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face-api
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face-api/dist/face-api.js

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(c,l,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),r&&this.addTapeNode(c,l,t,d,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:c,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let r=Qb(e);if(r!=null){let s=r.inputsToSave||[],a=r.outputsToSave||[],o;r.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(c=>t[c])):o=s.map(c=>t[c]);let 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Wt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},qr=class{constructor(e,t,n,r,s,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=s,this.outputTensorIndex=o,this.id=sS(),a!=null&&(this.originalName=XI(a),this.name=YI(this.originalName)),this.rank=t.length}},VW=0,Uf=class{constructor(e,t){this.callArgs=t,this.id=VW++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},UW=0,Xe=class extends ie.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=UW++,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=Es(n)+"_"+zf(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 s=null;e.batchSize!=null&&(s=e.batchSize),n=[s].concat(e.inputShape)}this.batchInputShape=n;let r=e.dtype;r==null&&(r=e.inputDType),r==null&&(r="float32"),this.dtype=r}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Gr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new H(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Bn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Bn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new _s(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let r=e[n],s=t[n];if(s==null)continue;let a=r.rank;if(s.ndim!=null&&a!==s.ndim)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${a}`);if(s.maxNDim!=null&&a>s.maxNDim)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${a}`);if(s.minNDim!=null&&a<s.minNDim)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${a}.`);if(s.dtype!=null&&r.dtype!==s.dtype)throw new H(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${r.dtype}.`);if(s.axes){let o=r.shape;for(let i in s.axes){let c=Number(i),l=s.axes[i],u=c>=0?o[c]:o[o.length+c];if(l!=null&&[l,null].indexOf(u)===-1)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected axis ${c} of input shape to have value ${l} but got shape ${o}.`)}}if(s.shape!=null)for(let o=0;o<s.shape.length;++o){let i=s.shape[o],c=r.shape[o];if(i!=null&&c!=null&&i!==c)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${r.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=xt(e),r=!0;for(let a of n)if(!(a instanceof qr)){r=!1;break}let s=!0;for(let a of n)if(a instanceof qr){s=!1;break}if(r===s)throw new H("Arguments to apply() must be all SymbolicTensors or all Tensors");return ai(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of xt(e))a.push(o.shape);this.build(Bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(e),s){let a=this.call(e,t),o=xt(a),i=[];for(let c of o)n.indexOf(c)!==-1&&(c=c.clone()),i.push(c);if(a=Bn(i),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=GW(e),o=this.computeOutputShape(a),i,c=HW(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((l,u)=>new qr(c,l,this,xt(e),t,this.name,u)):i=new qr(c,o,this,xt(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 _s(`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 _s(`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 Gr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Vf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Gv(e?this.trainableWeights:this.weights)}setWeights(e){M(()=>{let t=this.weights;if(t.length!==e.length)throw new H(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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ci(t);for(let f=0;f<d.length;++f){if(r!=null){let D=Vh().numTensors;D>r.maxNumTensors&&(r.maxNumTensors=D),D<r.minNumTensors&&(r.minNumTensors=D)}let m=d[f],g=m.sourceLayer;if(g instanceof yu)continue;let b=[],y=[],v=[],x=!1;for(let D of m.inputs){let P=h.getValue(D),F=h.getMask(D);b.push(P),y.push(F),F!=null&&(x=!0),s||(p[D.name]--,p[D.name]===0&&!t.hasKey(D)&&i.indexOf(D.name)===-1&&!P.isDisposed&&D.sourceLayer.stateful!==!0&&v.push(P))}x&&(n=n||{},n.mask=y[0]);let w=xt(g.apply(b,n)),T=null;g.supportsMasking&&(T=g.computeMask(b,y));let N=kV(m),$=Array.isArray(N)?N:[N];for(let D=0;D<$.length;++D){h.hasKey($[D])||h.add($[D],w[D],Array.isArray(T)?T[0]:T);let P=i.indexOf($[D].name);P!==-1&&(c[P]=w[D])}s||$e(v)}return h.disposeMasks(),a?c:c[0]}function xV(e,t){k.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],r={};if(e.length===1){let s=TS(e[0],t);n=s.sorted,r=s.recipientMap}else{let s=new Set;for(let a of e){let{sorted:o,recipientMap:i}=TS(a,t);for(let c 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Xe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=zf(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],ma(this.inputs).length!==this.inputs.length)throw new H(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);ma(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;cs(v===0,"input layer has >1 nodes"),cs(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 yu))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={},r={},s={},a={},o=[],i=(b,y,v,x,w,T)=>{(x==null||w==null||T==null)&&(x=b.sourceLayer,w=b.nodeIndex,T=b.tensorIndex);let N=x.inboundNodes[w];if(v.indexOf(N)!==-1)throw new Gr(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(N)!==-1)return;this.containerNodes.add(ls.nodeKey(x,w)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(N)===-1&&v.push(N);let $=N.inboundLayers.length;for(let D=0;D<$;D++){let P=N.inputTensors[D],F=N.inboundLayers[D],R=N.nodeIndices[D],C=N.tensorIndices[D];i(P,y,v,F,R,C)}for(y.push(N);v.indexOf(N)>=0;)v.splice(v.indexOf(N),1);o.push(N)},c=[],l=[];for(let b of this.outputs)i(b,c,l);let u=o.slice().reverse();for(let b of u){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=r[b.outboundLayer.id]==null?0:r[b.outboundLayer.id];y=Math.max(y,v),r[b.outboundLayer.id]=y,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let w=b.inboundLayers[x],T=b.nodeIndices[x],N=w.inboundNodes[T],$=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(y+1,$),n[N.id]=N}}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 r){let y=r[b];y in p||(p[y]=[]),p[y].push(s[b])}let h=Object.keys(p).map(b=>parseInt(b,10)).sort(_f);this.layers=[];for(let b of h){let y=p[b];y.sort((v,x)=>{let w=a[v.id],T=a[x.id];return w<T?-1:w>T?1:0});for(let v of y)v instanceof ls&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=p,h=Object.keys(d).map(b=>parseInt(b,10)).sort(_f);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 Gr(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=d;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new Gr(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Uf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 H("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={},r=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new H(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,r++}let s=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)s.push([n[o],e[a]]);else if(t)throw new H(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new H(`${a.length} of ${r} weights are not set: ${a}`)}Hv(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Qv}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Jv(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return M(()=>{e=xt(e);let n=new ci;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Sd(this.outputs,n,t)})}computeMask(e,t){return M(()=>{e=xt(e);let n;return t==null?n=ni(null,e.length):n=xt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Wf(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],c=t[o],l=i.name+"_0_0";n[l]=c}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(_f);if(r.length>1)for(let o of r){let i=this.nodesByDepth[o];for(let c of i){let l=c.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let u=[];for(let f=0;f<c.inboundLayers.length;f++){let m=c.inboundLayers[f],g=c.nodeIndices[f],b=c.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];u.push(v)}let d=l.computeOutputShape(Bn(u)),p=Wf(d),h=l.inboundNodes.indexOf(c);for(let f=0;f<p.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=p[f]}}}let s=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],c=this.outputLayersNodeIndices[o],l=this.outputLayersTensorIndices[o],u=`${i.name}_${c}_${l}`;a.push(u)}for(let o=0;o<a.length;o++){let i=a[o];cs(i in n),s.push(n[i])}return Bn(s)}runInternalGraph(e,t){t==null&&(t=ni(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let c=this.inputs[i],l=e[i],u=t[i];n[c.id]=[l,u]}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(_f);for(let i of r){let c=this.nodesByDepth[i];for(let l of c){let u=l.outboundLayer,d=l.inputTensors,p=l.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(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=xt(u.call(v,f)),y=xt(u.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=xt(u.call(m,f)),y=xt(u.computeMask(m,g));if(u.activityRegularizer)throw new Fe("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],w=b[v],T=y[v];n[x.id]=[w,T]}}}}let s=[],a=[],o=[];for(let i of this.outputs){cs(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[c,l]=n[i.id];o.push(c.shape),s.push(c),a.push(l)}return[s,a,o]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof ls?1:0;for(let s=0;s<r.inboundNodes.length;s++){let a=ls.nodeKey(r,s);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new H(`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 H("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new H(`No such layer: ${e}`)}calculateLosses(){return M(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=ls.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),c=[];for(let u=0;u<a.inboundNodes.length;u++){let d=a.inboundNodes[u],p=ls.nodeKey(a,u),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],b=d.nodeIndices[m],y=d.tensorIndices[m],v=ls.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}c.push(f)}}}let l={};l.name=a.name,l.className=o,l.config=i,l.inboundNodes=c,n.push(l)}e.layers=n;let r=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],c=ls.nodeKey(o,i);if(!this.containerNodes.has(c))continue;let l=t[c];l==null&&(l=0);let u=this.inputLayersTensorIndices[a];r.push([o.name,l,u])}e.inputLayers=r;let s=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],c=ls.nodeKey(o,i);if(!this.containerNodes.has(c))continue;let l=t[c];l==null&&(l=0);let u=this.outputLayersTensorIndices[a];s.push([o.name,l,u])}return e.outputLayers=s,e}static fromConfig(e,t,n={},r=!1){let s={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let b=[],y;for(let v of g){let x=v[0],w=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in s)){o(m,g);return}let N=s[x];if(N.inboundNodes.length<=w){o(m,g);return}let $=N.inboundNodes[w];b.push($.outputTensors[T])}b.length>0&&m.apply(Bn(b),y)}function c(m){let g=m.name,b=Kr(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(r),s[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${v}`);o(b,v)})}let l=t.name,u=t.layers;for(let m of u)c(m);for(;!Q4(a);)for(let m of u){let g=s[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)i(g,y)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];cs(g in s);let x=s[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];cs(g in s);let x=s[g].inboundNodes[b].outputTensors;p.push(x[y])}return new e({inputs:d,outputs:p,name:l})}get stateful(){if(this._stateful)throw new H("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(){M(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function IV(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>null);if(r===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!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let s=[];return t.forEach(a=>{a in e?s.push(e[a]):s.push(null)}),s}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function CS(e,t){return IV(e,t,"classWeight")}async function NS(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let s=M(()=>{if(e.shape.length===1)return Is(e);if(e.shape.length===2){if(e.shape[1]>1)return qo(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await s.data());$e(s);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),je(o,"float32")}else return null}function SV(e,t){return V(e,t)}var TV=32;function _S(e,t){let n,r,s=t;n=s.xs,r=s.ys,k.assert(n!=null&&r!=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=ES("input",e.inputNames,n),o=ES("output",e.outputNames,r),i=a[0].shape[0];k.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)})`),k.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let c=0;c<a.length;c++)k.assert(a[c].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[c]} has ${a[c].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let c=0;c<o.length;c++)k.assert(o[c].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[c]} has ${o[c].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function ES(e,t,n){if(n instanceof Ee)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let s of t){if(n[s]==null)throw new H(`The feature data generated by the dataset lacks the required ${e} key '${s}'.`);r.push(n[s])}return r}}function CV(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function NV(e,t,n){let r=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let s=n.validationData!=null,a,o;if(s)if(AS(n.validationData))k.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=CV(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),c=e.getDedupedMetricsNames(),l;s?l=c.slice().concat(c.map(g=>"val_"+g)):l=c.slice();let u=fS(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=mS(u,d,n.epochs,null,null,_V(t,n),null,s,l);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(r||(m=await t.iterator());r?b<n.batchesPerEpoch:!0;){let v=await m.next();if(r&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],o=()=>{let u=[];for(let f=0;f<this.inputs.length;++f)u.push({key:this.inputs[f],value:n[f]});let d=new ci(u),p=Sd(this.outputs,d,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](r[f],p[f]);s[f]!=null&&(g=SV(g,s[f]));let b=At(g);t.push(b),f===0?h=g:h=Y(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=At(g(r[b],p[b]))}Jt(m),a.push(m)}return h=At(h),this.calculateLosses().forEach(f=>{h=Y(h,f)}),h},i=this.collectedTrainableWeights.map(u=>u.read()),c=!0;return[this.optimizer_.minimize(o,c,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>M(()=>{let 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};px.className="ThresholdedReLU";ie.registerClass(px);var hx=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ax().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Me(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}};hx.className="Softmax";ie.registerClass(hx);function ku(e,t,n){if(typeof e=="number")return ni(e,t);if(e.length!==t)throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let s=e[r];if(!hW(s))throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${s}`)}return e}function Yr(e,t,n,r,s=1){if(e==null)return e;let a=t+(t-1)*(s-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+r-1)/r)}function ds(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+ba([n-t,0]);else if(r==="same")e=e*t;else throw new H(`Unsupport padding mode: ${r}.`);return e}function fx(e,t){return M(()=>(Lt(t),t==="channelsFirst"?Re(e,[0,2,3,1]):e))}function QS(e,t){return M(()=>(Lt(t),t==="channelsFirst"?Re(e,[0,2,3,4,1]):e))}function KV(e,t,n,r=1,s="valid",a,o=1){return M(()=>{if(a==null&&(a=Ur()),Lt(a),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Re(e,[0,2,1])),s==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=qh(e,t,r,s==="same"?"same":"valid","NWC",o);return n!=null&&(i=jr(i,n)),i})}function eT(e,t,n,r=[1,1],s="valid",a,o,i=null){return M(()=>{if(a==null&&(a=Ur()),Lt(a),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let c=fx(e,a);if(s==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return c=ha.conv2d({x:c,filter:t,strides:r,pad:s==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(c=Re(c,[0,3,1,2])),c})}function XV(e,t,n,r=[1,1,1],s="valid",a,o){return M(()=>{if(a==null&&(a=Ur()),Lt(a),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=QS(e,a);if(s==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=jy(i,t,r,s==="same"?"same":"valid","NDHWC",o),n!=null&&(i=jr(i,n)),a==="channelsFirst"&&(i=Re(i,[0,4,1,2,3])),i})}var mx=class extends Xe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",mx.verifyArgs(t),this.rank=e,en(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=ku(t.kernelSize,e,"kernelSize"),this.strides=ku(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,mr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Lt(this.dataFormat),this.activation=xa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ct(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Kt(t.biasConstraint),this.biasRegularizer=Nt(t.biasRegularizer),this.activityRegularizer=Nt(t.activityRegularizer),this.dilationRate=ku(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new H(`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 H(`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 H(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Cv(e.kernelSize,"number",1,3))throw new H(`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:va(this.activation),useBias:this.useBias,biasInitializer:Dt(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Nd=class extends mx{constructor(e,t){super(e,t);this.kernel=null,Nd.verifyArgs(t),this.filters=t.filters,en(this.filters,"filters"),this.kernelInitializer=Ct(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Kt(t.kernelConstraint),this.kernelRegularizer=Nt(t.kernelRegularizer)}build(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,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 M(()=>{e=Me(e);let n,r=this.bias==null?null:this.bias.read(),s=UI(this.activation.getClassName());if(s!=null&&this.rank===2)n=eT(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=KV(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=eT(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=XV(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ot(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=Yr(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Dt(this.kernelInitializer),kernelRegularizer:mt(this.kernelRegularizer),kernelConstraint:qt(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 H(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},_d=class extends Nd{constructor(e){super(2,e);_d.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Cv(e.kernelSize,"number",1,2))throw new H(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};_d.className="Conv2D";ie.registerClass(_d);var Ed=class extends Nd{constructor(e){super(3,e);Ed.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 H(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Ed.className="Conv3D";ie.registerClass(Ed);var gx=class extends _d{constructor(e){super(e);if(this.inputSpec=[new Wt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ot(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Wt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{let n=Me(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=r[a],c=r[o],l=this.kernelSize[0],u=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=ds(i,d,l,this.padding),f=ds(c,p,u,this.padding),m=[s,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Re(n,[0,2,3,1]));let g=Kh(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Re(g,[0,3,1,2])),this.bias!=null&&(g=jr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ot(e);let t=e.slice(),n,r,s;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3):(n=3,r=1,s=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],c=this.strides[1];return t[n]=this.filters,t[r]=ds(t[r],i,a,this.padding),t[s]=ds(t[s],c,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};gx.className="Conv2DTranspose";ie.registerClass(gx);var bx=class extends Ed{constructor(e){super(e);if(this.inputSpec=[new Wt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ot(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Wt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{let n=Me(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let c=r[i],l=r[a],u=r[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=ds(c,f,d,this.padding),y=ds(l,m,p,this.padding),v=ds(u,g,h,this.padding),x=[s,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Re(n,[0,2,3,4,1]));let w=Vk(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=Re(w,[0,4,1,2,3])),this.bias!==null&&(w=jr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=ot(e);let t=e.slice(),n,r,s,a;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3,a=4):(n=4,r=1,s=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],c=this.kernelSize[2],l=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[r]=ds(t[r],l,o,this.padding),t[s]=ds(t[s],u,i,this.padding),t[a]=ds(t[a],d,c,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};bx.className="Conv3DTranspose";ie.registerClass(bx);var tT=class extends Nd{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 H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("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 H(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ct(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Kt(t.depthwiseConstraint),this.pointwiseInitializer=Ct(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Kt(t.pointwiseConstraint)}build(e){if(e=ot(e),e.length<this.rank+2)throw new H(`Inputs to SeparableConv${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 H(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let o=0;o<this.rank;++o)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"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 Wt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{e=Me(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Re(e,[0,2,3,1])),n=ei(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=jr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Re(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=Dt(this.depthwiseInitializer),e.pointwiseInitializer=Dt(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseConstraint),e.pointwiseConstraint=qt(this.pointwiseConstraint),e}};tT.className="SeparableConv";var yx=class extends tT{constructor(e){super(2,e)}};yx.className="SeparableConv2D";ie.registerClass(yx);var Jf=class extends Nd{constructor(e){super(1,e);Jf.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"&&!Cv(e.kernelSize,"number",1,1))throw new H(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Jf.className="Conv1D";ie.registerClass(Jf);var vx=class extends Xe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return M(()=>{if(e=Me(e),this.dataFormat==="channelsLast"){let n=Af(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Af(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Af(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Af(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}};vx.className="Cropping2D";ie.registerClass(vx);var xx=class extends Xe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Lt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,lW(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 M(()=>{let n=Me(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Re(n,[0,2,3,1]);let s=this.size[0]*r[2],a=this.size[1]*r[3],o=this.interpolation==="nearest"?tr.resizeNearestNeighbor(n,[s,a]):tr.resizeBilinear(n,[s,a]);return Re(o,[0,3,1,2])}else{let s=this.size[0]*r[1],a=this.size[1]*r[2];return this.interpolation==="nearest"?tr.resizeNearestNeighbor(n,[s,a]):tr.resizeBilinear(n,[s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};xx.className="UpSampling2D";ie.registerClass(xx);function YV(e,t,n=[1,1],r="valid",s,a){return M(()=>{s==null&&(s=Ur()),Lt(s);let o=fx(e,s);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=la(o,t,n,r==="same"?"same":"valid","NHWC",a),s==="channelsFirst"&&(o=Re(o,[0,3,1,2])),o})}var wx=class extends mx{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ct(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Kt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=ot(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,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 M(()=>{e=Me(e);let n=YV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=jr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=Yr(t,this.kernelSize[0],this.padding,this.strides[0]),a=Yr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,s,a]:[e[0],s,a,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Dt(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseRegularizer),e}};wx.className="DepthwiseConv2D";ie.registerClass(wx);function nT(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new H("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function s(a){return a==null||Array.isArray(a)?a:[a]}return t=s(t),n=s(n),{inputs:e,initialState:t,constants:n}}function rT(e,t,n,r=!1,s,a,o=!1,i=!1){return M(()=>{let c=t.shape.length;if(c<3)throw new H(`Input should be at least 3D, but is ${c}D.`);let l=[1,0].concat(Hr(2,c));if(t=Re(t,l),a!=null)throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=ce(ce(s,"bool"),"float32"),s.rank===c-1&&(s=gn(s,-1)),s=Re(s,l)),r&&(t=er(t,0),s!=null&&(s=er(s,0)));let u=[],d,p=n,h=t.shape[0],f=ft(t),m;s!=null&&(m=ft(s));for(let b=0;b<h;++b){let y=f[b],v=M(()=>e(y,p));if(s==null)d=v[0],p=v[1];else{let x=M(()=>{let w=m[b],T=fe(Qn(w),w),N=Y(V(v[0],w),V(p[0],T)),$=p.map((D,P)=>Y(V(v[1][P],w),V(D,T)));return{output:N,newStates:$}});d=x.output,p=x.newStates}i&&u.push(d)}let g;return i&&(g=Mt(u,1)),[d,g,p]})}var ps=class extends Xe{constructor(e){super(e);let t;if(e.cell==null)throw new H("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new tm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new H("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 Wt({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 Hr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Uv(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[r].concat(s)}else return r}computeMask(e,t){return M(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(s=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");Uv(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Wt({shape:[n,null,...r]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Fe("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new H(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Wt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){M(()=>{if(!this.stateful)throw new _s("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Tt([n,r])):this.states_=[Tt([n,this.cell.stateSize])];else if(e==null)$e(this.states_),this.keptStates!=null&&($e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Tt([n,r])):this.states_[0]=Tt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):$e(this.states_);for(let r=0;r<this.states_.length;++r){let s=e[r],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,o=[n,a];if(!k.arraysEqual(s.shape,o))throw new H(`State ${r} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${s.shape}`);this.states_[r]=s}}this.states_=this.states_.map(r=>Jt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=nT(e,n,r,this.numConstants);e=s.inputs,n=s.initialState,r=s.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let c of n)this.stateSpec.push(new Wt({shape:c.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(t.constants=r,a=a.concat(r),this.numConstants=r.length),a[0]instanceof qr){let c=[e].concat(a),l=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=l;let d=super.apply(c,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return M(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;e=Me(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new H(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},c=rT((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),l=c[0],u=c[1],d=c[2];this.stateful&&this.resetStates(d,r);let p=this.returnSequences?u:l;return this.returnState?[p].concat(d):p})}getInitialState(e){return M(()=>{let t=Tt(e.shape);return t=xe(t,[1,2]),t=vd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Rv(t,[1,n]):t):this.cell.stateSize>1?[Rv(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()===ps.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,s=Kr(r,n);return new e(Object.assign(t,{cell:s}))}};ps.className="RNN";ie.registerClass(ps);var Ad=class extends Xe{},Qf=class extends Ad{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,en(this.units,"units"),this.activation=xa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ct(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Kt(e.kernelConstraint),this.recurrentConstraint=Kt(e.recurrentConstraint),this.biasConstraint=Kt(e.biasConstraint),this.dropout=bu([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=bu([1,ba([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ot(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 M(()=>{if(e=e,e.length!==2)throw new H(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wa({ones:()=>Qn(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wa({ones:()=>Qn(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));let s,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?s=us(V(e,a),this.kernel.read()):s=us(e,this.kernel.read()),this.bias!=null&&(s=jr(s,this.bias.read())),o!=null&&(n=V(n,o));let i=Y(s,us(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:va(this.activation),useBias:this.useBias,kernelInitializer:Dt(this.kernelInitializer),recurrentInitializer:Dt(this.recurrentInitializer),biasInitializer:Dt(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Qf.className="SimpleRNNCell";ie.registerClass(Qf);var kx=class extends ps{constructor(e){e.cell=new Qf(e);super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&($e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&($e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return new e(t)}};kx.className="SimpleRNN";ie.registerClass(kx);var em=class extends Ad{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 H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,en(this.units,"units"),this.activation=xa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ct(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Kt(e.kernelConstraint),this.recurrentConstraint=Kt(e.recurrentConstraint),this.biasConstraint=Kt(e.biasConstraint),this.dropout=bu([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=bu([1,ba([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=ot(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 M(()=>{if(e=e,e.length!==2)throw new H(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wa({ones:()=>Qn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wa({ones:()=>Qn(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,a=this.recurrentDropoutMask,o,i,c;0<this.dropout&&this.dropout<1&&(e=V(e,s[0]));let l=us(e,this.kernel.read());this.useBias&&(l=jr(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=V(r,a[0]));let u=this.recurrentKernel.read(),[d,p]=Ln(u,[2*this.units,this.units],u.rank-1),h=us(r,d),[f,m,g]=Ln(l,3,l.rank-1),[b,y]=Ln(h,2,h.rank-1);o=this.recurrentActivation.apply(Y(f,b)),i=this.recurrentActivation.apply(Y(m,y));let v=us(V(i,r),p);c=this.activation.apply(Y(g,v));let x=Y(V(o,r),V(Y(1,St(o)),c));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:va(this.activation),recurrentActivation:va(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Dt(this.kernelInitializer),recurrentInitializer:Dt(this.recurrentInitializer),biasInitializer:Dt(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};em.className="GRUCell";ie.registerClass(em);var Ix=class extends ps{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 em(e);super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&($e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&($e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Ix.className="GRU";ie.registerClass(Ix);var Dd=class extends Ad{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,en(this.units,"units"),this.activation=xa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ct(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Kt(e.kernelConstraint),this.recurrentConstraint=Kt(e.recurrentConstraint),this.biasConstraint=Kt(e.biasConstraint),this.dropout=bu([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=bu([1,ba([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=ot(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 r;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;r=new(t=class extends Er{apply(i,c){let l=s.apply([a]),u=new $f().apply([a]),d=s.apply([a*2]);return JI(JI(l,u),d)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return M(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wa({ones:()=>Qn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wa({ones:()=>Qn(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,c,l,u;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let d=us(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=V(r,o[0])),d=Y(d,us(r,this.recurrentKernel.read())),this.useBias&&(d=jr(d,this.bias.read()));let[p,h,f,m]=Ln(d,4,d.rank-1);i=this.recurrentActivation.apply(p),c=this.recurrentActivation.apply(h),l=Y(V(c,s),V(i,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=V(u,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:va(this.activation),recurrentActivation:va(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Dt(this.kernelInitializer),recurrentInitializer:Dt(this.recurrentInitializer),biasInitializer:Dt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Dd.className="LSTMCell";ie.registerClass(Dd);var Sx=class extends ps{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 Dd(e);super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&($e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&($e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Sx.className="LSTM";ie.registerClass(Sx);var tm=class extends Ad{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return M(()=>{e=e;let n=e.slice(1),r=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?r.push(n.splice(0,o.stateSize.length)):r.push(n.splice(0,1));r.reverse();let s=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=r[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),s.push(a.slice(1))}n=[];for(let o of s.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){Uv(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{ai(`RNNCell_${r}`,()=>{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=s=>({className:s.getClassName(),config:s.getConfig()}),r={cells:this.cells.map(t)};return Object.assign({},e,r)}static fromConfig(e,t,n={}){let r=[];for(let s of t.cells)r.push(Kr(s,n));return new e({cells:r})}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 Gv(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,s=e.splice(r);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}Hv(t)}};tm.className="StackedRNNCells";ie.registerClass(tm);function wa(e){let{ones:t,rate:n,training:r=!1,count:s=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):eS(t(),n),i=()=>wd(o,t,r);return!s||s<=1?Jt(i().clone()):Array(s).fill(void 0).map(i).map(l=>Jt(l.clone()))}var ZV=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,r=Object.getOwnPropertySymbols(e);s<r.length;s++)t.indexOf(r[s])<0&&Object.prototype.propertyIsEnumerable.call(e,r[s])&&(n[r[s]]=e[r[s]]);return n},sT=class extends ps{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Wt({ndim:5})]}call(e,t){return M(()=>{if(this.cell.dropoutMask!=null&&($e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&($e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new H("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}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 M(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)],a=Tt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){M(()=>{if(!this.stateful)throw new _s("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)];if(n[0]==null)throw new H("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(s)):this.states_=[Tt(s)];else if(e==null)$e(this.states_),this.keptStates!=null&&($e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Tt(s)):this.states_[0]=Tt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):$e(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],c=s;if(!k.arraysEqual(i.shape,c))throw new H(`State ${o} is incompatible with layer ${this.name}: expected shape=${c}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>Jt(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",c=e[i?3:2],l=e[i?4:3],u=Yr(c,r[0],s,a[0],o[0]),d=Yr(l,r[1],s,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};sT.className="ConvRNN2D";var nm=class extends Dd{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,en(this.filters,"filters"),this.kernelSize=ku(n,2,"kernelSize"),this.kernelSize.forEach(i=>en(i,"kernelSize")),this.strides=ku(r||1,2,"strides"),this.strides.forEach(i=>en(i,"strides")),this.padding=s||"valid",mr(this.padding),this.dataFormat=a||"channelsLast",Lt(this.dataFormat),this.dilationRate=ku(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>en(i,"dilationRate"))}build(e){var t;e=ot(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],s=4,a=this.kernelSize.concat([r,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let c=this.biasInitializer,l=this.filters;i=new(t=class extends Er{apply(d,p){let h=c.apply([l]),f=Jn([l]),m=c.apply([l*2]);return Fv([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return M(()=>{if(e.length!==3)throw new H(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],s=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wa({ones:()=>Qn(r),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,c=(Z,te,se)=>!te||!te[se]?Z:V(te[se],Z),l=c(r,i,0),u=c(r,i,1),d=c(r,i,2),p=c(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wa({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=c(s,h,0),m=c(s,h,1),g=c(s,h,2),b=c(s,h,3),y=3,[v,x,w,T]=Ln(this.kernel.read(),o,y),[N,$,D,P]=this.useBias?Ln(this.bias.read(),o):[null,null,null,null];l=this.inputConv(l,v,N,this.padding),u=this.inputConv(u,x,$,this.padding),d=this.inputConv(d,w,D,this.padding),p=this.inputConv(p,T,P,this.padding);let[F,R,C,L]=Ln(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,F),m=this.recurrentConv(m,R),g=this.recurrentConv(g,C),b=this.recurrentConv(b,L);let G=this.recurrentActivation.apply(Y(l,f)),j=this.recurrentActivation.apply(Y(u,m)),K=Y(V(j,a),V(G,this.activation.apply(Y(d,g)))),q=V(this.recurrentActivation.apply(Y(p,b)),this.activation.apply(K));return[q,q,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ZV(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let s=Pt(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?jr(s,n,this.dataFormat):s}recurrentConv(e,t){return Pt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};nm.className="ConvLSTM2DCell";ie.registerClass(nm);var Tx=class extends sT{constructor(e){let t=new nm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Tx.className="ConvLSTM2D";ie.registerClass(Tx);var rm=class extends Xe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,s=this.getNoiseShape(n);return wd(()=>eS(n,this.rate,s,this.seed),()=>n,r)}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()}};rm.className="Dropout";ie.registerClass(rm);var Cx=class extends rm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Cx.className="SpatialDropout1D";ie.registerClass(Cx);var Nx=class extends Xe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,en(this.units,"units"),this.activation=xa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Kt(e.kernelConstraint),this.biasConstraint=Kt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ot(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=ot(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e),r=UI(this.activation.getClassName()),s;return r!