelName:oo,backendName:"webgpu",kernelFunc:Ase},_se=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
${Le()}
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 = ${this.getInputSamplingString()};
setOutputAtIndex(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Rse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],l=i==="NHWC"?r.shape[3]:r.shape[1],d=u*a,p=c*a,h=l/(a*a),f=i==="NHWC"?[o,d,p,h]:[o,h,d,p],m=[{type:"int32",data:[a]}],g=new _se(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Dse={kernelName:uo,backendName:"webgpu",kernelFunc:Rse},QC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Qs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${r}
}`:e=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${r}
}
`,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
${e}
${uv()}
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)) {
${n}
${t}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},ZC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Qs(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${r}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${r}
}
`,t="dotProd = activation(dotProd, coords);"}let n=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);
}
}
${Or()}
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;
}
}
}
${n}
${t}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function Fse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:c}=s,l=u;l==null&&(l=[1,1]);let d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!0),p=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}],h;return d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4===0?h=new QC(d):(h=new ZC(d),p.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.outChannels/d.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,p)}var Ose={kernelName:_a,backendName:"webgpu",kernelFunc:Fse},JC=cn({opSnippet:0,cpuKernelImpl:fne,supportsComplex:!0}),Pse={kernelName:Xa,backendName:"webgpu",kernelFunc:JC},zse=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(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 (isNanCustom(candidate)) {
bestValue = uniforms.NAN;
} else if (!isNanCustom(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
{ bestValue = candidate; }`,t="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let n=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;
}
${Le()}
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.numbers[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) {
${n}
}
}
`}};function sc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,c=N.getAxesPermutation(u,a),l=e;c!=null&&(l=xi({inputs:{x:e},attrs:{perm:c},backend:r}),u=N.getInnerMostAxes(u.length,a),i.push(l)),N.assertAxesAreInnerMostDims(s,u,a);let[d,p]=N.computeOutAndReduceShapes(l.shape,u),h=d;n&&(h=N.expandShapeToKeepDim(d,o));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([l])){let m=r.tensorMap.get(l.dataId).values;switch(s){case"max":let g=dne(m,w.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=bne(l.shape,l.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(p),b=w.sizeFromShape(l.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":tp(e.dtype),x=[{type:"int32",data:[m]}],k=new zse(y,s),T=r.runWebGPUProgram(k,[l],v,x);i.push(T),f=Oe({inputs:{x:T},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function fv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,"sum",n)}var Mse={kernelName:ii,backendName:"webgpu",kernelFunc:fv};function Lse(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:c,steps:l}=N.getEinsumComputePath(o,u),d=l.length,p=null,h=i.length,f=[];for(let m=0;m<d;++m){for(let g of l[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=xi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Oe({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),p===null?p=v:(p=JC({inputs:{a:v,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=fv({inputs:{x:p},backend:n,attrs:{axis:c[m]-(i.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var Bse={kernelName:Wd,backendName:"webgpu",kernelFunc:Lse},Vse=Xt({opType:4}),Wse={kernelName:Da,backendName:"webgpu",kernelFunc:Vse},Use=cn({opSnippet:4,dtype:"bool",cpuKernelImpl:ene}),Gse={kernelName:lo,backendName:"webgpu",kernelFunc:Use},e2=Xt({opType:5,cpuKernelImpl:tne,dtype:"float32"}),Hse={kernelName:Fa,backendName:"webgpu",kernelFunc:e2};function zm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Oe({inputs:{x:a},backend:s,attrs:{shape:o}})}var qse={kernelName:co,backendName:"webgpu",kernelFunc:zm},jse=Xt({opType:6,cpuKernelImpl:nne}),Kse={kernelName:po,backendName:"webgpu",kernelFunc:jse},Xse=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
${Le()}
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.value);
}
}
`}};function uu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new Xse(s),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var Yse={kernelName:hl,backendName:"webgpu",kernelFunc:uu},Qse=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
${Le()}
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);
}
}
`}},Zse={kernelName:ho,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Qse(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Jse=Xt({opType:7,cpuKernelImpl:sne}),ere={kernelName:Oa,backendName:"webgpu",kernelFunc:Jse},tre=cn({opSnippet:12,dtype:"int32"}),nre={kernelName:Pa,backendName:"webgpu",kernelFunc:tre},sre=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},t2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=Eee(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function n2(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function uw(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),c=n.makeTensorInfo(r,"int32"),l=n.getFromPixelsProgram(a?"import":"copyExternal");l.updateOutputShape(r);let d=[c.shape],p=[c.dtype,a?"import":"copyExternal"],h=n2(l,d,p),f=l.getLayout(n.device),m=n.getAndSavePipeline(h,()=>t2(n.device,l,f.pipelineLayout,[],c,!0));l.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:l.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(c.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...l.dispatch];l.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=l.inputTexture.createView();return n.runFromPixelsProgram(l,g.bufferInfo.buffer,f,y,c.dataId),c}var rre={kernelName:ad,backendName:"webgpu",kernelFunc:are},Pi;function are(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,c=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[l,d]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[d,l,a];if(X().getBool("WEBGPU_USE_IMPORT")&&i)return uw({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((i||o)&&(Pi==null&&(Pi=document.createElement("canvas").getContext("2d")),Pi.canvas.width=l,Pi.canvas.height=d,Pi.drawImage(r,0,0,l,d),r=Pi.canvas),c||u||i||o)return uw({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(p,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var ire=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
