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

  /*
  Face-API
  homepage: <https://github.com/vladmandic/face-api>
  author: <https://github.com/vladmandic>'
  */

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a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=ty(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(ty(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Bd(h,this.backendName);$(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let b=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,b);let x=b.map(v=>{if(v.rank!=null)return v;let{dataId:k,shape:w,dtype:S}=v;return this.makeTensorFromDataId(k,w,S)});if(a){let v=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:c}=e,p=ty(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),a&&this.addTapeNode(l,u,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=qg(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?($(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&zr(e[0])&&(r=e.map(o=>fc(o)));let s=a.write(r,t,n),i=new Fe(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=Qw(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Fe(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new Hr(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*zg(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Hr||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*zg(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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KP=O({softmaxCrossEntropy_:XP}),YP={fft:Rc,ifft:Bl,rfft:Pc,irfft:wh},JP={hammingWindow:jR,hannWindow:z1,frame:W1,stft:YR},Ka={flipLeftRight:eP,resizeNearestNeighbor:j1,resizeBilinear:H1,rotateWithOffset:nP,cropAndResize:QR,nonMaxSuppression:rP,nonMaxSuppressionAsync:dP,nonMaxSuppressionWithScore:mP,nonMaxSuppressionWithScoreAsync:gP,nonMaxSuppressionPadded:bP,nonMaxSuppressionPaddedAsync:vP,transform:NP},X1={bandPart:SP,gramSchmidt:AP,qr:_P},QP={absoluteDifference:DP,computeWeightedLoss:yr,cosineDistance:RP,hingeLoss:OP,huberLoss:zP,logLoss:BP,meanSquaredError:UP,sigmoidCrossEntropy:jP,softmaxCrossEntropy:KP},br=class extends X0{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return Ee(r),t?a:(a.dispose(),null)}get iterations(){return 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All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);as(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(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let b=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let y of this.inputs){let b=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;Ja(x===0,"input layer has >1 nodes"),Ja(v===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let b=this.inputLayers[y];if(!(b instanceof jl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${b.getClassName()}.`);this.inputNames.push(b.name),this.feedInputShapes.push(b.batchInputShape),this.feedInputNames.push(b.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},a={},r={},s={},i=[],o=(y,b,x,v,k,w)=>{(v==null||k==null||w==null)&&(v=y.sourceLayer,k=y.nodeIndex,w=y.tensorIndex);let S=v.inboundNodes[k];if(x.indexOf(S)!==-1)throw new Fa(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(b.indexOf(S)!==-1)return;this.containerNodes.add(tr.nodeKey(v,k)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(S)===-1&&x.push(S);let A=S.inboundLayers.length;for(let F=0;F<A;F++){let D=S.inputTensors[F],R=S.inboundLayers[F],L=S.nodeIndices[F],W=S.tensorIndices[F];o(D,b,x,R,L,W)}for(b.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);i.push(S)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let c=i.slice().reverse();for(let y of c){n[y.id]=y,y.id in t||(t[y.id]=0);let b=t[y.id],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,x),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let v=0;v<y.inboundLayers.length;v++){let k=y.inboundLayers[v],w=y.nodeIndices[v],S=k.inboundNodes[w],A=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(b+1,A),n[S.id]=S}}let p={};for(let y in t){let b=t[y];b in p||(p[b]=[]),p[b].push(n[y])}let d={};for(let y in a){let b=a[y];b in d||(d[b]=[]),d[b].push(r[y])}let h=Object.keys(d).map(y=>parseInt(y,10)).sort(Oh);this.layers=[];for(let y of h){let b=d[y];b.sort((x,v)=>{let k=s[x.id],w=s[v.id];return k<w?-1:k>w?1:0});for(let x of b)x instanceof tr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(Oh);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of p[y]){let x=b.outboundLayer;if(x!=null){for(let v of b.inputTensors)if(m.indexOf(v)===-1)throw new Fa(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". 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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={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new V(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new V(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new V(`${s.length} of ${a} weights are not set: ${s}`)}Rb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${im}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Vb(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return M(()=>{e=gt(e);let n=new Li;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Xc(this.outputs,n,t)})}computeMask(e,t){return M(()=>{e=gt(e);let n;return t==null?n=$i(null,e.length):n=gt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Kh(e);if(t.length!==this.inputLayers.length)throw new V(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";n[u]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Oh);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(u.id)!==-1)continue;let c=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,x=n[b];c.push(x)}let p=u.computeOutputShape(Cn(c)),d=Kh(p),h=u.inboundNodes.indexOf(l);for(let m=0;m<d.length;m++){let f=`${u.name}_${h}_${m}`;n[f]=d[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],c=`${o.name}_${l}_${u}`;s.push(c)}for(let i=0;i<s.length;i++){let o=s[i];Ja(o in n),r.push(n[o])}return Cn(r)}runInternalGraph(e,t){t==null&&(t=$i(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],c=t[o];n[l.id]=[u,c]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Oh);for(let o of a){let l=this.nodesByDepth[o];for(let u of l){let c=u.outboundLayer,p=u.inputTensors,d=u.