=null?s=us(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=us(n,this.kernel.read()),this.bias!=null&&(s=jr(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:va(this.activation),useBias:this.useBias,kernelInitializer:Dt(this.kernelInitializer),biasInitializer:Dt(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Nx.className="Dense";ie.registerClass(Nx);var _x=class extends Xe{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ot(e);for(let t of e.slice(1))if(t==null)throw new H(`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],ga(e,1)]}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let s=2;s<n.rank;++s)r.push(s);r.push(1),n=Re(n,r)}return gW(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};_x.className="Flatten";ie.registerClass(_x);var Ex=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.activation=xa(e.activation)}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.activation.apply(n)})}getConfig(){let e={activation:va(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Ex.className="Activation";ie.registerClass(Ex);var Ax=class extends Xe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return M(()=>(e=Me(e),fW(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="RepeatVector";ie.registerClass(Ax);var Dx=class extends Xe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),s=1,a=null;for(let i=0;i<r.length;++i){let c=r[i];if(this.isUnknown(c))if(a===null)a=i;else throw new H("Can only specifiy one unknown dimension.");else s*=c}let o=ga(e);if(a!==null){if(s===0||o%s!==0)throw new H(n);r[a]=o/s}else if(o!==s)throw new H(n);return r}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 M(()=>{this.invokeCallHook(e,t);let n=Me(e),r=n.shape,s=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return U(n,s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="Reshape";ie.registerClass(Dx);var $x=class extends Xe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Hr(1,e.dims.length+1);if(!k.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Wt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ot(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return Re(Me(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};$x.className="Permute";ie.registerClass($x);var Fx=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Me(e),r=-1;return ed(Qo(n,this.maskValue),r)}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e),r=-1,s=!0,a=ed(Qo(n,this.maskValue),r,s);return V(n,ce(a,n.dtype))})}};Fx.className="Masking";ie.registerClass(Fx);var Rx=class extends Xe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(xt(e.inputLength))}this.inputDim=e.inputDim,en(this.inputDim,"inputDim"),this.outputDim=e.outputDim,en(this.outputDim,"outputDim"),this.embeddingsInitializer=Ct(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Nt(e.embeddingsRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.embeddingsConstraint=Kt(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 M(()=>this.maskZero?(e=Me(e),Qo(e,He(e))):null)}computeOutputShape(e){if(e=ot(e),this.inputLength==null)return[...e,this.outputDim];let t=xt(this.inputLength);if(t.length!==e.length-1)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let s=t[r],a=e[r+1];if(s!=null&&a!=null&&s!==a)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);n.dtype!=="int32"&&(n=Ef(n,"int32"));let r=QI(this.embeddings.read(),U(n,[n.size]));return U(r,ot(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Dt(this.embeddingsInitializer),embeddingsRegularizer:mt(this.embeddingsRegularizer),activityRegularizer:mt(this.activityRegularizer),embeddingsConstraint:qt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Rx.className="Embedding";ie.registerClass(Rx);var ui=class extends Xe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Fe}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 r=0;r<t.length;++r){let s=e[e.length-t.length+r],a=t[r];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new H("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ot(e)]),e=e,e.length<2)throw new H(`A merge layer should be called on an Array of at least 2 inputs. 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Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let r=e.map(s=>s.length);e.indexOf(null)===-1&&ma(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return M(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(s=>s.rank);if(r.indexOf(null)===-1){let s=ba(r);for(let a of e){let o=a.rank;for(let i=0;i<s-o;++i)a=vd(a,1);n.push(a)}return this.mergeFunction(n)}else{let s=!1;for(let i of e){let c=i.rank;if(c==null){let l=i.shape,u=l[0],d=l.slice(1).concat([u]),p=U(i,[u].concat(ga(l.slice(1))));p=Re(p,[1,0]),p=U(p,d),n.push(p),s=!0}else if(c>1){let l=Hr(1,c).concat([0]);n.push(Re(i,l)),s=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(s){if(o==null){let i=a.shape,c=i.length,l=i[c-1],u=[l].concat(i.slice(0,i.length-1));a=U(Re(U(a,[-1,l]),[1,0]),u)}else if(o>1){let i=[o-1].concat(Hr(0,o-1));a=Re(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=ma(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return M(()=>{if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an Array");if(!Array.isArray(e))throw new H("`inputs` should be an Array");if(t.length!==e.length)throw new H(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:gn(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=Nr(n,t[r]);return n})}},Px=class extends ui{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Y(t,e[n]);return t})}};Px.className="Add";ie.registerClass(Px);var Ox=class extends ui{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};Ox.className="Multiply";ie.registerClass(Ox);var Mx=class extends ui{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Y(t,e[n]);return V(1/e.length,t)})}};Mx.className="Average";ie.registerClass(Mx);var Lx=class extends ui{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=as(t,e[n]);return t})}};Lx.className="Maximum";ie.registerClass(Lx);var Bx=class extends ui{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=uu(t,e[n]);return t})}};Bx.className="Minimum";ie.registerClass(Bx);var zx=class extends ui{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 H("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let s=e[r].slice();s.splice(this.axis,1);let a=!1;for(let o of n)if(k.arraysEqual(o,s)){a=!0;break}a||n.push(s)}if(n.length>1)throw new H("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};zx.className="Concatenate";ie.registerClass(zx);function $d(e,t){for(;e<0;)e+=t;return e}function JV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,s=t.shape.length;n==null&&(n=[r-1,s-2]);let a=n;return M(()=>{let o;if(r>s){o=r-s;let c=[];for(let l=0;l<o;++l)c.push(1);t=U(t,t.shape.concat(c))}else if(s>r){o=s-r;let c=[];for(let l=0;l<o;++l)c.push(1);e=U(e,e.shape.concat(c))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=xe(V(e,t),a[0]):i=xe(V(Re(e,[1,0]),t),a[1]);else{let c=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;i=De(e,t,c,l)}if(o>0){let c;r>s?c=r+s-3:c=r-1;let l=[];for(let u=c;u<c+o;++u)l.push(u);i=os(i,l)}return i.shape.length===1&&(i=gn(i,1)),i})}var Wx=class extends ui{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new H(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new H(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((s,a)=>$d(s,e[a].shape.length)):r=[$d(this.axes,t.shape.length),$d(this.axes,n.shape.length)],this.normalize&&(t=Gf(t,r[0]),n=Gf(n,r[1])),JV(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[$d(this.axes,e.length),$d(this.axes,t.length)],n}computeOutputShape(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let s=t.concat(n);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Wx.className="Dot";ie.registerClass(Wx);var Vx=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);return wd(()=>Y(Df(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Vx.className="GaussianNoise";ie.registerClass(Vx);var Ux=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.rate>0&&this.rate<1?wd(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return V(n,Df(n.shape,1,s))},()=>n,t.training||!1):n})}};Ux.className="GaussianDropout";ie.registerClass(Ux);var Gx=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Me(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 M(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return wd(()=>{let s=Me(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,c=da(lu(n),this.rate);c=Ef(c,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=Y(V(s,c),V(Y(c,-1),i));return Y(V(d,l),u)},()=>Me(e),t.training||!1)}return e})}};Gx.className="AlphaDropout";ie.registerClass(Gx);function Fd(e,t,n,r,s,a=.001){let o;if(e.rank===2)o=Fk(e,t,n,r,s,a);else if(e.rank===3)o=Rk(e,t,n,r,s,a);else if(e.rank===4)o=Pk(e,t,n,r,s,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function QV(e,t,n,r,s=.001){return M(()=>{let a=nf(e,r),o=a.mean,i=a.variance;return[Fd(e,o,i,n,t,s),o,i]})}function eU(e,t,n,r,s=.001){return M(()=>{let a=nf(e,r),o=a.mean,i=a.variance,c=[];for(let f of Hr(0,e.rank))r.indexOf(f)!==-1?c.push(1):c.push(e.shape[f]);let l=U(o,c),u=U(i,c),d=t==null?null:U(t,c),p=n==null?null:U(n,c);return[Fd(e,l,u,p,d,s),o,i]})}function tU(e,t,n,r,s=.001){return k.arraysEqual(r.slice().sort(),Hr(0,e.rank-1))?QV(e,t,n,r,s):eU(e,t,n,r,s)}var Hx=class extends Xe{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ct(e.betaInitializer||"zeros"),this.gammaInitializer=Ct(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ct(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ct(e.movingVarianceInitializer||"ones"),this.betaConstraint=Kt(e.betaConstraint),this.gammaConstraint=Kt(e.gammaConstraint),this.betaRegularizer=Nt(e.betaRegularizer),this.gammaRegularizer=Nt(e.gammaRegularizer)}build(e){e=ot(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new H(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Wt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return M(()=>{let n=t.training==null?!1:t.training,r=Me(e),s=r.shape,a=s.length,o=Hr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let c=ni(1,a);c[i]=s[i];let l=o.slice();l.sort();let u=!k.arraysEqual(l,Hr(0,a).slice(0,a-1)),d=()=>{if(u){let b=U(this.movingMean.read(),c),y=U(this.movingVariance.read(),c),v=this.center?U(this.beta.read(),c):null,x=this.scale?U(this.gamma.read(),c):null;return Fd(r,b,y,v,x,this.epsilon)}else return Fd(r,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]=tU(r,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(b,y,v)=>{M(()=>{let x=1-v,w=b.read(),T=V(fe(w,y),x);b.write(fe(w,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:Dt(this.betaInitializer),gammaInitializer:Dt(this.gammaInitializer),movingMeanInitializer:Dt(this.movingMeanInitializer),movingVarianceInitializer:Dt(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:qt(this.betaConstraint),gammaConstraint:qt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Hx.className="BatchNormalization";ie.registerClass(Hx);var jx=class extends Xe{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=Ct(e.betaInitializer||"zeros"),this.gammaInitializer=Ct(e.gammaInitializer||"ones"),this.betaRegularizer=Nt(e.betaRegularizer),this.gammaRegularizer=Nt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ot(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==ma(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Me(e),r=n.shape,s=r.length;return M(()=>{let a=!0,{mean:o,variance:i}=nf(n,this.axis,a),c=ni(1,s);for(let f of this.axis)c[f]=r[f];let l=f=>f!=null&&f.shape.length!==s?U(f,c):f,u=l(this.gamma.read()),d=l(this.beta.read()),p=[],h=[];for(let f=0;f<s;++f)this.axis.indexOf(f)!==-1?(p.push(r[f]),h.push(1)):(p.push(1),h.push(r[f]));return o=On(o,p),i=On(i,p),u=On(u,h),d=On(d,h),Fd(n,o,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Dt(this.betaInitializer),gammaInitializer:Dt(this.gammaInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};jx.className="LayerNormalization";ie.registerClass(jx);function nU(e,t,n){return M(()=>{if(e.rank!==4)throw new H(`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 H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ur()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],fr(e,r)})}var qx=class extends Xe{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?Ur():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new H(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 H(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new H(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Wt({ndim:4})]}computeOutputShape(e){e=ot(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 M(()=>nU(Me(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};qx.className="ZeroPadding2D";ie.registerClass(qx);function sm(e,t,n,r,s,a){return M(()=>{Lt(s),qI(a),mr(r),n==null&&(n=[1,1]),r==null&&(r="valid"),s==null&&(s=Ur()),a==null&&(a="max"),e=fx(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=Ot(e,t,n,i):o=pr(e,t,n,i),s==="channelsFirst"&&(o=Re(o,[0,3,1,2])),o})}function aT(e,t,n,r,s,a){return M(()=>{Lt(s),qI(a),mr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),s==null&&(s=Ur()),a==null&&(a="max"),e=QS(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=sv(e,t,n,i):o=Vy(e,t,n,i),s==="channelsFirst"&&(o=Re(o,[0,4,1,2,3])),o})}var oT=class extends Xe{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 H(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(en(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 H(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,mr(this.padding),this.inputSpec=[new Wt({ndim:3})]}computeOutputShape(e){e=ot(e);let t=Yr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return M(()=>{this.invokeCallHook(e,t),e=vd(Me(e),2);let n=this.poolingFunction(Me(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return os(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Kx=class extends oT{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Lt(s),mr(r),sm(e,t,n,r,s,"max")}};Kx.className="MaxPooling1D";ie.registerClass(Kx);var Xx=class extends oT{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Lt(s),mr(r),sm(e,t,n,r,s,"avg")}};Xx.className="AveragePooling1D";ie.registerClass(Xx);var iT=class extends Xe{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 H(`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];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Lt(this.dataFormat),mr(this.padding),this.inputSpec=[new Wt({ndim:4})]}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Yr(t,this.poolSize[0],this.padding,this.strides[0]),n=Yr(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 M(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},Yx=class extends iT{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Lt(s),mr(r),sm(e,t,n,r,s,"max")}};Yx.className="MaxPooling2D";ie.registerClass(Yx);var Zx=class extends iT{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Lt(s),mr(r),sm(e,t,n,r,s,"avg")}};Zx.className="AveragePooling2D";ie.registerClass(Zx);var cT=class extends Xe{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 H(`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];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Lt(this.dataFormat),mr(this.padding),this.inputSpec=[new Wt({ndim:5})]}computeOutputShape(e){e=ot(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Yr(t,this.poolSize[0],this.padding,this.strides[0]),n=Yr(n,this.poolSize[1],this.padding,this.strides[1]),r=Yr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return M(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},Jx=class extends cT{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Lt(s),mr(r),aT(e,t,n,r,s,"max")}};Jx.className="MaxPooling3D";ie.registerClass(Jx);var Qx=class extends cT{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Lt(s),mr(r),aT(e,t,n,r,s,"avg")}};Qx.className="AveragePooling3D";ie.registerClass(Qx);var uT=class extends Xe{constructor(e){super(e);this.inputSpec=[new Wt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},ew=class extends uT{constructor(e){super(e||{})}call(e,t){return M(()=>{let n=Me(e);return At(n,1)})}};ew.className="GlobalAveragePooling1D";ie.registerClass(ew);var tw=class extends uT{constructor(e){super(e||{})}call(e,t){return M(()=>{let n=Me(e);return Cr(n,1)})}};tw.className="GlobalMaxPooling1D";ie.registerClass(tw);var lT=class extends Xe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Lt(this.dataFormat),this.inputSpec=[new Wt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},nw=class extends lT{call(e,t){return M(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?At(n,[1,2]):At(n,[2,3])})}};nw.className="GlobalAveragePooling2D";ie.registerClass(nw);var rw=class extends lT{call(e,t){return M(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?Cr(n,[1,2]):Cr(n,[2,3])})}};rw.className="GlobalMaxPooling2D";ie.registerClass(rw);var dT=class extends Xe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,s=Kr(r,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},sw=class extends dT{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ot(e),e.length<3)throw new H(`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=ot(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return M(()=>(e=Me(e),rT((a,o)=>[Me(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};sw.className="TimeDistributed";ie.registerClass(sw);function rU(e){si(uW,"BidirectionalMergeMode",e)}var sU="concat",aw=class extends dT{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Kr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Kr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?sU:e.mergeMode,rU(this.mergeMode),e.weights)throw new Fe("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,r,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Bn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=nT(e,n,r,this.numConstants);if(e=s.inputs,n=s.initialState,r=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let c=n.length;if(c%2>0)throw new H("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 l=n.map(u=>new Wt({shape:u.shape}));this.forwardLayer.stateSpec=l.slice(0,c/2),this.backwardLayer.stateSpec=l.slice(c/2),o.push(...l)}if(r!=null)throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof qr;for(let c of a)if(c instanceof qr!==i)throw new H("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let c=[e].concat(a),l=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=l;let d=super.apply(c,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return M(()=>{let n=t.initialState,r,s;if(n==null)r=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),c=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:c}))}let a;this.returnState&&(Array.isArray(r)&&(a=r.slice(1).concat(s.slice(1))),r=r[0],s=s[0]),this.returnSequences&&(s=er(s,1));let 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r=I("size",e,t,n),s=I("dtype",e,t,n),a=I("elementShape",e,t,n),o=I("dynamicSize",e,t,n),i=I("clearAfterRead",e,t,n),c=I("identicalElementShapes",e,t,n),l=I("name",e,t,n),u=new cH(l,s,r,a,c,o,i);return n.addTensorArray(u),[u.idTensor,Ie(1)]}case"TensorArrayWriteV3":{let r=I("tensorArrayId",e,t,n),s=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(r.id);return o.write(s,a),[o.idTensor]}case"TensorArrayReadV3":{let r=I("tensorArrayId",e,t,n),s=I("index",e,t,n);return[n.getTensorArray(r.id).read(s)]}case"TensorArrayGatherV3":{let r=I("tensorArrayId",e,t,n),s=I("indices",e,t,n),a=I("dtype",e,t,n);return[n.getTensorArray(r.id).gather(s,a)]}case"TensorArrayScatterV3":{let r=I("tensorArrayId",e,t,n),s=I("indices",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(r.id);return o.scatter(s,a),[o.idTensor]}case"TensorArrayConcatV3":{let r=I("tensorArrayId",e,t,n),s=n.getTensorArray(r.id),a=I("dtype",e,t,n);return[s.concat(a)]}case"TensorArraySplitV3":{let r=I("tensorArrayId",e,t,n),s=I("tensor",e,t,n),a=I("lengths",e,t,n),o=n.getTensorArray(r.id);return o.split(a,s),[o.idTensor]}case"TensorArraySizeV3":{let r=I("tensorArrayId",e,t,n),s=n.getTensorArray(r.id);return[Ie(s.size(),"int32")]}case"TensorArrayCloseV3":{let r=I("tensorArrayId",e,t,n),s=n.getTensorArray(r.id);return s.clearAndClose(),[s.idTensor]}case"TensorListSetItem":{let r=I("tensorListId",e,t,n),s=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorList(r.id);return o.setItem(s,a),[o.idTensor]}case"TensorListGetItem":{let r=I("tensorListId",e,t,n),s=I("index",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(s,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{let r=I("indices",e,t,n),s=I("tensor",e,t,n),a=I("elementShape",e,t,n),o=I("numElements",e,t,n),i=dH(s,r,a,o);return n.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=I(a,e,t,n),i=lH(r,s,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),s=I("indices",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(s,o,a)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),s=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(s,a,o)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),s=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=uH(r,s,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),s=n.getTensorList(r.id),a=I("dtype",e,t,n),o=I("elementShape",e,t,n);return[s.concat(a,o)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),s=I("tensor",e,t,n),a=n.getTensorList(r.id);return a.pushBack(s),[a.idTensor]}case"TensorListPopBack":{let 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g=I("leakyreluAlpha",e,t,n);return{stride:u,pad:d,dataFormat:p,dilations:h,biasArg:f,preluArg:m,activationFunc:s,leakyreluAlpha:g}}var fH=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=I("stride",e,t,n),s=I("pad",e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),o=I("dilation",e,t,n);return[qh(I("x",e,t,n),I("filter",e,t,n),r,s,a,o)]}case"Conv2D":{let r=I("strides",e,t,n),s=im(e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),o=I("dilations",e,t,n);return[Pt(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2]],s,a,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:r,pad:s,dataFormat:a,dilations:o,biasArg:i,preluArg:c,activationFunc:l,leakyreluAlpha:u}=KT(e,t,n);return[ha.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:s,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:l,preluActivationWeights:c,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:s,dataFormat:a,dilations:o,biasArg:i,preluArg:c,activationFunc:l,leakyreluAlpha:u}=KT(e,t,n);return[ha.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:s,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:l,preluActivationWeights:c,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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implemented`)}},NH=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:a,reverseIndexMap:o}=fd.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[r,s,a,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=fd.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[fd.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[fd.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`)}},_H=(e,t,n)=>{switch(e.op){case"FFT":return[pd(I("x",e,t,n))];case"IFFT":return[hu(I("x",e,t,n))];case"RFFT":return[hd(I("x",e,t,n))];case"IRFFT":return[pf(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},EH=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=wf.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[r,s]}case"StringSplit":{let{indices:r,values:s,shape:a}=wf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[r,s,a]}case"StringToHashBucketFast":return[wf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},AH=(e,t,n)=>{switch(e.op){case"Cast":return[ce(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let r=I("axis",e,t,n);return[gn(I("x",e,t,n),r)]}case"Squeeze":{let r=I("axis",e,t,n);return[os(I("x",e,t,n),r)]}case"Reshape":return[U(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[av(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[fr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let r=I("blockShape",e,t,n),s=I("paddings",e,t,n);return[cd(I("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=I("blockShape",e,t,n),s=I("crops",e,t,n);return[nd(I("x",e,t,n),r,s)]}case"DepthToSpace":{let r=I("blockSize",e,t,n),s=I("dataFormat",e,t,n).toUpperCase();return[qy(I("x",e,t,n),r,s)]}case"BroadcastTo":return[ou(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[Ok(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function XT(e,t,n,r){let s=((a,o,i)=>{switch(a.category){case"arithmetic":return M(()=>oH(a,o,i));case"basic_math":return M(()=>iH(a,o,i));case"control":return hH(a,o,i);case"convolution":return M(()=>fH(a,o,i));case"creation":return M(()=>mH(a,o,i));case"dynamic":return gH(a,o,i);case"evaluation":return M(()=>bH(a,o,i));case"image":return M(()=>wH(a,o,i));case"graph":return M(()=>yH(a,o,i));case"logical":return M(()=>kH(a,o,i));case"matrices":return M(()=>IH(a,o,i));case"normalization":return M(()=>SH(a,o,i));case"reduction":return M(()=>TH(a,o,i));case"slice_join":return M(()=>CH(a,o,i));case"sparse":return M(()=>NH(a,o,i));case"spectral":return M(()=>_H(a,o,i));case"string":return M(()=>EH(a,o,i));case"transformation":return M(()=>AH(a,o,i));case"hash_table":return xH(a,o,i,r);case"custom":let c=IT(a.op);if(c&&c.customExecutor)return c.customExecutor(new aH(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var YT=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,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 ZT(e,t,n,r){let s=new Set,a=[],o=null,i=null,c=new Set,l=Object.keys(e).map(p=>nr(p)[0]),u=[];r!=null&&(u=r.map(p=>nr(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((JT(p)||PH(p)||OH(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>s.has(h))),s.add(p.name),n[p.name]==null&&l.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{c.has(h.name)||(c.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}function DH(e,t,n){let{usedNodes:r,inputs:s}=n,a=[],o=Object.keys(s).map(u=>nr(u)[0]).map(u=>e.nodes[u]),i=e.initNodes;o.forEach(u=>{r.has(u.name)&&a.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&a.push(u)}),i!=null&&i.forEach(u=>{r.has(u.name)&&a.push(u)});let c=new Set,l=[];for(;a.length>0;){let u=a.pop();c.add(u.name),t[u.name]||l.push(u),u.children.forEach(d=>{!c.has(d.name)&&r.has(d.name)&&d.inputs.every(p=>c.has(p.name))&&a.push(d)})}return l}var $H=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],FH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],RH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function JT(e){return $H.indexOf(e.op)>=0}function PH(e){return FH.indexOf(e.op)>=0}function OH(e){return RH.indexOf(e.op)>=0}var Iw=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Iw(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(s=>s.name).sort(),r=t.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=ZT(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:a}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(r.length>0){let o=t.map(c=>c.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${r}]`)}return DH(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(u=>this.graph.nodes[nr(u)[0]]),s=t.map(u=>nr(u)[0]),a=s.map(u=>this.graph.nodes[u]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(r,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let c={},l={};return M(()=>{let u=new YT(this.weightMap,c,l,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=nr(f),b=[];b[g]=e[f],d[m]=b});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=XT(m,d,u,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. 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You can use model.execute() instead.");let b=i.filter(y=>!JT(y)&&!In(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw u!=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 [${s}]. 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l}processChildNodes(e,t,n,r,s,a){e.children.forEach(o=>{let[i]=hs(o.name,n);s[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(c=>!!In(c,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(c=>!!In(c,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=nr(t),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,c)=>a[c]===-1||a[c]===i);k.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&k.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=nr(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=nr(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},MH=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},LH="?tfjs-format=file",BH="model.json",QT=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new MH}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Zt.browserHTTPRequest(e,this.loadOptions);else{let t=Zt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Zt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let r=Zt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Iw(UT.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=UT.Instance.transformGraph(e.modelInitializer);this.initializer=new Iw(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Zt.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ee)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function zH(e,t={}){if(e==null)throw 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this.upstream.next()}},n6=class extends tn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},r6=class extends tn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},s6=class extends tn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async 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`).map(r=>(r.endsWith("\r")&&(r=r.slice(0,-1)),r))}},um='"',Od=Symbol("out"),lC=Symbol("field"),lm=Symbol("quote"),Nw=Symbol("quoteafterquote"),dC=Symbol("quoteinquote"),pC=class extends Su{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new uC(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,s)=>(r[s]=r[s]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[s],c=null;if(i==="")if(o&&o.default!==void 0)c=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);c=void 0}else{let l=Number(i);if(isNaN(l))o&&o.dtype==="bool"?c=this.getBoolean(i):c=i;else if(!o||!o.dtype)c=l;else switch(o.dtype){case"float32":c=l;break;case"int32":c=Math.floor(l);break;case"bool":c=this.getBoolean(i);break;default:c=l}}o&&o.isLabel?r[a]=c:n[a]=c}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,s=e.length,a=Od;for(let o=0;o<s;o++)switch(a){case Od:switch(e.charAt(o)){case um:r=o+1,a=lm;break;case this.delimiter:if(r=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Od;break;default:a=lC,r=o;break}break;case lC:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(r,o)),a=Od,r=o+1;break;default:}break;case lm:switch(e.charAt(o)){case um:a=Nw;break;default:}break;case Nw:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(r,o-1)),a=Od,r=o+1;break;case um:a=lm;break;default:a=dC;break}break;case dC:switch(e.charAt(o)){case um:a=lm;break;default:}break;default:}if(a===Nw?n.push(e.substring(r,s-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},hC=class extends tn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new hC(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 r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[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(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({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(s),r({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((r,s)=>n.set(r,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),Xn(n,t)}},fC=class extends tn{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=je([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-r)/2,o=s+n,i=r+a;this.cropBox=Wr([a,s,i,o],[1,4])}else this.cropBox=Wr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new fC(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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=Go.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return M(()=>{let t=gn(ce(e,"float32"),0),n;n=tr.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return U(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},mC=class{},gC=class extends tn{split(e){return new f6(this,e)}},f6=class extends gC{constructor(e,t){super();this.upstream=e,this.impl=new m6(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},m6=class extends Cw{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},g6=class extends tn{decodeUTF8(){return new b6(this)}},b6=class extends gC{constructor(e){super();this.upstream=e,this.impl=new y6(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},y6=class extends Cw{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=m1();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},bC=class extends g6{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,r)));else{let s=new FileReader;s.onload=o=>{let i=s.result;if(i instanceof 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mC{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return yC(this.url)?new vC(this.url,this.fileOptions).iterator():v6(this.url,this.fileOptions)}};function w6(e,t={}){return new pC(new xC(e),t)}function k6(e){let t=Tw(e);return rr(async()=>t)}function I6(e){return rr(async()=>{let t=await e();return Tw(()=>t.next())})}async function S6(e,t){return fC.create(e,t)}async function T6(e){return hC.create(e)}var C6="3.13.0";function ke(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var N6=is.whereImpl,_w=class extends kl{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new qp(this,ns())}nextDataId(){return _w.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&_.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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o=dm(n,s.shape,s.dtype),i=Ia({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),c=sr({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),c}if(s.dtype==="complex64"){let o=li({inputs:{input:s},backend:n}),i=Ia({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!k.hasEncodingLoss(s.dtype,a)){let o=fs({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32"){let o=n.data.get(s.dataId).values,i=Int32Array.from(o);return n.makeTensorInfo(s.shape,"int32",i)}if(a==="bool"){let o=n.data.get(s.dataId).values,i=k.toTypedArray([0],s.dtype),[c,l]=Vt((u,d)=>u!==d?1:0)(s.shape,[],o,i,"bool");return n.makeTensorInfo(l,"bool",c)}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var F6={kernelName:Ua,backendName:"cpu",kernelFunc:Ia};function nn(e,t,n,r){return n==null?({inputs:s,backend:a})=>{let{a:o,b:i}=s,c=a;ke([o,i],e);let 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P=k.locToIndex(D,v,x),F=e(m[$*2],m[$*2+1],g[P*2],g[P*2+1]);d[w]=F.real,p[w]=F.imag}return[d,p,i]}}var IC=Vt((e,t)=>e+t),R6=Ew((e,t,n,r)=>({real:e+n,imag:t+r})),Md=nn(Js,IC,R6),P6={kernelName:Js,backendName:"cpu",kernelFunc:Md};function Aw(e,t,n,r,s){let a=k.sizeFromShape(r),o=k.makeZerosTypedArray(s,n);for(let i=0;i<e.length;i++){let c=e[i];if(c<0)throw new Error("Input x must be non-negative!");c>=s||(a>0?o[c]+=t[i]:o[c]+=1)}return o}function SC(e,t,n,r=!1){let s=e.shape[0],a=e.shape[1],o=ze([s,n],t.dtype);for(let i=0;i<s;i++)for(let c=0;c<a;c++){let l=e.get(i,c);if(l<0)throw new Error("Input x must be non-negative!");l>=n||(r?o.set(1,i,l):t.size>0?o.set(o.get(i,l)+t.get(i,c),i,l):o.set(o.get(i,l)+1,i,l))}return o}function Sa(e){return(t,n,r)=>{let s=k.getTypedArrayFromDType(n,t.length);for(let a=0;a<t.length;++a)s[a]=e(t[a],r);return s}}function it(e,t,n){return({inputs:r,attrs:s,backend:a})=>{let{x:o}=r;if(ke(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc 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CC=Vt((e,t)=>e===t?1:0),NC=nn(dc,CC,null,"bool"),L6={kernelName:dc,backendName:"cpu",kernelFunc:NC},_C=Sa(e=>Math.exp(e)),EC=Tu(Qa,_C,"float32"),B6={kernelName:Qa,backendName:"cpu",kernelFunc:EC},AC=Sa(e=>Math.expm1(e)),z6=Tu(hc,AC),W6={kernelName:hc,backendName:"cpu",kernelFunc:z6},DC=Sa(e=>Math.floor(e)),V6=Tu(eo,DC),U6={kernelName:eo,backendName:"cpu",kernelFunc:V6};function $C(e,t,n,r,s,a,o,i,c){let l=ze([r,a],n);for(let u=0;u<r;u++){let d=[],p=0;for(let h=0;h<s;h++){let f=e[u*s+h];p+=f*o[h],d.push(f)}if(p<0||p>=c/a)throw new Error(`Invalid indices: ${d} does not index into ${i}`);for(let h=0;h<a;h++)l.values[u*a+h]=t.get(...t.indexToLoc(p*a+h))}return l}function FC(e,t,n){let r=ze(n,e.dtype);for(let s=0;s<r.size;++s){let o=r.indexToLoc(s).slice(),i=o[0],c=o[2],l=t.locToIndex([i,c]);o[2]=t.values[l];let u=e.locToIndex(o);0<=u&&u<e.values.length&&(r.values[s]=e.values[u])}return r}var RC=Vt((e,t)=>e>t?1:0),G6=nn(bc,RC,null,"bool"),H6={kernelName:bc,backendName:"cpu",kernelFunc:G6},PC=Vt((e,t)=>e>=t?1:0),j6=nn(ro,PC,null,"bool"),q6={kernelName:ro,backendName:"cpu",kernelFunc:j6},OC=Vt((e,t)=>e<t?1:0),K6=nn(wc,OC,null,"bool"),X6={kernelName:wc,backendName:"cpu",kernelFunc:K6},MC=Vt((e,t)=>e<=t?1:0),Y6=nn(kc,MC,null,"bool"),Z6={kernelName:kc,backendName:"cpu",kernelFunc:Y6};function LC(e,t,n){let r=(t-e)/(n-1),s=k.makeZerosTypedArray(n,"float32");s[0]=e;for(let a=1;a<s.length;a++)s[a]=s[a-1]+r;return s}var BC=Sa(e=>Math.log(e)),J6=Tu(oo,BC),Q6={kernelName:oo,backendName:"cpu",kernelFunc:J6};function zC(e,t,n,r){let s=k.getTypedArrayFromDType(r,k.sizeFromShape(n));for(let a=0;a<s.length;++a){let o=a*t,i=e[o];for(let c=0;c<t;++c){let l=e[o+c];(Number.isNaN(l)||l>i)&&(i=l)}s[a]=i}return s}var WC=Vt((e,t)=>Math.max(e,t)),e5=nn(co,WC),t5={kernelName:co,backendName:"cpu",kernelFunc:e5},VC=Vt((e,t)=>Math.min(e,t)),n5=nn(ho,VC),r5={kernelName:ho,backendName:"cpu",kernelFunc:n5},$w=Vt((e,t)=>e*t),s5=Ew((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),pm=nn(mo,$w,s5),a5={kernelName:mo,backendName:"cpu",kernelFunc:pm};function UC(e,t,n){let r=k.createScalarValue(-1,n);return $w([],t,r,e,n)}function o5(e){let{inputs:t,backend:n}=e,{x:r}=t;ke(r,"neg");let s=n.data.get(r.dataId).values,[a,o]=UC(s,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,a)}var i5={kernelName:Cc,backendName:"cpu",kernelFunc:o5},GC=Vt((e,t)=>e!==t?1:0),c5=nn(Nc,GC,null,"bool"),u5={kernelName:Nc,backendName:"cpu",kernelFunc:c5};function Fw(e,t,n,r,s){let a=t.length,o=k.sizeFromShape(t),i=k.computeStrides(t),c=k.computeStrides(s),l=k.getTypedArrayFromDType(n,k.sizeFromShape(s));for(let u=0;u<o;++u){let d=k.indexToLoc(u,a,i),p=new Array(d.length);for(let f=0;f<p.length;f++)p[f]=d[r[f]];let 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h=n.data.get(d.dataId).values,{outVals:f,outShape:m,outDtype:g}=HC(d.shape,d.dtype,h,u),b=m;return o&&(b=_.expandShapeToKeepDim(m,c)),p.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(b,g,f)}var p5={kernelName:Fc,backendName:"cpu",kernelFunc:d5};function Rw(e,t,n,r){let s=e===t,a=e<t&&n<0,o=t<e&&n>1;if(s||a||o)return k.makeZerosTypedArray(0,r);let i=Math.abs(Math.ceil((t-e)/n)),c=k.makeZerosTypedArray(i,r);t<e&&n===1&&(n=-1),c[0]=e;for(let l=1;l<c.length;l++)c[l]=c[l-1]+n;return c}var jC=Sa(e=>1/Math.sqrt(e)),h5=Tu(To,jC),f5={kernelName:To,backendName:"cpu",kernelFunc:h5},m5=Sa(e=>1/(1+Math.exp(-e))),qC=it(No,e=>1/(1+Math.exp(-e))),g5={kernelName:No,backendName:"cpu",kernelFunc:qC};function hm(e,t,n,r,s){let a=Ht.isSliceContinous(r,t,n),o=k.sizeFromShape(n),i=k.computeStrides(r);if(a){let d=Ht.computeFlatOffset(t,i);return s==="string"?e.slice(d,d+o):e.subarray(d,d+o)}let c=s==="string"?_.fromUint8ToStringArray(e):e,l=ze(r,s,c),u=ze(n,s);for(let d=0;d<u.size;++d){let 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b=f[g]===0;l[g]=b,m=m&&!b,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&p){let g=e,b=r;for(let y=0;y<i;++y)u[y]=y;return[g,[i,d],b,l,u]}else{let g=f[c-1],b=k.getArrayFromDType(n,g*d),y=k.getArrayFromDType(s,g),v=new Array(c).fill(0);for(let x=0;x<i;++x){let w=e[x*d],T=v[w],N=(w===0?0:f[w-1])+T;v[w]++;for(let $=0;$<d;++$)b[N*d+$]=e[x*d+$];y[N]=r[x],u[x]=N}for(let x=0;x<c;++x)if(v[x]===0){let T=x===0?0:f[x-1];b[T*d+0]=x;for(let N=1;N<d;++N)b[T*d+N]=0;y[T]=o}return[b,[g,d],y,l,u]}}function XC(e,t,n,r,s){let a=k.sizeFromShape(r),o=t[0],i=s.length,c=[],l=1,u=-1;for(let g=0;g<i;++g){let b=s[g];if(b===-1){if(u!