${Le()}
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)));
}
}
`}},ore={kernelName:za,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,c=n,l=[s,i,o],d=null;a!=null&&(d=a.shape,l.push(a));let p=null;r!=null&&(p=r.shape,l.push(r));let h=new ire(s.shape,i.shape,o.shape,d,p),f=[{type:"float32",data:[u]}];return c.runWebGPUProgram(h,l,s.dtype,f)}};function ure(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:c,dataFormat:l,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(l),g=N.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!1,m);return YC({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var lre={kernelName:ra,backendName:"webgpu",kernelFunc:ure};function cre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:c,dilations:l,dimRoundingMode:d,activation:p}=s,h=l;h==null&&(h=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let f=N.computeConv2DInfo(r.shape,a.shape,u,h,c,d,!0),m=[r,a],g=i!=null,b=o!=null;g&&m.push(i),b&&m.push(o);let y=[{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]}],v;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.filterHeight===3&&f.inChannels%4===0?v=new QC(f,g,p,b):(v=new ZC(f,g,p,b),y.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),n.runWebGPUProgram(v,m,"float32",y)}var dre={kernelName:aa,backendName:"webgpu",kernelFunc:cre},pre=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${Wt(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${Le()}
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 = ${e};
flattenIndex = flattenIndex + indexTemp * strideNum;
}
setOutputAtIndex(index, getA(flattenIndex, coords[1]));
}
}
`}};function hre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,c,l,d]=N.prepareAndValidate(s,r),p=Oe({inputs:{x:r},backend:n,attrs:{shape:[c,i]}}),h=Oe({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/l,l]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=rne(y,v,s.dtype,c,i,l,d,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new pre(i,[c,l]),m=[{type:"int32",data:[i]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),b=Oe({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var fre={kernelName:mo,backendName:"webgpu",kernelFunc:hre},mre=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=gre(this.aShape,"i32");return`
${Le()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`}};function gre(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function s2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],c=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),l=w.sizeFromShape(a.shape),d=[],p=Oe({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=Oe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,l/c.batchSize]}});d.push(p),d.push(h);let f=[c.batchSize,c.outerSize,l/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=_e(h.shape,h.dtype,v),T=n.tensorMap.get(p.dataId).values,C=_e(p.shape,p.dtype,T),R=ane(C,x,f);return d.forEach(_=>n.disposeData(_.dataId)),n.makeTensorInfo(c.outputShape,R.dtype,R.values)}let m=new mre(p.shape,f),g=n.runWebGPUProgram(m,[p,h],p.dtype);d.push(g);let b=Oe({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(y=>n.disposeData(y.dataId)),b}var bre={kernelName:fo,backendName:"webgpu",kernelFunc:s2},yre=cn({opSnippet:5,cpuKernelImpl:one,dtype:"bool"}),vre={kernelName:go,backendName:"webgpu",kernelFunc:yre},xre=cn({opSnippet:6,dtype:"bool",cpuKernelImpl:ine}),wre={kernelName:Ma,backendName:"webgpu",kernelFunc:xre};function kre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:"float32",data:[a]}],o=new tc(r.shape,15);return o.uniforms="alpha : f32;",n.runWebGPUProgram(o,[r],"float32",i)}var Ire={kernelName:Ba,backendName:"webgpu",kernelFunc:kre},Sre=cn({opSnippet:7,dtype:"bool",cpuKernelImpl:lne}),Cre={kernelName:bo,backendName:"webgpu",kernelFunc:Sre},Nre=cn({opSnippet:8,dtype:"bool",cpuKernelImpl:une}),Tre={kernelName:yo,backendName:"webgpu",kernelFunc:Nre},$re=Xt({opType:9,cpuKernelImpl:cne}),Are={kernelName:Va,backendName:"webgpu",kernelFunc:$re},Ere=cn({opSnippet:9,dtype:"bool"}),_re={kernelName:vo,backendName:"webgpu",kernelFunc:Ere},Rre=Xt({opType:10}),Dre={kernelName:yl,backendName:"webgpu",kernelFunc:Rre};function r2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return sc(r,a,i,"max",n)}var Fre={kernelName:Wa,backendName:"webgpu",kernelFunc:r2},Ore=cn({opSnippet:15,cpuKernelImpl:pne}),Pre={kernelName:Ua,backendName:"webgpu",kernelFunc:Ore};function zre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,c=1,l=N.computePool2DInfo(r.shape,a,i,c,o,u),d,p=[];if(l.filterHeight===1&&l.filterWidth===1){if(w.arraysEqual(l.inShape,l.outShape))return ns({inputs:{x:r},backend:n});d=new jC(l),p.push({type:"int32",data:[l.strideHeight,l.strideWidth]})}else d=new qC(l,"max"),p.push({type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.dilationHeight,l.dilationWidth]},{type:"int32",data:[l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterHeight,l.effectiveFilterWidth]});return n.runWebGPUProgram(d,[r],r.dtype,p)}var Mre={kernelName:Ga,backendName:"webgpu",kernelFunc:zre};function Lre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return sc(r,i,a,"mean",n)}var Bre={kernelName:Ha,backendName:"webgpu",kernelFunc:Lre};function Vre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,"min",n)}var Wre={kernelName:qa,backendName:"webgpu",kernelFunc:Vre},Ure=cn({opSnippet:16,cpuKernelImpl:hne}),Gre={kernelName:ja,backendName:"webgpu",kernelFunc:Ure},Hre=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),n=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Wt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Le()}
if (index < uniforms.size) {
let start = ${i}(${t});
let end = ${i}(${n});
var outC = getCoordsFromIndex(index);
for (var i = 0; i < ${e}; i = i + 1) {
if (${a} < ${s}) {
${a} = ${s} * 2 - ${a} - ${this.offset};
} else if(${a} >= ${r}) {
${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
}
}
let coords = outC - start;
setOutputAtIndex(index, getX(${o}));
}
}