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,g,y,b;if(u.callArgs!=null&&(m=u.callArgs),h.length===1){let[x,v]=h[0];m.mask==null&&(m.mask=v),y=gt(c.call(x,m)),b=gt(c.computeMask(x,v)),f=[x],g=[v]}else f=h.map(x=>x[0]),g=h.map(x=>x[1]),m.mask==null&&(m.mask=g),y=gt(c.call(f,m)),b=gt(c.computeMask(f,g));if(c.activityRegularizer)throw new $e("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<d.length;++x){let v=d[x],k=y[x],w=b[x];n[v.id]=[k,w]}}}}let r=[],s=[],i=[];for(let o of this.outputs){Ja(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=n[o.id];i.push(l.shape),r.push(l),s.push(u)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof tr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=tr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new V(`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 V("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new V(`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 a=tr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let c=0;c<s.inboundNodes.length;c++){let p=s.inboundNodes[c],d=tr.nodeKey(s,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],b=p.tensorIndices[f],x=tr.nodeKey(g,y),v=t[x];v==null&&(v=0),m.push([g.name,v,b,h])}l.push(m)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,n.push(u)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=tr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[s];a.push([i.name,u,c])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=tr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[s];r.push([i.name,u,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],b;for(let x of g){let v=x[0],k=x[1],w=x[2];if(b=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let S=r[v];if(S.inboundNodes.length<=k){i(f,g);return}let A=S.inboundNodes[k];y.push(A.outputTensors[w])}y.length>0&&f.apply(Cn(y),b)}function l(f){let g=f.name,y=$a(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new V(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let u=t.name,c=t.layers;for(let f of c)l(f);for(;!O3(s);)for(let f of c){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let b of y)o(g,b)}}let p=[],d=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];Ja(g in r);let x=r[g].inboundNodes[y].outputTensors;p.push(x[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];Ja(g in r);let x=r[g].inboundNodes[y].outputTensors;d.push(x[b])}return new e({inputs:p,outputs:d,name:u})}get stateful(){if(this._stateful)throw new V("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 dB(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===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!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Zk(e,t){return dB(e,t,"classWeight")}async function eI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=M(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await r.data());Ee(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Qe(i,"float32")}else return null}function hB(e,t){return B(e,t)}var mB=32;function nI(e,t){let n,a,r=t;n=r.xs,a=r.ys,I.assert(n!=null&&a!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let s=tI("input",e.inputNames,n),i=tI("output",e.outputNames,a),o=s[0].shape[0];I.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),I.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)I.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)I.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function tI(e,t,n){if(n instanceof Fe)return[n];if(Array.isArray(n))return I.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new V(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function fB(e){if(e.length===3)throw new $e("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function yB(e,t,n){let a=n.batchesPerEpoch!=null;if(I.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),I.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),I.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}`),I.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),I.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let h=0;h<this.inputs.length;++h)u.push({key:this.inputs[h],value:n[h]});let c=new Li(u),p=Xc(this.outputs,c,{training:!0}),d;for(let h=0;h<this.lossFunctions.length;++h){let m=this.lossFunctions[h](a[h],p[h]);r[h]!=null&&(m=hB(m,r[h]));let f=Ct(m);t.push(f),h===0?d=m:d=J(d,m)}for(let h=0;h<this.metricsTensors.length;++h){let m;if(this.outputs.length>1&&h<this.outputs.length)m=t[h];else{let f=this.metricsTensors[h][0],g=this.metricsTensors[h][1];m=Ct(f(a[g],p[g]))}qt(m),s.push(m)}return d=Ct(d),this.calculateLosses().forEach(h=>{d=J(d,h)}),d},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>M(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:a[l]});let i=new Li(s),o=Xc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=Ct(u(r[l],o[l]));l===0?n=c:n=J(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],p=Ct(u(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return wB(this,e,t,n)}async fitDataset(e,t){return yB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Ee(s),Cn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,a=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let s=0;s<a.length;++s)n&&!a[s].trainable||t.push({name:a[s].originalName,tensor:r[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Jd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Jd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=vr(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>vr(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let a of t)if(typeof n[a]=="string")e[a]=vr(n[a]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[vr(rm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>vr(rm(e)));{let e={};for(let t in this.metrics)e[t]=vr(rm(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=qc(e.optimizer_config),n=$a(t),a;if(typeof e.loss=="string")a=Di(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Di(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Di(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Di(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Di(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=jt.getSaveHandlers(e);if(i.length===0)throw new V(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new V(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new V("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await jt.