==-1)throw new Error(_.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(u,g));u=g,c.push(1)}else{if(b<0)throw new Error(_.getSparseReshapeNegativeOutputDimErrorMessage(g,b));l*=b,c.push(b)}}if(u!==-1){if(l<=0)throw new Error(_.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let g=Math.trunc(a/l);if(l*g!==a)throw new 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I5=class{constructor(e,t,n,r,s,a){this.separator=k.encodeString(e),this.nGramWidths=t,this.leftPad=k.encodeString(n),this.rightPad=k.encodeString(r),this.padWidth=s,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,r,s,a){for(let o=0;o<s;++o){let i=this.getPadWidth(a),c=Math.max(0,i-o),l=Math.max(0,i-(s-(o+1))),u=a-(c+l),d=t+(c>0?0:o-i),p=0;p+=c*this.leftPad.length;for(let b=0;b<u;++b)p+=e[d+b].length;p+=l*this.rightPad.length,p+=(c+l+u-1)*this.separator.length,n[r+o]=new Uint8Array(p);let f=n[r+o],m=0,g=b=>b.forEach(y=>f[m++]=y);for(let b=0;b<c;++b)g(this.leftPad),g(this.separator);for(let b=0;b<u-1;++b)g(e[d+b]),g(this.separator);if(u>0){g(e[d+u-1]);for(let b=0;b<l;++b)g(this.separator),g(this.rightPad)}else{for(let b=0;b<l-1;++b)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,r=t.length;if(r>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let c=1;c<r;++c){let l=t[c]>=i;if(l=l&&t[c]<=n,!l)throw new Error(`Invalid split value ${t[c]}, must be in [${i}, ${n}]`);i=t[c]}if(i!==n)throw new Error(`Last split value must be data size. 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i=e.locToIndex(o);r.values[s]=e.values[i]}return r}var Ld=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function r2(e,t,n=0,r=e.length-1){for(;r>n;){if(r-n>600){let i=r-n+1,c=t-n+1,l=Math.log(i),u=.5*Math.exp(2*l/3),d=.5*Math.sqrt(l*u*(i-u)/i)*Math.sign(c-i/2),p=Math.max(n,Math.floor(t-c*u/i+d)),h=Math.min(r,Math.floor(t+(i-c)*u/i+d));r2(e,t,p,h)}let s=e[t],a=n,o=r;for(k.swap(e,n,t),Ld(e[r],s)>0&&k.swap(e,n,r);a<o;){for(k.swap(e,a,o),a++,o--;Ld(e[a],s)<0;)a=a+1;for(;Ld(e[o],s)>0;)o=o-1}Ld(e[n],s)===0?k.swap(e,n,o):(o=o+1,k.swap(e,o,r)),o<=t&&(n=o+1),t<=o&&(r=o-1)}}function s2(e,t,n,r,s){let a=t[t.length-1],[o,i]=[e.length/a,a],c=k.getTypedArrayFromDType(n,o*r),l=k.getTypedArrayFromDType("int32",o*r);for(let d=0;d<o;d++){let p=d*i,h=e.subarray(p,p+i),f=new Array(h.length);h.forEach((y,v)=>f[v]={value:y,index:v}),r<f.length&&(r2(f,r),f=f.slice(0,r)),s&&f.sort(Ld);let m=d*r,g=c.subarray(m,m+r),b=l.subarray(m,m+r);for(let y=0;y<r;y++)g[y]=f[y].value,b[y]=f[y].index}let u=t.slice();return u[u.length-1]=r,[ze(u,n,c),ze(u,"int32",l)]}function a2(e,t,n,r){let s=k.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<s;f++)a[0]*=n[f];a[1]=n[s];for(let f=s+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[s]),c=new Gt(a,r,e),l=[],u=a[0]===1&&a[2]===1;for(let f=0;f<n[s];f++){let m;if(u)m=e[f].toString();else{let g=[];for(let b=0;b<a[0];b++)for(let y=0;y<a[2];y++)g.push(c.get(b,f,y));m=g.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let g=Object.keys(o).length;o[m]=g,i[f]=g,l.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let p=new Gt(d,r);l.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let b=0;b<a[2];b++)p.set(c.get(g,f,b),g,m,b)});let h=n.slice();return h[s]=d[1],{outputValues:p.values,outputShape:h,indices:i}}Uh("cpu",()=>new _w,1);var o2=it(Ja,e=>e>=0?e:Math.exp(e)-1),N5={kernelName:Ja,backendName:"cpu",kernelFunc:o2};function i2(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r;ke([s],"leakyRelu");let o=k.sizeFromShape(s.shape),i=n.data.get(s.dataId).values,c=k.getTypedArrayFromDType("float32",o);for(let l=0;l<i.length;l++)c[l]=i[l]<0?a*i[l]:i[l];return n.makeTensorInfo(s.shape,"float32",c)}var _5={kernelName:ao,backendName:"cpu",kernelFunc:i2},E5=Vt((e,t)=>e<0?t*e:e);function c2(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t;ke([r,s],"prelu");let a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,[i,c]=E5(r.shape,s.shape,a,o,"float32");return n.makeTensorInfo(c,"float32",i)}var A5={kernelName:vo,backendName:"cpu",kernelFunc:c2},u2=it(xo,e=>Math.max(0,e)),D5={kernelName:xo,backendName:"cpu",kernelFunc:u2},l2=it(ko,e=>Math.min(Math.max(0,e),6)),$5={kernelName:ko,backendName:"cpu",kernelFunc:l2};function Mw(e,t,n,r,s){if(n==="linear")return fs({inputs:{x:t},backend:e});if(n==="relu")return u2({inputs:{x:t},backend:e});if(n==="elu")return o2({inputs:{x:t},backend:e});if(n==="relu6")return l2({inputs:{x:t},backend:e});if(n==="prelu")return c2({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return i2({inputs:{x:t},backend:e,attrs:{alpha:s}});if(n==="sigmoid")return qC({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function _t(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=k.sizeFromShape(s.shape),i=k.inferFromImplicitShape(a,o),c=k.sizeFromShape(i);k.assert(o===c,()=>`The new shape (${i}) has ${c} elements and the old shape (${s.shape}) has ${o} elements. 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x=o?[g,u,p]:[g,p,u],w=i?[b,h,d]:[b,d,h],T=_t({inputs:{x:s},backend:n,attrs:{shape:x}}),N=_t({inputs:{x:a},backend:n,attrs:{shape:w}}),$=o?T.shape[1]:T.shape[2],D=o?T.shape[2]:T.shape[1],P=i?N.shape[1]:N.shape[2],F=Math.max(g,b),R=n.data.get(T.dataId).values,C=n.data.get(N.dataId).values,L=k.computeStrides(T.shape),G=k.computeStrides(N.shape),[j,K,q]=o?[L[0],1,L[1]]:[L[0],L[1],1],[Z,te,se]=i?[1,G[1],G[0]]:[G[1],1,G[0]],oe=D*P,re=ze([F,D,P],T.dtype),ue=re.values,ne=n.blockSize;for(let he=0;he<F;he++)for(let ye=0;ye<D;ye+=ne)for(let Ce=0;Ce<P;Ce+=ne)for(let Se=0;Se<$;Se+=ne){let _e=Math.min(ye+ne,D),Le=Math.min(Ce+ne,P),Ze=Math.min(Se+ne,$);for(let Ve=ye;Ve<_e;Ve++)for(let Ue=Ce;Ue<Le;Ue++){let ct=0;for(let Je=Se;Je<Ze;Je++){let dt=Math.min(he,g-1)*j,kt=Math.min(he,b-1)*se,Dn=R[dt+Ve*K+Je*q],Qe=C[Je*Z+Ue*te+kt];ct+=Dn*Qe}ue[he*oe+(Ve*P+Ue)]+=ct}}return n.disposeIntermediateTensorInfo(T),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(v,re.dtype,re.values)}var R5={kernelName:Va,backendName:"cpu",kernelFunc:d2};function P5(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:c,transposeB:l,activation:u,leakyreluAlpha:d}=r,p,h,f,m=[];p=d2({inputs:{a:s,b:a},attrs:{transposeA:c,transposeB:l},backend:n}),o&&(h=Md({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),u&&(f=Mw(n,p,u,i,d),m.push(p),p=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return p}var O5={kernelName:Oo,backendName:"cpu",kernelFunc:P5},M5=it(Zi,e=>Math.acos(e)),L5={kernelName:Zi,backendName:"cpu",kernelFunc:M5},B5=it(Ji,e=>Math.acosh(e)),z5={kernelName:Ji,backendName:"cpu",kernelFunc:B5};function W5(e){let{inputs:t,backend:n}=e,r=t;ke(t,"addN");let s=r.map(i=>n.data.get(i.dataId).values),a=ze(r[0].shape,r[0].dtype),o=a.values;for(let i=0;i<r.length;i++){let c=s[i];for(let l=0;l<o.length;l++)o[l]+=c[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var V5={kernelName:Ba,backendName:"cpu",kernelFunc:W5};function U5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;ke(s,"all");let i=k.parseAxisParam(a,s.shape),c=i,l=_.getAxesPermutation(c,s.shape.length),u=s;l!=null&&(u=gr({inputs:{x:s},backend:n,attrs:{perm:l}}),c=_.getInnerMostAxes(c.length,s.shape.length)),_.assertAxesAreInnerMostDims("all",c,u.shape.length);let[d,p]=_.computeOutAndReduceShapes(u.shape,c),h=k.sizeFromShape(p),f=k.makeZerosTypedArray(k.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let w=m[y+x];v=v&&w}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let b=_.expandShapeToKeepDim(d,i),y=_t({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var G5={kernelName:Qi,backendName:"cpu",kernelFunc:U5};function H5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;ke(s,"any");let i=k.parseAxisParam(a,s.shape),c=i,l=_.getAxesPermutation(c,s.shape.length),u=s;l!=null&&(u=gr({inputs:{x:s},backend:n,attrs:{perm:l}}),c=_.getInnerMostAxes(c.length,s.shape.length)),_.assertAxesAreInnerMostDims("any",c,u.shape.length);let[d,p]=_.computeOutAndReduceShapes(u.shape,c),h=k.sizeFromShape(p),f=k.makeZerosTypedArray(k.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let w=m[y+x];v=v||w}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let b=_.expandShapeToKeepDim(d,i),y=_t({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var j5={kernelName:ec,backendName:"cpu",kernelFunc:H5};function q5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r;ke(s,"argMax");let o=k.parseAxisParam(a,s.shape),i=_.getAxesPermutation(o,s.shape.length),c=s,l=[];i!=null&&(c=gr({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(c),o=_.getInnerMostAxes(o.length,c.shape.length)),o=[o[0]],_.assertAxesAreInnerMostDims("argMax",o,c.shape.length);let[u,d]=_.computeOutAndReduceShapes(c.shape,o),p=k.sizeFromShape(u),h=k.makeZerosTypedArray(p,"int32"),f=k.sizeFromShape(d),m=n.data.get(c.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let w=m[b+x];w>y&&(y=w,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var K5={kernelName:za,backendName:"cpu",kernelFunc:q5};function X5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r;ke(s,"argMin");let o=k.parseAxisParam(a,s.shape),i=_.getAxesPermutation(o,s.shape.length),c=s,l=[];i!=null&&(c=gr({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(c),o=_.getInnerMostAxes(o.length,c.shape.length)),o=[o[0]],_.assertAxesAreInnerMostDims("argMin",o,c.shape.length);let[u,d]=_.computeOutAndReduceShapes(c.shape,o),p=k.sizeFromShape(u),h=k.makeZerosTypedArray(p,"int32"),f=k.sizeFromShape(d),m=n.data.get(c.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let w=m[b+x];w<y&&(y=w,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var Y5={kernelName:Tl,backendName:"cpu",kernelFunc:X5},Z5=it(tc,e=>Math.asin(e)),J5={kernelName:tc,backendName:"cpu",kernelFunc:Z5},Q5=it(nc,e=>Math.asinh(e)),ej={kernelName:nc,backendName:"cpu",kernelFunc:Q5},tj=it(rc,e=>Math.atan(e)),nj={kernelName:rc,backendName:"cpu",kernelFunc:tj},rj=Vt((e,t)=>Math.atan2(e,t)),sj=nn(ac,rj),aj={kernelName:ac,backendName:"cpu",kernelFunc:sj},oj=it(sc,e=>Math.atanh(e)),ij={kernelName:sc,backendName:"cpu",kernelFunc:oj};function Lw(e,t,n,r,s,a){let o=s.strideHeight,i=s.strideWidth,c=s.dilationHeight,l=s.dilationWidth,u=s.effectiveFilterHeight,d=s.effectiveFilterWidth,p=s.padInfo.top,h=s.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=ze(s.outShape,n),g=m.values,b=s.outShape[1]*s.outShape[2]*s.outShape[3],y=s.outShape[2]*s.outShape[3],v=s.outShape[3];for(let x=0;x<s.batchSize;++x){let w=x*b,T=x*r[0];for(let N=0;N<s.inChannels;++N)for(let $=0;$<s.outHeight;++$){let D=$*o-p,P=Math.max(0,D),F=Math.min(s.inHeight,u+D),R=w+$*y;for(let C=0;C<s.outWidth;++C){let L=C*i-h,G=Math.max(0,L),j=Math.min(s.inWidth,d+L),K=f,q=0,Z=0;for(let se=P;se<F;se+=c){let oe=T+se*r[1];for(let re=G;re<j;re+=l){let ue=oe+re*r[2],ne=e[ue+N];a==="max"&&ne>K?K=ne:a==="avg"&&(q+=ne,Z++)}if(isNaN(K))break}let te=R+C*v+N;g[te]=a==="avg"?q/Z:K}}}return m}function p2(e,t,n,r,s=!1,a=!1){let o=ze(r.outShape,"int32"),i=r.strideHeight,c=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,d=r.effectiveFilterHeight,p=r.effectiveFilterWidth,h=r.padInfo.top,f=r.padInfo.left,m=ze(t,n,e);for(let g=0;g<r.batchSize;++g)for(let b=0;b<r.inChannels;++b)for(let y=0;y<r.outHeight;++y){let v=y*i-h,x=v;for(;x<0;)x+=l;let w=Math.min(r.inHeight,d+v);for(let T=0;T<r.outWidth;++T){let N=T*c-f,$=N;for(;$<0;)$+=u;let D=Math.min(r.inWidth,p+N),P=Number.NEGATIVE_INFINITY,F=-1;for(let R=x;R<w;R+=l){let C=R-v;for(let L=$;L<D;L+=u){let G=L-N,j=m.get(g,R,L,b);j>P&&(P=j,s?F=a?((g*r.inHeight+R)*r.inWidth+L)*r.inChannels+b:(R*r.inWidth+L)*r.inChannels+b:F=C*p+G)}}o.set(F,g,y,T,b)}}return o}function h2(e,t,n,r,s,a){let o=s.strideDepth,i=s.strideHeight,c=s.strideWidth,l=s.dilationDepth,u=s.dilationHeight,d=s.dilationWidth,p=s.effectiveFilterDepth,h=s.effectiveFilterHeight,f=s.effectiveFilterWidth,m=s.padInfo.front,g=s.padInfo.top,b=s.padInfo.left,y=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=ze(s.outShape,n),x=v.values,w=s.outShape[1]*s.outShape[2]*s.outShape[3]*s.outShape[4],T=s.outShape[2]*s.outShape[3]*s.outShape[4],N=s.outShape[3]*s.outShape[4],$=s.outShape[4];for(let D=0;D<s.batchSize;++D){let P=D*w,F=D*r[0];for(let R=0;R<s.inChannels;++R)for(let C=0;C<s.outDepth;++C){let L=C*o-m,G=L;for(;G<0;)G+=l;let j=Math.min(s.inDepth,p+L),K=P+C*T;for(let q=0;q<s.outHeight;++q){let Z=q*i-g,te=Z;for(;te<0;)te+=u;let se=Math.min(s.inHeight,h+Z),oe=K+q*N;for(let re=0;re<s.outWidth;++re){let ue=re*c-b,ne=ue;for(;ne<0;)ne+=d;let he=Math.min(s.inWidth,f+ue),ye=oe+re*$,Ce=y,Se=0,_e=0;for(let Ze=G;Ze<j;Ze+=l){let Ve=F+Ze*r[1];for(let Ue=te;Ue<se;Ue+=u){let ct=Ve+Ue*r[2];for(let Je=ne;Je<he;Je+=d){let dt=ct+Je*r[3],kt=e[dt+R];if(a==="max"&&kt>Ce?Ce=kt:a==="avg"&&(Se+=kt,_e++),isNaN(Ce))break}if(isNaN(Ce))break}if(isNaN(Ce))break}let Le=ye+R;x[Le]=a==="avg"?Se/_e:Ce}}}}return v}function cj(e,t){let n=ze(t.outShape,"int32"),r=t.strideDepth,s=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,u=t.effectiveFilterHeight,d=t.effectiveFilterWidth,p=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*r-p,v=y;for(;v<0;)v+=o;let x=Math.min(t.inDepth,l+y);for(let w=0;w<t.outHeight;++w){let T=w*s-h,N=T;for(;N<0;)N+=i;let $=Math.min(t.inHeight,u+T);for(let D=0;D<t.outWidth;++D){let P=D*a-f,F=P;for(;F<0;)F+=c;let R=Math.min(t.inWidth,d+P),C=Number.NEGATIVE_INFINITY,L=-1;for(let G=v;G<x;G+=o){let j=G-y;for(let K=N;K<$;K+=i){let q=K-T;for(let Z=F;Z<R;Z+=c){let te=Z-P,se=e.get(m,G,K,Z,g);se>=C&&(C=se,L=j*u*d+q*u+te)}}}n.set(L,m,b,w,D,g)}}}return n}function uj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;ke(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:c}=r,l=1;k.assert(_.eitherStridesOrDilationsAreOne(o,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let u=_.computePool2DInfo(s.shape,a,o,l,i,c),d;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))d=fs({inputs:{x:s},backend:n});else{let p=n.data.get(s.dataId).values,h=k.computeStrides(s.shape),f=Lw(p,s.shape,s.dtype,h,u,"avg");d=n.makeTensorInfo(u.outShape,s.dtype,f.values)}return d}var lj={kernelName:Wa,backendName:"cpu",kernelFunc:uj};function dj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:c,dataFormat:l}=r;ke(s,"avgPool3d");let u=_.computePool3DInfo(s.shape,a,o,1,i,c,l),d=n.data.get(s.dataId).values,p=h2(d,s.shape,s.dtype,k.computeStrides(s.shape),u,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}var pj={kernelName:Cl,backendName:"cpu",kernelFunc:dj};function hj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,{filterSize:o,strides:i,pad:c,dimRoundingMode:l}=r;ke([s,a],"avgPool3DGrad");let u=_.computePool3DInfo(a.shape,o,i,1,c,l),d=u.strideDepth,p=u.strideHeight,h=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,b=u.dilationDepth,y=u.dilationHeight,v=u.dilationWidth,x=u.effectiveFilterDepth,w=u.effectiveFilterHeight,T=u.effectiveFilterWidth,N=x-1-u.padInfo.front,$=T-1-u.padInfo.left,D=w-1-u.padInfo.top,P=ze(a.shape,"float32"),F=1/(f*m*g),R=n.bufferSync(s);for(let C=0;C<u.batchSize;++C)for(let L=0;L<u.inChannels;++L)for(let G=0;G<u.inDepth;++G)for(let j=0;j<u.inHeight;++j)for(let K=0;K<u.inWidth;++K){let q=G-N,Z=j-D,te=K-$,se=0;for(let oe=0;oe<x;oe+=b){let re=(q+oe)/d;if(!(re<0||re>=u.outDepth||Math.floor(re)!==re))for(let ue=0;ue<w;ue+=y){let ne=(Z+ue)/p;if(!(ne<0||ne>=u.outHeight||Math.floor(ne)!==ne))for(let he=0;he<T;he+=v){let ye=(te+he)/h;if(ye<0||ye>=u.outWidth||Math.floor(ye)!==ye)continue;se+=R.get(C,re,ne,ye,L)}}}P.set(se*F,C,G,j,K,L)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var fj={kernelName:Qp,backendName:"cpu",kernelFunc:hj};function mj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;ke([s,a],"avgPoolGrad");let{filterSize:i,strides:c,pad:l}=r,u=_.computePool2DInfo(o.shape,i,c,1,l),d=u.strideHeight,p=u.strideWidth,h=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,g=u.dilationWidth,b=u.effectiveFilterHeight,y=u.effectiveFilterWidth,v=y-1-u.padInfo.left,x=b-1-u.padInfo.top,w=ze(o.shape,"float32"),T=1/(h*f),N=n.data.get(s.dataId).values,$=ze(s.shape,"float32",N);for(let D=0;D<u.batchSize;++D)for(let P=0;P<u.inChannels;++P)for(let F=0;F<u.inHeight;++F)for(let R=0;R<u.inWidth;++R){let C=F-x,L=R-v,G=0;for(let j=0;j<b;j+=m){let K=(C+j)/d;if(!(K<0||K>=u.outHeight||Math.floor(K)!==K))for(let q=0;q<y;q+=g){let Z=(L+q)/p;if(Z<0||Z>=u.outWidth||Math.floor(Z)!==Z)continue;G+=$.get(D,K,Z,P)}}w.set(G*T,D,F,R,P)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var gj={kernelName:Jp,backendName:"cpu",kernelFunc:mj};function bj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,scale:a,offset:o,mean:i,variance:c}=t;k.assert(i.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ke([s,i,c,a,o],"batchNorm");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,p=n.data.get(c.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),g=f.length,b=h.length,y=p.length,v=d.length,x=0,w=0,T=0,N=0;for(let $=0;$<u.length;++$)m[$]=f[x++]+(u[$]-d[w++])*h[T++]/Math.sqrt(p[N++]+l),x>=g&&(x=0),w>=v&&(w=0),T>=b&&(T=0),N>=y&&(N=0);return n.makeTensorInfo(s.shape,s.dtype,m)}var yj={kernelName:no,backendName:"cpu",kernelFunc:bj};function vj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;ke([s],"batchToSpaceND");let i=a.reduce((b,y)=>b*y),c=_.getReshaped(s.shape,a,i),l=_.getPermuted(c.length,a.length),u=_.getReshapedPermuted(s.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(u,o,a.length),h=_t({inputs:{x:s},backend:n,attrs:{shape:c}}),f=gr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=_t({inputs:{x:f},backend:n,attrs:{shape:u}}),g=di({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var xj={kernelName:oc,backendName:"cpu",kernelFunc:vj};function wj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.data.get(s.dataId).values,c=n.data.get(a.dataId).values,l=Aw(i,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var kj={kernelName:eh,backendName:"cpu",kernelFunc:wj};function Ij(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,i=_.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var Sj={kernelName:th,backendName:"cpu",kernelFunc:Ij},Tj=it(Qs,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),Cj={kernelName:Qs,backendName:"cpu",kernelFunc:Tj},Nj=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(k.sizeFromShape(t.shape)),s=n.data.get(t.dataId),a=s.complexTensorInfos.real,o=s.complexTensorInfos.imag,i=n.data.get(a.dataId).values,c=n.data.get(o.dataId).values;for(let l=0;l<i.length;l++){let u=i[l],d=c[l];r[l]=Math.hypot(u,d)}return n.makeOutput(r,t.shape,"float32")},_j={kernelName:Nl,backendName:"cpu",kernelFunc:Nj};function Cu(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.imag,a=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,a)}var Ej={kernelName:gh,backendName:"cpu",kernelFunc:Cu};function Nu(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=k.parseAxisParam(s,t[0].shape)[0],o=_.computeOutShape(t.map(m=>m.shape),a);if(k.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>k.sizeFromShape(m.shape)>0);if(i.length===1)return fs({inputs:{x:i[0]},backend:n});let c=i.map(m=>m.shape);if(_.assertParamsConsistent(c,a),i[0].dtype==="complex64"){let m=i.map(x=>li({inputs:{input:x},backend:n})),g=i.map(x=>Cu({inputs:{input:x},backend:n})),b=Nu({inputs:m,backend:n,attrs:{axis:a}}),y=Nu({inputs:g,backend:n,attrs:{axis:a}}),v=sr({inputs:{real:b,imag:y},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),v}let l=i.map(m=>{let g=k.sizeFromShape(m.shape.slice(a));return _t({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),u=l.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=_.computeOutShape(l.map(m=>m.shape),1);let d=l[0].shape[0]===1,p=Dw(u,o,t[0].dtype,d),h=_.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Aj={kernelName:ic,backendName:"cpu",kernelFunc:Nu};function f2(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:c,dilations:l,dimRoundingMode:u}=r;ke([s,a],"conv2d");let d=_.convertConv2DDataFormat(c),p=_.computeConv2DInfo(s.shape,a.shape,o,l,i,u,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,b=p.padInfo.left,y=p.padInfo.top,v=p.dataFormat==="channelsLast",x=new Gt(p.outShape,s.dtype),w=k.computeStrides(s.shape),T=k.computeStrides(a.shape),N=w[0],$=v?w[1]:w[2],D=v?w[2]:1,P=v?1:w[1],F=x.strides[0],R=v?x.strides[1]:x.strides[2],C=v?x.strides[2]:1,L=v?1:x.strides[1],G=n.data.get(s.dataId).values,j=n.data.get(a.dataId).values,K=x.values;for(let q=0;q<p.batchSize;++q){let Z=q*N,te=q*F;for(let se=0;se<p.outHeight;++se){let oe=te+se*R,re=se*p.strideHeight-y;for(let ue=0;ue<h;++ue){let ne=re+ue*m;if(ne<0||ne>=p.inHeight)continue;let he=ue*T[0],ye=Z+ne*$;for(let Ce=0;Ce<p.outWidth;++Ce){let Se=oe+Ce*C,_e=Ce*p.strideWidth-b;for(let Le=0;Le<f;++Le){let Ze=_e+Le*g;if(Ze<0||Ze>=p.inWidth)continue;let Ve=he+Le*T[1],Ue=ye+Ze*D,ct=Ve;for(let Je=0;Je<p.inChannels;++Je){let dt=G[Ue+Je*P];for(let kt=0;kt<p.outChannels;++kt)K[Se+kt*L]+=dt*j[ct+kt];ct+=p.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,K)}var Dj={kernelName:Ha,backendName:"cpu",kernelFunc:f2};function $j(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:c,dimRoundingMode:l,filterShape:u}=r;ke([s,a],"conv2dBackpropFilter");let d=_.convertConv2DDataFormat(c),p=_.computeConv2DInfo(s.shape,u,o,1,i,l,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,b=p.dataFormat==="channelsLast",y=new Gt(p.filterShape,"float32"),v=p.padInfo.left,x=p.padInfo.top,w=n.data.get(s.dataId).values,T=n.data.get(a.dataId).values,N=new Gt(s.shape,s.dtype,w),$=new Gt(a.shape,a.dtype,T);for(let D=0;D<m;++D){let P=Math.max(0,Math.ceil((x-D)/h)),F=Math.min(p.outHeight,(p.inHeight+x-D)/h);for(let R=0;R<g;++R){let C=Math.max(0,Math.ceil((v-R)/f)),L=Math.min(p.outWidth,(p.inWidth+v-R)/f);for(let G=0;G<p.inChannels;++G)for(let j=0;j<p.outChannels;++j){let K=0;for(let q=0;q<p.batchSize;++q)for(let Z=P;Z<F;++Z){let te=D+Z*h-x;for(let se=C;se<L;++se){let oe=R+se*f-v;b?K+=N.get(q,te,oe,G)*$.get(q,Z,se,j):K+=N.get(q,G,te,oe)*$.get(q,j,Z,se)}}y.set(K,D,R,G,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var Fj={kernelName:rh,backendName:"cpu",kernelFunc:$j};function Rj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:c,dataFormat:l,dimRoundingMode:u}=r;ke([s,a],"conv2dBackpropInput");let d=k.computeStrides(a.shape),p=k.computeStrides(s.shape),h=_.convertConv2DDataFormat(l),f=_.computeConv2DInfo(o,a.shape,i,1,c,u,!1,h),m=new Gt(f.inShape,"float32"),g=m.values,b=n.data.get(s.dataId).values,y=n.data.get(a.dataId).values,[v,x,w]=d,{batchSize:T,filterHeight:N,filterWidth:$,inChannels:D,inHeight:P,inWidth:F,outChannels:R,outHeight:C,outWidth:L,strideHeight:G,strideWidth:j}=f;h=f.dataFormat;let K=N-1-f.padInfo.top,q=$-1-f.padInfo.left,Z=h==="channelsLast",te=m.strides[0],se=Z?m.strides[1]:m.strides[2],oe=Z?m.strides[2]:1,re=Z?1:m.strides[1],ue=p[0],ne=Z?p[1]:p[2],he=Z?p[2]:1,ye=Z?1:p[1];for(let Ce=0;Ce<T;++Ce)for(let Se=0;Se<D;++Se)for(let _e=0;_e<P;++_e){let Le=_e-K,Ze=Math.max(0,Math.ceil(Le/G)),Ve=Math.min(C,(N+Le)/G);for(let Ue=0;Ue<F;++Ue){let ct=Ue-q,Je=Math.max(0,Math.ceil(ct/j)),dt=Math.min(L,($+ct)/j),kt=0;for(let Qe=Ze;Qe<Ve;++Qe){let jn=Qe*G-Le;for(let sn=Je;sn<dt;++sn){let wr=sn*j-ct,$n=ue*Ce+ne*Qe+he*sn,qn=v*(N-1-jn)+x*($-1-wr)+w*Se;for(let ir=0;ir<R;++ir){let kr=b[$n+ye*ir],cr=y[qn+ir];kt+=kr*cr}}}let Dn=te*Ce+se*_e+oe*Ue+re*Se;g[Dn]=kt}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var Pj={kernelName:ja,backendName:"cpu",kernelFunc:Rj};function Oj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c}=r;ke([s,a],"conv3d");let l=_.computeConv3DInfo(s.shape,a.shape,o,c,i),{filterDepth:u,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Gt(l.outShape,s.dtype),w=n.data.get(s.dataId).values,T=n.data.get(a.dataId).values,N=x.values,$=k.computeStrides(s.shape),D=k.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let F=P*$[0],R=P*x.strides[0];for(let C=0;C<l.outDepth;++C){let L=R+C*x.strides[1],G=C*l.strideDepth-b;for(let j=0;j<u;++j){let K=G+j*h;if(K<0||K>=l.inDepth)continue;let q=j*D[0],Z=F+K*$[1];for(let te=0;te<l.outHeight;++te){let se=L+te*x.strides[2],oe=te*l.strideHeight-v;for(let re=0;re<d;++re){let ue=oe+re*f;if(ue<0||ue>=l.inHeight)continue;let ne=q+re*D[1],he=Z+ue*$[2];for(let ye=0;ye<l.outWidth;++ye){let Ce=se+ye*l.outChannels,Se=ye*l.strideWidth-y;for(let _e=0;_e<p;++_e){let Le=Se+_e*m;if(Le<0||Le>=l.inWidth)continue;let Ze=ne+_e*D[2],Ve=he+Le*l.inChannels,Ue=Ze;for(let ct=0;ct<l.inChannels;++ct){let Je=w[Ve+ct];for(let dt=0;dt<l.outChannels;++dt)N[Ce+dt]+=Je*T[Ue+dt];Ue+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var Mj={kernelName:_l,backendName:"cpu",kernelFunc:Oj};function Lj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:c}=r;ke([s,a],"conv3dBackpropFilterV2");let l=k.computeStrides(s.shape),u=k.computeStrides(a.shape),d=_.computeConv3DInfo(s.shape,c,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,b=d.filterWidth,y=new Gt(d.filterShape,"float32"),v=y.values,[x,w,T,N]=y.strides,$=n.data.get(a.dataId).values,[D,P,F,R]=u,C=n.data.get(s.dataId).values,[L,G,j,K]=l,q=d.padInfo.front,Z=d.padInfo.left,te=d.padInfo.top;for(let se=0;se<m;++se){let oe=Math.max(0,Math.ceil((q-se)/p)),re=Math.min(d.outDepth,(d.inDepth+q-se)/p),ue=se*x;for(let ne=0;ne<g;++ne){let he=Math.max(0,Math.ceil((te-ne)/h)),ye=Math.min(d.outHeight,(d.inHeight+te-ne)/h),Ce=ne*w+ue;for(let Se=0;Se<b;++Se){let _e=Math.max(0,Math.ceil((Z-Se)/f)),Le=Math.min(d.outWidth,(d.inWidth+Z-Se)/f),Ze=Se*T+Ce;for(let Ve=0;Ve<d.inChannels;++Ve){let Ue=Ve*N+Ze;for(let ct=0;ct<d.outChannels;++ct){let Je=0;for(let dt=0;dt<d.batchSize;++dt){let kt=dt*L,Dn=dt*D;for(let Qe=oe;Qe<re;++Qe){let sn=(se+Qe*p-q)*G+kt,wr=Qe*P+Dn;for(let $n=he;$n<ye;++$n){let ir=(ne+$n*h-te)*j+sn,kr=$n*F+wr;for(let cr=_e;cr<Le;++cr){let Ws=(Se+cr*f-Z)*K+ir,pn=cr*R+kr;Je+=C[Ws+Ve]*$[pn+ct]}}}}v[Ue+ct]=Je}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var Bj={kernelName:sh,backendName:"cpu",kernelFunc:Lj};function zj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:c}=r;ke([s],"conv3dBackpropInputV2");let l=k.computeStrides(s.shape),u=k.computeStrides(a.shape),d=_.computeConv3DInfo(c,a.shape,i,1,o),p=new Gt(d.inShape,"float32"),h=p.values,[f,m,g,b]=p.strides,y=n.data.get(s.dataId).values,[v,x,w,T]=l,N=n.data.get(a.dataId).values,[$,D,P,F]=u,{batchSize:R,filterDepth:C,filterHeight:L,filterWidth:G,inChannels:j,inDepth:K,inHeight:q,inWidth:Z,outChannels:te,outDepth:se,outHeight:oe,outWidth:re,strideDepth:ue,strideHeight:ne,strideWidth:he}=d,ye=C-1-d.padInfo.front,Ce=L-1-d.padInfo.top,Se=G-1-d.padInfo.left;for(let _e=0;_e<R;++_e)for(let Le=0;Le<j;++Le)for(let Ze=0;Ze<K;++Ze){let Ve=Ze-ye,Ue=Math.max(0,Math.ceil(Ve/ue)),ct=Math.min(se,(C+Ve)/ue);for(let Je=0;Je<q;++Je){let dt=Je-Ce,kt=Math.max(0,Math.ceil(dt/ne)),Dn=Math.min(oe,(L+dt)/ne);for(let Qe=0;Qe<Z;++Qe){let jn=Qe-Se,sn=Math.max(0,Math.ceil(jn/he)),wr=Math.min(re,(G+jn)/he),$n=0;for(let qn=Ue;qn<ct;++qn){let ir=qn*ue-Ve;for(let kr=kt;kr<Dn;++kr){let cr=kr*ne-dt;for(let Fn=sn;Fn<wr;++Fn){let Ws=Fn*he-jn,pn=v*_e+x*qn+w*kr+T*Fn,Vs=$*(C-1-ir)+D*(L-1-cr)+P*(G-1-Ws)+F*Le;for(let ur=0;ur<te;++ur){let cl=y[pn+ur],ul=N[Vs+ur];$n+=cl*ul}}}}h[f*_e+m*Ze+g*Je+b*Qe+Le]=$n}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var Wj={kernelName:ah,backendName:"cpu",kernelFunc:zj},Vj=it(qa,e=>Math.cos(e)),Uj={kernelName:qa,backendName:"cpu",kernelFunc:Vj},Gj=it(Ka,e=>Math.cosh(e)),Hj={kernelName:Ka,backendName:"cpu",kernelFunc:Gj};function jj(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:c,extrapolationValue:l}=r,[u,d,p,h]=s.shape,f=a.shape[0],[m,g]=i,b=ze([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(o.dataId).values,x=n.data.get(s.dataId).values,w=k.computeStrides(s.shape),T=k.computeStrides(b.shape);for(let N=0;N<f;N++){let $=N*4,D=y[$],P=y[$+1],F=y[$+2],R=y[$+3],C=v[N];if(C>=u)continue;let L=m>1?(F-D)*(d-1)/(m-1):0,G=g>1?(R-P)*(p-1)/(g-1):0;for(let j=0;j<m;j++){let K=m>1?D*(d-1)+j*L:.5*(D+F)*(d-1);if(K<0||K>d-1){for(let q=0;q<g;q++)for(let Z=0;Z<h;Z++){let te=Z+q*T[2]+j*T[1]+N*T[0];b.values[te]=l}continue}if(c==="bilinear"){let q=Math.floor(K),Z=Math.ceil(K),te=K-q;for(let se=0;se<g;se++){let oe=g>1?P*(p-1)+se*G:.5*(P+R)*(p-1);if(oe<0||oe>p-1){for(let he=0;he<h;he++){let ye=he+se*T[2]+j*T[1]+N*T[0];b.values[ye]=l}continue}let re=Math.floor(oe),ue=Math.ceil(oe),ne=oe-re;for(let he=0;he<h;he++){let ye=he+re*w[2]+q*w[1]+C*w[0],Ce=x[ye];ye=he+ue*w[2]+q*w[1]+C*w[0];let Se=x[ye];ye=he+re*w[2]+Z*w[1]+C*w[0];let _e=x[ye];ye=he+ue*w[2]+Z*w[1]+C*w[0];let Le=x[ye],Ze=Ce+(Se-Ce)*ne,Ve=_e+(Le-_e)*ne;ye=he+se*T[2]+j*T[1]+N*T[0],b.values[ye]=Ze+(Ve-Ze)*te}}}else for(let q=0;q<g;++q){let Z=g>1?P*(p-1)+q*G:.5*(P+R)*(p-1);if(Z<0||Z>p-1){for(let oe=0;oe<h;oe++){let re=oe+q*T[2]+j*T[1]+N*T[0];b.values[re]=l}continue}let te=Math.round(Z),se=Math.round(K);for(let oe=0;oe<h;oe++){let re=oe+te*w[2]+se*w[1]+C*w[0],ue=oe+q*T[2]+j*T[1]+N*T[0];b.values[ue]=x[re]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var qj={kernelName:cc,backendName:"cpu",kernelFunc:jj};function Kj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;ke(s,"cumsum");let c=_.getAxesPermutation([a],s.shape.length),l=s;c!=null&&(l=gr({inputs:{x:s},backend:n,attrs:{perm:c}}));let u=_.getInnerMostAxes(1,s.shape.length)[0];if(u!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${u}`);let d=Tr(l.dtype,"int32"),p=k.makeZerosTypedArray(k.sizeFromShape(l.shape),d),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=i?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)p[v]=o?0:h[v];else{let x=m(b,y-1);p[v]=o?h[x]+p[x]:h[v]+p[x]}}let g=n.makeTensorInfo(l.shape,d,p);if(c!=null){let b=_.getUndoAxesPermutation(c),y=gr({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var Xj={kernelName:Xa,backendName:"cpu",kernelFunc:Kj};function Yj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let c=n.data.get(s.dataId).values,l=n.data.get(a.dataId).values,u=Aw(c,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(s.shape.length===2){let c=n.bufferSync(s),l=n.bufferSync(a),u=SC(c,l,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var Zj={kernelName:oh,backendName:"cpu",kernelFunc:Yj};function Jj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r;k.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);let i=s.shape[0],c=s.shape[1],l=s.shape[2],u=s.shape[3],d=c*a,p=l*a,h=u/(a*a),f=n.data.get(s.dataId).values,m=new Float32Array(i*d*p*h),g=0;for(let b=0;b<i;++b)for(let y=0;y<d;++y){let v=Math.floor(y/a),x=y%a;for(let w=0;w<p;++w){let T=Math.floor(w/a),N=w%a,$=(x*a+N)*h;for(let D=0;D<h;++D){let F=D+$+u*(T+l*(v+c*b));m[g++]=f[F]}}}return n.makeTensorInfo([i,d,p,h],s.dtype,m)}var Qj={kernelName:uc,backendName:"cpu",kernelFunc:Jj};function m2(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c,dimRoundingMode:l}=r;ke([s,a],"depthwiseConv2DNative");let u=k.computeStrides(s.shape),d=k.computeStrides(a.shape),p=c;p==null&&(p=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(o,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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tq(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:c,dimRoundingMode:l,filterShape:u}=r;ke([s,a],"depthwiseConv2dNativeBackpropFilter");let d=_.computeConv2DInfo(s.shape,u,o,i,c,l,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new Gt(d.filterShape,"float32"),b=d.padInfo.left,y=d.padInfo.top,v=d.outChannels/d.inChannels,x=n.data.get(s.dataId).values,w=new Gt(s.shape,s.dtype,x),T=n.data.get(a.dataId).values,N=new Gt(a.shape,a.dtype,T);for(let $=0;$<f;++$){let D=Math.max(0,Math.ceil((y-$)/p)),P=Math.min(d.outHeight,(d.inHeight+y-$)/p);for(let F=0;F<m;++F){let R=Math.max(0,Math.ceil((b-F)/h)),C=Math.min(d.outWidth,(d.inWidth+b-F)/h);for(let L=0;L<d.outChannels;++L){let G=Math.trunc(L/v),j=L%v,K=0;for(let q=0;q<d.batchSize;++q)for(let Z=D;Z<P;++Z){let te=$+Z*p-y;for(let se=R;se<C;++se){let oe=F+se*h-b;K+=w.get(q,te,oe,G)*N.get(q,Z,se,L)}}g.set(K,$,F,G,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var nq={kernelName:ih,backendName:"cpu",kernelFunc:tq};function rq(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:c,dimRoundingMode:l,inputShape:u}=r;ke([s,a],"depthwiseConv2DNativeBackpropInput");let d=k.computeStrides(s.shape),p=k.computeStrides(a.shape),h=_.computeConv2DInfo(u,a.shape,o,i,c,l,!0),f=new Gt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(s.dataId).values,[x,w,T]=d,N=n.data.get(a.dataId).values,[$,D,P]=p,{batchSize:F,filterHeight:R,filterWidth:C,inChannels:L,inHeight:G,inWidth:j,outChannels:K,outHeight:q,outWidth:Z,strideHeight:te,strideWidth:se}=h,oe=R-1-h.padInfo.top,re=C-1-h.padInfo.left,ue=K/L;for(let ne=0;ne<F;++ne)for(let he=0;he<L;++he)for(let ye=0;ye<G;++ye){let Ce=ye-oe,Se=Math.max(0,Math.ceil(Ce/te)),_e=Math.min(q,(R+Ce)/te);for(let Le=0;Le<j;++Le){let Ze=Le-re,Ve=Math.max(0,Math.ceil(Ze/se)),Ue=Math.min(Z,(C+Ze)/se),ct=0;for(let Je=Se;Je<_e;++Je){let dt=Je*te-Ce;for(let kt=Ve;kt<Ue;++kt){let Dn=kt*se-Ze,Qe=x*ne+w*Je+T*kt,jn=$*(R-1-dt)+D*(C-1-Dn)+P*he;for(let sn=0;sn<ue;++sn){let wr=he*ue+sn,$n=v[Qe+wr],qn=N[jn+sn];ct+=$n*qn}}}m[g*ne+b*ye+y*Le+he]=ct}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var sq={kernelName:ch,backendName:"cpu",kernelFunc:rq};function aq(e){let{inputs:t,backend:n}=e,{x:r}=t,s=k.sizeFromShape(r.shape),a=n.data.get(r.dataId).values,o=ze([s,s],r.dtype),i=o.values;for(let l=0;l<a.length;l++)i[l*s+l]=a[l];let c=[...r.shape,...r.shape];return n.makeTensorInfo(c,o.dtype,o.values)}var oq={kernelName:uh,backendName:"cpu",kernelFunc:aq},iq={kernelName:El,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s}=e,{strides:a,pad:o,dilations:i}=n,c=t,l=c.data.get(r.dataId).values,u=r.shape.length,d=c.data.get(s.dataId).values,p=s.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:w,filterHeight:T,filterWidth:N,dilationHeight:$,dilationWidth:D,outShape:P}=_.computeDilation2DInfo(r.shape,s.shape,a,o,"NHWC",i),F=k.sizeFromShape(P),R=P.length,C=k.getArrayFromDType(r.dtype,F);for(let G=0;G<h;++G)for(let j=0;j<b;++j){let K=j*x-v.top;for(let q=0;q<y;++q){let Z=q*w-v.left;for(let te=0;te<g;++te){let se=Number.MIN_SAFE_INTEGER;for(let re=0;re<T;++re){let ue=K+re*$;if(ue>=0&&ue<f)for(let ne=0;ne<N;++ne){let he=Z+ne*D;if(he>=0&&he<m){let ye=k.locToIndex([G,ue,he,te],u,k.computeStrides(r.shape)),Ce=k.locToIndex([re,ne,te],p,k.computeStrides(s.shape)),Se=l[ye]+d[Ce];Se>se&&(se=Se)}}}let oe=k.locToIndex([G,j,q,te],R,k.computeStrides(P));C[oe]=se}}}return{dataId:c.write(k.toTypedArray(C,r.dtype),P,r.dtype),shape:P,dtype:r.dtype}}},cq={kernelName:dh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s,dy:a}=e,{strides:o,pad:i,dilations:c}=n,l=t,u=k.toNestedArray(r.shape,l.data.get(r.dataId).values),d=k.toNestedArray(s.shape,l.data.get(s.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:w,filterWidth:T,dilationHeight:N,dilationWidth:$,outShape:D}=_.computeDilation2DInfo(r.shape,s.shape,o,i,"NHWC",c);k.assert(a.rank===D.length,()=>`Error in ${dh}, dy must have the same rank as output ${D.length}, but got ${a.rank}`);let P=k.toNestedArray(D,l.data.get(a.dataId).values),F=k.makeZerosNestedTypedArray(s.shape,s.dtype);for(let C=0;C<p;++C)for(let L=0;L<g;++L){let G=L*v-y.top;for(let j=0;j<b;++j){let K=j*x-y.left;for(let q=0;q<m;++q){let Z=Number.MIN_SAFE_INTEGER,te=0,se=0;for(let oe=0;oe<w;++oe){let re=G+oe*N;if(re>=0&&re<h)for(let ue=0;ue<T;++ue){let ne=K+ue*$;if(ne>=0&&ne<f){let he=u[C][re][ne][q]+d[oe][ue][q];he>Z&&(Z=he,te=oe,se=ue)}}}F[te][se][q]+=P[C][L][j][q]}}}return{dataId:l.write(k.toTypedArray(F,r.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},uq={kernelName:lh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s,dy:a}=e,{strides:o,pad:i,dilations:c}=n,l=t,u=k.toNestedArray(r.shape,l.data.get(r.dataId).values),d=k.toNestedArray(s.shape,l.data.get(s.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:w,filterWidth:T,dilationHeight:N,dilationWidth:$,outShape:D}=_.computeDilation2DInfo(r.shape,s.shape,o,i,"NHWC",c);k.assert(a.rank===D.length,()=>`Error in ${lh}, dy must have the same rank as output ${D.length}, but got ${a.rank}`);let P=k.toNestedArray(D,l.data.get(a.dataId).values),F=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let C=0;C<p;++C)for(let L=0;L<g;++L){let G=L*v-y.top;for(let j=0;j<b;++j){let K=j*x-y.left;for(let q=0;q<m;++q){let Z=Number.MIN_SAFE_INTEGER,te=G<0?0:G,se=K<0?0:K;for(let oe=0;oe<w;++oe){let re=G+oe*N;if(re>=0&&re<h)for(let ue=0;ue<T;++ue){let ne=K+ue*$;if(ne>=0&&ne<f){let he=u[C][re][ne][q]+d[oe][ue][q];he>Z&&(Z=he,te=re,se=ne)}}}F[C][te][se][q]+=P[C][L][j][q]}}}return{dataId:l.write(k.toTypedArray(F,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function Bd(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;ke(s,"sum");let i;s.dtype==="bool"?i=Ia({inputs:{x:s},backend:n,attrs:{dtype:"int32"}}):i=fs({inputs:{x:s},backend:n});let c=i.shape.length,l=k.parseAxisParam(a,i.shape),u=_.getAxesPermutation(l,c),d=l,p=i;u!=null&&(p=gr({inputs:{x:i},backend:n,attrs:{perm:u}}),d=_.getInnerMostAxes(d.length,c)),_.assertAxesAreInnerMostDims("sum",d,p.shape.length);let[h,f]=_.computeOutAndReduceShapes(p.shape,d),m=_.upcastType(p.dtype,"int32"),g=dm(n,h,m),b=k.sizeFromShape(f),y=n.data.get(g.dataId).values,v=n.data.get(p.dataId).values;for(let x=0;x<y.length;++x){let w=x*b,T=0;for(let N=0;N<b;++N)T+=v[w+N];y[x]=T}if(o){let x=_.expandShapeToKeepDim(g.shape,l),w=g;g=_t({inputs:{x:g},backend:n,attrs:{shape:x}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(i),u!=null&&n.disposeIntermediateTensorInfo(p),g}var lq={kernelName:Eo,backendName:"cpu",kernelFunc:Bd};function dq(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:c}=_.decodeEinsumEquation(s,a.length);_.checkEinsumDimSizes(o.length,c,a);let{path:l,steps:u}=_.getEinsumComputePath(i,c),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:b,expandDims:y}=_.getEinsumPermutation(h,c[g]),v;_.isIdentityPermutation(b)?v=a[g]:(v=gr({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let w=0;w<y.length;++w)x.splice(y[w],0,1);k.arraysEqual(v.shape,x)||(v=_t({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),p===null?p=v:(p=pm({inputs:{a:v,b:p},backend:n}),f.push(p))}m<d-1&&(l[m]>=0&&(p=Bd({inputs:{x:p},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var pq={kernelName:ph,backendName:"cpu",kernelFunc:dq};function hq(e){let{inputs:t,backend:n}=e,{dy:r,y:s}=t;ke([r,s],"eluGrad");let a=new Float32Array(k.sizeFromShape(s.shape)),o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values;for(let c=0;c<o.length;++c){let l=o[c];l>=1?a[c]=i[c]:a[c]=i[c]*(l+1)}return n.makeTensorInfo(s.shape,"float32",a)}var fq={kernelName:hh,backendName:"cpu",kernelFunc:hq},mq=_.ERF_P,gq=_.ERF_A1,bq=_.ERF_A2,yq=_.ERF_A3,vq=_.ERF_A4,xq=_.ERF_A5,wq=it(lc,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+mq*n);return t*(1-((((xq*r+vq)*r+yq)*r+bq)*r+gq)*r*Math.exp(-n*n))}),kq={kernelName:lc,backendName:"cpu",kernelFunc:wq};function fm(e){let{inputs:t,backend:n,attrs:r}=e,{input:s}=t,{dim:a}=r,o=s.shape.length,i=s.shape.slice(),c=a;return a<0&&(k.