`}},qre={kernelName:Ka,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(l=>({type:"int32",data:[l[0],l[1]]})),u=new Hre(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function jre(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=mne(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new tc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var Kre={kernelName:xo,backendName:"webgpu",kernelFunc:jre};function Xre(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,c=n.readSync(r.dataId),l=n.readSync(a.dataId),{selectedIndices:d}=ys.nonMaxSuppressionV3Impl(c,l,i,o,u);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Yre={kernelName:ko,backendName:"webgpu",kernelFunc:Xre};function Qre(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:c}=s,l=n.readSync(r.dataId),d=n.readSync(a.dataId),p=i,h=o,f=u,m=c,{selectedIndices:g,selectedScores:b}=ys.nonMaxSuppressionV5Impl(l,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Zre={kernelName:Io,backendName:"webgpu",kernelFunc:Qre};function Ed(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=Ed({inputs:{x:r},backend:n}),i=Up({inputs:{input:s},backend:n}),o=Ed({inputs:{x:i},backend:n}),u=iu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return uu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Jre={kernelName:Wo,backendName:"webgpu",kernelFunc:Ed};function a2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=a2({inputs:{x:r},backend:n}),i=Up({inputs:{input:s},backend:n}),o=Ed({inputs:{x:i},backend:n}),u=iu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return uu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var eae={kernelName:So,backendName:"webgpu",kernelFunc:a2};function tae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return zm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(l=>{w.assertShapesMatch(a,l.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(l=>{let d=zm({inputs:{input:l},backend:n,attrs:{dim:r}});return o.push(d),d}),c=XC({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(l=>n.disposeData(l.dataId)),c}var nae={kernelName:No,backendName:"webgpu",kernelFunc:tae},sae=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((l,d)=>`uniforms.pad${d}[0]`).join(","),s=this.xShape.map((l,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Le()}
if (index < uniforms.size) {
let start = ${r};
let end = ${a};
let outC = getCoordsFromIndex(index);
if (${i} || ${o}) {
setOutputAtIndex(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputAtIndex(index, getX(${u}));
}
}
}
`}},i2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(c=>w.arraysEqual(c,[0,0])))return ns({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let c=a.map((l,d)=>l[0]+r.shape[d]+l[1]);return uu({backend:n,attrs:{shape:c,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];a.map(c=>o.push({type:"int32",data:[c[0],c[1]]}));let u=new sae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},rae={kernelName:Ya,backendName:"webgpu",kernelFunc:i2},aae=cn({opSnippet:13}),iae={kernelName:Qa,backendName:"webgpu",kernelFunc:aae};function oae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new GC(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var uae={kernelName:Za,backendName:"webgpu",kernelFunc:oae};function lae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,"prod",n)}var cae={kernelName:To,backendName:"webgpu",kernelFunc:lae},dae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=yne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},pae={kernelName:wl,backendName:"webgpu",kernelFunc:dae},o2=cn({opSnippet:3}),hae={kernelName:Ra,backendName:"webgpu",kernelFunc:o2},fae=Xt({opType:13}),mae={kernelName:Ja,backendName:"webgpu",kernelFunc:fae},gae=Xt({opType:14}),bae={kernelName:ti,backendName:"webgpu",kernelFunc:gae},yae=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
${Le()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC =
(vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutputAtIndex(index, newValue);
}
}
`}};function vae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,c]=i,l=a&&u>1?1:0,d=a&&c>1?1:0,h=[{type:"float32",data:[l,d]},{type:"float32",data:[o?.5:0]}],f=new yae(r.shape,u,c);return n.runWebGPUProgram(f,[r],"float32",h)}var xae={kernelName:ei,backendName:"webgpu",kernelFunc:vae},wae=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; roundBase : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}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",`
${Le()}
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 = ${e};
// Compute the coordinators of nearest neighbor point.
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
let sourceNearestRC = vec2<i32>(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutputAtIndex(index, newValue);
}
}
`}};function kae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,c]=o,l=a&&u>1?1:0,d=a&&c>1?1:0,h=[{type:"float32",data:[l,d]},{type:"float32",data:[a?.5:0]}],f=new wae(r.shape,u,c,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Iae={kernelName:Il,backendName:"webgpu",kernelFunc:kae},Sae=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(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`
${Le()}
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));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputAtIndex(index, outputValue);
}
}
`}},Cae={kernelName:Uo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new Sae(s.shape,a),[c,l]=N.getImageCenter(i,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[l]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),o.runWebGPUProgram(u,[s],s.dtype,d)}},Nae=Xt({opType:16,cpuKernelImpl:vne}),Tae={kernelName:ni,backendName:"webgpu",kernelFunc:Nae},$ae=class{constructor(e,t,n,s,r,a,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=ze(e),this.dispatch=$e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32; strides: ${o}; size: i32;`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
return index;
}
`):this.updatesRank===2&&(s="coords[0], coords[1]",r="vec2<i32>(flattenedIndex, coords[1])",a=`
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(${s})`,o=this.type==="int32"?"atomicAdd(&(result.numbers[flatIndex]), i32(updateValue));":`
var assumed = atomicLoad(&(result.numbers[flatIndex]));
var success = 0;
for (; success == 0;) {
let new = bitcast<f32>(assumed) + updateValue;
let newI32 = bitcast<i32>(new);
let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
assumed = resValue[0];
success = resValue[1];
}
`;return`
${a}
${Le()}