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:SB,generatedBy:`TensorFlow.js tfjs-layers v${im}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await jt.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=jt.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;Kk(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){Kk(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};wr.className="Model";re.registerClass(wr);var lI=class extends wr{};lI.className="Functional";re.registerClass(lI);async function CB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=qc(n),r=$a(a,t);if(e.weightsManifest!=null){let s=await jt.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),Ee(s)}return r}async function FB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=jt.getLoadHandlers(e,t);if(n.length===0)n.push(jt.browserHTTPRequest(e,t));else if(n.length>1)throw new V(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return AB(e,void 0,t)}async function AB(e,t,n){if(n==null&&(n={}),e.load==null)throw new V("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let a=await e.load(),r=a.modelTopology;r.model_config!=null&&(r=r.model_config);let s=n.strict==null?!0:n.strict,i=a.weightData!=null&&a.weightSpecs!=null&&s,o=$a(qc(r),t,i),l=a.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),a.userDefinedMetadata!=null&&o.setUserDefinedMetadata(a.userDefinedMetadata),a.weightData!=null){if(a.weightSpecs==null)throw new V("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=_B(a.weightData,a.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Ee(u),Ee(c.map(p=>p.tensor))}return o}function _B(e,t){let n=jt.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var Kl=class extends wr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Xh("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new V(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Kl||e instanceof wr,n;if(t){if(n=e,n.outputs.length!==1)throw new V("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new V("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new V("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=Rk({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(a)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new V(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new V("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=Mk(this.outputs[0])}this.inboundNodes=[],new Jh({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:$i(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(a=>a.shape),outputShapes:this.outputs[0].shape})}else{let a=e.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(pt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new wr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Fa("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Fa("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Fa("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Fa("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");r=t}else I.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Kl))throw new $e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=$a(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Kl.className="Sequential";re.registerClass(Kl);function EB(e){return new wr(e)}function $B(e){return new Kl(e)}function DB(e,t){return t==null&&(t={}),FB(e,t)}function Sk(e){return Rk(e)}function MB(e,t){fa.registerCallbackConstructor(e,t)}var Wn=class extends re.Serializable{getConfig(){return{}}},uI=class extends Wn{apply(e,t=1){return dz(e,t)}};uI.className="elu";re.registerClass(uI);var cI=class extends Wn{apply(e){return yh(e)}};cI.className="selu";re.registerClass(cI);var pI=class extends Wn{apply(e){return qe(e)}};pI.className="relu";re.registerClass(pI);var dI=class extends Wn{apply(e){return M(()=>Ll(6,qe(e)))}};dI.className="relu6";re.registerClass(dI);var hI=class extends Wn{apply(e){return e}};hI.className="linear";re.registerClass(hI);var mI=class extends Wn{apply(e){return ca(e)}};mI.className="sigmoid";re.registerClass(mI);var fI=class extends Wn{apply(e){return mz(e)}};fI.className="hardSigmoid";re.registerClass(fI);var gI=class extends Wn{apply(e){return Pl(e)}};gI.className="softplus";re.registerClass(gI);var yI=class extends Wn{apply(e){return hz(e)}};yI.className="softsign";re.registerClass(yI);var bI=class extends Wn{apply(e){return Dl(e)}};bI.className="tanh";re.registerClass(bI);var Xb=class extends Wn{apply(e,t=-1){return Ta(e,t)}};Xb.className="softmax";re.registerClass(Xb);var xI=class extends Wn{apply(e,t=-1){return ch(e,t)}};xI.className="logSoftmax";re.registerClass(xI);var vI=class extends Wn{apply(e,t=1){return M(()=>ca(e.mul(t)).mul(e))}};vI.className="swish";re.registerClass(vI);function os(e){return e.getClassName()}function Kb(e,t={}){return Lc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function ls(e){if(e==null){let t={};return t.className="linear",t.config={},Kb(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Kb(t)}else return e instanceof Wn?e:Kb(e)}function Yb(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var wI=class extends re.Serializable{},Yc=class extends wI{constructor(e){super();Yb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return M(()=>{let t=vt([1]);return this.hasL1&&(t=J(t,Se(B(this.l1,zt(e))))),this.hasL2&&(t=J(t,Se(B(this.l2,Uc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Yc.className="L1L2";re.registerClass(Yc);function RB(e){return Yb(e),new Yc({l1:e!