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),c=o+a+1),i.splice(c,0,1),_t({inputs:{x:s},backend:n,attrs:{shape:i}})}var Iq={kernelName:pc,backendName:"cpu",kernelFunc:fm},Sq=Vt((e,t)=>e/t),Bw=nn(Za,Sq),zw={kernelName:Za,backendName:"cpu",kernelFunc:Bw};function g2(e,t,n){let r=e.shape,s=r[0],a=r[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,c=o.complexTensorInfos.imag,l=[s,a],u=k.sizeFromShape(l),d=k.getTypedArrayFromDType("float32",u),p=k.getTypedArrayFromDType("float32",u);for(let g=0;g<s;g++){let b=di({inputs:{x:i},backend:n,attrs:{begin:[g,0],size:[1,a]}}),y=di({inputs:{x:c},backend:n,attrs:{begin:[g,0],size:[1,a]}}),v=sr({inputs:{real:b,imag:y},backend:n}),{real:x,imag:w}=Tq(v,t,n),T=_.mergeRealAndImagArrays(x,w);for(let N=0;N<a;N++){let $=_.getComplexWithIndex(T,N);d[g*a+N]=$.real,p[g*a+N]=$.imag}n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(v)}let h=n.makeTensorInfo(l,"float32",d),f=n.makeTensorInfo(l,"float32",p),m=sr({inputs:{real:h,imag:f},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}function Tq(e,t,n){let r=k.sizeFromShape(e.shape),s=n.data.get(e.dataId),a=n.data.get(s.complexTensorInfos.real.dataId).values,o=n.data.get(s.complexTensorInfos.imag.dataId).values;if(Cq(r)){let i=Ww(a,o,r,t,n),c=[e.shape[0],e.shape[1]];if(t){let l=n.makeTensorInfo(c,"float32",i.real),u=n.makeTensorInfo(c,"float32",i.imag),d=n.makeTensorInfo([],"float32",k.createScalarValue(r,"float32")),p=fs({inputs:{x:d},backend:n}),h=zw.kernelFunc({inputs:{a:l,b:d},backend:n}),f=zw.kernelFunc({inputs:{a:u,b:p},backend:n}),m=n.data.get(h.dataId).values,g=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),{real:m,imag:g}}return i}else{let i=_.mergeRealAndImagArrays(a,o),c=Nq(i,r,t);return _.splitRealAndImagArrays(c)}}function Cq(e){return(e&e-1)===0}function Ww(e,t,n,r,s){if(n===1)return{real:e,imag:t};let a=_.mergeRealAndImagArrays(e,t),o=n/2,i=_.complexWithEvenIndex(a),c=i.real,l=i.imag,u=[c.length],d=s.makeTensorInfo(u,"float32",c),p=s.makeTensorInfo(u,"float32",l),h=sr({inputs:{real:d,imag:p},backend:s}),f=_.complexWithOddIndex(a),m=f.real,g=f.imag,b=[m.length],y=s.makeTensorInfo(b,"float32",m),v=s.makeTensorInfo(b,"float32",g),x=sr({inputs:{real:y,imag:v},backend:s}),w=Ww(c,l,o,r,s),T=w.real,N=w.imag,$=[T.length],D=s.makeTensorInfo($,"float32",T),P=s.makeTensorInfo($,"float32",N),F=sr({inputs:{real:D,imag:P},backend:s}),R=Ww(m,g,o,r,s),C=R.real,L=R.imag,G=[C.length],j=s.makeTensorInfo(G,"float32",C),K=s.makeTensorInfo(G,"float32",L),q=sr({inputs:{real:j,imag:K},backend:s}),Z=_.exponents(n,r),te=[Z.real.length],se=s.makeTensorInfo(te,"float32",Z.real),oe=s.makeTensorInfo(te,"float32",Z.imag),re=sr({inputs:{real:se,imag:oe},backend:s}),ue=pm({inputs:{a:re,b:q},backend:s}),ne=Md({inputs:{a:F,b:ue},backend:s}),he=Ow({inputs:{a:F,b:ue},backend:s}),ye=li({inputs:{input:ne},backend:s}),Ce=li({inputs:{input:he},backend:s}),Se=Cu({inputs:{input:ne},backend:s}),_e=Cu({inputs:{input:he},backend:s}),Le=Nu({inputs:[ye,Ce],backend:s,attrs:{axis:0}}),Ze=Nu({inputs:[Se,_e],backend:s,attrs:{axis:0}}),Ve=s.data.get(Le.dataId).values,Ue=s.data.get(Ze.dataId).values;return s.disposeIntermediateTensorInfo(d),s.disposeIntermediateTensorInfo(p),s.disposeIntermediateTensorInfo(h),s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(v),s.disposeIntermediateTensorInfo(x),s.disposeIntermediateTensorInfo(D),s.disposeIntermediateTensorInfo(P),s.disposeIntermediateTensorInfo(F),s.disposeIntermediateTensorInfo(j),s.disposeIntermediateTensorInfo(K),s.disposeIntermediateTensorInfo(q),s.disposeIntermediateTensorInfo(se),s.disposeIntermediateTensorInfo(oe),s.disposeIntermediateTensorInfo(re),s.disposeIntermediateTensorInfo(ue),s.disposeIntermediateTensorInfo(ne),s.disposeIntermediateTensorInfo(he),s.disposeIntermediateTensorInfo(ye),s.disposeIntermediateTensorInfo(Se),s.disposeIntermediateTensorInfo(Ce),s.disposeIntermediateTensorInfo(_e),s.disposeIntermediateTensorInfo(Le),s.disposeIntermediateTensorInfo(Ze),{real:Ve,imag:Ue}}function Nq(e,t,n){let r=new Float32Array(t*2);for(let s=0;s<t;s++){let a=0,o=0;for(let i=0;i<t;i++){let c=_.exponent(s*i,t,n),l=_.getComplexWithIndex(e,i);a+=l.real*c.real-l.imag*c.imag,o+=l.real*c.imag+l.imag*c.real}n&&(a/=t,o/=t),_.assignToTypedArray(r,a,o,s)}return r}function _q(e){let{inputs:t,backend:n}=e,{input:r}=t,s=k.sizeFromShape(r.shape),a=r.shape[r.shape.length-1],o=s/a,i=_t({inputs:{x:r},backend:n,attrs:{shape:[o,a]}}),c=g2(i,!1,n),l=_t({inputs:{x:c},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),l}var Eq={kernelName:fh,backendName:"cpu",kernelFunc:_q};function Vw(e){let{backend:t,attrs:n}=e,{shape:r,value:s,dtype:a}=n,o=a||k.inferDtype(s),i=k.getArrayFromDType(o,k.sizeFromShape(r));return Dq(i,s,o),t.makeTensorInfo(r,o,i)}var Aq={kernelName:Al,backendName:"cpu",kernelFunc:Vw};function Dq(e,t,n){e.fill(t)}var $q={kernelName:fc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,s=n,a=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(r.shape)),[o,i,c,l]=r.shape,u=s.data.get(r.dataId).values;for(let p=0;p<o;p++){let h=p*c*i*l;for(let f=0;f<i;f++){let m=f*(c*l);for(let g=0;g<c;g++){let b=g*l;for(let y=0;y<l;y++){let v=Math.round(c-g-1),x=h+m+b+y,w=u[x];if(v>=0&&v<c){let T=v*l,N=h+m+T+y;w=u[N]}a[x]=w}}}}return{dataId:s.write(a,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Fq=Vt((e,t)=>Math.floor(e/t)),Rq=nn(to,Fq,null,"int32"),Pq={kernelName:to,backendName:"cpu",kernelFunc:Rq};function Oq(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:l,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=r,m=f2({inputs:{x:s,filter:a},backend:n,attrs:{strides:c,pad:l,dataFormat:u,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=Md({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=Mw(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var Mq={kernelName:Mo,backendName:"cpu",kernelFunc:Oq};function Lq(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:l,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=r,m=m2({inputs:{x:s,filter:a},backend:n,attrs:{strides:c,pad:l,dataFormat:u,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=Md({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=Mw(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var Bq={kernelName:Lo,backendName:"cpu",kernelFunc:Lq};function zq(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=k.sizeFromShape(r.shape),o=s.shape,i=o[o.length-1],[c,l,u,d]=_.prepareAndValidate(r,s);if(l===0)return n.makeTensorInfo(c,r.dtype,[]);let p=n.data.get(s.dataId).values,h=n.bufferSync(r),f=$C(p,h,r.dtype,l,i,u,d,r.shape,a);return n.makeTensorInfo(c,r.dtype,f.values)}var Wq={kernelName:gc,backendName:"cpu",kernelFunc:zq};function Vq(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r;ke([s,a],"gatherV2");let c=k.parseAxisParam(o,s.shape)[0],l=n.data.get(a.dataId).values,u=s.shape[c];for(let x=0;x<l.length;++x){let w=l[x];k.assert(w<=u-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=i;i==null&&(d=0);let p=k.sizeFromShape(a.shape),h=_.segment_util.collectGatherOpShapeInfo(s,a,c,d),f=_t({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),m=_t({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,p/h.batchSize]}}),g=[h.batchSize,h.outerSize,p/h.batchSize,h.sliceSize],b=n.bufferSync(m),y=n.bufferSync(f),v=FC(y,b,g);return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.makeTensorInfo(h.outputShape,v.dtype,v.values)}var Uq={kernelName:mc,backendName:"cpu",kernelFunc:Vq};function 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x8={kernelName:xh,backendName:"cpu",kernelFunc:v8};function w8(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;ke([a,o],"maxPoolGrad");let{filterSize:c,strides:l,pad:u,dimRoundingMode:d}=r,p=_.computePool2DInfo(i.shape,c,l,1,u,d),h=n.data.get(i.dataId).values,f=ze(p.outShape,i.dtype,p2(h,i.shape,i.dtype,p).values),m=p.strideHeight,g=p.strideWidth,b=p.dilationHeight,y=p.dilationWidth,v=p.effectiveFilterHeight,x=p.effectiveFilterWidth,w=x-1-p.padInfo.left,T=v-1-p.padInfo.top,N=ze(i.shape,"float32"),$=n.data.get(s.dataId).values,D=ze(s.shape,"float32",$);for(let P=0;P<p.batchSize;++P)for(let F=0;F<p.inChannels;++F)for(let R=0;R<p.inHeight;++R)for(let C=0;C<p.inWidth;++C){let L=R-T,G=C-w,j=0;for(let K=0;K<v;K+=b){let q=(L+K)/m;if(!(q<0||q>=p.outHeight||Math.floor(q)!==q))for(let Z=0;Z<x;Z+=y){let te=(G+Z)/g;if(te<0||te>=p.outWidth||Math.floor(te)!==te)continue;let 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g=n.makeTensorInfo(d,u.dtype,f);if(o){let b=_.expandShapeToKeepDim(d,i),y=_t({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var _8={kernelName:po,backendName:"cpu",kernelFunc:N8};function E8(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,mode:o}=r;ke(s,"mirrorPad");let i=a.map((v,x)=>v[0]+s.shape[x]+v[1]),c=a.map(v=>v[0]),l=a.map((v,x)=>v[0]+s.shape[x]),u=o==="reflect"?0:1,d=n.data.get(s.dataId).values,p=s.shape.length,h=k.computeStrides(s.shape),f=k.sizeFromShape(i),m=i.length,g=k.computeStrides(i),b=k.getTypedArrayFromDType(s.dtype,f);for(let v=0;v<f;v++){let x=k.indexToLoc(v,m,g);for(let T=0;T<m;T++)x[T]<c[T]?x[T]=c[T]*2-x[T]-u:x[T]>=l[T]&&(x[T]=(l[T]-1)*2-x[T]+u);x=x.map((T,N)=>T-c[N]);let w=k.locToIndex(x,p,h);b[v]=d[w]}return{dataId:n.write(b,i,s.dtype),shape:i,dtype:s.dtype}}var A8={kernelName:fo,backendName:"cpu",kernelFunc:E8},D8=Vt((e,t)=>{let n=e%t;return 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c=i?s:y2({inputs:{logits:s},backend:n,attrs:{dim:-1}}),l=c.shape[0],u=c.shape[1],d=n.data.get(c.dataId).values,p=[l,a],h=k.makeZerosTypedArray(k.sizeFromShape(p),"int32");for(let f=0;f<l;++f){let m=f*u,g=new Float32Array(u-1);g[0]=d[m];for(let v=1;v<g.length;++v)g[v]=g[v-1]+d[m+v];let b=R8.alea(o.toString()),y=f*a;for(let v=0;v<a;++v){let x=b();h[y+v]=g.length;for(let w=0;w<g.length;w++)if(x<g[w]){h[y+v]=w;break}}}return i||n.disposeIntermediateTensorInfo(c),n.makeTensorInfo(p,"int32",h)}var M8={kernelName:kh,backendName:"cpu",kernelFunc:O8},L8=is.nonMaxSuppressionV3Impl;function B8(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c}=r;ke(s,"NonMaxSuppression");let l=n.data.get(s.dataId).values,u=n.data.get(a.dataId).values,{selectedIndices:d}=L8(l,u,o,i,c);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var z8={kernelName:_c,backendName:"cpu",kernelFunc:B8},W8=is.nonMaxSuppressionV4Impl;function V8(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c,padToMaxOutputSize:l}=r;ke(s,"NonMaxSuppressionPadded");let u=n.data.get(s.dataId).values,d=n.data.get(a.dataId).values,{selectedIndices:p,validOutputs:h}=W8(u,d,o,i,c,l);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var U8={kernelName:Ec,backendName:"cpu",kernelFunc:V8},G8=is.nonMaxSuppressionV5Impl;function H8(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c,softNmsSigma:l}=r;ke(s,"NonMaxSuppressionWithScore");let u=n.data.get(s.dataId).values,d=n.data.get(a.dataId).values,p=o,h=i,f=c,m=l,{selectedIndices:g,selectedScores:b}=G8(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var j8={kernelName:Ac,backendName:"cpu",kernelFunc:H8};function q8(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{depth:a,onValue:o,offValue:i}=r;ke(s,"oneHot");let c=k.sizeFromShape(s.shape),l=new Float32Array(c*a);l.fill(i);let u=n.data.get(s.dataId).values;for(let d=0;d<c;++d)u[d]>=0&&u[d]<a&&(l[d*a+u[d]]=o);return n.makeTensorInfo([...s.shape,a],"int32",l)}var K8={kernelName:go,backendName:"cpu",kernelFunc:q8};function mm(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(r.dtype==="complex64"){let s=li({inputs:{input:r},backend:n}),a=mm({inputs:{x:s},backend:n}),o=Cu({inputs:{input:r},backend:n}),i=mm({inputs:{x:o},backend:n}),c=sr({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),c}else return Vw({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}var X8={kernelName:Yc,backendName:"cpu",kernelFunc:mm};function v2(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(r.dtype==="complex64"){let s=li({inputs:{input:r},backend:n}),a=v2({inputs:{x:s},backend:n}),o=Cu({inputs:{input:r},backend:n}),i=mm({inputs:{x:o},backend:n}),c=sr({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),c}else return Vw({backend:n,attrs:{shape:r.shape,value:1,dtype:r.dtype}})}var Y8={kernelName:Dc,backendName:"cpu",kernelFunc:v2};function x2(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return fm({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],c=t.map(u=>{let 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t.makeTensorInfo([i.length],a,i)}var rK={kernelName:Pl,backendName:"cpu",kernelFunc:nK},sK=it(Rc,e=>1/e),aK={kernelName:Rc,backendName:"cpu",kernelFunc:sK};function oK(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r;ke(s,"resizeBilinear");let c=k.computeStrides(s.shape),[l,u]=i,[d,p,h,f]=s.shape,m=n.data.get(s.dataId).values,g=new Float32Array(k.sizeFromShape([d,l,u,f])),b=[a&&l>1?p-1:p,a&&u>1?h-1:h],y=[a&&l>1?l-1:l,a&&u>1?u-1:u],v=0,x=b[0]/y[0],w=b[1]/y[1];for(let T=0;T<d;T++)for(let N=0;N<l;N++){let $;o?$=x*(N+.5)-.5:$=x*N;let D=Math.max(0,Math.floor($)),P=$-D,F=Math.min(p-1,Math.ceil($)),R=T*c[0]+D*c[1],C=T*c[0]+F*c[1];for(let L=0;L<u;L++){let G;o?G=w*(L+.5)-.5:G=w*L;let j=Math.max(0,Math.floor(G)),K=G-j,q=Math.min(h-1,Math.ceil(G)),Z=R+j*c[2],te=C+j*c[2],se=R+q*c[2],oe=C+q*c[2];for(let re=0;re<f;re++){let ue=m[Z+re],ne=m[te+re],he=m[se+re],ye=m[oe+re],Ce=ue+(he-ue)*K,Se=ne+(ye-ne)*K,_e=Ce+(Se-Ce)*P;g[v++]=_e}}}return 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lK(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r;ke(s,"resizeNearestNeighbor");let c=k.computeStrides(s.shape),[l,u]=i,[d,p,h,f]=s.shape,m=n.data.get(s.dataId).values,g=new Float32Array(d*l*u*f),b=[a&&l>1?p-1:p,a&&u>1?h-1:h],y=[a&&l>1?l-1:l,a&&u>1?u-1:u],v=b[0]/y[0],x=b[1]/y[1],w=0;for(let T=0;T<d;T++){let N=T*c[0];for(let $=0;$<l;$++){let D=o?v*($+.5):v*$,P=Math.min(p-1,a?Math.round(D):Math.floor(D));o&&(P=Math.max(0,P));let F=N+P*c[1];for(let R=0;R<u;R++){let C=o?x*(R+.5):x*R,L=Math.min(h-1,a?Math.round(C):Math.floor(C));o&&(L=Math.max(0,L));let G=F+L*c[2];for(let j=0;j<f;j++){let K=m[G+j];g[w++]=K}}}}return n.makeTensorInfo([d,l,u,f],s.dtype,g)}var dK={kernelName:Ol,backendName:"cpu",kernelFunc:lK};function pK(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r;ke([a,s],"resizeNearestNeighborGrad");let i=k.computeStrides(s.shape),c=k.computeStrides(a.shape),[l,u,d,p]=s.shape,[,h,f]=a.shape,m=new 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wK(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t;ke([r,s,a],"select");let o=r.shape.length,i=n.data.get(r.dataId).values,c=n.data.get(s.dataId).values,l=n.data.get(a.dataId).values,u=Tr(s.dtype,a.dtype),d=k.makeZerosTypedArray(k.sizeFromShape(s.shape),u),p=0,h=o===0||o>1||s.shape.length===1?1:k.sizeFromShape(s.shape.slice(1));for(let f=0;f<i.length;f++)for(let m=0;m<h;m++)i[f]===1?d[p++]=c[f]:d[p++]=l[f];return n.makeTensorInfo(s.shape,u,d)}var kK={kernelName:Mc,backendName:"cpu",kernelFunc:wK},IK=_.SELU_SCALEALPHA,SK=_.SELU_SCALE,TK=it(Lc,e=>e>=0?SK*e:IK*(Math.exp(e)-1)),CK={kernelName:Lc,backendName:"cpu",kernelFunc:TK},NK=it(Wc,e=>e<0?-1:e>0?1:0),_K={kernelName:Wc,backendName:"cpu",kernelFunc:NK},EK=it(Co,e=>Math.sin(e)),AK={kernelName:Co,backendName:"cpu",kernelFunc:EK},DK=it(zc,e=>Math.sinh(e)),$K={kernelName:zc,backendName:"cpu",kernelFunc:DK},FK=11920928955078125e-23,I2=Math.log(FK)+2,RK=it(Vc,e=>{let t=e>-I2,n=e<I2,r=Math.exp(e),s;return 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${r.shape}`);if(s.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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h=di({inputs:{x:s},backend:n,attrs:{begin:l,size:p}});return l[i]+=d,h})}var XK={kernelName:Gc,backendName:"cpu",kernelFunc:KK},YK={kernelName:zl,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t;ke(n,"square");let s=r.data.get(n.dataId).values,a=new Float32Array(s.length);for(let i=0;i<s.length;++i){let c=s[i];a[i]=c*c}return{dataId:r.write(a,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},ZK=it(ta,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),JK={kernelName:ta,backendName:"cpu",kernelFunc:ZK};function QK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:c,endMask:l,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=r;ke(s,"stridedSlice");let{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=Ht.sliceInfo(s.shape,a,o,i,c,l,u,d,p),w;if(m)w=_t({inputs:{x:s},backend:n,attrs:{shape:f}});else if(g||b){k.assert(s.shape.length>=1,()=>`Input must have rank at least 1, got: ${s.shape.length}`);let 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j2(e){if(km==null){let t=Zr(e);km=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,km)}function q2(e){if(e===0)return 0;let t,n=Zr(e);return yr(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:yr(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function yr(e,t){return e.getExtension(t)!=null}function qw(e){try{if(Zr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function K2(e){if(e===0)return!1;let t=Zr(e);if(e===1){if(!yr(t,"OES_texture_float"))return!1}else if(!yr(t,"EXT_color_buffer_float"))return!1;return Kw(t)}function X2(e){if(e===0)return!1;let t=Zr(e);if(e===1){if(!yr(t,"OES_texture_float")||!yr(t,"WEBGL_color_buffer_float"))return!1}else{if(yr(t,"EXT_color_buffer_float"))return Kw(t);let r="EXT_color_buffer_half_float";if(yr(t,r)){let s=t.getExtension(r);return GX(t,s)}return!1}return Kw(t)}function Kw(e){let t=Gw(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,s,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 o=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),o}function GX(e,t){let n=Gw(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let s=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,s,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(o),i}function Y2(e){return e!==2?!1:Zr(e).fenceSync!=null}function Eu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=J();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>qw(2)?2:qw(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",()=>H2(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>j2(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:q2(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Zl.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>K2(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",()=>X2(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Y2(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",()=>Zl.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 Sn(){let e,t,n,r,s,a,o,i,c,l;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",s="texture",a="outputColor",o="out vec4 outputColor;",i=`
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)
`,c="",l=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",r="varying",s="texture2D",a="gl_FragColor",o="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,c=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,l=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:s,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:c,defineRound:l}}function mi(e,t,n="index"){let r=k.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / ${s}`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${s}`:`index -= ${e[a]} * ${s}`;return`${o}; ${i};`}).join("")}function Im(e,t,n="index"){let r=k.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function HX(e,t){let n=e.length,r=e.map(a=>`${t}[${a}]`),s=new Array(n-1);s[n-2]=r[n-1];for(let a=n-3;a>=0;--a)s[a]=`(${s[a+1]} * ${r[a+1]})`;return s}function jX(e,t,n="index"){let r=e.map((a,o)=>o),s=HX(r,t);return s.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${s[o]}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${s[o]}`:`index -= ${e[o]} * ${s[o]}`;return`${i}; ${c};`}).join("")}function Xw(e){let t=k.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function Yw(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var Z2=`
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:J2}=_;function qX(e,t,n){let r=[];if(e.forEach(h=>{let f=k.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Zw(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:r.push(`uniform int ${h.name}Shape;`);break;case 2:r.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:r.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:r.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}r.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:r.push("uniform int outShape;");break;case 2:r.push("uniform ivec2 outShape;"),r.push("uniform int outShapeStrides;");break;case 3:r.push("uniform ivec3 outShape;"),r.push("uniform ivec2 outShapeStrides;");break;case 4:r.push("uniform ivec4 outShape;"),r.push("uniform ivec3 outShapeStrides;");break;default:break}r.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{r.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let s=r.join(`
`),a=e.map(h=>KX(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=Sn(),c=ZX(i),l,u,d=e7(i);return t.isPacked?(l=XX(t.logicalShape,o,n.enableShapeUniforms),u=QX(i)):(l=YX(t.logicalShape,o,n.enableShapeUniforms),u=JX(i)),n.packedInputs&&(d+=s7),[d,c,u,s,l,a,n.userCode].join(`
`)}function Au(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return g7(e,t);case 1:return y7(e,t);case 2:return x7(e,t);case 3:return k7(e,t);case 4:return S7(e,t);case 5:return T7(e);case 6:return C7(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function Q2(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return m7(e);case 1:return b7(e,t);case 2:return v7(e,t);case 3:return w7(e,t);default:return I7(e,t)}}function KX(e,t,n=!1,r){let s="";n?s+=Q2(e,r):s+=Au(e,r);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?s+=N7(e,t):s+=_7(e,t)),s}function XX(e,t,n){switch(e.length){case 0:return eN();case 1:return a7(e,t,n);case 2:return h7(e,t,n);case 3:return i7(e,t,n);default:return u7(e,t,n)}}function YX(e,t,n){switch(e.length){case 0:return eN();case 1:return o7(e,t,n);case 2:return f7(e,t,n);case 3:return c7(e,t,n);case 4:return l7(e,t,n);case 5:return d7(e,t);case 6:return p7(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function ZX(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function JX(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function QX(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function e7(e){return`${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);
}
${t7}
${n7}
${r7}
`}var t7=`
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);
}
`,n7=`
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);
}
`,r7=`
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);
}
`,s7=`
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 eN(){return`
int getOutputCoords() {
return 0;
}
`}function a7(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return r[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${r[1]}.0);
}
`:r[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${r[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);
}
`}function o7(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 * ${t[1]} + resTexRC.y;
}
`}function i7(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),a=s*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec3(b, r, c);
}
`}function c7(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;
${Im(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let r=mi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
return ivec3(r, c, d);
}
`}function u7(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),a=s*Math.ceil(e[e.length-2]/2),o=a,i="",c="b, r, c";for(let l=2;l<e.length-1;l++)o*=e[e.length-l-1],i=`
int b${l} = index / ${o};
index -= b${l} * ${o};
`+i,c=`b${l}, `+c;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec${e.length}(${c});
}
`}function l7(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;
${Im(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let r=mi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
return ivec4(r, c, d, d2);
}
`}function d7(e,t){let n=mi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function p7(e,t){let n=mi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function h7(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${r[0]}, ${r[1]}));
}
`;let s=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec2(r, c);
}
`}function f7(e,t,n){return k.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
return ivec2(r, c);
}
`}function gi(e){return`offset${e}`}function m7(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=Sn();return`
vec4 ${n}() {
return ${r.texture2D}(${t}, halfCR);
}
`}function g7(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${r}() {return ${n};}`;let[s,a]=e.shapeInfo.texShape;if(s===1&&a===1)return`
float ${r}() {
return sampleTexture(${n}, halfCR);
}
`;let o=gi(n);if(t)return`
float ${r}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
return sampleTexture(${n}, uv);
}
`;let[i,c]=e.shapeInfo.texShape;return`
float ${r}() {
vec2 uv = uvFromFlat(${i}, ${c}, ${o});
return sampleTexture(${n}, uv);
}
`}function b7(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,a=Sn();if(t)return`
vec4 ${r}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let o=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];return`
vec4 ${r}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function y7(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${r}(int index) {
${Du(e)}
}
`;let s=e.shapeInfo.texShape,a=s[0],o=s[1];if(o===1&&a===1)return`
float ${r}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let i=gi(n);return o===1?t?`
float ${r}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${r}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${r}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
return sampleTexture(${n}, uv);
}
`}function v7(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],c=Sn();if(a!=null&&k.arraysEqual(n,a))return t?`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return ${c.texture2D}(${r}, uv);
}
`:`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
return ${c.texture2D}(${r}, uv);
}
`;if(t)return`
vec4 ${s}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${r}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${c.texture2D}(${r}, uv);
}
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(n[1]/2);return`
vec4 ${s}(int row, int col) {
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
return ${c.texture2D}(${r}, uv);
}
`}function x7(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape;if(a!=null&&k.arraysEqual(n,a)){if(t)return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`;let p=a[0],h=a[1];return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`}let{newShape:o,keptDims:i}=k.squeezeShape(n),c=o;if(c.length<n.length){let p=$u(e,c),h=["row","col"];return`
${Au(p,t)}
float ${s}(int row, int col) {
return ${s}(${Fu(h,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${Du(e)}
}
`;let l=a[0],u=a[1],d=gi(r);return u===1?t?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${r}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${r}TexShape[0]));
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${r}, uv);
}
`:l===1?t?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${r}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${r}TexShape[1]), 0.5);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
return sampleTexture(${r}, uv);
}
`:t?`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r}Shape[1] + col + ${d};
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${d};
vec2 uv = uvFromFlat(${l}, ${u}, index);
return sampleTexture(${r}, uv);
}
`}function w7(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=$u(e,p),m=["b","row","col"];return`
${Q2(f,t)}
vec4 ${s}(int b, int row, int col) {
return ${s}(${Fu(m,h)});
}
`}let i=Sn();if(t)return`
vec4 ${s}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${r}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${r}, uv);
}
`;let c=o[0],l=o[1],u=Math.ceil(n[2]/2),d=u*Math.ceil(n[1]/2);return`
vec4 ${s}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${c}, ${l}, ${d}, ${u}, b, row, col);
return ${i.texture2D}(${r}, uv);
}
`}function k7(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:c}=k.squeezeShape(n),l=i;if(l.length<n.length){let m=$u(e,l),g=["row","col","depth"];return`
${Au(m,t)}
float ${s}(int row, int col, int depth) {
return ${s}(${Fu(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${o}, 1)));
${Du(e)}
}
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
float ${s}(int row, int col, int depth) {
int stride1 = ${r}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${r}, uv);
}
`;if(p===o&&h==null)return t?`
float ${s}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${r}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
return sampleTexture(${r}, uv);
}
`;let f=gi(r);return t?`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${r}Shape[1] * ${r}Shape[2];
int stride1 = ${r}Shape[2];
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${r}, uv);
}
`}function I7(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=Sn();if(t)return`
vec4 ${r}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${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 ${s.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,c=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],l=c[0],u=c[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
vec4 ${r}(${h}) {
int index = ${f};
int texR = index / ${u};
int texC = index - texR * ${u};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${l});
return ${s.texture2D}(${n}, uv);
}
`}function S7(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:c,keptDims:l}=k.squeezeShape(n);if(c.length<n.length){let y=$u(e,c),v=["row","col","depth","depth2"];return`
${Au(y,t)}
float ${s}(int row, int col, int depth, int depth2) {
return ${s}(${Fu(v,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, 1)));
${Du(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${r}Shape[3];`,m=`int stride1 = ${r}Shape[2] * stride2;`,g=`int stride0 = ${r}Shape[1] * stride1;`;if(h===i&&u==null)return t?`
float ${s}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;if(h===a&&u==null)return t?`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${r}Shape[1] * ${r}Shape[2], ${r}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;let b=gi(r);return t?`
float ${s}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${b});
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${p}, ${h}, index + ${b});
return sampleTexture(${r}, uv);
}
`}function T7(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[4],a=t[3]*s,o=t[2]*a,i=t[1]*o,{newShape:c,keptDims:l}=k.squeezeShape(t);if(c.length<t.length){let m=$u(e,c),g=["row","col","depth","depth2","depth3"];return`
${Au(m)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${Fu(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, ${s})) +
depth3;
${Du(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&u==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${o}, ${a}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(h===s&&u==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${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=gi(n);return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} + depth * ${a} +
depth2 * ${s} + depth3 + ${f};
vec2 uv = uvFromFlat(${p}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function C7(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:s,keptDims:a}=k.squeezeShape(t);if(s.length<t.length){let g=$u(e,s),b=["row","col","depth","depth2","depth3","depth4"];return`
${Au(g)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${Fu(b,a)});
}
`}let o=t[5],i=t[4]*o,c=t[3]*i,l=t[2]*c,u=t[1]*l;if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${l}, ${c}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${o}, 1)));
${Du(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===u&&d==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${l}, ${c}, ${i}, ${o})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===o&&d==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${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=gi(n);return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${u} + col * ${l} + depth * ${c} +
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function Du(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function N7(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=J2(e.shapeInfo.logicalShape,t.logicalShape),c=gt(o),l=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(y=>`coords.${d[y+l]} = 0;`).join(`
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((y,v)=>`coords.${d[v+l]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,b=k.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!b)o===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(i.length){let y=a-2,v=a-1;i.indexOf(y)>-1&&i.indexOf(v)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${s}() {
${c} coords = getOutputCoords();
${u}
vec4 outputValue = get${r}(${p});
${h}
}
`}function _7(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,c=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===c&&e.shapeInfo.flatOffset==null&&k.arraysEqual(o,a))return`
float ${s}() {
return sampleTexture(${n}, resultUV);
}
`;let l=gt(c),u=J2(e.shapeInfo.logicalShape,t.logicalShape),d=c-i,p,h=["x","y","z","w","u","v"];i===0?p="":c<2&&u.length>=1?p="coords = 0;":p=u.map(m=>`coords.${h[m+d]} = 0;`).join(`
`);let f="";return c<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
float ${s}() {
${l} coords = getOutputCoords();
${p}
return get${r}(${f});
}
`}function gt(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 Zw(e,t,n){let{newShape:r,keptDims:s}=k.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):r,c=!e&&a>1&&!k.arraysEqual(t,n)&&r.length<a||o;return{useSqueezeShape:c,uniformShape:c?i:t,keptDims:s}}function $u(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Fu(e,t){return t.map(n=>e[n]).join(", ")}function E7(e,t,n,r){let s=n.map((x,w)=>{let T={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(T.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[w],shapeInfo:T}}),a=s.map(x=>x.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},i=qX(s,o,t),c=A2(e.gl,i),l=e.createProgram(c),u=null,d=e.getUniformLocation(l,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(l,"INFINITY",!1));let p=!1,h={},f={},m={};for(let x=0;x<t.variableNames.length;x++){let w=t.variableNames[x];h[w]=e.getUniformLocation(l,w,p),h[`offset${w}`]=e.getUniformLocation(l,`offset${w}`,p),t.enableShapeUniforms&&(f[`${w}Shape`]=e.getUniformLocation(l,`${w}Shape`,p),m[`${w}TexShape`]=e.getUniformLocation(l,`${w}TexShape`,p))}let g,b,y;t.enableShapeUniforms&&(g=e.getUniformLocation(l,"outShape",p),y=e.getUniformLocation(l,"outShapeStrides",p),b=e.getUniformLocation(l,"outTexShape",p));let v=[];return t.customUniforms&&t.customUniforms.forEach((x,w)=>{v[w]=e.getUniformLocation(l,x.name,p)}),{program:t,fragmentShader:c,source:i,webGLProgram:l,uniformLocations:h,customUniformLocations:v,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:d,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:y,outTexShapeLocation:b}}function tN(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,r)=>{let s=n.logicalShape,a=t[r],o=a.shape;if(!k.arraysEqual(s,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${s} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,c=a.isUniform?null:a.texData.texShape;if(!k.arraysEqual(i,c))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${c} must match`)})}function A7(e,t,n,r,s){t.program.enableShapeUniforms||(tN(t.inShapeInfos,n),tN([t.outShapeInfo],[r]));let a=r.texData.texture,o=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,l)=>{let u=t.program.variableNames[l],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=Zw(t.program.packedInputs,c.shape,c.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,c.texData.texShape[0],c.texData.texShape[1]),d!=null){if(c.isUniform){if(k.sizeFromShape(c.shape)<2)e.gl.uniform1f(d,c.uniformValues[0]);else{let m=c.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}c.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture.texture,d,l)}});let i=t.outShapeLocation;if(i)switch(r.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(r.shape));break;default:break}if(t.outShapeStridesLocation){let c=k.computeStrides(r.shape);switch(r.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(c));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(c));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(c));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&s&&t.program.customUniforms.forEach((c,l)=>{let u=t.customUniformLocations[l],d=s[l];if(c.type==="float")e.gl.uniform1fv(u,d);else if(c.type==="vec2")e.gl.uniform2fv(u,d);else if(c.type==="vec3")e.gl.uniform3fv(u,d);else if(c.type==="vec4")e.gl.uniform4fv(u,d);else if(c.type==="int")e.gl.uniform1iv(u,d);else if(c.type==="ivec2")e.gl.uniform2iv(u,d);else if(c.type==="ivec3")e.gl.uniform3iv(u,d);else if(c.type==="ivec4")e.gl.uniform4iv(u,d);else throw Error(`uniform type ${c.type} is not supported yet.`)}),e.executeProgram()}function D7(e,t,n){let r="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let c=o.texData.texShape,{useSqueezeShape:l,uniformShape:u,keptDims:d}=Zw(e.packedInputs,o.shape,c),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(c[0]/2),Math.ceil(c[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(u.length===2&&!e.packedInputs)h=`${u[0]>1}_${u[1]>1}`;else if(u.length>2&&!e.packedInputs){let w=k.computeStrides(u);f=`${w[0]===c[1]}_${w[w.length-1]===c[1]}`}let m=o.shape.length,g=u.length===2&&k.arraysEqual(o.shape,c),b=k.sizeFromShape(o.shape)===1,y=_.getBroadcastDims(o.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&k.arraysEqual(c,n.texData.texShape),x=e.packedInputs||u.length>2?"":`${c[0]>1}_${c[1]>1}`;r+=`${m}_${v}_${l?d:""}_${u.length}_${b}_${y}_${g}_${p}_${h}_${f}_${x}_${i}`}else{let c=o.isUniform?"uniform":o.texData.texShape;r+=`${o.shape}_${c}_${i}`}});let s=e.userCode,a=e.constructor.name;return a+="_"+r+"_"+s+`${J().getNumber("WEBGL_VERSION")}`,a}function Vn(e){return J().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var $7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Wd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Sn();this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Im(["r","c","d"],e):mi(["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;
}
`}},F7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Wd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Sn();this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Im(["r","c","d"],e):mi(["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;
}
`}},R7=class{constructor(e){this.variableNames=["A"],this.outTexUsage=br.DOWNLOAD;let t=Sn();this.outputShape=e,this.userCode=`
${Z2}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},P7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=br.DOWNLOAD;let t=Sn();this.outputShape=e,this.userCode=`
${Z2}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},O7=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Sn();this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?Yw():Xw(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];
}
${n.output} = vec4(${r}, 0., 0., 0.);
}
`}},M7=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Sn();this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length);let r="",s="result";t&&(s="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;r+=`
localCoords = coords;
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${o};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${i}] = values[0];
} else if (offset == 1) {
result[${i}] = values[1];
} else if (offset == 2) {
result[${i}] = values[2];
} else {
result[${i}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?Yw():Xw(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${r}
${n.output} = ${s};
}