if (index < uniforms.size) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t}));
flattenedIndex = flattenedIndex + indexInside * ${n};
}
let updateValue = ${i};
let flatIndex = getOutputIndexFromCoords(${r});
${o}
}
}`}};function Aae(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:c,strides:l,outputSize:d}=N.calculateShapes(a,r,i),p=[d/c,c];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=Oe({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Oe({inputs:{x:a},backend:n,attrs:{shape:[u,c]}}),m=f.dtype,g=uu({backend:n,attrs:{shape:p,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:l},{type:"int32",data:[b]}],v=new $ae(f.shape,o,h.shape.length,f.shape.length,l,p,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Oe({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var Eae={kernelName:_o,backendName:"webgpu",kernelFunc:Aae},_ae=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`
${Le()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputAtIndex(index, getA(${t}));
} else {
setOutputAtIndex(index, getB(${t}));
}
}
}
`}};function Rae(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new _ae(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],vn(r.dtype,a.dtype))}var Dae={kernelName:Ro,backendName:"webgpu",kernelFunc:Rae},Fae=Xt({opType:19}),Oae={kernelName:ri,backendName:"webgpu",kernelFunc:Fae},Pae=Xt({opType:17}),zae={kernelName:si,backendName:"webgpu",kernelFunc:Pae},Mae=Xt({opType:18}),Lae={kernelName:Fo,backendName:"webgpu",kernelFunc:Mae},u2=cn({opSnippet:2,cpuKernelImpl:Sne,supportsComplex:!0}),Bae={kernelName:li,backendName:"webgpu",kernelFunc:u2};function Vae(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=r2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),c=Oe({inputs:{x:o},backend:n,attrs:{shape:u}}),l=u2({inputs:{a:r,b:c},backend:n}),d=e2({inputs:{x:l},backend:n}),p=fv({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=Oe({inputs:{x:p},backend:n,attrs:{shape:u}}),f=o2({inputs:{a:d,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(c.dataId),n.disposeData(l.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var Wae={kernelName:oi,backendName:"webgpu",kernelFunc:Vae},Uae=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let c=[],l=i2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),d=N.getReshaped(l.shape,a,o,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(l.shape,a,o,!1),f=Oe({inputs:{x:l},backend:n,attrs:{shape:d}}),m=xi({inputs:{x:f},backend:n,attrs:{perm:p}}),g=Oe({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(l),c.push(f),c.push(m),c.forEach(b=>n.disposeData(b.dataId)),g},Gae={kernelName:Oo,backendName:"webgpu",kernelFunc:Uae},Hae=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${u};`;let c="";n===1?c="i":n===2&&(c="i, j"),this.indicesSnippet=`getIndices(${c})`;let l="";s===1?l="i":s===2&&(l="i, coords[1]"),this.updatesSnippet=`getUpdates(${l})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
${Le()}
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 < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
if (curIndex < uniforms.size)
{
setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function qae(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:c,strides:l,outputSize:d}=N.calculateShapes(a,r,o),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[u]},{type:"int32",data:l}],f=new Hae(c,u,r.shape.length,a.shape.length,l,[d,1],p),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Oe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var jae={kernelName:Qd,backendName:"webgpu",kernelFunc:qae};function Kae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),c=r.shape.length,l=new Array(c).fill(0),d=r.shape.slice();return u.map(p=>{let h=[...d];h[o]=p;let f=ou({inputs:{x:r},backend:n,attrs:{begin:l,size:h}});return l[o]+=p,f})}var Xae={kernelName:Po,backendName:"webgpu",kernelFunc:Kae},Yae=Xt({opType:20}),Qae={kernelName:ai,backendName:"webgpu",kernelFunc:Yae},Zae={kernelName:$l,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new tc(n.shape,21);return s.runWebGPUProgram(r,[n],n.dtype)}},Jae=cn({opSnippet:11}),eie={kernelName:ui,backendName:"webgpu",kernelFunc:Jae},tie=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t}; strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
${Le()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, getX(${t}));
}
}
`}};function nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:c,ellipsisMask:l,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,i,o,u,c,l,d,p),k;if(m)k=Oe({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=kt.computeOutShape(y,v,x),C=ou({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=Oe({inputs:{x:C},backend:n,attrs:{shape:f}}),n.disposeData(C.dataId)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),R=_e(r.shape,r.dtype,C),_=kne(h,R,x,y);k=n.makeTensorInfo(f,r.dtype,_.values)}else{let C=new tie(h),R=[{type:"int32",data:y},{type:"int32",data:x}],_=n.runWebGPUProgram(C,[r],r.dtype,R);k=Oe({inputs:{x:_},backend:n,attrs:{shape:f}}),n.disposeData(_.dataId)}return k}var sie={kernelName:zo,backendName:"webgpu",kernelFunc:nie};function rie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:c}=s,{data:l,dataSplits:d}=t,p=n.readSync(l.dataId),h=n.readSync(d.dataId),[f,m]=Ine(p,h,r,a,i,o,u,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var aie={kernelName:Zd,backendName:"webgpu",kernelFunc:rie},iie=Xt({opType:22}),oie={kernelName:ci,backendName:"webgpu",kernelFunc:iie},uie=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=lie(this.rank,"uniforms.");return`
${Le()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`}};function lie(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function cie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let u=n.readSync(r.dataId),c=r.dtype==="string"?u.map(p=>w.decodeString(p)):u,l=_e(r.shape,r.dtype,c),d=Cne(l,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new uie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var die={kernelName:Tr,backendName:"webgpu",kernelFunc:cie},pie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
dir : i32; inc : i32;`,this.shaderKey="swap"}getUserCode(){return`
${Le()}
if (index < uniforms.size) {
let outC = getCoordsFromIndex(index);
let batch = outC[0];
let elemIdx = outC[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced
// above, Figure5(a) shows that element[1] is in the second half of
// the group when group size is 2, but it is in the first half of
// the group when group size is 4.