=null?e.l1:null,l2:0})}function PB(e){return Yb(e),new Yc({l2:e!=null?e.l2:null,l1:0})}var kI={l1l2:"L1L2"};function dt(e){return pb(e)}function II(e,t={}){return Lc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function kt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in kI?kI[e]:e,config:{}};return II(t)}else return e instanceof wI?e:II(e)}var Jb=class extends je{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Re(e);let n=qe(e);return this.maxValue!=null&&(n=Xt(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Jb.className="ReLU";re.registerClass(Jb);var Qb=class extends je{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Re(e);return Fc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Qb.className="LeakyReLU";re.registerClass(Qb);var Zb=class extends 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e={alphaInitializer:At(this.alphaInitializer),alphaRegularizer:dt(this.alphaRegularizer),alphaConstraint:Vt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Zb.className="PReLU";re.registerClass(Zb);var ex=class extends je{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new $e(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Re(e);return Ml(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};ex.className="ELU";re.registerClass(ex);var tx=class extends je{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Re(e);return n.mul(Bc(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};tx.className="ThresholdedReLU";re.registerClass(tx);var nx=class extends je{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Xb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Re(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}};nx.className="Softmax";re.registerClass(nx);function Yl(e,t,n){if(typeof e=="number")return $i(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!lz(r))throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Da(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function om(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+ss([n-t,0]);else if(a==="same")e=e*t;else throw new V(`Unsupport padding mode: ${a}.`);return e}function ax(e,t){return M(()=>(Mt(t),t==="channelsFirst"?Ve(e,[0,2,3,1]):e))}function NI(e,t){return M(()=>(Mt(t),t==="channelsFirst"?Ve(e,[0,2,3,4,1]):e))}function OB(e,t,n,a=1,r="valid",s,i=1){return M(()=>{if(s==null&&(s=Aa()),Mt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ve(e,[0,2,1])),r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=nh(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Za(o,n)),o})}function TI(e,t,n,a=[1,1],r="valid",s,i,o=null){return M(()=>{if(s==null&&(s=Aa()),Mt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=ax(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ns.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ve(l,[0,3,1,2])),l})}function LB(e,t,n,a=[1,1,1],r="valid",s,i){return M(()=>{if(s==null&&(s=Aa()),Mt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=NI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=My(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Za(o,n)),s==="channelsFirst"&&(o=Ve(o,[0,4,1,2,3])),o})}var rx=class extends je{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",rx.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Yl(t.kernelSize,e,"kernelSize"),this.strides=Yl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ta(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Mt(this.dataFormat),this.activation=ls(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ut(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=Yl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ja("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!hb(e.kernelSize,"number",1,3))throw new V(`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:os(this.activation),useBias:this.useBias,biasInitializer:At(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Jc=class extends rx{constructor(e,t){super(e,t);this.kernel=null,Jc.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ut(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,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=Re(e);let n,a=this.bias==null?null:this.bias.read(),r=ck(this.activation.getClassName());if(r!=null&&this.rank===2)n=TI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=OB(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=TI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=LB(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=pt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Da(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:At(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:Vt(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 V(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Qc=class extends Jc{constructor(e){super(2,e);Qc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hb(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Qc.className="Conv2D";re.registerClass(Qc);var lm=class extends Jc{constructor(e){super(3,e);lm.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 V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};lm.className="Conv3D";re.registerClass(lm);var sx=class extends Qc{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=pt(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{let n=Re(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=om(o,p,u,this.padding),m=om(l,d,c,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ve(n,[0,2,3,1]));let g=ah(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ve(g,[0,3,1,2])),this.bias!