`}},nN={};Ae(nN,{bindVertexProgramAttributeStreams:()=>dN,createBufferFromOutputTexture:()=>fN,createFloat16MatrixTexture:()=>iN,createFloat16PackedMatrixTexture:()=>lN,createFloat32MatrixTexture:()=>oN,createIndexBuffer:()=>aN,createPackedMatrixTexture:()=>uN,createUnsignedBytesMatrixTexture:()=>cN,createVertexBuffer:()=>sN,createVertexShader:()=>rN,downloadByteEncodedFloatMatrixFromOutputTexture:()=>gN,downloadFloat32MatrixFromBuffer:()=>mN,downloadMatrixFromPackedOutputTexture:()=>yN,downloadPackedMatrixFromBuffer:()=>bN,getInternalFormatForFloat16MatrixTexture:()=>Qw,getInternalFormatForFloat16PackedMatrixTexture:()=>n0,getInternalFormatForFloat32MatrixTexture:()=>Jw,getInternalFormatForPackedMatrixTexture:()=>t0,getInternalFormatForUnsignedBytesMatrixTexture:()=>e0,uploadDenseMatrixToTexture:()=>pN,uploadPixelDataToTexture:()=>hN});function rN(e){let t=Sn(),n=`${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 E2(e,n)}function sN(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 F2(e,t)}function aN(e){let t=new Uint16Array([0,1,2,2,1,3]);return R2(e,t)}function jd(e,t,n,r,s,a){O2(t,n);let o=P2(e),i=e.TEXTURE_2D;return be(e,()=>e.bindTexture(i,o)),be(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),be(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),J().getNumber("WEBGL_VERSION")===1?be(e,()=>e.texImage2D(i,0,r,t,n,0,s,a,null)):be(e,()=>e.texStorage2D(i,1,r,t,n)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:o,texShape:[n,t]}}function Jw(e){return e.internalFormatFloat}function oN(e,t,n,r){let[s,a]=Vd(t,n);return jd(e,s,a,Jw(r),r.textureFormatFloat,e.FLOAT)}function Qw(e){return e.internalFormatHalfFloat}function iN(e,t,n,r){let[s,a]=Vd(t,n);return jd(e,s,a,Qw(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function e0(e){return e.downloadTextureFormat}function cN(e,t,n,r){let[s,a]=Vd(t,n);return jd(e,s,a,e0(r),e.RGBA,e.UNSIGNED_BYTE)}function t0(e){return e.internalFormatPackedFloat}function uN(e,t,n,r){let[s,a]=_u(t,n);return jd(e,s,a,t0(r),e.RGBA,e.FLOAT)}function n0(e){return e.internalFormatPackedHalfFloat}function lN(e,t,n,r){let[s,a]=_u(t,n);return jd(e,s,a,n0(r),e.RGBA,r.textureTypeHalfFloat)}function dN(e,t,n){let r=0,s=3*4,a=3*4+2*4;return be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Hw(e,t,"clipSpacePos",n,3,a,r)&&Hw(e,t,"uv",n,2,a,s)}function pN(e,t,n,r,s,a){be(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,c;s instanceof Uint8Array?(o=new Uint8Array(n*r*4),i=e.UNSIGNED_BYTE,c=e.RGBA):(o=new Float32Array(n*r*4),i=e.FLOAT,c=a.internalFormatPackedFloat),o.set(s),J().getNumber("WEBGL_VERSION")===2?be(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,e.RGBA,i,o)):be(e,()=>e.texImage2D(e.TEXTURE_2D,0,c,n,r,0,e.RGBA,i,o)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function hN(e,t,n){be(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?J().getNumber("WEBGL_VERSION")===2?be(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):J().getNumber("WEBGL_VERSION")===2?be(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function fN(e,t,n,r){let s=e.createBuffer();be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,s));let i=4*4*t*n;return be(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),s}function mN(e,t,n){let r=e,s=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,s),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),s}function gN(e,t,n,r){let[s,a]=Vd(t,n),o=4,i=new Uint8Array($X(t*n,o));return be(e,()=>e.readPixels(0,0,s,a,r.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function bN(e,t,n,r,s,a,o,i){let c=e,l=new Float32Array(FX(a,o));return c.bindBuffer(c.PIXEL_PACK_BUFFER,t),c.getBufferSubData(c.PIXEL_PACK_BUFFER,0,l),c.bindBuffer(c.PIXEL_PACK_BUFFER,null),l}function yN(e,t,n){let r=new Float32Array(t*n*4);return be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var Sm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,C2(t,e)):this.gl=Zr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Ud(this.gl,s),yr(this.gl,a))this.textureHalfFloatExtension=Ud(this.gl,a);else if(J().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),yr(this.gl,r))this.colorBufferHalfFloatExtension=Ud(this.gl,r);else if(J().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",yr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(yr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=sN(this.gl),this.indexBuffer=aN(this.gl),this.framebuffer=M2(this.gl),this.textureConfig=Gw(this.gl,this.textureHalfFloatExtension)}get debug(){return J().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;be(e,()=>e.finish()),be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),be(e,()=>e.deleteFramebuffer(this.framebuffer)),be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),be(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),be(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),oN(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),iN(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),cN(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),hN(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),pN(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),lN(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),uN(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(jw(this.gl,this.framebuffer),this.outputTexture=null),be(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>gN(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,s,a){return bN(this.gl,e,t,n,r,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return mN(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=fN(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,s=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=r.clientWaitSync(s,0,0);return a===r.ALREADY_SIGNALED||a===r.CONDITION_SATISFIED},t=s}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>yN(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=rN(t));let n=D2(t);return be(t,()=>t.attachShader(n,this.vertexShader)),be(t,()=>t.attachShader(n,e)),$2(t,n),this.debug&&bm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=dN(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&be(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&bm(this.gl,this.program),be(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?B2(this.gl,e,t):z2(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),be(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(),W2(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,s]=_u(t,n);this.setOutputMatrixTextureDriver(e,r,s)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&bm(this.gl,this.program),Gd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),be(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),be(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Ud(this.gl,J().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(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().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 k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().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,r=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=L7(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)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ym(this.gl,e,this.framebuffer),this.debug&&Gd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ym(this.gl,this.outputTexture,this.framebuffer),this.debug&&Gd(this.gl)):jw(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;ym(r,e,this.framebuffer),this.debug&&Gd(r),this.outputTexture=e,be(r,()=>r.viewport(0,0,t,n)),be(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),be(this.gl,()=>this.gl.scissor(e,t,n,r))}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 L7(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:B7,bincountImpl:vN,bincountReduceImpl:z7,ceilImpl:W7,concatImpl:V7,equalImpl:U7,expImpl:G7,expm1Impl:H7,floorImpl:j7,gatherNdImpl:q7,gatherV2Impl:K7,greaterImpl:X7,greaterEqualImpl:Y7,lessImpl:Z7,lessEqualImpl:J7,linSpaceImpl:Q7,logImpl:e9,maxImpl:t9,maximumImpl:n9,minimumImpl:r9,multiplyImpl:s9,negImpl:a9,notEqualImpl:o9,prodImpl:i9,rangeImpl:c9,rsqrtImpl:u9,sigmoidImpl:l9,simpleAbsImpl:xN,sliceImpl:d9,sparseFillEmptyRowsImpl:p9,sparseReshapeImpl:h9,sparseSegmentReductionImpl:wN,sqrtImpl:f9,stridedSliceImpl:m9,stringNGramsImpl:g9,stringSplitImpl:b9,stringToHashBucketFastImpl:y9,subImpl:v9,tileImpl:x9,topKImpl:w9,transposeImpl:r0,uniqueImpl:k9}=wC;function kN(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Tn(e,t){return t===1?[e]:kN(e,t)}function I9(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var S9=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Vn(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let t=Tn("rc",this.rank),n=gt(this.rank),r=this.getOutOfBoundsCondition(t),s=this.getSetup(t),a=this.getOutput(t);this.userCode=`
void main() {
${n} rc = getOutputCoords();
if(${r}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${a}));
}
}
`}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let r=0;r<=1;r++){let s=`${n===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let a=2;a<this.rank;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}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[${this.rank} - 1]`:this.outputShape[this.rank-1],r=this.enableShapeUniforms?`outShape[${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 >= ${r};
`}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]})`}},IN=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length);let n="";for(let r=0;r<4;r++){let s="thisRC = rc;";r%2===1&&(s+="thisRC.z += 1;"),r>1&&(s+="thisRC.y += 1;"),n+=`
${s}
${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${r}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${r>0?"}":""}
`}this.userCode=`
${T9(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?Yw():Xw(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 T9(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?jX(["r","c","d"],"inputShape"):mi(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var C9=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=TN(t,n),s=CN(e,r,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=SN(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[s].shift();return this.usedTextures[s].push(i),i}let o;return r===cn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===cn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===cn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===cn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===cn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let s=TN(n,r),a=CN(t,s,r);a in this.freeTextures||(this.freeTextures[a]=[]);let o=SN(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,r),i=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let c=this.usedTextures[a],l=c.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");c.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 N9(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 SN(e,t,n,r,s){let a=_9(t,r),o;if(s){let[c,l]=_u(e[0],e[1]);o=c*l}else{let[c,l]=Vd(e[0],e[1]);o=c*l}let i=N9(n,a);return o*i}function _9(e,t){switch(e){case cn.PACKED_2X2_FLOAT32:return t0(t);case cn.PACKED_2X2_FLOAT16:return n0(t);case cn.UNPACKED_FLOAT32:return Jw(t);case cn.UNPACKED_FLOAT16:return Qw(t);case cn.PACKED_4X1_UNSIGNED_BYTE:return e0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function E9(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?cn.PACKED_2X2_FLOAT32:cn.UNPACKED_FLOAT32:e?cn.PACKED_2X2_FLOAT16:cn.UNPACKED_FLOAT16}function TN(e,t){if(e===br.UPLOAD)return cn.PACKED_2X2_FLOAT32;if(e===br.RENDER||e==null)return E9(t);if(e===br.DOWNLOAD||e===br.PIXELS)return cn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function CN(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Fs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},Fr="if (isnan(x)) return x;",A9="return x;",NN="return abs(x);",D9="return (x >= 0.0) ? x : (exp(x) - 1.0);",$9=Fr+`
return (x < 0.0) ? 0.0 : x;
`,F9=Fr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Ru="return x;",R9="return 1.0 / (1.0 + exp(-1.0 * x));",P9="return x;",O9=`
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;
`,M9=`
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;
`,L9=`
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;
`,B9="return 1.0 / (1.0 + exp(-1.0 * x));",bi=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},z9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Vn(this.outputShape.length);let t=e.length,n=Tn("rc",t),r=gt(t),s=I9(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${o}));
}
`}},W9=is.whereImpl,V9=1e-7,U9=1e-4,Tm={};function G9(e){return e in Tm||(Tm[e]={}),Tm[e]}var H9=J().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),j9=600;function q9(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*j9/1024/1024}var Cm=class extends kl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Sm)t=e;else{let n=Zr(J().getNumber("WEBGL_VERSION"),e);t=new Sm(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Zr(J().getNumber("WEBGL_VERSION"));t=new Sm(n),this.binaryCache=G9(J().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new C9(this.gpgpu),this.numMBBeforeWarning=q9(),this.texData=new qp(this,ns())}nextDataId(){return Cm.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:br.UPLOAD,refCount:1}),r}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,r,s){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:br.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:s,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new bi(o,Ru):d=new Fs(o,Ru);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:r}],r),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let c=this.activeTimers!=null,l;c&&(l=k.now());let u;if(r==="complex64"){let d=this.readSync(s.real.dataId),p=this.readSync(s.imag.dataId);u=_.mergeRealAndImagArrays(d,p)}else u=this.getValuesFromTexture(e);return c&&(this.downloadWaitMs+=k.now()-l),this.convertAndCacheOnCPU(e,u)}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:r,slice:s,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(s!=null){let h;i?h=new bi(r,Ru):h=new Fs(r,Ru);let f=this.runWebGLProgram(h,[{dataId:e,shape:r,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(J().getBool("DEBUG")&&!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let c=null,l;if(a!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);c=this.gpgpu.createBufferFromTexture(h.texture.texture,...gm(r))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];u=_.mergeRealAndImagArrays(f,m)}else if(c==null)u=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(c,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),c!=null){let h=this.gpgpu.gl;be(h,()=>h.deleteBuffer(c))}let d=this.convertAndCacheOnCPU(e,u),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)&&ns().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:r,shape:s,slice:a,dtype:o,isPacked:i,texture:c}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let p;i?p=new bi(s,Ru):p=new Fs(s,Ru);let h=this.runWebGLProgram(p,[{dataId:e,shape:s,dtype:o}],o),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(c==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),u=ns().makeTensorFromDataId(l.dataId,l.shape,l.dtype),d=this.texData.get(l.dataId);return Object.assign({tensorRef:u},d.texture)}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>k.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!N2(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),s=k.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...gm(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(d),h}let a=J().getBool("WEBGL_PACK")&&r===!0,o=a?vm(t):t,i=a?new P7(o):new R7(o),c=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),l=this.texData.get(c.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(c),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=k.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=k.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(s);o.kernelMs=k.sum(i),o.getExtraProfileInfo=()=>i.map((c,l)=>({name:a[l],ms:c})).map(c=>`${c.name}: ${c.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:s,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,c=this.dataRefCount.get(i);c>1?this.dataRefCount.set(i,c-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,s,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=H9){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&k.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){_.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return W9(e.shape,t)}packedUnaryOp(e,t,n){let r=new bi(e.shape,t),s=this.compileAndRun(r,[e],n);return ns().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=xN(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,NN,e.dtype);let t=new Fs(e.shape,NN),n=this.compileAndRun(t,[e]);return ns().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let s=n.map(a=>k.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return ns().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new z9(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new S9(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[hi(e.shape),...fi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[hi(t),...fi(t)],a=new IN(s,n),o=!0,i=[n],c=this.runWebGLProgram(a,[r],e.dtype,i,o);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:r,shape:s,dtype:a}=n;if(t!=null){let d=k.sizeFromShape(s),p=t[0]*t[1]*4;k.assert(d<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=vm(s),i;r?i=new F7(o):i=new $7(o);let c=!0,l=[t!=null?t:gm(o)],u=this.runWebGLProgram(i,[{shape:o,dtype:a,dataId:e}],a,l,c,t);return{dtype:a,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,r,s=!1,a){let o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Wd.DENSE){let g=a!=null?a:gm(e.outputShape);i.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(o.shape)===0)return i.values=k.getTypedArrayFromDType(o.dtype,0),o;let c=[],l=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&&k.sizeFromShape(g.shape)<=J().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),c.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!Hd(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),c.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(o.dataId);let u={shape:o.shape,texData:i,isUniform:!1},d=D7(e,l,u),p=this.getAndSaveBinary(d,()=>E7(this.gpgpu,e,l,u)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),A7(this.gpgpu,p,l,u,r),c.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=k.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),g}return o}compileAndRun(e,t,n,r,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(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=M(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?V9:U9}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:s,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let c=this.activeTimers!=null,l;c&&(l=k.now());let u=t.texShape;if(u==null&&(u=G2(n,i),t.texShape=u),s!=null){let d=vm(n),p,h=u[1],f=u[0],m=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(i||!m)&&([h,f]=_u(u[0],u[1])),i?p=new M7(d,m):p=new O7(d,m);let g=m?[f,h]:u,b=this.makeTensorInfo(g,r),y=this.texData.get(b.dataId);m?y.usage=br.PIXELS:y.usage=br.UPLOAD,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,s);let v=[[f,h]],x=!0,w=this.runWebGLProgram(p,[b],r,v,x),T=this.texData.get(w.dataId);t.texture=T.texture,t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,this.disposeIntermediateTensorInfo(b),this.texData.delete(w.dataId),t.values=null,c&&(this.uploadWaitMs+=k.now()-l)}else{let d=this.acquireTexture(u,o,r,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=K9(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};Cm.nextDataId=0;function K9(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 r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var X9="3.13.0";function _N(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Zl.isBrowser()&&Uh("webgl",()=>new Cm,2);var Y9={forceHalfFloat:_N},EN=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Pu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Vn(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Nm=`
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;
`,qd=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=_.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length;this.enableShapeUniforms=Vn(s);let a="";if(r)if(s===0||k.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${gt(s)} coords = getOutputCoords();
`,s===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=Tn("coords",s);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${i[s-2]} + 1) >= outShape[${s} - 2];
bool nextColOutOfBounds =
(${i[s-1]} + 1) >= outShape[${s} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${i[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
(${i[s-1]} + 1) >= ${this.outputShape[s-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);
${a}
setOutput(result);
}
`}};function ar(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Z9={kernelName:so,backendName:"webgl",kernelFunc:ar};function Ta(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(a.dataId),i=ar({inputs:{x:r},backend:n}),c=ar({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:c},a}var J9={kernelName:nh,backendName:"webgl",kernelFunc:Ta},AN="return (a < 0.) ? b * a : a;",DN=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Q9(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r,o=n.makeTensorInfo([],"float32",k.createScalarValue(a,"float32")),i=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qd(DN,s.shape,o.shape):new Pu(AN,s.shape,o.shape),c=n.runWebGLProgram(i,[s,o],"float32");return n.disposeIntermediateTensorInfo(o),c}var eY={kernelName:ao,backendName:"webgl",kernelFunc:Q9},$N="return (a < 0.) ? b * a : a;",FN=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function tY(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qd(FN,r.shape,s.shape):new Pu($N,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],"float32")}var nY={kernelName:vo,backendName:"webgl",kernelFunc:tY},Ou="if (isnan(x)) return x;",rY=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,sY=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:s,backend:a})=>{let{x:o}=s,i=a,c=r||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,c);return i.makeTensorInfo(o.shape,c,p)}let l=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return l?u=new bi(o.shape,t):u=new Fs(o.shape,e),i.runWebGLProgram(u,[o],c)}}function un({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:a}){return({inputs:o,backend:i})=>{let{a:c,b:l}=o,u=i;if(r&&c.dtype==="complex64"){let f=u.texData.get(c.dataId),m=u.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,w]=v,T={dataId:x.dataId,dtype:x.dtype,shape:c.shape},N={dataId:w.dataId,dtype:w.dtype,shape:l.shape},$=new Pu(e,c.shape,l.shape);return u.runWebGLProgram($,[T,N],Tr(x.dtype,w.dtype))}),y=Ta({inputs:{real:g,imag:b},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(b),y}let d=a||Tr(c.dtype,l.dtype);if((c.dtype==="string"||l.dtype==="string"||u.shouldExecuteOnCPU([c,l]))&&s!=null){let f=u.texData.get(c.dataId).values,m=u.texData.get(l.dataId).values,g=c.dtype==="string"?_.fromUint8ToStringArray(f):f,b=c.dtype==="string"?_.fromUint8ToStringArray(m):m,[y,v]=s(c.shape,l.shape,g,b,d),x=u.makeTensorInfo(v,d),w=u.texData.get(x.dataId);return w.values=y,x}let p=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new qd(t,c.shape,l.shape,n):h=new Pu(e,c.shape,l.shape),u.runWebGLProgram(h,[c,l],d)}}function _m(e,t=!1){if(e==="linear")return t?P9:A9;if(e==="relu")return t?M9:$9;if(e==="elu")return t?O9:D9;if(e==="relu6")return t?L9:F9;if(e==="prelu")return t?FN:$N;if(e==="leakyrelu")return t?DN:AN;if(e==="sigmoid")return t?B9:R9;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var RN=class{constructor(e,t,n,r=!1,s=!1,a=!1,o=null,i=!1,c=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Vn(this.outputShape.length);let l=r?e[1]:e[2],u=Math.ceil(l/2),d=r?"i * 2, rc.y":"rc.y, i * 2",p=s?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:c?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:m=`vec4 activation(vec4 x) {
${o}
}`,g="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),c&&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 = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; 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);
}
`}},PN={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},ON=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},MN="return a * b;";function s0(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=_.upcastType(r.dtype,s.dtype);if(r.dtype==="complex64"){let i=n.texData.get(r.dataId),c=n.texData.get(s.dataId),l=new ON(PN.REAL,r.shape,s.shape),u=new ON(PN.IMAG,r.shape,s.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:r.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:s.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:s.shape}],p=n.runWebGLProgram(l,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Ta({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([r,s])){let i=n.texData.get(r.dataId),c=n.texData.get(s.dataId),[l,u]=s9(r.shape,s.shape,i.values,c.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=l,d}let o;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new qd(MN,r.shape,s.shape):o=new Pu(MN,r.shape,s.shape),n.runWebGLProgram(o,[r,s],a)}var aY={kernelName:mo,backendName:"webgl",kernelFunc:s0};function oY(e,t,n){let r=[hi(e.shape),...fi(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[hi(t),...fi(t)],o=new IN(a,r),i=!0,c=[r],l=n.runWebGLProgram(o,[s],e.dtype,c,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ge(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=n,i=k.sizeFromShape(s.shape),c=k.inferFromImplicitShape(a,i),l=k.sizeFromShape(c);k.assert(i===l,()=>`The new shape (${c}) has ${l} elements and the old shape (${s.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let u=o.texData.get(s.dataId);return u.isPacked&&!Hd(s.shape,c)&&!(u.texture!==null&&Hd(u.shape,c))?oY(s,c,o):(o.incRef(s.dataId),{dataId:s.dataId,shape:c,dtype:s.dtype})}var iY={kernelName:Pc,backendName:"webgl",kernelFunc:ge},LN=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o=Math.floor(n/4)*4,i=n%4,c="sumValue += dot(values, ones);";if(t!=null){let u=1/t;c=`sumValue += dot(values * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let l="";s%n>0&&(l=`
if (inIdx < 0 || inIdx >= ${s}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${l}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${o}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${c}
}
int inIdx = inOffset + ${o};
if (${i===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${c}
} else if (${i===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${c}
} else if (${i===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${c}
}
setOutput(sumValue);
}
`}},cY=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let c=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?c="sumValue":t==="prod"?c="prodValue":t==="all"?c="allValue":t==="any"&&(c="anyValue");let l=Math.floor(n/4)*4,u=n%4,d=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${i}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${i}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,p="vec4";t==="all"?(o="1.0",d=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(o="0.0",d=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let h="";s%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${o};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${o});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${l}; 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 + ${l};
if (${u===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${d}
} else if (${u===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${d}
} else if (${u===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${d}
}
setOutput(${c});
}
`}};function uY(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function yi(e,t,n,r){let s=uY(e.shape),a=e;for(let o=0;o<s.length;o++){let{inSize:i,windowSize:c,outSize:l}=s[o],u,d;n==="mean"?u=o===0?new LN({windowSize:c,inSize:i,batchSize:e.shape[0],outSize:l},i):new LN({windowSize:c,inSize:i,batchSize:e.shape[0],outSize:l}):u=new cY({windowSize:c,inSize:i,batchSize:e.shape[0],outSize:l},n),d=a,a=r.runWebGLProgram(u,[a],t),d.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(d)}return a}var lY=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 r=gt(this.rank),s=dY(t);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function dY(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"],r=new Array(t);for(let s=0;s<e.length;s++)r[e[s]]=n[s];return r.join()}var pY=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=gt(this.rank),s=kN("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=s[l];let o=`vec2(${a.slice(-2).join()})`,i=`++${s[this.rank-1]} < ${n[this.rank-1]}`,c=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${c};
if(${i}) {
result[1] = ${c};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${c};
if(${i}) {
result[3] = ${c};
}
}
setOutput(result);
}
`}};function Em(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new pY(e.shape,t):new lY(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function hY(e,t,n,r){let s=t,a=e.shape.length,o=k.parseAxisParam(s,e.shape),i=o,c=_.getAxesPermutation(i,a),l=c!=null,u=e;l&&(u=Em(e,c,r),i=_.getInnerMostAxes(i.length,a)),_.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=_.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=_.expandShapeToKeepDim(d,o));let f=k.sizeFromShape(p),g=k.sizeFromShape(e.shape)/f,b=ge({inputs:{x:u},attrs:{shape:[g,f]},backend:r}),y=Lh(e.dtype),v=yi(b,y,"sum",r),x=ge({inputs:{x:v},attrs:{shape:h},backend:r});return r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(v),l&&r.disposeIntermediateTensorInfo(u),x}function Am(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return hY(s,a,o,n)}var fY={kernelName:Eo,backendName:"webgl",kernelFunc:Am};function Cn(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,c=new Array(i);for(let u=0;u<c.length;u++)c[u]=s.shape[a[u]];let l;if(o.shouldExecuteOnCPU([s])){let d=o.texData.get(s.dataId).values,p=r0(d,s.shape,s.dtype,a,c);l=o.makeTensorInfo(c,s.dtype);let h=o.texData.get(l.dataId);h.values=p}else l=Em(s,a,o);return l}var mY={kernelName:Po,backendName:"webgl",kernelFunc:Cn},BN=1e3;function Dm({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:c=null}){let l=e.shape.length,u=t.shape.length,d=n?e.shape[l-2]:e.shape[l-1],p=r?t.shape[u-1]:t.shape[u-2],h=n?e.shape[l-1]:e.shape[l-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=k.sizeFromShape(m),y=k.sizeFromShape(g),x=su.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);k.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[b,d,h]:[b,h,d],T=r?[y,f,p]:[y,p,f],N=ge({inputs:{x:e},backend:s,attrs:{shape:w}}),$=ge({inputs:{x:t},backend:s,attrs:{shape:T}}),D=[N,$],P=Math.max(b,y),F=n?N.shape[1]:N.shape[2],R=a!=null,C=o!=null,L=c==="leakyrelu",G=c!=null?_m(c,!0):null,j=R||C||L||G!=null,K;if((h===1||f===1)&&F>BN&&j===!1){let Z=N,te=$;n&&(Z=Cn({inputs:{x:N},backend:s,attrs:{perm:[0,2,1]}}),D.push(Z)),r&&(te=Cn({inputs:{x:$},backend:s,attrs:{perm:[0,2,1]}}),D.push(te));let se=f!==1,oe=f===1,re=Z;se&&(re=ge({inputs:{x:Z},backend:s,attrs:{shape:[P,F,1]}}),D.push(re));let ue=f===1?2:1,ne=te;oe&&(ne=ge({inputs:{x:te},backend:s,attrs:{shape:[P,1,F]}}),D.push(ne));let he=s0({inputs:{a:re,b:ne},backend:s});K=Am({inputs:{x:he},backend:s,attrs:{axis:ue,keepDims:!0}}),D.push(he)}else{let Z=Tr(e.dtype,t.dtype),te=new RN(w,T,[P,h,f],n,r,R,G,C,L),se=[N,$];if(a!=null&&se.push(a),C&&se.push(o),L){let oe=s.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));se.push(oe),D.push(oe)}K=s.runWebGLProgram(te,se,Z)}let q=ge({inputs:{x:K},backend:s,attrs:{shape:x}});D.push(K);for(let Z of D)s.disposeIntermediateTensorInfo(Z);return q}function gY(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:c,transposeB:l,activation:u,leakyreluAlpha:d}=r;return Dm({a:s,b:a,transposeA:c,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var bY={kernelName:Oo,backendName:"webgl",kernelFunc:gY},zN="return abs(x);";function yY(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let a=n.texData.get(r.dataId),o=xN(a.values);return n.makeTensorInfo(r.shape,r.dtype,o)}let s;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new bi(r.shape,zN):s=new Fs(r.shape,zN),n.runWebGLProgram(s,[r],r.dtype)}var vY={kernelName:Yi,backendName:"webgl",kernelFunc:yY},xY=Fr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,wY=Ye({opSnippet:xY}),kY={kernelName:Zi,backendName:"webgl",kernelFunc:wY},IY=Fr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,SY=Ye({opSnippet:IY}),TY={kernelName:Ji,backendName:"webgl",kernelFunc:SY},WN="return a + b;",CY=un({opSnippet:WN,packedOpSnippet:WN,supportsComplex:!0,cpuKernelImpl:B7}),NY={kernelName:Js,backendName:"webgl",kernelFunc:CY},_Y=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let r=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${r};
setOutput(result);
}
`}},EY=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let r=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${r};
setOutput(result);
}
`}};function $m(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return ar({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(r.length/2),l=$m({inputs:r.slice(0,c),backend:n}),u=$m({inputs:r.slice(c),backend:n});return $m({inputs:[l,u],backend:n})}let s=r.map(c=>c.dtype).reduce((c,l)=>Tr(c,l)),a=r.map(c=>c.shape),i=J().getBool("WEBGL_PACK")?new EY(r[0].shape,a):new _Y(r[0].shape,a);return n.runWebGLProgram(i,r,s)}var AY={kernelName:Ba,backendName:"webgl",kernelFunc:$m};function DY(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=k.parseAxisParam(a,s.shape),l=c,u=_.getAxesPermutation(l,i),d=s;u!=null&&(d=Cn({inputs:{x:s},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,i)),_.assertAxesAreInnerMostDims("all",l,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,l),f=k.sizeFromShape(h),m=ge({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"all",n),b;if(o){let y=_.expandShapeToKeepDim(p,c);b=ge({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ge({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),b}var $Y={kernelName:Qi,backendName:"webgl",kernelFunc:DY};function FY(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=k.parseAxisParam(a,s.shape),l=c,u=_.getAxesPermutation(l,i),d=s;u!=null&&(d=Cn({inputs:{x:s},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,i)),_.assertAxesAreInnerMostDims("any",l,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,l),f=k.sizeFromShape(h),m=ge({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"any",n),b;if(o){let y=_.expandShapeToKeepDim(p,c);b=ge({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ge({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),b}var RY={kernelName:ec,backendName:"webgl",kernelFunc:FY},PY=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${r}; i++) {
int inIdx = ${i};
float candidate = getA(batch, inIdx);
if (candidate ${o} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},OY=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),r||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,c=gt(i),l=Tn("coords",i),u,d;if(a===1){d=i+1;let N=gt(d);u=`
${N} sourceLocR = ${N}(${l.join()}, 0);
++${l[i-1]};
${N} sourceLocG = ${N}(${l.join()}, 0);
++${l[i-2]};
${N} sourceLocA = ${N}(${l.join()}, 0);
--${l[i-1]};
${N} sourceLocB = ${N}(${l.join()}, 0);
--${l[i-2]};`}else d=i,u=`
${c} sourceLocR = coords;
++${l[i-1]};
${c} sourceLocG = coords;
++${l[i-2]};
${c} sourceLocA = coords;
--${l[i-1]};
${c} sourceLocB = coords;
--${l[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(N=>"int "+N),m=Tn("sourceLocR",d-1).concat("inIdx.r"),g=Tn("sourceLocG",d-1).concat("inIdx.g"),b=Tn("sourceLocB",d-1).concat("inIdx.b"),y=Tn("sourceLocA",d-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",x=r?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${y.join()})));`,w=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,T=r?"":`
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() {
${c} coords = getOutputCoords();
bool hasNextCol = ${l[i-1]} < ${o[i-1]-1};
bool hasNextRow = ${l[i-2]} < ${o[i-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${w};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${x}
vec4 candidate = ${w};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${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 VN(e,t,n,r=null){let s=t.shape[0],a=t.shape[1];r!=null&&(s=r.shape[0],a=r.shape[1]);let o=_.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:s,outSize:Math.ceil(a/o)},c=new PY(i,n,r==null),l=[t];r!=null&&l.push(r);let u=e.runWebGLProgram(c,l,"int32");if(u.shape[1]===1)return u;let d=VN(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function UN(e,t,n,r=null){let s=r!=null?r.shape:t.shape,a=s[s.length-1],o=_.computeOptimalWindowSize(a),i=new OY(s,o,n,r==null),c=r==null?[t]:[t,r],l=e.runWebGLProgram(i,c,"int32");if(l.shape.length===t.shape.length){let u=UN(e,t,n,l);return e.disposeIntermediateTensorInfo(l),u}return l}function GN(e,t,n,r){let s=[n];if(_.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),s,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,c=t;i&&(c=e.unpackTensor(t),a.push(c));let[l,u]=_.computeOutAndReduceShapes(c.shape,s),d=k.sizeFromShape(u),p=ge({inputs:{x:c},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=VN(e,p,r);a.push(h);let f=ge({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return UN(e,t,r)}function MY(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=k.parseAxisParam(a,s.shape),i=_.getAxesPermutation(o,s.shape.length),c=s,l=[];i!=null&&(c=Cn({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(c),o=_.getInnerMostAxes(o.length,c.shape.length)),_.assertAxesAreInnerMostDims("argMax",[o[0]],c.shape.length);let u=GN(n,c,o[0],"max");return l.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var LY={kernelName:za,backendName:"webgl",kernelFunc:MY};function BY(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=k.parseAxisParam(a,s.shape),i=_.getAxesPermutation(o,s.shape.length),c=s,l=[];i!=null&&(c=Cn({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(c),o=_.getInnerMostAxes(o.length,c.shape.length)),_.assertAxesAreInnerMostDims("argMin",[o[0]],c.shape.length);let u=GN(n,c,o[0],"min");return l.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var zY={kernelName:Tl,backendName:"webgl",kernelFunc:BY},WY=Fr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,VY=Ye({opSnippet:WY}),UY={kernelName:tc,backendName:"webgl",kernelFunc:VY},GY=Fr+"return log(x + sqrt(x * x + 1.0));",HY=Ye({opSnippet:GY}),jY={kernelName:nc,backendName:"webgl",kernelFunc:HY},qY=Fr+`
return atan(x);
`,KY=Ye({opSnippet:qY}),XY={kernelName:rc,backendName:"webgl",kernelFunc:KY},YY=rY+`
return atan(a, b);
`,ZY=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+sY+`
return result;
`,JY=un({opSnippet:YY,packedOpSnippet:ZY}),QY={kernelName:ac,backendName:"webgl",kernelFunc:JY},eZ=Fr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tZ=Ye({opSnippet:eZ}),nZ={kernelName:sc,backendName:"webgl",kernelFunc:tZ},Kd=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,u=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${u};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d};
wC += ${l}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${N} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?s?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,w=a%4,T=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${y}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${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 < ${u};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; wC += 4) {
int xC = xCCorner + wC * ${l};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
getValue(batch, xR, xC + 2 * ${l}, d),
getValue(batch, xR, xC + 3 * ${l}, d)
);
${T}
}
int xC = xCCorner + ${x};
if (${w===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${T}
} else if (${w===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
initializationValue,
initializationValue
);
${T}
} else if (${w===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
getValue(batch, xR, xC + 2 * ${l}, d),
initializationValue
);
${T}
}
}
setOutput(${v});
}
`}},a0=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,c=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let D=">=";this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${c});
const ivec3 pads = ivec3(${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 += ${l}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${d}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${D} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?s?`(((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 * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let T=Math.floor(a/4)*4,N=a%4,$=`
if (${y}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${c});