let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
var i = 0;
if (isFirstInPair) {
i = elemIdx;
} else {
i = elemIdx - uniforms.inc;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.inc;
} else {
i1 = i32(getIndices(batch, i + uniforms.inc));
}
var x0 = f32(0.0);
var x1 = f32(0.0);
if (i0 < uniforms.inputSize) {
x0 = getX(batch, i0);
} else {
x0 = uniforms.negativeInf;
}
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = uniforms.negativeInf;
}
let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) {
// Elements in opposite order of direction
let iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutputAtIndex(index, f32(i0));
} else {
setOutputAtIndex(index, f32(i1));
}
}
}
`}},hie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge"}getUserCode(){return`
${Le()}
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 zi(e,t){t!==null&&e.disposeData(t.dataId)}function lw(e){let t=1;for(;t<e;)t*=2;return t}function fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[T,C]=Nne(k,o,r.dtype,a,i);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(C.shape,C.dtype,C.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,"int32",[])];if(u===1)return[r,uu({attrs:{shape:o,dtype:"int32",value:0},backend:n})];let l=w.sizeFromShape(o)/u,d=Oe({inputs:{x:r},attrs:{shape:[l,u]},backend:n}),p=lw(a),h=lw(u),f=null,m=()=>f===null?[d,d]:[d,f],g=(k,T,C)=>{let R=m(),_=new pie(C),D=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[T]}],F=f;f=n.runWebGPUProgram(_,R,"int32",D),zi(n,F)};for(let k=1;k<p;k*=2){let T=k*2;for(let C=k;C>=1;C/=2)g(T,C,[l,h])}for(let k=h;k>p;k/=2){let T=m(),C=new hie([l,k/2]),_=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[p]}],P=f;f=n.runWebGPUProgram(C,T,"int32",_),zi(n,P);let D=p/2,F=D*2;for(let $=D;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=ou({inputs:{x:f},backend:n,attrs:{begin:0,size:[l,a]}}),zi(n,b);let y=s2({inputs:{x:d,indices:f},backend:n,attrs:{axis:1,batchDims:1}});zi(n,d);let v=o.slice(0,-1);v.push(a),b=f,f=Oe({inputs:{x:f},attrs:{shape:v},backend:n}),zi(n,b);let x=y;return y=Oe({inputs:{x:y},attrs:{shape:v},backend:n}),zi(n,x),[y,f]}var mie={kernelName:Lo,backendName:"webgpu",kernelFunc:fie},gie=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=ze(this.outputShape),this.dispatch=$e(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;
}
${Le()}
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 bie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:c}=s,[l,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[l,f,m,h],b=new gie(g),y=i==="nearest"?1:2,v;switch(o){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var yie={kernelName:Bo,backendName:"webgpu",kernelFunc:bie};function vie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],c=new Array(o-1),l=0;for(let m=0;m<o;m++)m!==a&&(c[l++]=i.shape[m]);let d=[],p=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){p[a]=m;let g=ou({inputs:{x:i},backend:n,attrs:{begin:p,size:h}}),b=Oe({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=b,d.push(g)}return d.forEach(m=>n.disposeData(m.dataId)),f}var xie={kernelName:Vo,backendName:"webgpu",kernelFunc:vie},wie=[qte,Ane,_ne,Fne,Bne,Wne,Gne,qne,Qne,tse,sse,ose,Yte,dse,vse,Ise,Cse,Tse,Ese,Dse,Ose,Bse,Wse,Gse,Hse,qse,Kse,Yse,Zse,rre,ere,nre,ore,lre,dre,fre,bre,vre,wre,Xte,lse,Ire,Cre,Tre,Are,_re,Dre,Fre,Pre,Mre,Bre,Wre,Gre,qre,Pse,Kre,Yre,Zre,Zne,eae,nae,rae,iae,uae,cae,pae,Jne,hae,mae,bae,Gte,xae,Iae,Cae,Tae,Eae,Dae,Oae,zae,Lae,Xne,sie,aie,Wae,Gae,jae,Xae,Qae,Zae,eie,Bae,Mse,oie,die,mie,yie,Mne,xie,Jre];for(let e of wie)Al(e);var kie=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,n=!1){let s=cw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=cw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function cw(e,t){return`${e}_${t}`}var l2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=ze(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
@binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
${Le()}
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.numbers[flatIndex] = i32(floor(255.0 * values[i]));
}
}
}
`}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Iie=class extends l2{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Sie=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),c2=class extends tl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!dv())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 kie(this.device),this.tensorMap=new Fd(this,ks()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),X().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 c2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*Dm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*Dm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new l2),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Iie),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=N.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=BC(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.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,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type==="int32"?new Int32Array(s,o,a.data.length).set(a.data):a.type==="uint32"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let C=this.tensorMap.get(r.dataId);return C.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}let a=[{type:"float32",data:[NaN]}],i=t.concat(r).map(C=>C.shape),o="int32";i.map(C=>{a.push({type:o,data:C})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let C=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?C/4:C]})}s&&(a=[...a,...s]);let c=this.makeUniforms(a),l=t.map((C,R)=>{if(C.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(C.dataId),{dtype:this.tensorMap.get(C.dataId).dtype,shape:C.shape,name:e.variableNames[R]}}),d=l.map(C=>C.dtype).concat(r.dtype),p=l.map(C=>N.getBroadcastDims(C.shape,r.shape)),h=l.map(C=>w.arraysEqual(C.shape,r.shape)).join("_"),f=p.map(C=>C.join("_")).join(";"),m=n2(e,i,d,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>t2(this.device,e,b,l,r)),v=this.activeTimers!