=null&&(g=Za(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=pt(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=om(t[a],o,s,this.padding),t[r]=om(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};sx.className="Conv2DTranspose";re.registerClass(sx);var SI=class extends Jc{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 V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("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 V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Ut(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Ut(t.pointwiseConstraint)}build(e){if(e=pt(e),e.length<this.rank+2)throw new V(`Inputs to SeparableConv${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 V(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Yt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{e=Re(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ve(e,[0,2,3,1])),n=Fi(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Za(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ve(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=At(this.depthwiseInitializer),e.pointwiseInitializer=At(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseConstraint),e.pointwiseConstraint=Vt(this.pointwiseConstraint),e}};SI.className="SeparableConv";var ix=class extends SI{constructor(e){super(2,e)}};ix.className="SeparableConv2D";re.registerClass(ix);var um=class extends Jc{constructor(e){super(1,e);um.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"&&!hb(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};um.className="Conv1D";re.registerClass(um);var ox=class extends je{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=Re(e),this.dataFormat==="channelsLast"){let n=Lh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Lh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Lh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Lh(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}};ox.className="Cropping2D";re.registerClass(ox);var lx=class extends je{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,Mt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,sz(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=Re(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ve(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ve(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};lx.className="UpSampling2D";re.registerClass(lx);function zB(e,t,n=[1,1],a="valid",r,s){return M(()=>{r==null&&(r=Aa()),Mt(r);let i=ax(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Qr(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ve(i,[0,3,1,2])),i})}var ux=class extends rx{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=wt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ut(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=pt(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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=Re(e);let n=zB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Za(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Da(t,this.kernelSize[0],this.padding,this.strides[0]),s=Da(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=At(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseRegularizer),e}};ux.className="DepthwiseConv2D";re.registerClass(ux);function CI(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function AI(e,t,n,a=!1,r,s,i=!1,o=!1){return M(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(_a(2,l));if(t=Ve(t,u),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=Rn(r,-1)),r=Ve(r,u)),a&&(t=Ln(t,0),r!=null&&(r=Ln(r,0)));let c=[],p,d=n,h=t.shape[0],m=ct(t),f;r!=null&&(f=ct(r));for(let y=0;y<h;++y){let b=m[y],x=M(()=>e(b,d));if(r==null)p=x[0],d=x[1];else{let v=M(()=>{let k=f[y],w=On(k).sub(k),S=x[0].mul(k).add(d[0].mul(w)),A=d.map((F,D)=>x[1][D].mul(k).add(F.mul(w)));return{output:S,newStates:A}});p=v.output,d=v.newStates}o&&c.push(p)}let g;return o&&(g=Dt(c,1)),[p,g,d]})}var er=class extends je{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new cm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Yt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return _a(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Db(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return M(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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 $e("Constants support is not implemented in RNN yet.");Db(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Yt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!I.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Yt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){M(()=>{if(!this.stateful)throw new xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("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(a=>vt([n,a])):this.states_=[vt([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>vt([n,a])):this.states_[0]=vt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!I.arraysEqual(r.shape,i))throw new V(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>qt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=CI(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Yt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Ea){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return M(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Re(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=AI((d,h)=>{let m=this.cell.call([d].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,a);let p=this.returnSequences?u:l;return this.returnState?[p].concat(c):p})}getInitialState(e){return M(()=>{let t=vt(e.shape);return t=Se(t,[1,2]),t=Vc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?xb(t,[1,n]):t):this.cell.stateSize>1?[xb(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===er.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=$a(a,n);return new e(Object.assign(t,{cell:r}))}};er.className="RNN";re.registerClass(er);var Hc=class extends je{},pm=class extends Hc{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,Kt(this.units,"units"),this.activation=ls(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=Hl([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=pt(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 V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=us({ones:()=>On(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=us({ones:()=>On(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Qa(B(e,s),this.kernel.read()):r=Qa(e,this.kernel.read()),this.bias!