const ivec3 pads = ivec3(${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 += ${l}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${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)
);
${$}
}
int xC = xCCorner + ${T};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${$}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
initializationValue,
initializationValue
);
${$}
} else if (${N===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
);
${$}
}
}
setOutput(${w});
}
}
`}};function rZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Eu(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:c}=r,l=1;k.assert(_.eitherStridesOrDilationsAreOne(o,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let u=_.computePool2DInfo(s.shape,a,o,l,i,c);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return ar({inputs:{x:s},backend:n});let d=new Kd(u,"avg",!1);return n.runWebGLProgram(d,[s],"float32")}var sZ={kernelName:Wa,backendName:"webgl",kernelFunc:rZ};function aZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:c,dataFormat:l}=r,u=[1,1,1],d=_.computePool3DInfo(s.shape,a,o,u,i,c,l),p=new a0(d,"avg",!1);return n.runWebGLProgram(p,[s],"float32")}var oZ={kernelName:Cl,backendName:"webgl",kernelFunc:aZ},iZ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=i-1-e.padInfo.top,u=c-1-e.padInfo.left,d=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${l}, ${u});
const float avgMultiplier = float(${d});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${i};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC+= ${o}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},cZ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,c=e.dilationHeight,l=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`
const ivec3 pads = ivec3(${h}, ${f}, ${m});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
wD += ${i}) {
float dyD = float(dyDCorner + wD) / ${s}.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 += ${c}) {
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 += ${l}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function uZ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:c,pad:l,dimRoundingMode:u}=r,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,c,d,l,u),h=new cZ(p);return n.runWebGLProgram(h,[s],o.dtype)}var lZ={kernelName:Qp,backendName:"webgl",kernelFunc:uZ};function dZ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;Eu([s,a],"avgPoolGrad");let{filterSize:i,strides:c,pad:l}=r,u=_.computePool2DInfo(o.shape,i,c,1,l),d=new iZ(u);return n.runWebGLProgram(d,[s],o.dtype)}var pZ={kernelName:Jp,backendName:"webgl",kernelFunc:dZ};function hZ(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return Dm({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var fZ={kernelName:Va,backendName:"webgl",kernelFunc:hZ},mZ=class{constructor(e,t,n,r,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let o="0.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";s!=null&&(_.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${o};
float scale = ${i};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},gZ=class{constructor(e,t,n,r,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";s!=null&&(_.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${o};
vec4 scale = ${i};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}},bZ=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:s,variance:a,offset:o,scale:i}=e;k.assert(s.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(o==null||s.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(i==null||s.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:c}=n;c==null&&(c=.001);let l=[r,s,a],u=null;o!=null&&(u=o.shape,l.push(o));let d=null;i!=null&&(d=i.shape,l.push(i));let p=J().getBool("WEBGL_PACK_NORMALIZATION")?new gZ(r.shape,s.shape,a.shape,u,d,c):new mZ(r.shape,s.shape,a.shape,u,d,c);return t.runWebGLProgram(p,l,l[0].dtype)},yZ={kernelName:no,backendName:"webgl",kernelFunc:bZ},vZ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=xZ(this.rank),r,s=e.map((a,o)=>`sourceLoc.${o0[o]} = start[${o}] + coords.${o0[o]};`);r=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
void main() {
${r}
setOutput(getSource(${n}));
}
`}},o0=["x","y","z","w","u","v"];function xZ(e){if(e===1)return"sourceLoc";if(e<=6)return o0.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var wZ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),n=Tn("coords",this.rank),r=Tn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,a=`getChannel(getSource(${r.join()}), ${s})`,o=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.y = ${a};
--${r[this.rank-1]};
}
`,i=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${r[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.w = ${a};
}
}
`,c=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((l,u)=>`start[${u}]`).join()});`:e.map((l,u)=>`${r[u]} = ${n[u]} + start[${u}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${c}
vec4 result = vec4(0.);
${o}
${i}
setOutput(result);
}
`}};function kZ(e,t,n,r){let s=r.texData.get(e.dataId),a=r.makeTensorInfo(n,e.dtype),o=r.texData.get(a.dataId);Object.assign(o,s),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Ht.computeFlatOffset(t,k.computeStrides(e.shape));s.slice&&(i+=s.slice.flatOffset),o.slice={flatOffset:i,origDataId:s.slice&&s.slice.origDataId||e.dataId};let c=r.dataRefCount.get(o.slice.origDataId)||1;return r.dataRefCount.set(o.slice.origDataId,c+1),a}function Mu(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,c]=Ht.parseSliceParams(s,a,o);if(Ht.assertParamsValid(s,i,c),k.sizeFromShape(c)===0)return n.makeTensorInfo(c,s.dtype,[]);if(n.shouldExecuteOnCPU([s])||s.dtype==="string"){let d=n.texData.get(s.dataId),p=d9(d.values,i,c,s.shape,s.dtype);return n.makeTensorInfo(c,s.dtype,p)}let{isPacked:l}=n.texData.get(s.dataId),u=Ht.isSliceContinous(s.shape,i,c);if(l||!u){let d=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wZ(c):new vZ(c),p=[i];return n.runWebGLProgram(d,[s],s.dtype,p)}return n.uploadToGPU(s.dataId),kZ(s,i,c,n)}var IZ={kernelName:Bc,backendName:"webgl",kernelFunc:Mu},SZ=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;k.assert(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,v)=>y*v),c=_.getReshaped(s.shape,a,i),l=_.getPermuted(c.length,a.length),u=_.getReshapedPermuted(s.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(u,o,a.length),h=[],f=ge({inputs:{x:s},backend:n,attrs:{shape:c}}),m=Cn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=ge({inputs:{x:m},backend:n,attrs:{shape:u}}),b=Mu({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},TZ={kernelName:oc,backendName:"webgl",kernelFunc:SZ};function CZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.readSync(s.dataId),c=n.readSync(a.dataId),l=vN(i,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var NZ={kernelName:eh,backendName:"webgl",kernelFunc:CZ};function _Z(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.readSync(r.dataId),o=n.readSync(s.dataId),i=_.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var EZ={kernelName:th,backendName:"webgl",kernelFunc:_Z},AZ="return float(a != b);",HN=un({opSnippet:AZ,cpuKernelImpl:o9,dtype:"bool"}),DZ={kernelName:Nc,backendName:"webgl",kernelFunc:HN};function Xd(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return ar({inputs:{x:s.complexTensorInfos.real},backend:n})}var $Z={kernelName:Ih,backendName:"webgl",kernelFunc:Xd},FZ="return float(int(x));";function RZ(e,t){let n=new Fs(e.shape,FZ),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function i0(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return ar({inputs:{x:s},backend:n});let o=Tt(s.shape),i=i0({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),c=Ta({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),c}if(s.dtype==="complex64"){let o=Xd({inputs:{input:s},backend:n}),i=i0({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!k.hasEncodingLoss(s.dtype,a)){let o=ar({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return RZ(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),c=HN({inputs:{a:s,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),c}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var PZ={kernelName:Ua,backendName:"webgl",kernelFunc:i0},jN="return ceil(x);",OZ=Ye({opSnippet:jN,packedOpSnippet:jN,cpuKernelImpl:W7}),MZ={kernelName:Ga,backendName:"webgl",kernelFunc:OZ},LZ=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));
}
`}},BZ=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 zZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i;J().getBool("WEBGL_PACK_CLIP")?i=new BZ(s.shape):i=new LZ(s.shape);let c=[[a],[o]];return n.runWebGLProgram(i,[s],s.dtype,c)}var WZ={kernelName:Qs,backendName:"webgl",kernelFunc:zZ},VZ=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 qN(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function UZ(e){let{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),a=new VZ(r.shape),o=[qN(r,s.complexTensorInfos.real),qN(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var GZ={kernelName:Nl,backendName:"webgl",kernelFunc:UZ},HZ=class{constructor(e){this.outputShape=[],this.outputShape=_.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let r=t.length,s=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},jZ=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,r=n.length,s=gt(r),a=Tn("coords",r),o=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let c=o[t],l=o.slice(-2),u=o.join(),d=`if (${c} < ${i[0]}) {
return getChannel(
getT0(${u}), vec2(${l.join()}));
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
if (${c} < ${i[f]} && ${c} >= ${i[f-1]}) {
return getChannel(
getT${f}(${Fm(o,c,m)}),
vec2(${Fm(l,c,m)}));
}`}let p=i.length,h=i[i.length-1];d+=`
return getChannel(
getT${p}(${Fm(o,c,h)}),
vec2(${Fm(l,c,h)}));`,this.userCode=`
float getValue(${o.map(f=>"int "+f)}) {
${d}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[r-1]} = ${a[r-1]} + 1;
if (${a[r-1]} < ${n[r-1]}) {
result.g = getValue(${a});
}
${a[r-2]} = ${a[r-2]} + 1;
if (${a[r-2]} < ${n[r-2]}) {
result.a = getValue(${a});
}
${a[r-1]} = ${a[r-1]} - 1;
if (${a[r-2]} < ${n[r-2]} &&
${a[r-1]} < ${n[r-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function Fm(e,t,n){let r=e.indexOf(t);return e.map((a,o)=>o===r?`${a} - ${n}`:a).join()}function Rm(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return ar({inputs:{x:s.complexTensorInfos.imag},backend:n})}var qZ={kernelName:gh,backendName:"webgl",kernelFunc:Rm};function Lu(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(m=>Xd({inputs:{input:m},backend:n})),d=e.map(m=>Rm({inputs:{input:m},backend:n})),p=Lu(u,t,n),h=Lu(d,t,n),f=Ta({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let u=e.map(b=>{let y=k.sizeFromShape(b.shape.slice(t));return ge({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),d=u.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),p=_.computeOutShape(u.map(b=>b.shape),1),h=u[0].shape[0]===1,f=V7(d,p,r,h),m=_.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,r,f);return u.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=Lu(e.slice(0,u),t,n),p=Lu(e.slice(u),t,n),h=Lu([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new jZ(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:o}=KZ(e,t,n),i=new HZ(a.map(u=>u.shape)),c=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=ge({inputs:{x:c},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(c),l}function KZ(e,t,n){let r=_.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ge({inputs:{x:a},attrs:{shape:[-1,k.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function KN(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=k.parseAxisParam(s,t[0].shape)[0],o=_.computeOutShape(t.map(l=>l.shape),a);if(k.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(l=>k.sizeFromShape(l.shape)>0);if(i.length===1)return ar({inputs:{x:i[0]},backend:n});let c=i.map(l=>l.shape);return _.assertParamsConsistent(c,a),Lu(i,a,n)}var XZ={kernelName:ic,backendName:"webgl",kernelFunc:KN},XN=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,c=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(r?v=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?v=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:v=`
float activation(float x) {
${n}
}
`,x="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
const ivec2 strides = ivec2(${i}, ${c});
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${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 * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${w}
${x}
setOutput(result);
}
`}},YZ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,c=e.dilationHeight,l=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${a}, ${o});
const ivec3 pads = ivec3(${t}, ${n}, ${r});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${u}; wF++) {
int xF = xFCorner + wF * ${i};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${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 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${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);
}
`}},ZZ=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=Vn(this.outputShape.length);let{dataFormat:n}=t,r=Sn(),s=n==="channelsLast",a=s?0:1,o=s?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,c="";for(let l=0;l<=1;l++)for(let u=0;u<=1;u++)c+=`
blockIndex = rc.y + ${u};
pos = rc.x + ${l};
${i}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${o}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${s}) {
innerDims = vec2(d1, ch);
result[${l*2+u}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${l*2+u}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${c}
${r.output} = result;
}
`}};function YN({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let c=e.shape,l=r.texData.get(e.dataId),u=n.inChannels,d=c[0]*c[1]*c[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((d===1||p===1)&&u>BN)&&l.isPacked&&h&&l.texture!=null&&c[2]%2!==0&&k.arraysEqual(l.shape.slice(-3),c.slice(-3))){let x=c[0]*c[1]*(c[2]+1),w={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,k.assert(Hd(l.shape,w.shape),()=>`packed reshape ${l.shape} to ${w.shape} isn't free`);let N=ge({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let $=Dm({a:w,b:N,backend:r,transposeA:f,transposeB:m,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),D=r.texData.get($.dataId);k.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=T,D.shape=n.outShape,g=ar({inputs:{x:$},backend:r}),g.shape=n.outShape,b.push($)}else{let x=h?c[0]*c[1]*c[2]:c[0]*c[2]*c[3],w=ge({inputs:{x:e},backend:r,attrs:{shape:[1,x,n.inChannels]}}),T=ge({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Dm({a:w,b:T,transposeA:f,transposeB:m,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ge({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),b.push(w),b.push(T),b.push(N)}for(let x of b)r.disposeIntermediateTensorInfo(x);return g}function ZN({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:c,filterHeight:l,inChannels:u,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=c*l*u,g=p*d,b=[m,g],y=!0,v=!1,x=[],w=ge({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),T=ge({inputs:{x:t},backend:r,attrs:{shape:[1,m,k.sizeFromShape(t.shape)/m]}});x.push(w),x.push(T);let N=new ZZ(b,n),$=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],D=r.runWebGLProgram(N,[w],"float32",$),P=ge({inputs:{x:D},backend:r,attrs:{shape:[1,b[0],b[1]]}});x.push(D),x.push(P);let F=s!=null,R=a!=null,C=i==="leakyrelu",L=i?_m(i,!0):null,G=new RN(P.shape,T.shape,[1,g,n.outChannels],y,v,F,L,R,C),j=[P,T];if(s&&j.push(s),R&&j.push(a),C){let te=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));j.push(te),x.push(te)}let K=r.runWebGLProgram(G,j,"float32"),q=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],Z=ge({inputs:{x:K},backend:r,attrs:{shape:q}});x.push(K);for(let te of x)r.disposeIntermediateTensorInfo(te);return Z}function JZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:c,dilations:l,dimRoundingMode:u}=r,d=_.convertConv2DDataFormat(c),p=_.computeConv2DInfo(s.shape,a.shape,o,l,i,u,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=YN({x:s,filter:a,convInfo:p,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)h=ZN({x:s,filter:a,convInfo:p,backend:n});else{let m=new XN(p);h=n.runWebGLProgram(m,[s,a],"float32")}let f=ge({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var QZ={kernelName:Ha,backendName:"webgl",kernelFunc:JZ},eJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=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} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
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);
}
`}},tJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,c=a?1:2,l=a?2:3,u=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${u}];
ivec2 dyCorner = ivec2(coords[${c}], coords[${l}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.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) / ${s}.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);
}
`}},nJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${s};
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 * ${r} - ${o};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},rJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,c=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${i}, ${c}, ${l});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${s}.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 < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 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 sJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:c,dimRoundingMode:l,filterShape:u}=r,d=_.convertConv2DDataFormat(c),p=_.computeConv2DInfo(s.shape,u,o,1,i,l,!1,d),h=new eJ(p);return n.runWebGLProgram(h,[s,a],"float32")}var aJ={kernelName:rh,backendName:"webgl",kernelFunc:sJ};function oJ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:c,dataFormat:l,dimRoundingMode:u}=r,d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(o,a.shape,i,1,c,u,!1,d),h=new tJ(p);return n.runWebGLProgram(h,[s,a],"float32")}var iJ={kernelName:ja,backendName:"webgl",kernelFunc:oJ};function cJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c}=r,l=_.computeConv3DInfo(s.shape,a.shape,o,c,i),u=new YZ(l);return n.runWebGLProgram(u,[s,a],"float32")}var uJ={kernelName:_l,backendName:"webgl",kernelFunc:cJ};function lJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:c}=r,l=_.computeConv3DInfo(s.shape,c,o,1,i),u=new nJ(l);return n.runWebGLProgram(u,[s,a],"float32")}var dJ={kernelName:sh,backendName:"webgl",kernelFunc:lJ};function pJ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:c}=r,l=_.computeConv3DInfo(c,a.shape,i,1,o),u=new rJ(l);return n.runWebGLProgram(u,[s,a],"float32")}var hJ={kernelName:ah,backendName:"webgl",kernelFunc:pJ},fJ=Ou+`
return cos(x);
`,mJ=Ye({opSnippet:fJ}),gJ={kernelName:qa,backendName:"webgl",kernelFunc:mJ},bJ=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,yJ=Ye({opSnippet:bJ}),vJ={kernelName:Ka,backendName:"webgl",kernelFunc:yJ},xJ=class{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,c]=e,[l]=t,[u,d]=n;this.outputShape=[l,u,d,c];let p=r==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,b]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${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 > ${h} ) {
setOutput(float(${s}));
return;
}
float in_x = ${x};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},wJ=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:c,extrapolationValue:l}=r,u=new xJ(s.shape,a.shape,i,c,l);return n.runWebGLProgram(u,[s,a,o],"float32")},kJ={kernelName:cc,backendName:"webgl",kernelFunc:wJ},JN=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let r=e.length,s=t?"0.0":`getX(${QN(r,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${gt(r)} coords = getOutputCoords();
int end = ${e_(r,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${o}) {
int idx = ${i};
${e_(r,"coords")} = idx;
val += getX(${QN(r,"coords")});
}
setOutput(val);
}
`}};function QN(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 ${e} is not yet supported`)}function e_(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function IJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,c=s.shape.length,l=_.getAxesPermutation([a],c),u=s;l!=null&&(u=Cn({inputs:{x:s},backend:n,attrs:{perm:l}}));let d=_.getInnerMostAxes(1,c)[0];if(d!==c-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${s.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=ar({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new JN(u.shape,!1,i),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(o){let f=new JN(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=_.getUndoAxesPermutation(l),m=Cn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var SJ={kernelName:Xa,backendName:"webgl",kernelFunc:IJ};function TJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let c=n.readSync(s.dataId),l=n.readSync(a.dataId),u=vN(c,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(s.shape.length===2){let c=n.bufferSync(s),l=n.bufferSync(a),u=z7(c,l,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var CJ={kernelName:oh,backendName:"webgl",kernelFunc:TJ},NJ=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 _J(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],c=o==="NHWC"?s.shape[1]:s.shape[2],l=o==="NHWC"?s.shape[2]:s.shape[3],u=o==="NHWC"?s.shape[3]:s.shape[1],d=c*a,p=l*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new NJ(f,a,o);return n.runWebGLProgram(m,[s],s.dtype)}var EJ={kernelName:uc,backendName:"webgl",kernelFunc:_J},t_=class{constructor(e,t=!1,n=null,r=!1,s=!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=Vn(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,c="",l="";n&&(r?c=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?c=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:c=`
float activation(float x) {
${n}
}
`,l="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${c}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${o}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${u}
${l}
setOutput(result);
}
`}},n_=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Vn(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,c=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,d=u,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;p+=`
for (int r = 0; r < ${l}; r++) {
`;for(let g=0;g<u;g++)p+=`
xTexelC${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 + ${b*c};
`,i===1){if(b<u&&(o%2===1?(p+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${b}Ready = 1;
}
`,c===1&&b>0?p+=`
xC${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<u)){let y=o%2===0?k.nearestLargerEven(c):c;c%2===0&&o%2===1||c%2!==0&&o%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${b+1}Ready = 1;
}
`,c>1&&(p+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${b+1};
`}}else b<u&&(o%2===1?(p+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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<u&&(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${b+1}.xy);
`,b+1<u&&(p+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`)));b<u&&(p+=`
wTexel = getW(r, ${b}, d1, q);
dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
`,b+1<u&&(p+=`
wTexel = getW(r, ${b+1}, d1, q);
dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
`))}p+=`
}
`,p+=`
}
`;let h="",f="";n&&(r?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?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"),r&&this.variableNames.push("preluActivationWeights"),s&&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 AJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c,dimRoundingMode:l}=r,u=c;u==null&&(u=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=_.computeConv2DInfo(s.shape,a.shape,o,u,i,l,!0),p;J().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?p=new n_(d):p=new t_(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,[s,a],"float32",h)}var DJ={kernelName:Ya,backendName:"webgl",kernelFunc:AJ},$J=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=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} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},FJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.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) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${i}; dm++) {
int d2 = d1 * ${i} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function RJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:c,dimRoundingMode:l,filterShape:u}=r,d=_.computeConv2DInfo(s.shape,u,o,i,c,l,!0),p=new $J(d);return n.runWebGLProgram(p,[s,a],"float32")}var PJ={kernelName:ih,backendName:"webgl",kernelFunc:RJ};function OJ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:c,dimRoundingMode:l,inputShape:u}=r,d=_.computeConv2DInfo(u,a.shape,o,i,c,l,!0),p=new FJ(d);return n.runWebGLProgram(p,[s,a],"float32")}var MJ={kernelName:ch,backendName:"webgl",kernelFunc:OJ},LJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function BJ(e){let{inputs:t,backend:n}=e,{x:r}=t,s=[...r.shape,...r.shape],a=k.sizeFromShape(r.shape),o=ge({inputs:{x:r},backend:n,attrs:{shape:[a]}}),i=new LJ(a),c=n.runWebGLProgram(i,[o],o.dtype),l=ge({inputs:{x:c},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),l}var zJ={kernelName:uh,backendName:"webgl",kernelFunc:BJ},WJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:c,dilationWidth:l}=e,{top:u,left:d}=r;this.userCode=`
const ivec2 strides = ivec2(${s}, ${a});
const ivec2 pads = ivec2(${u}, ${d});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${o}; h++) {
int hIn = hBeg + h * ${c};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${i}; w++) {
int wIn = wBeg + w * ${l};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function VJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c}=r,l=_.computeDilation2DInfo(s.shape,a.shape,o,i,"NHWC",c),u,d=new WJ(l);u=n.runWebGLProgram(d,[s,a],"float32");let p=ge({inputs:{x:u},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(u),p}var UJ={kernelName:El,backendName:"webgl",kernelFunc:VJ};function GJ(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:c}=_.decodeEinsumEquation(s,a.length);_.checkEinsumDimSizes(o.length,c,a);let{path:l,steps:u}=_.getEinsumComputePath(i,c),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:b,expandDims:y}=_.getEinsumPermutation(h,c[g]),v;_.isIdentityPermutation(b)?v=a[g]:(v=Cn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let w=0;w<y.length;++w)x.splice(y[w],0,1);k.arraysEqual(v.shape,x)||(v=ge({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),p===null?p=v:(p=s0({inputs:{a:v,b:p},backend:n}),f.push(p))}m<d-1&&(l[m]>=0&&(p=Am({inputs:{x:p},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var HJ={kernelName:ph,backendName:"webgl",kernelFunc:GJ},jJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",qJ=`
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;
`,KJ=Ye({opSnippet:jJ,packedOpSnippet:qJ}),XJ={kernelName:Ja,backendName:"webgl",kernelFunc:KJ},YJ="return (b >= 1.0) ? a : a * (b + 1.0);",ZJ=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,JJ=e=>{let{inputs:t,backend:n}=e,{dy:r,y:s}=t,a=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qd(ZJ,r.shape,s.shape):new Pu(YJ,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)},QJ={kernelName:hh,backendName:"webgl",kernelFunc:JJ},eQ=`
return vec4(equal(a, b));
`,tQ="return float(a == b);",nQ=un({opSnippet:tQ,packedOpSnippet:eQ,dtype:"bool",cpuKernelImpl:U7}),rQ={kernelName:dc,backendName:"webgl",kernelFunc:nQ},sQ=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${_.ERF_P};
float a1 = ${_.ERF_A1};
float a2 = ${_.ERF_A2};
float a3 = ${_.ERF_A3};
float a4 = ${_.ERF_A4};
float a5 = ${_.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,aQ=Ye({opSnippet:sQ}),oQ={kernelName:lc,backendName:"webgl",kernelFunc:aQ},iQ=Ou+`
return exp(x);
`,cQ=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,r_=Ye({opSnippet:iQ,packedOpSnippet:cQ,cpuKernelImpl:G7,dtype:"float32"}),uQ={kernelName:Qa,backendName:"webgl",kernelFunc:r_};function c0(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),c=s;return s<0&&(k.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),c=o+s+1),i.splice(c,0,1),ge({inputs:{x:a},backend:r,attrs:{shape:i}})}var lQ={kernelName:pc,backendName:"webgl",kernelFunc:c0},s_="return exp(x) - 1.0;",dQ=Ye({opSnippet:s_,packedOpSnippet:s_,cpuKernelImpl:H7}),pQ={kernelName:hc,backendName:"webgl",kernelFunc:dQ},a_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${r}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${o}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${r});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${r}; 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 o_(e,t,n){let r=n.texData.get(e.dataId),s=k.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=s/a,i=ge({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),c=i.shape,l=new a_("real",c,t),u=new a_("imag",c,t),d=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:c},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:c}],p=n.runWebGLProgram(l,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Ta({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=ge({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function hQ(e){let{inputs:t,backend:n}=e,{input:r}=t;return o_(r,!1,n)}var fQ={kernelName:fh,backendName:"webgl",kernelFunc:hQ},mQ=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 Yd(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||k.inferDtype(s),a==="string"){let o=k.getArrayFromDType(a,k.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new mQ(r,s),i=[[s]];return t.runWebGLProgram(o,[],a,i)}}var gQ={kernelName:Al,backendName:"webgl",kernelFunc:Yd},bQ=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},yQ={kernelName:fc,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new bQ(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},i_="return floor(x);",vQ=Ye({opSnippet:i_,packedOpSnippet:i_,cpuKernelImpl:j7}),xQ={kernelName:eo,backendName:"webgl",kernelFunc:vQ},wQ=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,kQ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,IQ=un({opSnippet:wQ,packedOpSnippet:kQ,dtype:"int32"}),SQ={kernelName:to,backendName:"webgl",kernelFunc:IQ},TQ=class{constructor(e){this.variableNames=["A"];let t=Sn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},CQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Sn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}.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);
}
}
${t.output} = result;
}
`}},NQ={kernelName:Dh,backendName:"webgl",kernelFunc:_Q},Bu;function _Q(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r,o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,[c,l]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],u=[l,c],d=[l,c,a];(i||o)&&(Bu==null&&(Bu=document.createElement("canvas").getContext("2d")),Bu.canvas.width=c,Bu.canvas.height=l,Bu.drawImage(s,0,0,c,l),s=Bu.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=br.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),s);let h=J().getBool("WEBGL_PACK")?new CQ(d):new TQ(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function EQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:l,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=r,m=_.convertConv2DDataFormat(u),g=_.computeConv2DInfo(s.shape,a.shape,c,d,l,p,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=YN({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)b=ZN({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let x=o!=null,w=i!=null,T=h==="leakyrelu",N=h?_m(h,!1):null,$=new XN(g,x,N,w,T),D=[s,a];if(o&&D.push(o),i&&D.push(i),T){let P=n.makeTensorInfo([],"float32",k.createScalarValue(f,"float32"));D.push(P),y.push(P)}b=n.runWebGLProgram($,D,"float32")}let v=ge({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var AQ={kernelName:Mo,backendName:"webgl",kernelFunc:EQ};function DQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:l,dilations:u,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=r,f=[],m=u;m==null&&(m=[1,1]),k.assert(_.eitherStridesOrDilationsAreOne(c,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${c} and dilations '${m}'`);let g=_.computeConv2DInfo(s.shape,a.shape,c,m,l,d,!0),b=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=p?_m(p,b):null,v=[s,a],x=o!=null,w=i!=null,T=p==="leakyrelu";if(x&&v.push(o),w&&v.push(i),T){let P=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));v.push(P),f.push(P)}let N;b?N=new n_(g,x,y,w,T):N=new t_(g,x,y,w,T);let $=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],D=n.runWebGLProgram(N,v,"float32",$);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),D}var $Q={kernelName:Lo,backendName:"webgl",kernelFunc:DQ},FQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=gt(t.length),s=gt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${r} strides = ${r}(${this.strides});
void main() {
${s} 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 RQ(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=k.sizeFromShape(r.shape),[c,l,u,d]=_.prepareAndValidate(r,s),p=ge({inputs:{x:s},backend:n,attrs:{shape:[l,o]}}),h=ge({inputs:{x:r},backend:n,attrs:{shape:[k.sizeFromShape(r.shape)/u,u]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.readSync(s.dataId),y=n.bufferSync(r),v=q7(b,y,r.dtype,l,o,u,d,r.shape,i);return n.makeTensorInfo(c,r.dtype,v.values)}let f=new FQ(o,d,[l,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=ge({inputs:{x:m},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var PQ={kernelName:gc,backendName:"webgl",kernelFunc:RQ},OQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=gt(this.rank),r=MQ(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${r}));
}
`}};function MQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e.length;s++)s===2?r.push("index"):r.push(`${n[s]}`);return r.join()}function c_(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r,c=k.parseAxisParam(o,s.shape)[0];if(J().get("DEBUG")){let y=n.readSync(a.dataId),v=s.shape[c];for(let x=0;x<y.length;++x){let w=y[x];k.assert(w<=v-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${v-1}]`)}}let l=_.segment_util.collectGatherOpShapeInfo(s,a,c,i),u=k.sizeFromShape(a.shape),d=[],p=ge({inputs:{x:s},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=ge({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,u/l.batchSize]}});d.push(p),d.push(h);let f=[l.batchSize,l.outerSize,u/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([s,a])||s.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(p),x=K7(v,y,f);return d.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new OQ(p.shape,f),g=n.runWebGLProgram(m,[p,h],p.dtype);d.push(g);let b=ge({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var LQ={kernelName:mc,backendName:"webgl",kernelFunc:c_},BQ="return float(a > b);",zQ=`
return vec4(greaterThan(a, b));
`,WQ=un({opSnippet:BQ,packedOpSnippet:zQ,cpuKernelImpl:X7,dtype:"bool"}),VQ={kernelName:bc,backendName:"webgl",kernelFunc:WQ},UQ="return float(a >= b);",GQ=`
return vec4(greaterThanEqual(a, b));
`,HQ=un({opSnippet:UQ,packedOpSnippet:GQ,dtype:"bool",cpuKernelImpl:Y7}),jQ={kernelName:ro,backendName:"webgl",kernelFunc:HQ};function qQ(e){let{inputs:t,backend:n}=e,{input:r}=t;return o_(r,!0,n)}var KQ={kernelName:mh,backendName:"webgl",kernelFunc:qQ},XQ="return float(!isnan(x) && !isinf(x));",YQ=Ye({opSnippet:XQ,dtype:"bool"}),ZQ={kernelName:yc,backendName:"webgl",kernelFunc:YQ},JQ="return float(isinf(x));",QQ=Ye({opSnippet:JQ,dtype:"bool"}),eee={kernelName:vc,backendName:"webgl",kernelFunc:QQ},tee="return float(isnan(x));",nee=Ye({opSnippet:tee,dtype:"bool"}),ree={kernelName:xc,backendName:"webgl",kernelFunc:nee},see="return float(a < b);",aee=`
return vec4(lessThan(a, b));
`,oee=un({opSnippet:see,packedOpSnippet:aee,cpuKernelImpl:Z7,dtype:"bool"}),iee={kernelName:wc,backendName:"webgl",kernelFunc:oee},cee="return float(a <= b);",uee=`
return vec4(lessThanEqual(a, b));
`,lee=un({opSnippet:cee,packedOpSnippet:uee,cpuKernelImpl:J7,dtype:"bool"}),dee={kernelName:kc,backendName:"webgl",kernelFunc:lee};function pee(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=Q7(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var hee={kernelName:bh,backendName:"webgl",kernelFunc:pee},fee=Ou+`
return x < 0.0 ? 0./0. : log(x);
`,mee=`
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;
`,gee=Ye({opSnippet:fee,packedOpSnippet:mee,cpuKernelImpl:e9}),bee={kernelName:oo,backendName:"webgl",kernelFunc:gee},yee=Ou+`
return log(1.0 + x);
`,vee=Ye({opSnippet:yee}),xee={kernelName:Ic,backendName:"webgl",kernelFunc:vee},wee="return float(a >= 1.0 && b >= 1.0);",kee=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,Iee=un({opSnippet:wee,packedOpSnippet:kee,dtype:"bool"}),See={kernelName:Sc,backendName:"webgl",kernelFunc:Iee},Tee="return float(!(x >= 1.0));",Cee=Ye({opSnippet:Tee}),Nee={kernelName:Dl,backendName:"webgl",kernelFunc:Cee},_ee="return float(a >= 1.0 || b >= 1.0);",Eee=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Aee=un({opSnippet:_ee,packedOpSnippet:Eee,dtype:"bool"}),Dee={kernelName:$l,backendName:"webgl",kernelFunc:Aee},$ee=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,c=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${c})`:s===1?i=`1.0/(${c})`:i=`exp(log(${c}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${o}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${i};
setOutput(val);
}
`}},Fee=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,c=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${c})`:s===1?i=`1.0/(${c})`:i=`exp(log(${c}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${i};
setOutput(result);
}
`}},Ree=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{depthRadius:a,bias:o,alpha:i,beta:c}=r,l=J().getBool("WEBGL_PACK_NORMALIZATION")?new Fee(s.shape,a,o,i,c):new $ee(s.shape,a,o,i,c);return n.runWebGLProgram(l,[s],s.dtype)},Pee={kernelName:Fl,backendName:"webgl",kernelFunc:Ree},Oee=class{constructor(e,t,n,r,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=s,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 = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${r}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${r})
* float(${s})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},Mee=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:c,alpha:l,beta:u}=r,d=new Oee(s.shape,i,c,l,u);return n.runWebGLProgram(d,[s,a,o],s.dtype)},Lee={kernelName:yh,backendName:"webgl",kernelFunc:Mee};function Bee(e,t,n,r){let s=k.sizeFromShape(t),o=k.sizeFromShape(e.shape)/s,i=ge({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),c=yi(i,e.dtype,"max",r),l=ge({inputs:{x:c},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(c),l}function u_(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=s.shape.length,c=k.parseAxisParam(a,s.shape),l=c,u=_.getAxesPermutation(l,i),d=u!=null,p=n.shouldExecuteOnCPU([s]),h=s;if(d){if(p){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let N=0;N<x.length;N++)x[N]=s.shape[u[N]];let w=r0(v,s.shape,s.dtype,u,x);h=n.makeTensorInfo(x,s.dtype);let T=n.texData.get(h.dataId);T.values=w}else h=Em(s,u,n);l=_.getInnerMostAxes(l.length,i)}_.assertAxesAreInnerMostDims("max",l,i);let[f,m]=_.computeOutAndReduceShapes(h.shape,l),g=f;o&&(g=_.expandShapeToKeepDim(f,c));let b;if(p){let v=n.texData.get(h.dataId).values,x=t9(v,k.sizeFromShape(m),g,s.dtype);b=n.makeTensorInfo(g,s.dtype);let w=n.texData.get(b.dataId);w.values=x}else b=Bee(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),b}var zee={kernelName:io,backendName:"webgl",kernelFunc:u_},Wee=EN+`
return max(a, b);
`,Vee=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Nm+`
return result;