=null,x=sre(this.device,g,t.map(C=>this.tensorToBinding(C)),this.tensorToBinding(r),c);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(C=>{this.commandQueueOwnedIds.add(C.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let T={byteSize:c.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:c.buffer};return this.uniformDisposalQueue.push(T),X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Sie){return X().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},mv=c2;mv.nextDataId=0;var Cie={};Ae(Cie,{WebGPUBackend:()=>mv,webgpu_util:()=>MC});dv()&&sp("webgpu",async()=>{X().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:X().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has("timestamp-query");s?n={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let r=await t.requestDevice(n);return new mv(r,s)},3);var Nt=(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))(Nt||{}),Gp=(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))(Gp||{}),d2;function Nie(e){d2=e.wasm.cwrap(sa,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Tie(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet 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Rie(e){p2=e.wasm.cwrap(wa,null,["array","number","number","number"])}function Die(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return p2(a,r.length,Nt[s.dtype],i),s}var Fie={kernelName:wa,backendName:"wasm",setupFunc:Rie,kernelFunc:Die};function Hp(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var Oie={kernelName:La,backendName:"wasm",kernelFunc:Hp},h2;function Pie(e){h2=e.wasm.cwrap(di,null,["number","array","number","number","number","array","number"])}function to(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Mie(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=zie(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=Hp({inputs:t,backend:n});return f.shape=o,f}let 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d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==d&&(c=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:c}}var f2;function Bie(e){f2=e.wasm.cwrap(rl,null,["number, number, number"])}function Vie(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,c=i,{transposed:l,axes:d,originalAxes:p,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(l.dataId).id;c=l,u=v}let f=c.shape.length;N.assertAxesAreInnerMostDims("all",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;f2(u,b,v)}if(h&&t.disposeData(l.dataId),a){let v=N.expandShapeToKeepDim(y.shape,p);y.shape=v}return y}var Wie={kernelName:rl,backendName:"wasm",setupFunc:Bie,kernelFunc:Vie},m2;function Uie(e){m2=e.wasm.cwrap(al,null,["number, number, number"])}function Gie(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,c=i,{transposed:l,axes:d,originalAxes:p,inputWasTransposed:h}=Pr(i,r,t);if(h){let v=t.dataIdMap.get(l.dataId).id;c=l,u=v}let f=c.shape.length;N.assertAxesAreInnerMostDims("any",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;m2(u,b,v)}if(h&&t.disposeData(l.dataId),a){let v=N.expandShapeToKeepDim(y.shape,p);y.shape=v}return y}var Hie={kernelName:al,backendName:"wasm",setupFunc:Uie,kernelFunc:Gie},g2;function qie(e){g2=e.wasm.cwrap(ka,null,["number","number","number","number","number"])}function jie(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:c,axes:l,inputWasTransposed:d}=Pr(a,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;b!==i&&(u=c,o=b)}let p=u.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[l[0]];return g2(o,Nt[u.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var Kie={kernelName:ka,backendName:"wasm",kernelFunc:jie,setupFunc:qie},b2;function Xie(e){b2=e.wasm.cwrap(Ia,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Yie(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:c}=n,l=N.computePool2DInfo(r.shape,i,o,1,u,c),d=l.filterHeight,p=l.filterWidth,h=l.padInfo.top,f=l.padInfo.right,m=l.padInfo.bottom,g=l.padInfo.left,b=l.strideHeight,y=l.strideWidth,v=l.inChannels;if(l.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${l.dataFormat}'. Please use 'channelsLast'.`);if(l.dilationWidth!==1||l.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${l.dilationHeight}, ${l.dilationWidth}].`);let x=s.makeOutput(l.outShape,"float32"),k=s.dataIdMap.get(x.dataId).id;return b2(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,b,y,v,k),x}var Qie={kernelName:Ia,backendName:"wasm",setupFunc:Xie,kernelFunc:Yie};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. 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transposeB=${o} must match.`);let x=i?[g,l,p]:[g,p,l],k=o?[b,h,d]:[b,d,h],T=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),C=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),R=n.dataIdMap.get(T.dataId).id,_=n.dataIdMap.get(C.dataId).id,P=i?T.shape[2]:T.shape[1],D=o?C.shape[1]:C.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,D],T.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(T.shape).buffer),q=new Uint8Array(new Int32Array(C.shape).buffer);return y2(R,W,T.shape.length,_,q,C.shape.length,i,o,z),n.disposeData(T.dataId),n.disposeData(C.dataId),$.shape=v,$}var toe={kernelName:Sa,backendName:"wasm",setupFunc:Jie,kernelFunc:eoe};function ya(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=kt.parseSliceParams(t,n,s),o=kt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),c=r.makeOutput(i,t.dtype),l=w.computeStrides(t.shape),d=r.dataIdMap.get(c.dataId);if(o){let f=kt.computeFlatOffset(a,l);return t.dtype==="string"?d.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(c).set(u.subarray(f,f+w.sizeFromShape(i))),c}if(t.dtype==="string"){let f=Cd(u,a,i,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)noe(u,l[0],p,a,i);else if(h===3)soe(u,l[0],l[1],p,a,i);else if(h===4)roe(u,l[0],l[1],l[2],p,a,i);else{let f=Cd(u,a,i,t.shape,t.dtype);p.set(f)}return c}function noe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let c=i;c<u;c++){let l=c*t+o;n.set(e.subarray(l,l+r[1]),a),a+=r[1]}}function soe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],c=r[2],l=o+a[0],d=u+a[1];for(let p=o;p<l;p++)for(let h=u;h<d;h++){let f=p*t+h*n+c;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function roe(e,t,n,s,r,a,i){let o=0,u=a[0],c=a[1],l=a[2],d=u+i[0],p=c+i[1],h=l+i[2],f=a[3];for(let m=u;m<d;m++)for(let g=c;g<p;g++)for(let b=l;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var aoe={kernelName:Do,backendName:"wasm",kernelFunc:ya};function ioe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),c=N.getPermuted(u.length,a.length),l=N.getReshapedPermuted(r.shape,a,o),d=N.getSliceBeginCoords(i,a.length),p=N.getSliceSize(l,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=to({inputs:{x:h},backend:n,attrs:{perm:c}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:l}}),g=ya({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var ooe={kernelName:ro,backendName:"wasm",kernelFunc:ioe};function rc(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var uoe={kernelName:Ca,backendName:"wasm",kernelFunc:rc},loe=Yt(Na),v2;function coe(e){v2=e.wasm.cwrap(Nr,null,["number","number","number","number"])}function 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y.stringBytes=N.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),c=0,l=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return c+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*c;for(let m=0;m<d.length;m++){let g=l[m],b=h*g,y=d[m].subarray(b,b+g);p.set(y,f),f+=g}}return i}var hoe={kernelName:ao,backendName:"wasm",kernelFunc:x2},w2;function foe(e){w2=e.wasm.cwrap(Ta,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function moe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:c,pad:l,dimRoundingMode:d,dataFormat:p}=n,h=N.convertConv2DDataFormat(p),f=N.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,T=f.dilationWidth,C=f.strideHeight,R=f.strideWidth,_=f.inChannels,P=f.outChannels,D=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),ne=s.dataIdMap.get(J.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return _2(b,K,j,Q,y,k,T,x,C,R,_,P,q,D,F,$,z,W,v,g,se,f||0,ne),J}var Qoe={kernelName:ra,backendName:"wasm",setupFunc:Xoe,kernelFunc:Yoe},R2;function Zoe(e){R2=e.wasm.cwrap(aa,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 Joe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:c,dilations:l,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!0),g=Gp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let te=s.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,T=m.filterWidth,C=m.padInfo.top,R=m.padInfo.right,_=m.padInfo.bottom,P=m.padInfo.left,D=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type==="SAME"?1:0,K=m.batchSize,j=m.inHeight,Q=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. 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Fue={kernelName:qa,backendName:"wasm",setupFunc:Rue,kernelFunc:Due},Oue=!1,Pue=dn(ja,Oue),B2=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(B2||{}),V2;function zue(e){V2=e.wasm.cwrap(Ka,null,["number","array","number","number","array","array","number","number"])}function Mue(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),l=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(l).buffer),h=new Uint8Array(new Int32Array(d).buffer);return V2(i,c,t.shape.length,Nt[t.dtype],p,h,B2[r],u),o}var Lue={kernelName:Ka,backendName:"wasm",kernelFunc:Mue,setupFunc:zue},Bue=!0,Vue=dn(Xa,Bue),Wue=Yt(xo);function gv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var W2;function Uue(e){W2=e.wasm.cwrap(ko,"number",["number","number","number","number","number"])}function Gue(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,c=t.dataIdMap.get(o.dataId).id,l=t.dataIdMap.get(u.dataId).id,d=W2(c,l,a,r,i),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=gv(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var Hue={kernelName:ko,backendName:"wasm",setupFunc:Uue,kernelFunc:Gue},U2;function que(e){U2=e.wasm.cwrap(xl,"number",["number","number","number","number","number","bool"])}function jue(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:c}=n,l=t.dataIdMap.get(u.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=U2(l,d,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=gv(t,p);t.wasm._free(m);let 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ile={kernelName:No,backendName:"wasm",kernelFunc:ale},q2;function ole(e){q2=e.wasm.cwrap(Ya,null,["number","array","number","number","array","array","number","number"])}function ule(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return $2({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),c=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),d=s.map(m=>m[0]),p=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(d).buffer),f=new Uint8Array(new Int32Array(p).buffer);return q2(i,l,t.shape.length,Nt[t.dtype],h,f,r,c),o}var j2={kernelName:Ya,backendName:"wasm",kernelFunc:ule,setupFunc:ole},lle=!1,cle=dn(Qa,lle),K2;function dle(e){K2=e.wasm.cwrap(Za,null,["number","number","number"])}function 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Kle={kernelName:Oo,backendName:"wasm",kernelFunc:jle},sN;function Xle(e){sN=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function Yle(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=n,o=s.shape[0],u=s.shape[1],c=t.readSync(a.dataId)[0],l=[o+c,u],d=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(l,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(l.slice(0,1),r.dtype),b=t.dataIdMap.get(g.dataId).id,y=t.makeOutput([c],"bool"),v=t.dataIdMap.get(y.dataId).id,x=t.makeOutput([o],s.dtype),k=t.dataIdMap.get(x.dataId).id,T=t.makeOutput([4],"int32"),C=t.dataIdMap.get(T.dataId).id,R=sN(d,p,Nt[r.dtype],o,c,u,h,m,b,v,k,C),_=t.readSync(T.dataId),P;switch(_[0]){case 1:{P=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(_[1]);break}case 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Identity,Ud as Imag,Rt as InputSpec,fl as IsFinite,ml as IsInf,gl as IsNan,tl as KernelBackend,Hd as LRN,ig as LRNGrad,eL as LayerVariable,mr as LayersModel,Ba as LeakyRelu,bo as Less,yo as LessEqual,ag as LinSpace,Va as Log,bl as Log1p,n$ as LogSoftmax,vo as LogicalAnd,yl as LogicalNot,Gd as LogicalOr,cS as MathBackendCPU,uC as MathBackendWebGL,Wa as Max,Ga as MaxPool,qd as MaxPool3D,ug as MaxPool3DGrad,og as MaxPoolGrad,lg as MaxPoolWithArgmax,Ua