`,Uee=un({opSnippet:Wee,packedOpSnippet:Vee,cpuKernelImpl:n9}),Gee={kernelName:co,backendName:"webgl",kernelFunc:Uee};function Hee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Eu(s,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:c}=r,l=1;k.assert(_.eitherStridesOrDilationsAreOne(o,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let u=_.computePool2DInfo(s.shape,a,o,l,i,c);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return ar({inputs:{x:s},backend:n});let d=new Kd(u,"max",!1);return n.runWebGLProgram(d,[s],s.dtype)}var jee={kernelName:uo,backendName:"webgl",kernelFunc:Hee};function qee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dataFormat:c,dimRoundingMode:l}=r,u=[1,1,1],d=_.computePool3DInfo(s.shape,a,o,u,i,l,c),p=new a0(d,"max",!1);return n.runWebGLProgram(p,[s],s.dtype)}var Kee={kernelName:Rl,backendName:"webgl",kernelFunc:qee},Xee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=s-1-e.padInfo.top,i=a-1-e.padInfo.left,c=s*a-1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${s};
wR += ${r}) {
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 = ${c} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Yee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,c=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=c-1-e.padInfo.top,p=l-1-e.padInfo.left,h=i*c*l-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${d}, ${p});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${i};
wD += ${s}) {
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 < ${c};
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 < ${l};
wC += ${o}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(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 * ${c} * ${l} +
wR * ${l} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Zee(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:c,pad:l,dimRoundingMode:u}=r,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,c,d,l,u),h=new a0(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Yee(p),g=n.runWebGLProgram(m,[s,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Jee={kernelName:xh,backendName:"webgl",kernelFunc:Zee};function Qee(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;Eu([a,o],"maxPoolGrad");let{filterSize:c,strides:l,pad:u,dimRoundingMode:d}=r,p=_.computePool2DInfo(i.shape,c,l,1,u,d),h=!0,f=new Kd(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Xee(p),b=n.runWebGLProgram(g,[s,m],i.dtype);return n.disposeIntermediateTensorInfo(m),b}var ete={kernelName:vh,backendName:"webgl",kernelFunc:Qee};function tte(e,t,n,r){let s=new Kd(n,"max",!1),a=r.runWebGLProgram(s,[e],"float32");s=new Kd(n,"max",!0,!0,t);let o=r.runWebGLProgram(s,[e],"float32");return[a,o]}var nte={kernelName:wh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:s,strides:a,pad:o,includeBatchInIndex:i}=t,c=n;k.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let l=[1,1];k.assert(_.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let u=_.computePool2DInfo(r.shape,s,a,l,o),[d,p]=tte(r,i,u,c);return[d,p]}};function rte(e,t,n,r){let s=k.sizeFromShape(t),o=k.sizeFromShape(e.shape)/s,i=ge({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),c=yi(i,"float32","mean",r),l=ge({inputs:{x:c},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(c),l}var ste={kernelName:lo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:s,axis:a}=t,o=n,i=r.shape.length,c=k.parseAxisParam(a,r.shape),l=c,u=_.getAxesPermutation(l,i),d=u!=null,p=o.shouldExecuteOnCPU([r]),h=[],f=r;if(d){if(p){let x=o.texData.get(f.dataId).values,w=new Array(i);for(let $=0;$<w.length;$++)w[$]=r.shape[u[$]];let T=r0(x,r.shape,r.dtype,u,w);f=o.makeTensorInfo(w,r.dtype);let N=o.texData.get(f.dataId);N.values=T}else f=Em(r,u,o);h.push(f),l=_.getInnerMostAxes(l.length,i)}_.assertAxesAreInnerMostDims("sum",l,i);let[m,g]=_.computeOutAndReduceShapes(f.shape,l),b=m;s&&(b=_.expandShapeToKeepDim(m,c));let y=rte(f,g,b,o);for(let v of h)o.disposeIntermediateTensorInfo(v);return y}};function ate(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=k.parseAxisParam(a,s.shape),l=c,u=_.getAxesPermutation(l,i),d=s;u!=null&&(d=Cn({inputs:{x:s},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,s.shape.length)),_.assertAxesAreInnerMostDims("min",l,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,l),f=k.sizeFromShape(h),m=ge({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"min",n),b;if(o){let y=_.expandShapeToKeepDim(p,c);b=ge({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ge({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),b}var ote={kernelName:po,backendName:"webgl",kernelFunc:ate},ite=EN+`
return min(a, b);
`,cte=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Nm+`
return result;
`,ute=un({opSnippet:ite,packedOpSnippet:cte,cpuKernelImpl:r9}),lte={kernelName:ho,backendName:"webgl",kernelFunc:ute},dte=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,s=gt(r),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),c=n==="reflect"?0:1;if(r===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${c};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${c};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${o});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${r}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${c};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${c};
}
}
${s} coords = outC - start;
setOutput(getX(${i}));
}
`}},pte=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 r=e.length,s=gt(r),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Tn("rc",r),c=Tn("source",r),l=`${i[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${c.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(r===1){let h=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${d};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${d};
}
source -= start;
`;p=`
${s} rc = outputLoc;
${h}
result[0] = getChannel(getX(${c.join()}), ${u});
${i[r-1]} += 1;
if(${l}) {
${h}
result[1] = getChannel(getX(${c.join()}), ${u});
}
`}else{let h=`
${s} source = rc;
${s} lt = ${s}(lessThan(source, start));
${s} gte = ${s}(greaterThanEqual(source, end));
${s} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${d}) +
gte * ((end - 1) * 2 - source + ${d});
source -= start;
`;p=`
${s} rc = outputLoc;
${h}
result[0] = getChannel(getX(${c.join()}), ${u});
${i[r-1]} += 1;
if(${l}) {
${h}
result[1] = getChannel(getX(${c.join()}), ${u});
}
rc = outputLoc;
${i[r-2]} += 1;
if(${i[r-2]} < ${this.outputShape[r-2]}) {
${h}
result[2] = getChannel(getX(${c.join()}), ${u});
${i[r-1]} += 1;
if(${l}) {
${h}
result[3] = getChannel(getX(${c.join()}), ${u});
}
}
`}this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${o});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},hte=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:s,mode:a}=n,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new pte(r.shape,s,a):new dte(r.shape,s,a);return t.runWebGLProgram(o,[r],r.dtype)},fte={kernelName:fo,backendName:"webgl",kernelFunc:hte},mte=`if (b == 0.0) return NAN;
return mod(a, b);`,gte=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Nm+`
return result;
`,bte=un({opSnippet:mte,packedOpSnippet:gte}),yte={kernelName:Tc,backendName:"webgl",kernelFunc:bte},vte=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}));
}
`}},xte=`
if (a == b) {
return 1.0;
};
return a / b;`,wte=`
// 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;
`,l_=un({opSnippet:xte,packedOpSnippet:wte,checkOutOfBounds:!0}),kte={kernelName:Za,backendName:"webgl",kernelFunc:l_},d_="return a - b;",p_=un({opSnippet:d_,packedOpSnippet:d_,supportsComplex:!0,cpuKernelImpl:v9}),Ite={kernelName:$o,backendName:"webgl",kernelFunc:p_};function h_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=k.parseAxisParam([a],s.shape),i=u_({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),c=_.expandShapeToKeepDim(i.shape,o),l=ge({inputs:{x:i},backend:n,attrs:{shape:c}}),u=p_({inputs:{a:s,b:l},backend:n}),d=r_({inputs:{x:u},backend:n}),p=Am({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ge({inputs:{x:p},backend:n,attrs:{shape:c}}),f=l_({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var Ste={kernelName:Ao,backendName:"webgl",kernelFunc:h_};function Tte(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,c=i?s:h_({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),l=c.shape[0],u=c.shape[1],d=new vte(l,u,a),p=[[o]],h=n.runWebGLProgram(d,[c],"int32",p);return i||n.disposeIntermediateTensorInfo(c),h}var Cte={kernelName:kh,backendName:"webgl",kernelFunc:Tte},Nte=Fr+`
return -x;
`,_te=`
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 Ete(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.texData.get(r.dataId),[o,i]=a9(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new bi(r.shape,_te):s=new Fs(r.shape,Nte),n.runWebGLProgram(s,[r],r.dtype)}var Ate={kernelName:Cc,backendName:"webgl",kernelFunc:Ete},Dte=is.nonMaxSuppressionV3Impl;function $te(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c}=r,l=n.readSync(s.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Dte(l,u,o,i,c);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Fte={kernelName:_c,backendName:"webgl",kernelFunc:$te},Rte=is.nonMaxSuppressionV4Impl;function Pte(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c,padToMaxOutputSize:l}=r,u=n.readSync(s.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=Rte(u,d,o,i,c,l);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Ote={kernelName:Ec,backendName:"webgl",kernelFunc:Pte},Mte=is.nonMaxSuppressionV5Impl;function Lte(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c,softNmsSigma:l}=r,u=n.readSync(s.dataId),d=n.readSync(a.dataId),p=o,h=i,f=c,m=l,{selectedIndices:g,selectedScores:b}=Mte(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Bte={kernelName:Ac,backendName:"webgl",kernelFunc:Lte},zte=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${r}), float(${n}),
float(index == coords.y)));
}
`}},Wte=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{depth:a,onValue:o,offValue:i}=r,c=k.sizeFromShape(s.shape),l=new zte(c,a,o,i),u=ge({inputs:{x:s},backend:n,attrs:{shape:[c]}}),d=n.runWebGLProgram(l,[u],s.dtype);n.disposeIntermediateTensorInfo(u);let p=[...s.shape,a],h=ge({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},Vte={kernelName:go,backendName:"webgl",kernelFunc:Wte};function Pm(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=Xd({inputs:{input:r},backend:n}),a=Pm({inputs:{x:s},backend:n}),o=Rm({inputs:{input:r},backend:n}),i=Pm({inputs:{x:o},backend:n}),c=Ta({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),c}else return Yd({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Ute={kernelName:Yc,backendName:"webgl",kernelFunc:Pm};function f_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=Xd({inputs:{input:r},backend:n}),a=f_({inputs:{x:s},backend:n}),o=Rm({inputs:{input:r},backend:n}),i=Pm({inputs:{x:o},backend:n}),c=Ta({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),c}else return Yd({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Gte={kernelName:Dc,backendName:"webgl",kernelFunc:f_};function Hte(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return c0({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],c=t.map(u=>{let d=c0({inputs:{input:u},backend:n,attrs:{dim:s}});return i.push(d),d}),l=KN({inputs:c,backend:n,attrs:{axis:s}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),l}var jte={kernelName:$c,backendName:"webgl",kernelFunc:Hte},qte=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((c,l)=>c[0]+e[l]+c[1]);let r=e.length,s=gt(r),a=t.map(c=>c[0]).join(","),o=t.map((c,l)=>c[0]+e[l]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${o});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${s} coords = outC - start;
setOutput(getX(${i}));
}
}
`}},Kte=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 r=e.length,s=gt(r),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Tn("rc",r),c=Tn("source",r),l=`${i[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${c.slice(-2).join()})`,d=[`${s} rc = outputLoc;`,`${i[r-1]} += 1;
if(${l}) {
`,r===1?"":`}
rc = outputLoc;
${i[r-2]} += 1;
if(${i[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${i[r-1]} += 1;
if(${l}) {`],p=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=r===1?2:4;f<m;f++)h+=`
${d[f]}
if (${p}) {
result[${f}] = float(value);
} else {
${s} source = rc - start;
result[${f}] = getChannel(getX(${c.join()}), ${u});
}
`;h+=r===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${o});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},m_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;if(k.sizeFromShape(s.shape)===0){let l=a.map((u,d)=>u[0]+s.shape[d]+u[1]);return Yd({backend:n,attrs:{shape:l,value:o,dtype:s.dtype}})}let i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kte(s.shape,a,o):new qte(s.shape,a,o),c=[[o]];return n.runWebGLProgram(i,[s],s.dtype,c)},Xte={kernelName:bo,backendName:"webgl",kernelFunc:m_},Yte=`
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);
`,Zte=`
// 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));
`+Nm+`
return result;
`,Jte=un({opSnippet:Yte,packedOpSnippet:Zte}),Qte={kernelName:yo,backendName:"webgl",kernelFunc:Jte};function ene(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=[],l=k.parseAxisParam(a,s.shape),u=l,d=_.getAxesPermutation(u,i),p=s;d!=null&&(p=Cn({inputs:{x:s},backend:n,attrs:{perm:d}}),u=_.getInnerMostAxes(u.length,i),c.push(p)),_.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:b}=i9(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=_.computeOutAndReduceShapes(p.shape,u),g=k.sizeFromShape(m),b=ge({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),y=Lh(s.dtype),v=yi(b,y,"prod",n);h=ge({inputs:{x:v},backend:n,attrs:{shape:f}}),c.push(b),c.push(v)}if(o){c.push(h);let f=_.expandShapeToKeepDim(h.shape,l);h=ge({inputs:{x:h},backend:n,attrs:{shape:f}})}return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var tne={kernelName:Fc,backendName:"webgl",kernelFunc:ene},g_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=c9(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},nne={kernelName:Pl,backendName:"webgl",kernelFunc:g_},rne="return 1.0 / x;",sne=Ye({opSnippet:rne}),ane={kernelName:Rc,backendName:"webgl",kernelFunc:sne},one=Fr+`
return (x < 0.0) ? 0.0 : x;
`,ine=`
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;
`,cne=Ye({opSnippet:one,packedOpSnippet:ine}),une={kernelName:xo,backendName:"webgl",kernelFunc:cne},lne=Fr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,dne=`
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;
`,pne=Ye({opSnippet:lne,packedOpSnippet:dne}),hne={kernelName:ko,backendName:"webgl",kernelFunc:pne},fne=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let l=[r&&t>1?o-1:o,r&&n>1?i-1:i],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],d;s?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${l[0]/u[0]},
${l[1]/u[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}},mne=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let l=[r&&t>1?o-1:o,r&&n>1?i-1:i],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],d;s?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${l[0]/u[0]},
${l[1]/u[1]},
${l[1]/u[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${d};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${c-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 gne(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[c,l]=i,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new mne(s.shape,c,l,a,o):new fne(s.shape,c,l,a,o);return n.runWebGLProgram(u,[s],"float32")}var bne={kernelName:wo,backendName:"webgl",kernelFunc:gne},yne=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],c=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/c[0],u=i[1]/c[1],d=1/l,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${l});
const float widthScale = float(${u});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${r-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), ${s-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 vne(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new yne(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var xne={kernelName:Th,backendName:"webgl",kernelFunc:vne},wne=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let l=[r&&t>1?o-1:o,r&&n>1?i-1:i],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0",p;s?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${l[0]/u[0]},
${l[1]/u[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},kne=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let l=[r&&t>1?o-1:o,r&&n>1?i-1:i],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0",p;s?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${l[0]/u[0]},
${l[1]/u[1]},
${l[1]/u[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${c-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function Ine(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[c,l]=i,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new kne(s.shape,c,l,a,o):new wne(s.shape,c,l,a,o);return n.runWebGLProgram(u,[s],s.dtype)}var Sne={kernelName:Ol,backendName:"webgl",kernelFunc:Ine},Tne=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],c=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/c[0],u=i[1]/c[1],d=1/l,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${l});
const float widthScale = float(${u});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float sourceFracRow =
float(${i[0]}) *
(float(dyR) / float(${c[0]}));
float sourceFracCol =
float(${i[1]}) *
(float(dyC) / float(${c[1]}));
int sourceNearestRow = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Cne(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new Tne(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Nne={kernelName:Sh,backendName:"webgl",kernelFunc:Cne},_ne=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let r=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,s=e.map((o,i)=>r(i)).join(","),a=gt(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}},Ene=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-${n} tensor is not yet supported`);this.outputShape=e;let r=Tn("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=gt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${s}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${i(r.slice())};
if(${s}){
result.g = ${c(r.slice())};
}
if(${a}) {
result.b = ${l(r.slice())};
if(${s}) {
result.a = ${u(r.slice())};
}
}
setOutput(result);
}
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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]));
${s}
if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
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}
setOutput(outputValue);
}
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// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
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${i} strides = ${i}(${s});
void main() {
${c} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${u});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${p};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function zne(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:c,sliceSize:l,strides:u,outputSize:d}=_.calculateShapes(a,s,o),p=[d/l,l];if(d===0)return n.makeTensorInfo(o,s.dtype);let h=ge({inputs:{x:s},backend:n,attrs:{shape:[c,i]}}),f=ge({inputs:{x:a},backend:n,attrs:{shape:[c,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new b_(c,i,h.shape.length,f.shape.length,u,p),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=ge({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var Wne={kernelName:Oc,backendName:"webgl",kernelFunc:zne},Vne=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",r="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],c=[];for(let l=0;l<t.length;l++)c.push(`${o[l]}`),l<e&&i.push(`${o[l]}`);r=i.join(),s=c.join()}let a=gt(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${r});
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setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function Une(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new Vne(r.shape.length,s.shape,s.shape.length);return n.runWebGLProgram(o,[r,s,a],Tr(s.dtype,a.dtype))}var Gne={kernelName:Mc,backendName:"webgl",kernelFunc:Une},Hne=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${_.SELU_SCALEALPHA};
float scale = ${_.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,jne=Ye({opSnippet:Hne}),qne={kernelName:Lc,backendName:"webgl",kernelFunc:jne},Kne=Ou+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,Xne=`
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
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if (isnan(x)) { return 0.0; }
return sign(x);
`,Qne=Ye({opSnippet:Jne}),ere={kernelName:Wc,backendName:"webgl",kernelFunc:Qne},tre=Ou+`
return sin(x);
`,nre=Ye({opSnippet:tre}),rre={kernelName:Co,backendName:"webgl",kernelFunc:nre},sre=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,are=Ye({opSnippet:sre}),ore={kernelName:zc,backendName:"webgl",kernelFunc:are},ire=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
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${s} begin = ${s}(${e});
${s} strides = ${s}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${o}));
}
`}};function Rre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:c,endMask:l,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=r,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=Ht.sliceInfo(s.shape,a,o,i,c,l,u,d,p),w;if(m)w=ge({inputs:{x:s},backend:n,attrs:{shape:f}});else if(g||b){k.assert(s.shape.length>=1,()=>`Input must have rank at least 1, got: ${s.shape.length}`);let N=Ht.computeOutShape(y,v,x),$=Mu({inputs:{x:s},backend:n,attrs:{begin:y,size:N}});w=ge({inputs:{x:$},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo($)}else if(n.shouldExecuteOnCPU([s])){let $=n.readSync(s.dataId),D=ze(s.shape,s.dtype,$),P=m9(h,D,x,y);w=n.makeTensorInfo(f,s.dtype,P.values)}else{let $=new Fre(y,x,h);w=n.runWebGLProgram($,[s],s.dtype)}let T=ge({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),T}var Pre={kernelName:jc,backendName:"webgl",kernelFunc:Rre};function Ore(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:c,preserveShortSequences:l}=r,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=g9(p,h,s,a,o,i,c,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Mre={kernelName:Nh,backendName:"webgl",kernelFunc:Ore};function Lre(e){let{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[l,u,d]=b9(i,c,s),p=u.length;return[n.makeTensorInfo([p,2],"int32",l),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Bre={kernelName:_h,backendName:"webgl",kernelFunc:Lre};function zre(e){let{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=y9(o,s);return n.makeTensorInfo(a.shape,"int32",i)}var Wre={kernelName:Eh,backendName:"webgl",kernelFunc:zre},Vre="return tan(x);",Ure=Ye({opSnippet:Vre}),Gre={kernelName:Fo,backendName:"webgl",kernelFunc:Ure},Hre=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,jre=Ye({opSnippet:Hre}),qre={kernelName:Ro,backendName:"webgl",kernelFunc:jre},Kre=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 r=gt(this.rank),s=Xre(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function Xre(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<e.length;s++)r.push(`imod(${n[s]}, ${e[s]})`);return r.join()}function x_(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(s.dtype==="string"||s.shape.length>5){let c=n.readSync(s.dataId),l=s.dtype==="string"?c.map(p=>k.decodeString(p)):c,u=ze(s.shape,s.dtype,l),d=x9(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Kre(s.shape,a);return n.runWebGLProgram(o,[s],s.dtype)}var Yre={kernelName:ea,backendName:"webgl",kernelFunc:x_},Zre=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));
}
}
`}},Jre=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 vi(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function w_(e){let t=1;for(;t<e;)t*=2;return t}function Qre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r,i=J().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),c=J().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=s.shape,u=l[l.length-1];if(n.shouldExecuteOnCPU([s])||u<i||a>c){let P=n.readSync(s.dataId),[F,R]=w9(P,l,s.dtype,a,o);return[n.makeTensorInfo(F.shape,F.dtype,F.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,s.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(u===1)return[s,Yd({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let d=n.texData.get(s.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(s):s,m=k.sizeFromShape(l)/u,g=ge({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&vi(n,h);let b=w_(a),y=w_(u),v=null,x=()=>v===null?[g,g]:[g,v],w=(P,F,R)=>{let C=x(),L=new Zre(R),j=[[u],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[F]],K=v;v=n.runWebGLProgram(L,C,"int32",j),vi(n,K)};for(let P=1;P<b;P*=2){let F=P*2;for(let R=P;R>=1;R/=2)w(F,R,[m,y])}for(let P=y;P>b;P/=2){let F=x(),R=new Jre([m,P/2]),L=[[u],[v===null?1:0],[b]],G=v;v=n.runWebGLProgram(R,F,"int32",L),vi(n,G);let j=b/2,K=j*2;for(let q=j;q>=1;q/=2)w(K,q,v.shape)}let T=v;v=Mu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),vi(n,T);let N=c_({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});vi(n,g);let $=l.slice(0,-1);$.push(a),T=v,v=ge({inputs:{x:v},attrs:{shape:$},backend:n}),vi(n,T);let D=N;return N=ge({inputs:{x:N},attrs:{shape:$},backend:n}),vi(n,D),[N,v]}var ese={kernelName:qc,backendName:"webgl",kernelFunc:Qre},tse=class{constructor(e,t,n,r,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(r){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${i} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${s});
}
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(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${o} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function nse(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:c,outputShape:l}=r,[u,d,p,h]=s.shape,[f,m]=l!=null?l:[d,p],g=[u,f,m,h],b=new tse(d,p,o,i,c,g);return n.runWebGLProgram(b,[s,a],"float32")}var rse={kernelName:Kc,backendName:"webgl",kernelFunc:nse};function sse(e){let{inputs:t,attrs:n,backend:r}=e,{axis:s}=n,{x:a}=t;Eu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=r.readSync(a.dataId),{outputValues:i,outputShape:c,indices:l}=k9(o,s,a.shape,a.dtype);return[r.makeTensorInfo(c,a.dtype,i),r.makeTensorInfo([l.length],"int32",l)]}var ase={kernelName:Ah,backendName:"webgl",kernelFunc:sse};function ose(e){let{inputs:t,backend:n,attrs:r}=e,{value:s}=t,{axis:a}=r;a<0&&(a+=s.shape.length);let o=s,i=o.shape.length,c=s.shape[a],l=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(l[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(c);for(let m=0;m<f.length;m++){p[a]=m;let g=Mu({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),b=ge({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var ise={kernelName:Xc,backendName:"webgl",kernelFunc:ose},cse=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,s=e.inSize,a=e.numSegments,o=a*Math.ceil(s/n);this.outputShape=[r,o];let i="0.0",c="sumValue",l=Math.floor(n/4)*4,u=n%4,d=`
sumValue += dot(values, segFilter);
`,p="";s%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`);let h="";s%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${s}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${i};
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${l}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${d}
}
int inIdx = inOffset + ${l};
if (${u===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${d}
} else if (${u===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${d}
} else if (${u===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${d}
}
setOutput(${c});
}
`}};function use(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:a}=t,{numSegments:o}=r,i=s.shape.length,c=[],l=0,u=_.getAxesPermutation([l],i),d=s;u!=null&&(d=Cn({inputs:{x:s},backend:n,attrs:{perm:u}}),c.push(d),l=_.getInnerMostAxes(1,i)[0]);let p=_.segment_util.computeOutShape(d.shape,l,o),h=k.sizeFromShape([d.shape[l]]),f=ge({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});c.push(f);let m=Lh(s.dtype),g=(x,w,T,N,$)=>{let D=x.shape[0],P=x.shape[1],F=_.segment_util.segOpComputeOptimalWindowSize(P,$),R={windowSize:F,inSize:P,batchSize:D,numSegments:$},C=new cse(R,w),L=n.compileAndRun(C,[x,T],N);if(c.push(L),L.shape[1]===$)return L;let G=g_({backend:n,attrs:{start:0,stop:$,step:1,dtype:"float32"}}),j=x_({inputs:{x:G},backend:n,attrs:{reps:[P/F]}});return c.push(G),c.push(j),g(L,w,j,N,$)},b=g(f,"unsortedSegmentSum",a,m,o),y=ge({inputs:{x:b},backend:n,attrs:{shape:p}}),v=y;if(u!=null){c.push(y);let x=_.getUndoAxesPermutation(u);v=Cn({inputs:{x:v},backend:n,attrs:{perm:x}})}return c.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var lse={kernelName:Wl,backendName:"webgl",kernelFunc:use},dse=[bY,vY,kY,TY,NY,AY,$Y,RY,LY,zY,UY,jY,XY,QY,nZ,sZ,oZ,lZ,pZ,fZ,yZ,TZ,NZ,EZ,PZ,MZ,WZ,J9,GZ,XZ,QZ,aJ,iJ,uJ,dJ,hJ,gJ,vJ,kJ,SJ,CJ,EJ,DJ,PJ,MJ,zJ,UJ,HJ,XJ,QJ,rQ,oQ,uQ,lQ,pQ,fQ,gQ,yQ,xQ,SQ,NQ,AQ,$Q,PQ,LQ,VQ,jQ,Z9,KQ,qZ,ZQ,eee,ree,eY,iee,dee,hee,bee,xee,See,Nee,Dee,Pee,Lee,zee,Gee,jee,Kee,Jee,ete,nte,ste,ote,lte,fte,yte,Cte,aY,Ate,Fte,Ote,Bte,DZ,Vte,Gte,jte,Xte,Qte,nY,tne,nne,$Z,kte,ane,une,hne,iY,bne,xne,Sne,Nne,Dne,Fne,One,Bne,Wne,Gne,qne,Zne,ere,rre,ore,IZ,Ste,ure,dre,hre,mre,bre,vre,wre,Ire,Tre,_re,Are,$re,Pre,Mre,Bre,Wre,Ite,fY,Gre,qre,Yre,ese,rse,mY,ase,ise,lse,Ute];for(let e of dse)Ul(e);var Rt;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Rt||(Rt={}));var Zd;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Zd||(Zd={}));var k_;function pse(e){k_=e.wasm.cwrap(Oo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function hse(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:c,transposeB:l,activation:u,leakyreluAlpha:d}=r,p=n.dataIdMap.get(s.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let $=n.dataIdMap.get(o.dataId);if($.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${$.shape.length}.`);f=$.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=Zd[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let b=c?s.shape[2]:s.shape[1],y=l?a.shape[1]:a.shape[2],v=su.assertAndGetBroadcastShape(s.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],s.dtype),w=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(s.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return k_(p,T,s.shape.length,h,N,a.shape.length,c,l,g,f,m,d||0,w),x}var fse={kernelName:Oo,backendName:"wasm",setupFunc:pse,kernelFunc:hse};function ln(e,t){let n;function r(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function s(a){let{backend:o,inputs:{x:i}}=a,c=o.dataIdMap.get(i.dataId).id,l=o.makeOutput(i.shape,t||i.dtype),u=o.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||n(c,Rt[i.dtype],u),l}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var mse=ln(Yi);function Nn(e,t,n){let r;function s(o){r=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:c}=o,{a:l,b:u}=c,d=i.dataIdMap.get(l.dataId).id,p=i.dataIdMap.get(u.dataId).id,h=n!=null?n:l.dtype,f=_.assertAndGetBroadcastShape(l.shape,u.shape),m=i.makeOutput(f,h);if(k.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(u.shape).buffer),y=i.dataIdMap.get(m.dataId).id;return(()=>r(d,g,l.shape.length,p,b,u.shape.length,Rt[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:a}}var gse=!0,bse=Nn(Js,gse),I_;function yse(e){I_=e.wasm.cwrap(Ba,null,["array","number","number","number"])}function vse(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(r.shape)===0)return r;let s=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(s).buffer),o=n.dataIdMap.get(r.dataId).id;return I_(a,s.length,Rt[r.dtype],o),r}var xse={kernelName:Ba,backendName:"wasm",setupFunc:yse,kernelFunc:vse};function Om(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var wse={kernelName:so,backendName:"wasm",kernelFunc:Om},S_;function kse(e){S_=e.wasm.cwrap(Po,null,["number","array","number","number","number","array","number"])}function zu(e){let{inputs:t,backend:n,attrs:r}=e,[s,a]=Sse(t.x.shape,r.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=Ise(t.x.shape,r.perm),c={dataId:t.x.dataId,shape:s,dtype:t.x.dtype};if(o){let f=Om({inputs:t,backend:n});return f.shape=i,f}let l=n.makeOutput(i,c.dtype),u=n.dataIdMap.get(c.dataId).id,d=n.dataIdMap.get(l.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(c.shape).buffer);return S_(u,h,c.shape.length,Rt[c.dtype],d,p,a.length),l}function Ise(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function Sse(e,t){let n=[],r=[];for(let s=0;s<e.length;++s)e[s]!==1&&n.push(e[s]),e[t[s]]!==1&&r.push(t[s]);for(let s=0;s<r.length;++s){let a=-1;for(let o=0;o<r.length;++o)r[o]>=s&&(a===-1||r[a]>r[o])&&(a=o);r[a]=s}return[n,r]}var Tse={kernelName:Po,backendName:"wasm",kernelFunc:zu,setupFunc:kse};function Ca(e,t,n){let r=e.shape,s=e.shape.length,a=k.parseAxisParam(t,r),o=a,i=_.getAxesPermutation(o,s),c=null,l=!1;if(i!=null){let u=new Array(s);for(let h=0;h<u.length;h++)u[h]=r[i[h]];o=_.getInnerMostAxes(o.length,s),c=zu({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(c.dataId).id!==d&&(l=!0)}return{transposed:c,originalAxes:a,axes:o,inputWasTransposed:l}}var T_;function Cse(e){T_=e.wasm.cwrap(Qi,null,["number, number, number"])}function Nse(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,c=t.dataIdMap.get(o.dataId).id,l=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ca(o,s,t);if(h){let v=t.dataIdMap.get(u.dataId).id;l=u,c=v}let f=l.shape.length;_.assertAxesAreInnerMostDims("all",d,f);let[m,g]=_.computeOutAndReduceShapes(l.shape,d),b=k.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(k.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;T_(c,b,v)}if(h&&t.disposeData(u.dataId),a){let v=_.expandShapeToKeepDim(y.shape,p);y.shape=v}return y}var _se={kernelName:Qi,backendName:"wasm",setupFunc:Cse,kernelFunc:Nse},C_;function Ese(e){C_=e.wasm.cwrap(ec,null,["number, number, number"])}function Ase(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,c=t.dataIdMap.get(o.dataId).id,l=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ca(o,s,t);if(h){let v=t.dataIdMap.get(u.dataId).id;l=u,c=v}let f=l.shape.length;_.assertAxesAreInnerMostDims("any",d,f);let[m,g]=_.computeOutAndReduceShapes(l.shape,d),b=k.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(k.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;C_(c,b,v)}if(h&&t.disposeData(u.dataId),a){let v=_.expandShapeToKeepDim(y.shape,p);y.shape=v}return y}var Dse={kernelName:ec,backendName:"wasm",setupFunc:Ese,kernelFunc:Ase},N_;function $se(e){N_=e.wasm.cwrap(za,null,["number","number","number","number","number"])}function Fse(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s}=r,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,c=a,{transposed:l,axes:u,inputWasTransposed:d}=Ca(a,s,t);if(d){let b=t.dataIdMap.get(l.dataId).id;b!==o&&(c=l,i=b)}let p=c.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=k.sizeFromShape(h.shape),g=c.shape[u[0]];return N_(i,Rt[c.dtype],m,g,f),d&&t.disposeData(l.dataId),h}var Rse={kernelName:za,backendName:"wasm",kernelFunc:Fse,setupFunc:$se},__;function Pse(e){__=e.wasm.cwrap(Wa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ose(e){let{inputs:t,attrs:n,backend:r}=e,s=t.x,a=r.dataIdMap.get(s.dataId).id,{filterSize:o,strides:i,pad:c,dimRoundingMode:l}=n,u=_.computePool2DInfo(s.shape,o,i,1,c,l),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,b=u.strideHeight,y=u.strideWidth,v=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let x=r.makeOutput(u.outShape,"float32"),w=r.dataIdMap.get(x.dataId).id;return __(a,s.shape[0],s.shape[1],s.shape[2],d,p,h,f,m,g,b,y,v,w),x}var Mse={kernelName:Wa,backendName:"wasm",setupFunc:Pse,kernelFunc:Ose};function Un(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:s}=n,a=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,a);return k.assert(a===k.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${r.shape}. 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transposeB=${i} must match.`);let x=o?[g,u,p]:[g,p,u],w=i?[b,h,d]:[b,d,h],T=Un({inputs:{x:s},backend:n,attrs:{shape:x}}),N=Un({inputs:{x:a},backend:n,attrs:{shape:w}}),$=n.dataIdMap.get(T.dataId).id,D=n.dataIdMap.get(N.dataId).id,P=o?T.shape[2]:T.shape[1],F=i?N.shape[1]:N.shape[2],R=Math.max(g,b),C=n.makeOutput([R,P,F],T.dtype),L=n.dataIdMap.get(C.dataId).id,G=new Uint8Array(new Int32Array(T.shape).buffer),j=new Uint8Array(new Int32Array(N.shape).buffer);return E_($,G,T.shape.length,D,j,N.shape.length,o,i,L),n.disposeData(T.dataId),n.disposeData(N.dataId),C.shape=v,C}var Wse={kernelName:Va,backendName:"wasm",setupFunc:Bse,kernelFunc:zse};function xi(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:s}=e,[a,o]=Ht.parseSliceParams(t,n,r),i=Ht.isSliceContinous(t.shape,a,o),c=s.readSync(t.dataId),l=s.makeOutput(o,t.dtype),u=k.computeStrides(t.shape),d=s.dataIdMap.get(l.dataId);if(i){let f=Ht.computeFlatOffset(a,u);return t.dtype==="string"?d.stringBytes=c.slice(f,f+k.sizeFromShape(o)):s.typedArrayFromHeap(l).set(c.subarray(f,f+k.sizeFromShape(o))),l}if(t.dtype==="string"){let f=hm(c,a,o,t.shape,t.dtype);return d.stringBytes=f,l}let p=s.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Vse(c,u[0],p,a,o);else if(h===3)Use(c,u[0],u[1],p,a,o);else if(h===4)Gse(c,u[0],u[1],u[2],p,a,o);else{let f=hm(c,a,o,t.shape,t.dtype);p.set(f)}return l}function Vse(e,t,n,r,s){let a=0,o=r[0],i=r[1],c=o+s[0];for(let l=o;l<c;l++){let u=l*t+i;n.set(e.subarray(u,u+s[1]),a),a+=s[1]}}function Use(e,t,n,r,s,a){let o=0,i=s[0],c=s[1],l=s[2],u=i+a[0],d=c+a[1];for(let p=i;p<u;p++)for(let h=c;h<d;h++){let f=p*t+h*n+l;r.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function Gse(e,t,n,r,s,a,o){let i=0,c=a[0],l=a[1],u=a[2],d=c+o[0],p=l+o[1],h=u+o[2],f=a[3];for(let m=c;m<d;m++)for(let g=l;g<p;g++)for(let b=u;b<h;b++){let y=m*t+g*n+b*r+f;s.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var Hse={kernelName:Bc,backendName:"wasm",kernelFunc:xi};function jse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r,i=a.reduce((b,y)=>b*y),c=_.getReshaped(s.shape,a,i),l=_.getPermuted(c.length,a.length),u=_.getReshapedPermuted(s.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(u,o,a.length),h=Un({inputs:{x:s},backend:n,attrs:{shape:c}}),f=zu({inputs:{x:h},backend:n,attrs:{perm:l}}),m=Un({inputs:{x:f},backend:n,attrs:{shape:u}}),g=xi({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 qse={kernelName:oc,backendName:"wasm",kernelFunc:jse};function Jd(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,s=r.makeOutput(t.shape,n),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(s).set(a),s}var Kse={kernelName:Ua,backendName:"wasm",kernelFunc:Jd},Xse=ln(Ga),A_;function Yse(e){A_=e.wasm.cwrap(Qs,null,["number","number","number","number"])}function Zse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(s.shape,s.dtype),l=n.dataIdMap.get(c.dataId).id;return A_(i,a,o,l),c}var Jse={kernelName:Qs,backendName:"wasm",setupFunc:Yse,kernelFunc:Zse};function D_(e){let{inputs:t,backend:n}=e,r=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],s=_.computeOutShape(t.map(h=>h.shape),r),a=t.filter(h=>k.sizeFromShape(h.shape)>0);if(a.length===1)return Om({inputs:{x:a[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(k.sizeFromShape(s)===0)return o;let i=a.map(h=>h.shape);if(_.assertParamsConsistent(i,r),a[0].dtype==="string"){let h=a.map(v=>{let x=k.sizeFromShape(v.shape.slice(r));return Un({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));s=_.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=Dw(f,s,t[0].dtype,m),b=_.computeOutShape(a.map(v=>v.shape),r);o.shape=b;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=_.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),o}let c=k.sizeFromShape(a[0].shape.slice(0,r)),l=0,u=a.map(h=>{let f=k.sizeFromShape(h.shape.slice(r));return l+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<c;h++){let f=h*l;for(let m=0;m<d.length;m++){let g=u[m],b=h*g,y=d[m].subarray(b,b+g);p.set(y,f),f+=g}}return o}var Qse={kernelName:ic,backendName:"wasm",kernelFunc:D_},$_;function eae(e){$_=e.wasm.cwrap(Ha,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tae(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:c,dilations:l,pad:u,dimRoundingMode:d,dataFormat:p}=n,h=_.convertConv2DDataFormat(p),f=_.computeConv2DInfo(s.shape,a.shape,c,l,u,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,w=f.dilationHeight,T=f.dilationWidth,N=f.strideHeight,$=f.strideWidth,D=f.inChannels,P=f.outChannels,F=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let R=r.makeOutput(f.outShape,"float32"),C=r.dataIdMap.get(R.dataId).id;return $_(o,s.shape[0],s.shape[1],s.shape[2],i,m,g,b,y,v,x,F,w,T,N,$,D,P,C),R}var nae={kernelName:Ha,backendName:"wasm",setupFunc:eae,kernelFunc:tae},F_;function rae(e){F_=e.wasm.cwrap(ja,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function sae(e){let{backend:t,inputs:n,attrs:r}=e,{dy:s,filter:a}=n,{strides:o,pad:i,dataFormat:c,dimRoundingMode:l,inputShape:u}=r,d=1,p=_.convertConv2DDataFormat(c),h=_.computeConv2DInfo(u,a.shape,o,d,i,l,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:w,outWidth:T,strideHeight:N,strideWidth:$}=h,D=m-1-h.padInfo.top,P=g-1-h.padInfo.left,F=h.dataFormat==="channelsLast",R=k.computeStrides(h.inShape),C=k.computeStrides(s.shape),[L,G,j]=k.computeStrides(a.shape),K=R[0],q=F?R[1]:R[2],Z=F?R[2]:1,te=F?1:R[1],se=C[0],oe=F?C[1]:C[2],re=F?C[2]:1,ue=F?1:C[1],ne=t.makeOutput(h.inShape,"float32"),he=t.dataIdMap.get(ne.dataId).id,ye=t.dataIdMap.get(s.dataId).id,Ce=t.dataIdMap.get(a.dataId).id;return F_(ye,Ce,f,m,g,y,v,b,w,T,x,N,$,D,P,L,G,j,K,q,Z,te,se,oe,re,ue,he),ne}var aae={kernelName:ja,backendName:"wasm",setupFunc:rae,kernelFunc:sae},oae=ln(qa),iae=ln(Ka),u0;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(u0||(u0={}));var R_;function cae(e){R_=e.wasm.cwrap(cc,null,["number","number","number","number","array","number","number","number","number","number"])}function uae(e){let{backend:t,inputs:n,attrs:r}=e,{method:s,extrapolationValue:a,cropSize:o}=r,{image:i,boxes:c,boxInd:l}=n,u=c.shape[0],[d,p]=o,h=[u,d,p,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=Jd({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return R_(g,b,y,u,w,d,p,u0[s],a,x),m!=null&&t.disposeData(m.dataId),v}var lae={kernelName:cc,backendName:"wasm",setupFunc:cae,kernelFunc:uae},P_;function dae(e){P_=e.wasm.cwrap(Xa,null,["number","number","number","number","number","number"])}function pae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,c=s.shape.length;k.assert(s.dtype==="float32"||s.dtype==="int32",()=>`cumsum does not support ${s.dtype} tensors in the WASM backend`);let l=_.getAxesPermutation([a],c),u=s;l!==null&&(u=zu({inputs:{x:s},attrs:{perm:l},backend:n}));let d=_.getInnerMostAxes(1,c)[0];_.assertAxesAreInnerMostDims("cumsum",[d],c);let p=n.makeOutput(u.shape,u.dtype),h=u.shape[d],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(p.dataId).id;P_(f,o?1:0,i?1:0,h,m,Rt[s.dtype]);let g=p;if(l!==null){let b=_.getUndoAxesPermutation(l);g=zu({inputs:{x:p},attrs:{perm:b},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var hae={kernelName:Xa,backendName:"wasm",setupFunc:dae,kernelFunc:pae},O_;function fae(e){O_=e.wasm.cwrap(uc,null,["number","number","number","array","number","array","array","number","number"])}function mae(e){let{backend:t,inputs:n,attrs:r}=e,{x:s}=n,{blockSize:a,dataFormat:o}=r,i=s.shape[0],c=o==="NHWC"?s.shape[1]:s.shape[2],l=o==="NHWC"?s.shape[2]:s.shape[3],u=o==="NHWC"?s.shape[3]:s.shape[1],d=c*a,p=l*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(s.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(s.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return O_(b,a,o==="NHWC"?1:0,y,s.shape.length-1,v,x,f.length,w),m}var gae={kernelName:uc,backendName:"wasm",setupFunc:fae,kernelFunc:mae},M_;function bae(e){M_=e.wasm.cwrap(Ya,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yae(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:c,dilations:l,pad:u,dimRoundingMode:d}=n,p=l==null?[1,1]:l,h=_.computeConv2DInfo(s.shape,a.shape,c,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,w=h.dilationWidth,T=h.strideHeight,N=h.strideWidth,$=h.inChannels,D=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let F=r.makeOutput(h.outShape,"float32"),R=r.dataIdMap.get(F.dataId).id;return M_(o,s.shape[0],s.shape[1],s.shape[2],i,f,m,g,b,y,v,P,x,w,T,N,$,D,R),F}var vae={kernelName:Ya,backendName:"wasm",setupFunc:bae,kernelFunc:yae},xae=ln(Ja),wae=!1,kae=Nn(dc,wae,"bool"),Iae=ln(Qa,"float32");function l0(e){let{inputs:t,attrs:n,backend:r}=e,{input:s}=t,{dim:a}=n,o=s.shape.length,i=s.shape.slice(),c=a;return a<0&&(k.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),c=o+a+1),i.splice(c,0,1),Un({inputs:{x:s},backend:r,attrs:{shape:i}})}var Sae={kernelName:pc,backendName:"wasm",kernelFunc:l0};function L_(e){let{attrs:{shape:t,value:n,dtype:r},backend:s}=e,a=s.makeOutput(t,r);return s.typedArrayFromHeap(a).fill(n),a}var Tae={kernelName:Al,backendName:"wasm",kernelFunc:L_},B_;function Cae(e){B_=e.wasm.cwrap(fc,null,["number","number","number","number","number","number"])}function Nae(e){let{inputs:t,backend:n}=e,{image:r}=t,s=n.makeOutput(r.shape,r.dtype),a=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,[i,c,l,u]=r.shape;return B_(a,i,c,l,u,o),s}var _ae={kernelName:fc,backendName:"wasm",kernelFunc:Nae,setupFunc:Cae},Eae=ln(eo),Aae=!1,Dae=Nn(to,Aae),z_;function $ae(e){z_=e.wasm.cwrap(no,null,["number","number","number","number","number","number","number"])}function Fae(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:s}=r,{x:a,mean:o,variance:i,offset:c,scale:l}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=c!=null?t.dataIdMap.get(c.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(k.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return z_(u,d,p,h,f,s,g),m}var Rae={kernelName:no,backendName:"wasm",setupFunc:$ae,kernelFunc:Fae},W_;function Pae(e){W_=e.wasm.cwrap(Mo,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 Oae(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:l,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=_.computeConv2DInfo(s.shape,a.shape,c,u,l,p),g=Zd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=r.dataIdMap.get(s.dataId).id,y=r.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let re=r.dataIdMap.get(o.dataId);if(re.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${re.shape.length}.`);if(re.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${re.shape}) does not match the number of output channels (${v})`);x=re.id}let w=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,$=m.padInfo.right,D=m.padInfo.bottom,P=m.padInfo.left,F=m.dilationHeight,R=m.dilationWidth,C=m.strideHeight,L=m.strideWidth,G=m.inChannels,j=m.padInfo.type==="SAME"?1:0,K=m.batchSize,q=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let te=r.makeOutput(m.outShape,"float32"),se=r.dataIdMap.get(te.dataId).id,oe=i==null?0:r.dataIdMap.get(i.dataId).id;return W_(b,K,q,Z,y,w,T,x,N,$,D,P,j,F,R,C,L,G,v,g,oe,f||0,se),te}var Mae={kernelName:Mo,backendName:"wasm",setupFunc:Pae,kernelFunc:Oae},V_;function Lae(e){V_=e.wasm.cwrap(Lo,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 Bae(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:l,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=_.computeConv2DInfo(s.shape,a.shape,c,u,l,p,!0),g=Zd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=r.dataIdMap.get(s.dataId).id,y=r.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let re=r.dataIdMap.get(o.dataId);if(re.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${re.shape.length}.`);if(re.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${re.shape}) does not match the number of output channels (${v})`);x=re.id}let w=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,$=m.padInfo.right,D=m.padInfo.bottom,P=m.padInfo.left,F=m.dilationHeight,R=m.dilationWidth,C=m.strideHeight,L=m.strideWidth,G=m.inChannels,j=m.padInfo.type==="SAME"?1:0,K=m.batchSize,q=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let te=r.makeOutput(m.outShape,"float32"),se=r.dataIdMap.get(te.dataId).id,oe=i==null?0:r.dataIdMap.get(i.dataId).id;return V_(b,K,q,Z,y,w,T,x,N,$,D,P,j,F,R,C,L,G,v,g,oe,f||0,se),te}var zae={kernelName:Lo,backendName:"wasm",setupFunc:Lae,kernelFunc:Bae},U_;function Wae(e){U_=e.wasm.cwrap(gc,null,["number","number","number","number","number","number","array","number"])}function Vae(e){let{backend:t,inputs:n}=e,{params:r,indices:s}=n,[a,o,i,c]=Iy.prepareAndValidate(r,s),l=t.makeOutput(a,r.dtype);if(o===0)return l;let u=s.shape,d=u[u.length-1],h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,g=new Uint8Array(new Int32Array(c).buffer),b=t.dataIdMap.get(l.dataId).id;return U_(h,Rt[r.dtype],m,o,d,i,g,b),l}var Uae={kernelName:gc,backendName:"wasm",setupFunc:Wae,kernelFunc:Vae},G_;function Gae(e){G_=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Hae(e){let{backend:t,inputs:n,attrs:r}=e,{x:s,indices:a}=n,{axis:o,batchDims:i}=r,c=k.parseAxisParam(o,s.shape)[0],l=t.readSync(a.dataId),u=s.shape[c];for(let D=0;D<l.length;++D){let P=l[D];k.assert(P<=u-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${u-1}]`)}let d=_.segment_util.collectGatherOpShapeInfo(s,a,c,i),p=Un({inputs:{x:s},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=k.sizeFromShape(a.shape),f=Un({inputs:{x:a},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),m=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(m,s.dtype);if(k.sizeFromShape(s.shape)===0)return g;let b=p.shape.length-1,v=t.dataIdMap.get(p.dataId).id,w=t.dataIdMap.get(f.dataId).id,T=t.dataIdMap.get(g.dataId).id,N=new Uint8Array(new Int32Array(k.computeStrides(p.shape)).buffer),$=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer);return G_(v,Rt[s.dtype],N,b,w,d.batchSize,$,T),t.disposeData(p.dataId),t.disposeData(f.dataId),g.shape=d.outputShape,g}var jae={kernelName:mc,backendName:"wasm",setupFunc:Gae,kernelFunc:Hae},qae=!1,Kae=Nn(bc,qae,"bool"),Xae=!1,Yae=Nn(ro,Xae,"bool"),H_;function Zae(e){H_=e.wasm.cwrap(ao,null,["number","number","number","number"])}function Jae(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,s=r.dataIdMap.get(t.dataId).id,a=r.makeOutput(t.shape,"float32");if(k.sizeFromShape(t.shape)!==0){let o=r.dataIdMap.get(a.dataId).id;H_(s,Rt[t.dtype],n,o)}return a}var Qae={kernelName:ao,backendName:"wasm",setupFunc:Zae,kernelFunc:Jae},eoe=!1,toe=Nn(wc,eoe,"bool"),noe=!1,roe=Nn(kc,noe,"bool"),soe=ln(oo),aoe=!1,ooe=Nn(Sc,aoe,"bool"),j_;function ioe(e){j_=e.wasm.cwrap(io,null,["number","number","number","number"])}function coe(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:s,keepDims:a}=r,{x:o}=n,c=t.dataIdMap.get(o.dataId).id,l=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ca(o,s,t);if(h){let v=t.dataIdMap.get(u.dataId).id;l=u,c=v}let f=l.shape.length;_.assertAxesAreInnerMostDims("max",d,f);let[m,g]=_.computeOutAndReduceShapes(l.shape,d),b=k.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(k.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;j_(c,Rt[o.dtype],b,v)}if(h&&t.disposeData(u.dataId),a){let v=_.expandShapeToKeepDim(y.shape,p);y.shape=v}return y}var uoe={kernelName:io,backendName:"wasm",setupFunc:ioe,kernelFunc:coe},loe=!1,doe=Nn(co,loe),q_;function poe(e){q_=e.wasm.cwrap(uo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hoe(e){let{inputs:t,attrs:n,backend:r}=e,s=t.x,a=r.dataIdMap.get(s.dataId).id;k.assert(s.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${s.dtype}.`);let{filterSize:o,strides:i,pad:c,dimRoundingMode:l}=n,u=_.computePool2DInfo(s.shape,o,i,1,c,l),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,b=u.dilationHeight,y=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,w=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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x=_.expandShapeToKeepDim(v.shape,p);v.shape=x}return l.dtype!=="float32"&&t.disposeData(y.dataId),v}var boe={kernelName:lo,backendName:"wasm",setupFunc:moe,kernelFunc:goe},X_;function yoe(e){X_=e.wasm.cwrap(po,null,["number","number","number","number"])}function voe(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,c=i,l=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ca(o,s,t);if(h){let v=t.dataIdMap.get(u.dataId).id;v!==i&&(l=u,c=v)}let f=l.shape.length;_.assertAxesAreInnerMostDims("min",d,f);let[m,g]=_.computeOutAndReduceShapes(l.shape,d),b=k.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(k.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;X_(c,Rt[o.dtype],b,v)}if(h&&t.disposeData(u.dataId),a){let v=_.expandShapeToKeepDim(y.shape,p);y.shape=v}return y}var xoe={kernelName:po,backendName:"wasm",setupFunc:yoe,kernelFunc:voe},woe=!1,koe=Nn(ho,woe),d0;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(d0||(d0={}));var Y_;function Ioe(e){Y_=e.wasm.cwrap(fo,null,["number","array","number","number","array","array","number","number"])}function Soe(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,mode:s}}=e,a=r.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),c=n.dataIdMap.get(i.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),u=r.map(f=>f[0]),d=r.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(d).buffer);return Y_(o,l,t.shape.length,Rt[t.dtype],p,h,d0[s],c),i}var Toe={kernelName:fo,backendName:"wasm",kernelFunc:Soe,setupFunc:Ioe},Coe=!0,Noe=Nn(mo,Coe),_oe=ln(Cc);function p0(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],s=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:s,pSelectedScores:a,pValidOutputs:o}}var Z_;function Eoe(e){Z_=e.wasm.cwrap(_c,"number",["number","number","number","number","number"])}function Aoe(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:s,maxOutputSize:a,scoreThreshold:o}=r,{boxes:i,scores:c}=n,l=t.dataIdMap.get(i.dataId).id,u=t.dataIdMap.get(c.dataId).id,d=Z_(l,u,a,s,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=p0(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var Doe={kernelName:_c,backendName:"wasm",setupFunc:Eoe,kernelFunc:Aoe},J_;function $oe(e){J_=e.wasm.cwrap(Ec,"number",["number","number","number","number","number","bool"])}function 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lt({x:r,y:s,width:a,height:o})}clipAtImageBorders(t,n){let{x:r,y:s,right:a,bottom:o}=this,i=Math.max(r,0),c=Math.max(s,0),l=a-i,u=o-c,d=Math.min(l,t-i),p=Math.min(u,n-c);return new lt({x:i,y:c,width:d,height:p}).floor()}shift(t,n){let{width:r,height:s}=this,a=this.x+t,o=this.y+n;return new lt({x:a,y:o,width:r,height:s})}padAtBorders(t,n){let r=this.width+1,s=this.height+1,a=1,o=1,i=r,c=s,l=this.left,u=this.top,d=this.right,p=this.bottom;return d>n&&(i=-d+n+r,d=n),p>t&&(c=-p+t+s,p=t),l<1&&(c=2-l,l=1),u<1&&(c=2-u,u=1),{dy:o,edy:c,dx:a,edx:i,y:u,ey:p,x:l,ex:d,w:r,h:s}}calibrate(t){return new lt({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var Vu=class extends lt{constructor(t,n,r,s,a=!1){super({left:t,top:n,right:r,bottom:s},a)}};var Na=class{constructor(t,n,r,s,a){this._imageDims=new _n(a.width,a.height),this._score=t,this._classScore=n,this._className=r,this._box=new lt(s).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new lt(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Na(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var wt=class extends Na{constructor(t,n,r){super(t,t,"",n,r)}forSize(t,n){let{score:r,relativeBox:s,imageDims:a}=super.forSize(t,n);return new wt(r,s,a)}};function x0(e,t,n=!0){let r=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),s=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),a=r*s;return n?a/(e.area+t.area-a):a/Math.min(e.area,t.area)}function w0(e){let t=e.map(i=>i.x),n=e.map(i=>i.y),r=t.reduce((i,c)=>c<i?c:i,1/0),s=n.reduce((i,c)=>c<i?c:i,1/0),a=t.reduce((i,c)=>i<c?c:i,0),o=n.reduce((i,c)=>i<c?c:i,0);return new Vu(r,s,a,o)}function k0(e,t,n,r=!0){let s=t.map((o,i)=>({score:o,boxIndex:i})).sort((o,i)=>o.score-i.score).map(o=>o.boxIndex),a=[];for(;s.length>0;){let o=s.pop();a.push(o);let i=s,c=[];for(let l=0;l<i.length;l++){let u=i[l],d=e[o],p=e[u];c.push(x0(d,p,r))}s=s.filter((l,u)=>c[u]<=n)}return a}function Qr(e,t){return M(()=>{let[n,r,s]=t,a=wn([...e.shape.slice(0,3),1],n,"float32"),o=wn([...e.shape.slice(0,3),1],r,"float32"),i=wn([...e.shape.slice(0,3),1],s,"float32"),c=tt([a,o,i],3);return fe(e,c)})}function I0(e,t=!1){return M(()=>{let[n,r]=e.shape.slice(1);if(n===r)return e;let s=Math.abs(n-r),a=Math.round(s*(t?.5:1)),o=n>r?2:1,i=p=>{let h=e.shape.slice();return h[o]=p,wn(h,0,"float32")},c=i(a),l=s-c.shape[o],d=[t&&l?i(l):null,e,c].filter(p=>!!p).map(p=>ce(p,"float32"));return tt(d,o)})}function jce(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let r=Math.floor(Math.random()*(n+1)),s=t[n];t[n]=t[r],t[r]=s}return t}function np(e){return 1/(1+Math.exp(-e))}function qce(e){return Math.log(e/(1-e))}var Uu=class extends lt{constructor(t,n,r,s,a=!1){super({x:t,y:n,width:r,height:s},a)}};var Kce=.5,Xce=.43,Yce=.45,xr=class{constructor(t,n,r=new Oe(0,0)){let{width:s,height:a}=n;this._imgDims=new _n(s,a),this._shift=r,this._positions=t.map(o=>o.mul(new Oe(s,a)).add(r))}get shift(){return new Oe(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new Oe(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new Oe(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let a=t instanceof wt?t.box.floor():new lt(t);return this.shiftBy(a.x,a.y).align(null,n)}let{useDlibAlignment:r,minBoxPadding:s}={useDlibAlignment:!1,minBoxPadding:.2,...n};return r?this.alignDlib():this.alignMinBbox(s)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,r,s]=t,a=d=>s.sub(d).magnitude(),o=(a(n)+a(r))/2,i=Math.floor(o/Yce),c=Ii(t),l=Math.floor(Math.max(0,c.x-Kce*i)),u=Math.floor(Math.max(0,c.y-Xce*i));return new Uu(l,u,Math.min(i,this.imageWidth+l),Math.min(i,this.imageHeight+u))}alignMinBbox(t){let n=w0(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var NE=class extends xr{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Ii([t[3],t[4]])]}};var Gu=class extends xr{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Ii)}};var rp=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${ki(this.distance)})`:""}`}};var sp=class extends lt{static assertIsValidLabeledBox(t,n){if(lt.assertIsValidBox(t,n),!Jr(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var Os=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(r=>new Float32Array(r));return new Os(t.label,n)}};var _E=class extends sp{static assertIsValidPredictedBox(t,n){if(sp.assertIsValidLabeledBox(t,n),!Wu(t.score)||!Wu(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,r,s){super(t,n);this._score=r,this._classScore=s}get score(){return this._score}get classScore(){return this._classScore}};function gs(e){return e.detection instanceof wt}function Si(e,t){return{...e,...{detection:t}}}function S0(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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implementation for nodejs environment")},s=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},a=()=>{if(n)return new n;throw new Error("createVideoElement - missing Video implementation for nodejs environment")},o=global.fetch,i=zm();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:r,createImageElement:s,createVideoElement:a,fetch:o,...i}}function C0(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var rn;function Zce(){if(!rn)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return rn}function N0(e){rn=e}function _0(){return C0()?N0(S0()):ap()?N0(T0()):null}function Jce(e){if(rn||_0(),!rn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=rn.Canvas,Image:n=rn.Image}=e;rn.Canvas=t,rn.Image=n,rn.createCanvasElement=e.createCanvasElement||(()=>new t),rn.createImageElement=e.createImageElement||(()=>new n),rn.ImageData=e.ImageData||rn.ImageData,rn.Video=e.Video||rn.Video,rn.fetch=e.fetch||rn.fetch,rn.readFile=e.readFile||rn.readFile}var nt={getEnv:Zce,setEnv:N0,initialize:_0,createBrowserEnv:S0,createFileSystem:zm,createNodejsEnv:T0,monkeyPatch:Jce,isBrowser:C0,isNodejs:ap};_0();function Ti(e){return!nt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Gn(e){let{Canvas:t,CanvasRenderingContext2D:n}=nt.getEnv();if(e instanceof n)return e;let r=Ti(e);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let s=r.getContext("2d");if(!s)throw new Error("resolveContext2d - canvas 2d context is null");return s}var E0=(s=>(s.TOP_LEFT="TOP_LEFT",s.TOP_RIGHT="TOP_RIGHT",s.BOTTOM_LEFT="BOTTOM_LEFT",s.BOTTOM_RIGHT="BOTTOM_RIGHT",s))(E0||{}),op=class{constructor(t={}){let{anchorPosition:n,backgroundColor:r,fontColor:s,fontSize:a,fontStyle:o,padding:i}=t;this.anchorPosition=n||"TOP_LEFT",this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=s||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=o||"Georgia",this.padding=i||4}},_a=class{constructor(t,n,r={}){this.text=typeof t=="string"?[t]:t instanceof _a?t.text:t,this.anchor=n,this.options=new op(r)}measureWidth(t){let{padding:n}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,s)=>r<s?s:r,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:r}=this.options,s=r==="BOTTOM_RIGHT"||r==="TOP_RIGHT",a=r==="BOTTOM_LEFT"||r==="BOTTOM_RIGHT",o=this.measureWidth(t),i=this.measureHeight(),c=s?this.anchor.x-o:this.anchor.x,l=a?this.anchor.y-i:this.anchor.y;if(n){let{width:u,height:d}=n,p=Math.max(Math.min(c,u-o),0),h=Math.max(Math.min(l,d-i),0);return{x:p,y:h}}return{x:c,y:l}}draw(t){let n=Ti(t),r=Gn(n),{backgroundColor:s,fontColor:a,fontSize:o,fontStyle:i,padding:c}=this.options;r.font=`${o}px ${i}`;let l=this.measureWidth(r),u=this.measureHeight();r.fillStyle=s;let d=this.getUpperLeft(r,n);r.fillRect(d.x,d.y,l,u),r.fillStyle=a,this.text.forEach((p,h)=>{let f=c+d.x,m=c+d.y+(h+1)*o;r.fillText(p,f,m)})}};var A0=class{constructor(t={}){let{boxColor:n,lineWidth:r,label:s,drawLabelOptions:a}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=s;let o={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new op({...o,...a})}},Wm=class{constructor(t,n={}){this.box=new lt(t),this.options=new A0(n)}draw(t){let n=Gn(t),{boxColor:r,lineWidth:s}=this.options,{x:a,y:o,width:i,height:c}=this.box;n.strokeStyle=r,n.lineWidth=s,n.strokeRect(a,o,i,c);let{label:l}=this.options;l&&new _a([l],{x:a-s/2,y:o},this.options.drawLabelOptions).draw(t)}};function Qce(e,t){(Array.isArray(t)?t:[t]).forEach(r=>{let s=r instanceof wt?r.score:gs(r)?r.detection.score:void 0,a=r instanceof wt?r.box:gs(r)?r.detection.box:new lt(r),o=s?`${ki(s)}`:void 0;new Wm(a,{label:o}).draw(e)})}function ip(e){let{Image:t,Video:n}=nt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function D0(e){return new Promise((t,n)=>{(e instanceof nt.getEnv().Canvas||ip(e))&&t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",s),a.currentTarget.removeEventListener("error",r),n(a))}function s(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",s),a.currentTarget.removeEventListener("error",r),t(a))}e.addEventListener("load",s),e.addEventListener("error",r)})}function $0(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let s=nt.getEnv().createImageElement();s.onload=()=>t(s),s.onerror=n,s.src=r.result},r.onerror=n,r.readAsDataURL(e)})}function Ci(e){let{Image:t,Video:n}=nt.getEnv();return e instanceof t?new _n(e.naturalWidth,e.naturalHeight):e instanceof n?new _n(e.videoWidth,e.videoHeight):new _n(e.width,e.height)}function Ni({width:e,height:t}){let{createCanvasElement:n}=nt.getEnv(),r=n();return r.width=e,r.height=t,r}function cp(e,t){let{ImageData:n}=nt.getEnv();if(!(e instanceof n)&&!ip(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:s}=t||Ci(e),a=Ni({width:r,height:s});return e instanceof n?Gn(a).putImageData(e,0,0):Gn(a).drawImage(e,0,0,r,s),a}async function F0(e,t){let n=t||nt.getEnv().createCanvasElement(),[r,s,a]=e.shape.slice(vr(e)?1:0),o=M(()=>e.as3D(r,s,a).toInt());return await Go.toPixels(o,n),o.dispose(),n}function Vm(e){let{Image:t,Canvas:n,Video:r}=nt.getEnv();return e instanceof t||e instanceof n||e instanceof r}function R0(e,t,n=!1){let{Image:r,Canvas:s}=nt.getEnv();if(!(e instanceof r||e instanceof s))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return Ni({width:1,height:1});let a=Ci(e),o=t/Math.max(a.height,a.width),i=o*a.width,c=o*a.height,l=Ni({width:t,height:t}),u=e instanceof s?e:cp(e),d=Math.abs(i-c)/2,p=n&&i<c?d:0,h=n&&c<i?d:0;return u.width>0&&u.height>0&&Gn(l).drawImage(u,p,h,i,c),l}var Ms=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((r,s)=>{if(Ps(r)){this._imageTensors[s]=r,this._inputDimensions[s]=r.shape;return}if(vr(r)){let o=r.shape[0];if(o!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${o} passed, but not supported in input array`);this._imageTensors[s]=r,this._inputDimensions[s]=r.shape.slice(1);return}let a=r instanceof nt.getEnv().Canvas?r:cp(r);this._canvases[s]=a,this._inputDimensions[s]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return ms(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),r=this.getInputHeight(t);return y0({width:n,height:r},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,M(()=>{let r=ms(this.batchSize,0,1).map(a=>{let o=this.getInput(a);if(o instanceof Ee){let i=vr(o)?o:gn(o);return i=I0(i,n),(i.shape[1]!==t||i.shape[2]!==t)&&(i=tr.resizeBilinear(i,[t,t],!1,!1)),i.as3D(t,t,3)}if(o instanceof nt.getEnv().Canvas)return Go.fromPixels(R0(o,t,n));throw new 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Iue(e){let t=ft(Re(e,[1,0])),n=[fe(t[2],t[0]),fe(t[3],t[1])],r=[Y(t[0],me(n[0],2)),Y(t[1],me(n[1],2))];return{sizes:n,centers:r}}function Sue(e,t){let{sizes:n,centers:r}=Iue(e),s=ft(Re(t,[1,0])),a=me(V(mn(me(s[2],5)),n[0]),2),o=Y(V(me(s[0],10),n[0]),r[0]),i=me(V(mn(me(s[3],5)),n[1]),2),c=Y(V(me(s[1],10),n[1]),r[1]);return Re(Mt([fe(o,a),fe(c,i),Y(o,a),Y(c,i)]),[1,0])}function ZE(e,t,n){return M(()=>{let r=e.shape[0],s=Sue(U(On(n.extra_dim,[r,1,1]),[-1,4]),U(e,[-1,4]));s=U(s,[r,s.shape[0]/r,4]);let a=hr(We(t,[0,0,1],[-1,-1,-1])),o=We(a,[0,0,0],[-1,-1,1]);o=U(o,[r,o.shape[1]]);let i=ft(s),c=ft(o);return{boxes:i,scores:c}})}function Ai(e,t){return M(()=>{let n=e.shape[0],r=U(_i(e,t.box_encoding_predictor),[n,-1,1,4]),s=U(_i(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:r,classPrediction:s}})}function JE(e,t,n){return M(()=>{let 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u=Array.from(l.dataSync()),p=YE(c,u,r,.5,s),h=a.getReshapedInputDimensions(0),f=a.inputSize,m=f/h.width,g=f/h.height,b=c.arraySync(),y=p.map(v=>{let[x,w]=[Math.max(0,b[v][0]),Math.min(1,b[v][2])].map($=>$*g),[T,N]=[Math.max(0,b[v][1]),Math.min(1,b[v][3])].map($=>$*m);return new wt(u[v],new Uu(T,x,N-T,w-x),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),l.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return KE(t)}extractParams(t){return qE(t)}};function QE(e){let t=new Di;return t.extractWeights(e),t}function Tue(e){return QE(e)}var eA=class extends Di{};var tA=.4,nA=[new Oe(.738768,.874946),new Oe(2.42204,2.65704),new Oe(4.30971,7.04493),new Oe(10.246,4.59428),new Oe(12.6868,11.8741)],rA=[new Oe(1.603231,2.094468),new Oe(6.041143,7.080126),new Oe(2.882459,3.518061),new Oe(4.266906,5.178857),new Oe(9.041765,10.66308)],sA=[117.001,114.697,97.404],aA="tiny_yolov2_model",oA="tiny_yolov2_separable_conv_model";var og=e=>typeof e=="number";function q0(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!og(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>og(t.x)&&og(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(og)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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iA(e,t,n,r){let{extractWeights:s,getRemainingWeights:a}=An(e),o=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:l}=Cue(s,o),u;if(t.withSeparableConvs){let[d,p,h,f,m,g,b,y,v]=r,x=t.isFirstLayerConv2d?i(d,p,3,"conv0"):l(d,p,"conv0"),w=l(p,h,"conv1"),T=l(h,f,"conv2"),N=l(f,m,"conv3"),$=l(m,g,"conv4"),D=l(g,b,"conv5"),P=y?l(b,y,"conv6"):void 0,F=v?l(y,v,"conv7"):void 0,R=i(v||y||b,5*n,1,"conv8");u={conv0:x,conv1:w,conv2:T,conv3:N,conv4:$,conv5:D,conv6:P,conv7:F,conv8:R}}else{let[d,p,h,f,m,g,b,y,v]=r,x=c(d,p,"conv0"),w=c(p,h,"conv1"),T=c(h,f,"conv2"),N=c(f,m,"conv3"),$=c(m,g,"conv4"),D=c(g,b,"conv5"),P=c(b,y,"conv6"),F=c(y,v,"conv7"),R=i(v,5*n,1,"conv8");u={conv0:x,conv1:w,conv2:T,conv3:N,conv4:$,conv5:D,conv6:P,conv7:F,conv8:R}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:u,paramMappings:o}}function Nue(e,t){let n=or(e,t);function r(i){let c=n(`${i}/sub`,1),l=n(`${i}/truediv`,1);return{sub:c,truediv:l}}function s(i){let c=n(`${i}/filters`,4),l=n(`${i}/bias`,1);return{filters:c,bias:l}}function a(i){let c=s(`${i}/conv`),l=r(`${i}/bn`);return{conv:c,bn:l}}let o=Xu(n);return{extractConvParams:s,extractConvWithBatchNormParams:a,extractSeparableConvParams:o}}function cA(e,t){let n=[],{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}=Nue(e,n),o;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else o={conv0:s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:s("conv6"),conv7:s("conv7"),conv8:r("conv8")};return En(e,n),{params:o,paramMappings:n}}var bs=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var K0=class extends dn{constructor(t){super("TinyYolov2");q0(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let r=Bs(t,n.conv0);return r=Ot(r,[2,2],[2,2],"same"),r=Bs(r,n.conv1),r=Ot(r,[2,2],[2,2],"same"),r=Bs(r,n.conv2),r=Ot(r,[2,2],[2,2],"same"),r=Bs(r,n.conv3),r=Ot(r,[2,2],[2,2],"same"),r=Bs(r,n.conv4),r=Ot(r,[2,2],[2,2],"same"),r=Bs(r,n.conv5),r=Ot(r,[2,2],[1,1],"same"),r=Bs(r,n.conv6),r=Bs(r,n.conv7),_i(r,n.conv8,"valid",!1)}runMobilenet(t,n){let r=this.config.isFirstLayerConv2d?Qu(_i(t,n.conv0,"valid",!1)):zs(t,n.conv0);return r=Ot(r,[2,2],[2,2],"same"),r=zs(r,n.conv1),r=Ot(r,[2,2],[2,2],"same"),r=zs(r,n.conv2),r=Ot(r,[2,2],[2,2],"same"),r=zs(r,n.conv3),r=Ot(r,[2,2],[2,2],"same"),r=zs(r,n.conv4),r=Ot(r,[2,2],[2,2],"same"),r=zs(r,n.conv5),r=Ot(r,[2,2],[1,1],"same"),r=n.conv6?zs(r,n.conv6):r,r=n.conv7?zs(r,n.conv7):r,_i(r,n.conv8,"valid",!1)}forwardInput(t,n){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let s=ce(t.toBatchTensor(n,!1),"float32");return s=this.config.meanRgb?Qr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(t,n){return this.forwardInput(await yt(t),n)}async detect(t,n={}){let{inputSize:r,scoreThreshold:s}=new bs(n),a=await yt(t),o=await this.forwardInput(a,r),i=M(()=>ft(o)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},l=await this.extractBoxes(i,a.getReshapedInputDimensions(0),s);o.dispose(),i.dispose();let u=l.map(g=>g.box),d=l.map(g=>g.score),p=l.map(g=>g.classScore),h=l.map(g=>this.config.classes[g.label]);return k0(u.map(g=>g.rescale(r)),d,this.config.iouThreshold,!0).map(g=>new Na(d[g],p[g],h[g],u[g],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return cA(t,this.config)}extractParams(t){let n=this.config.filterSizes||K0.DEFAULT_FILTER_SIZES,r=n?n.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return iA(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,r){let{width:s,height:a}=n,o=Math.max(s,a),i=o/s,c=o/a,l=t.shape[1],u=this.config.anchors.length,[d,p,h]=M(()=>{let b=t.reshape([l,l,u,this.boxEncodingSize]),y=b.slice([0,0,0,0],[l,l,u,4]),v=b.slice([0,0,0,4],[l,l,u,1]),x=this.withClassScores?zr(b.slice([0,0,0,5],[l,l,u,this.config.classes.length]),3):Ie(0);return[y,v,x]}),f=[],m=await p.array(),g=await d.array();for(let b=0;b<l;b++)for(let y=0;y<l;y++)for(let v=0;v<u;v++){let x=np(m[b][y][v][0]);if(!r||x>r){let w=(y+np(g[b][y][v][0]))/l*i,T=(b+np(g[b][y][v][1]))/l*c,N=Math.exp(g[b][y][v][2])*this.config.anchors[v].x/l*i,$=Math.exp(g[b][y][v][3])*this.config.anchors[v].y/l*c,D=w-N/2,P=T-$/2,F={row:b,col:y,anchor:v},{classScore:R,label:C}=this.withClassScores?await this.extractPredictedClass(h,F):{classScore:1,label:0};f.push({box:new Vu(D,P,D+N,P+$),score:x,classScore:x*R,label:C,...F})}}return d.dispose(),p.dispose(),h.dispose(),f}async extractPredictedClass(t,n){let{row:r,col:s,anchor:a}=n,o=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>o[r][s][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},el=K0;el.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var tl=class extends el{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:tA,classes:["face"],...t?{anchors:rA,meanRgb:sA}:{anchors:nA,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new wt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?oA:aA}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function _ue(e,t=!0){let n=new tl(t);return n.extractWeights(e),n}var ig=class extends bs{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Or=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function $i(e,t,n,r,s=({alignedRect:a})=>a){let a=e.map(c=>Ei(c)?s(c):c.detection),o=r||(t instanceof Ee?await ju(t,a):await Hu(t,a)),i=await n(o);return o.forEach(c=>c instanceof Ee&&c.dispose()),i}async function nl(e,t,n,r,s){return $i([e],t,async a=>n(a[0]),r,s)}var uA=.4,lA=[new Oe(1.603231,2.094468),new Oe(6.041143,7.080126),new Oe(2.882459,3.518061),new Oe(4.266906,5.178857),new Oe(9.041765,10.66308)],dA=[117.001,114.697,97.404];var rl=class extends el{constructor(){let t={withSeparableConvs:!0,iouThreshold:uA,classes:["face"],anchors:lA,meanRgb:dA,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new wt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new Di,tinyFaceDetector:new rl,tinyYolov2:new tl,faceLandmark68Net:new Zu,faceLandmark68TinyNet:new tg,faceRecognitionNet:new Ju,faceExpressionNet:new Zm,ageGenderNet:new eg},pA=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),Eue=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),Aue=(e,t)=>rt.tinyYolov2.locateFaces(e,t),hA=e=>rt.faceLandmark68Net.detectLandmarks(e),Due=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),$ue=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),Fue=e=>rt.faceExpressionNet.predictExpressions(e),Rue=e=>rt.ageGenderNet.predictAgeAndGender(e),fA=e=>rt.ssdMobilenetv1.load(e),Pue=e=>rt.tinyFaceDetector.load(e),Oue=e=>rt.tinyYolov2.load(e),Mue=e=>rt.faceLandmark68Net.load(e),Lue=e=>rt.faceLandmark68TinyNet.load(e),Bue=e=>rt.faceRecognitionNet.load(e),zue=e=>rt.faceExpressionNet.load(e),Wue=e=>rt.ageGenderNet.load(e),Vue=fA,Uue=pA,Gue=hA;var X0=class extends Or{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},sl=class extends X0{async run(){let t=await this.parentTask,n=await $i(t,this.input,async r=>Promise.all(r.map(s=>rt.faceExpressionNet.predictExpressions(s))),this.extractedFaces);return t.map((r,s)=>Jm(r,n[s]))}withAgeAndGender(){return new ol(this,this.input)}},al=class extends X0{async run(){let t=await this.parentTask;if(!t)return;let n=await nl(t,this.input,r=>rt.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Jm(t,n)}withAgeAndGender(){return new il(this,this.input)}},Fi=class extends sl{withAgeAndGender(){return new Pi(this,this.input)}withFaceDescriptors(){return new Aa(this,this.input)}},Ri=class extends al{withAgeAndGender(){return new Oi(this,this.input)}withFaceDescriptor(){return new Da(this,this.input)}};var Y0=class extends Or{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},ol=class extends Y0{async run(){let t=await this.parentTask,n=await $i(t,this.input,async r=>Promise.all(r.map(s=>rt.ageGenderNet.predictAgeAndGender(s))),this.extractedFaces);return t.map((r,s)=>{let{age:a,gender:o,genderProbability:i}=n[s];return sg(ag(r,o,i),a)})}withFaceExpressions(){return new sl(this,this.input)}},il=class extends Y0{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:r,genderProbability:s}=await nl(t,this.input,a=>rt.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return sg(ag(t,r,s),n)}withFaceExpressions(){return new al(this,this.input)}},Pi=class extends ol{withFaceExpressions(){return new Fi(this,this.input)}withFaceDescriptors(){return new Aa(this,this.input)}},Oi=class extends il{withFaceExpressions(){return new Ri(this,this.input)}withFaceDescriptor(){return new Da(this,this.input)}};var cg=class extends Or{constructor(t,n){super();this.parentTask=t;this.input=n}},Aa=class extends cg{async run(){let t=await this.parentTask;return(await $i(t,this.input,r=>Promise.all(r.map(s=>rt.faceRecognitionNet.computeFaceDescriptor(s))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,s)=>rg(t[s],r))}withFaceExpressions(){return new Fi(this,this.input)}withAgeAndGender(){return new Pi(this,this.input)}},Da=class extends cg{async run(){let t=await this.parentTask;if(!t)return;let n=await nl(t,this.input,r=>rt.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return rg(t,n)}withFaceExpressions(){return new Ri(this,this.input)}withAgeAndGender(){return new Oi(this,this.input)}};var ug=class extends Or{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},lg=class extends ug{async run(){let t=await this.parentTask,n=t.map(a=>a.detection),r=this.input instanceof Ee?await ju(this.input,n):await Hu(this.input,n),s=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Ee&&a.dispose()),t.map((a,o)=>Yu(a,s[o]))}withFaceExpressions(){return new Fi(this,this.input)}withAgeAndGender(){return new Pi(this,this.input)}withFaceDescriptors(){return new Aa(this,this.input)}},dg=class extends ug{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,r=this.input instanceof Ee?await ju(this.input,[n]):await Hu(this.input,[n]),s=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ee&&a.dispose()),Yu(t,s)}withFaceExpressions(){return new Ri(this,this.input)}withAgeAndGender(){return new Oi(this,this.input)}withFaceDescriptor(){return new Da(this,this.input)}};var pg=class extends Or{constructor(t,n=new Pr){super();this.input=t;this.options=n}},mp=class extends pg{async run(){let{input:t,options:n}=this,r;if(n instanceof ig)r=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Pr)r=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof bs)r=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(r=>t(r.map(s=>Si({},s)))).catch(r=>n(r))})}withFaceLandmarks(t=!1){return new lg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new sl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ol(this.runAndExtendWithFaceDetections(),this.input)}},hg=class extends pg{async run(){let t=await new mp(this.input,this.options),n=t[0];return t.forEach(r=>{r.score>n.score&&(n=r)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Si({},n):void 0)})}withFaceLandmarks(t=!1){return new dg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new al(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new il(this.runAndExtendWithFaceDetection(),this.input)}};function Hue(e,t=new Pr){return new hg(e,t)}function fg(e,t=new Pr){return new mp(e,t)}async function mA(e,t){return fg(e,new Pr(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function jue(e,t={}){return fg(e,new bs(t)).withFaceLandmarks().withFaceDescriptors()}var que=mA;function Z0(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),r=Array.from(t);return Math.sqrt(n.map((s,a)=>s-r[a]).reduce((s,a)=>s+a**2,0))}var mg=class{constructor(t,n=.6){this._distanceThreshold=n;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let s=1,a=()=>`person ${s++}`;this._labeledDescriptors=r.map(o=>{if(o instanceof Os)return o;if(o instanceof Float32Array)return new Os(a(),[o]);if(o.descriptor&&o.descriptor instanceof Float32Array)return new Os(a(),[o.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(r=>Z0(r,t)).reduce((r,s)=>r+s,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:r})=>new rp(r,this.computeMeanDistance(t,n))).reduce((n,r)=>n.distance<r.distance?n:r)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this._distanceThreshold?n:new rp("unknown",n.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(r=>Os.fromJSON(r));return new mg(n,t.distanceThreshold)}};function Kue(e){let t=new rl;return t.extractWeights(e),t}function gA(e,t){let{width:n,height:r}=new _n(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(s=>gA(s,{width:n,height:r}));if(Ei(e)){let s=e.detection.forSize(n,r),a=e.unshiftedLandmarks.forSize(s.box.width,s.box.height);return Yu(Si(e,s),a)}return gs(e)?Si(e,e.detection.forSize(n,r)):e instanceof xr||e instanceof wt?e.forSize(n,r):e}var Xue=RE;return fD(Yue);})();
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 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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