as Maximum,Ha as Mean,qa as Min,ja as Minimum,Ka as MirrorPad,vl as Mod,sb as MomentumOptimizer,cg as Multinomial,Xa as Multiply,xo as Neg,ko as NonMaxSuppressionV3,xl as NonMaxSuppressionV4,Io as NonMaxSuppressionV5,wo as NotEqual,F$ as OP_SCOPE_SUFFIX,Co as OneHot,So as OnesLike,_r as Optimizer,Wr as OptimizerConstructors,No as Pack,Ya as PadV2,qce as Pool,Qa as Pow,Za as Prelu,To as Prod,rb as RMSPropOptimizer,Rr as RNN,wl as Range,C$ as Rank,jd as Real,Ra as RealDiv,kl as Reciprocal,EF as Reduction,Ja as Relu,ti as Relu6,$o as Reshape,ei as ResizeBilinear,pg as ResizeBilinearGrad,Il as ResizeNearestNeighbor,dg as ResizeNearestNeighborGrad,Ao as Reverse,Uo as RotateWithOffset,Eo as Round,ni as Rsqrt,fp as SGDOptimizer,_o as ScatterNd,Ro as Select,Sl as Selu,$b as Sequential,ri as Sigmoid,Cl as Sign,si as Sin,Fo as Sinh,Do as Slice,oi as Softmax,Nl as Softplus,Oo as SpaceToBatchND,Kd as SparseFillEmptyRows,Tl as SparseReshape,Xd as SparseSegmentMean,Yd as SparseSegmentSum,Qd as SparseToDense,Po as SplitV,ai as Sqrt,$l as Square,ui as SquaredDifference,pi as Step,zo as StridedSlice,Zd as StringNGrams,hg as StringSplit,fg as StringToHashBucketFast,li as Sub,ii as Sum,Ns as SymbolicTensor,Mo as Tan,ci as Tanh,tt as Tensor,Vt as TensorBuffer,Tr as Tile,Lo as TopK,Bo as Transform,di as Transpose,mg as Unique,Vo as Unpack,Jd as UnsortedSegmentSum,od as Variable,Wo as ZerosLike,sa as _FusedMatMul,Mt as abs,mE as acos,bE as acosh,oe as add,vE as addN,yk as all,Zf as any,Gu as argMax,SE as argMin,NE as asin,$E as asinh,EE as atan,RE as atan2,FE as atanh,Ag as avgPool,kk as avgPool3d,uE as backend,N as backend_util,cde as basicLSTMCell,qu as batchNorm,JE as batchNorm2d,t_ as batchNorm3d,s_ as batchNorm4d,Eg as batchToSpaceND,Ik as bincount,Mde as booleanMaskAsync,i_ as broadcastArgs,jc as broadcastTo,Go as broadcast_util,Zw as browser,_e as buffer,Kde as callbacks,pe as cast,l_ as ceil,Vn as clipByValue,pr as clone,ia as complex,Dt as concat,p_ as concat1d,f_ as concat2d,g_ as concat3d,y_ as concat4d,uM as constraints,Sk as conv1d,la as conv2d,Ck as conv2dTranspose,Nk as conv3d,N_ as conv3dTranspose,Xce as copyRegisteredKernels,Rg as cos,$k as cosh,Jk as cosineWindow,Ak as cumsum,qs as customGrad,nW as data,__ as denseBincount,gk as deprecationWarn,D_ as depthToSpace,rp as depthwiseConv2d,Yde as deregisterOp,np as device_util,dde as diag,z_ as dilation2d,Jce as disableDeprecationWarnings,Ee as dispose,ede as disposeVariables,Se as div,W_ as divNoNan,pde as dot,C3 as dropout,H_ as einsum,ap as elu,Zce as enableDebugMode,Qce as enableProdMode,N3 as enclosingPowerOfTwo,ks as engine,X as env,Yn as equal,K_ as erf,Qn as exp,Pn as expandDims,Z_ as expm1,Ek as eye,Kg as fft,Fl as fill,ode as findBackend,ude as findBackendFactory,ip as floor,bk as floorDiv,hX as forceHalfFloat,pa as fused,ju as gather,k3 as gatherND,ek as gather_util,ade as getBackend,Rv as getGradient,Mf as getKernel,Lf as getKernelsForBackend,ipe as getThreadsCount,tK as gpgpu_util,mde as grad,gde as grads,Un as greater,Ho as greaterEqual,fd as ifft,Dg as imag,ls as image,Bde as inTopKAsync,gM as initializers,aB as input,An as io,qk as irfft,hde as isFinite,fde as isInf,lR as isNaN,jt as keep,ys as kernel_impls,ZM as layers,Fg as leakyRelu,_k as less,qo as lessEqual,cO as linalg,hR as linspace,Qde as loadGraphModel,qde as loadLayersModel,mR as localResponseNormalization,Zn as log,Og as log1p,vde as logSigmoid,Rk as logSoftmax,AR as logSumExp,_s as logicalAnd,Mg as logicalNot,Pk as logicalOr,xde as logicalXor,Ude as losses,Me as matMul,_A as math,$s as max,Lg as maxPool,zk as maxPool3d,zR as maxPoolWithArgmax,Er as maximum,St as mean,Qf as memory,wde as meshgrid,$4 as metrics,em as min,up as minimum,UR as mirrorPad,HR as mod,Gde as model,G4 as models,Bg as moments,Lde as movingAverage,V as mul,kde as multiRNNCell,YR as multinomial,It as neg,pO as nextFrame,Qk as norm,Ku as notEqual,cd as oneHot,Mn as ones,Jn as onesLike,B as op,Ide as outerProduct,hi as pad,Sde as pad1d,Cde as pad2d,Nde as pad3d,Tde as pad4d,$de as pool,da as pow,Wg as prelu,fA as print,Mk as prod,tde as profile,Ade as rand,Ede as randomGamma,bD as randomNormal,Pl as randomUniform,Xu as range,rde as ready,pd as real,wD as reciprocal,sp as registerBackend,jde as registerCallbackConstructor,r$ as registerGradient,Al as registerKernel,Xde as registerOp,H4 as regularizers,Ks as relu,Lk as relu6,ide as removeBackend,G as reshape,es as reverse,_de as reverse1d,Rde as reverse2d,Dde as reverse3d,Fde as reverse4d,Xg as rfft,Bk as round,Vk as rsqrt,Ce as scalar,y3 as scatterND,nk as scatter_util,Wk as selu,DD as separableConv2d,Hde as sequential,ae as serialization,sde as setBackend,lde as setPlatform,ape as setThreadsCount,spe as setWasmPath,rpe as setWasmPaths,qj as setWebGLContext,OD as setdiff1dAsync,zy as shared,Hs as sigmoid,zD as sign,Wde as signal,Uk as sin,Gk as sinh,He as slice,Hg as slice1d,Hk as slice2d,qg as slice3d,hd as slice4d,kt as slice_util,jg as softmax,Ol as softplus,Vg as spaceToBatchND,Dc as sparse,Zk as sparseToDense,Vde as spectral,Bn as split,rn as sqrt,ct as square,jk as squaredDifference,yr as squeeze,ts as stack,lp as step,n3 as stridedSlice,Sf as string,be as sub,ve as sum,tp as sumOutType,r3 as tan,Hu as tanh,ds as tensor,Jt as tensor1d,Hi as tensor2d,zA as tensor3d,Ode as tensor4d,Pde as tensor5d,zde as tensor6d,Ts as tensor_util,eE as test_util,H as tidy,us as tile,nde as time,i3 as topk,_i as train,qe as transpose,Yg as truncatedNormal,Uv as unique,Kce as unregisterGradient,jce as unregisterKernel,c3 as unsortedSegmentSum,Rs as unstack,vn as upcastType,w as util,bde as valueAndGrad,yde as valueAndGrads,p3 as variable,yR as variableGrads,upe as version,Zde as version_converter,Yce as version_core,Jde as version_cpu,DI as version_layers,ope as version_wasm,epe as version_webgl,tpe as webgl,Hj as webgl_util,Cie as webgpu,xn as where,Xk as whereAsync,Tt as zeros,je as zerosLike};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use backend file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
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