=null&&(r=Za(r,this.bias.read())),i!=null&&(n=B(n,i));let o=J(r,Qa(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:os(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};pm.className="SimpleRNNCell";re.registerClass(pm);var cx=class extends er{constructor(e){e.cell=new pm(e),super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};cx.className="SimpleRNN";re.registerClass(cx);var dm=class extends Hc{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 V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=Hl([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=pt(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 V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=us({ones:()=>On(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=us({ones:()=>On(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=B(e,r[0]));let u=Qa(e,this.kernel.read());this.useBias&&(u=Za(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,s[0]));let c=this.recurrentKernel.read(),[p,d]=zn(c,[2*this.units,this.units],c.rank-1),h=Qa(a,p),[m,f,g]=zn(u,3,u.rank-1),[y,b]=zn(h,2,h.rank-1);i=this.recurrentActivation.apply(J(m,y)),o=this.recurrentActivation.apply(J(f,b));let x=Qa(B(o,a),d);l=this.activation.apply(J(g,x));let v=J(B(i,a),B(J(1,St(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:os(this.activation),recurrentActivation:os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};dm.className="GRUCell";re.registerClass(dm);var px=class extends er{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 dm(e),super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};px.className="GRU";re.registerClass(px);var Zc=class extends Hc{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,Kt(this.units,"units"),this.activation=ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=Hl([1,ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=pt(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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends ma{apply(i,o){let l=r.apply([s]),u=new Wh().apply([s]),c=r.apply([s*2]);return vk(vk(l,u),c)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,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 V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=us({ones:()=>On(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=us({ones:()=>On(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0<this.dropout&&this.dropout<1&&(e=B(e,s[0]));let p=Qa(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,i[0])),p=J(p,Qa(a,this.recurrentKernel.read())),this.useBias&&(p=Za(p,this.bias.read()));let[d,h,m,f]=zn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),u=J(B(l,r),B(o,this.activation.apply(m))),c=this.recurrentActivation.apply(f);let g=B(c,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:os(this.activation),recurrentActivation:os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),recurrentInitializer:At(this.recurrentInitializer),biasInitializer:At(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Zc.className="LSTMCell";re.registerClass(Zc);var dx=class extends er{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 Zc(e),super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};dx.className="LSTM";re.registerClass(dx);var cm=class extends Hc{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),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Db(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Ri(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push($a(r,n));return new e({cells:a})}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 Mb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Rb(t)}};cm.className="StackedRNNCells";re.registerClass(cm);function us(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>kk(t(),n),i=()=>Gc(s,t,a);return!r||r<=1?qt(i().clone()):Array(r).fill(void 0).map(i).map(o=>qt(o.clone()))}var WB=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},FI=class extends er{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Yt({ndim:5})]}call(e,t){return M(()=>{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return M(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=vt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){M(()=>{if(!this.stateful)throw new xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("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(()=>vt(r)):this.states_=[vt(r)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>vt(r)):this.states_[0]=vt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!I.arraysEqual(i.shape,o))throw new V(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>qt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=Da(l,a[0],r,s[0],i[0]),p=Da(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};FI.className="ConvRNN2D";var hm=class extends Zc{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=Yl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=Yl(a||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=r||"valid",ta(this.padding),this.dataFormat=s||"channelsLast",Mt(this.dataFormat),this.dilationRate=Yl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Kt(o,"dilationRate"))}build(e){var t;e=pt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends ma{apply(c,p){let d=l.apply([u]),h=Xa([u]),m=l.apply([u*2]);return wb([d,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return M(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=us({ones:()=>On(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Q,se,ne)=>!se||!se[ne]?Q:B(se[ne],Q),u=l(a,o,0),c=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=us({ones:()=>On(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,v,k,w]=zn(this.kernel.read(),i,b),[S,A,F,D]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),c=this.inputConv(c,v,A,this.padding),p=this.inputConv(p,k,F,this.padding),d=this.inputConv(d,w,D,this.padding);let[R,L,W,U]=zn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,R),f=this.recurrentConv(f,L),g=this.recurrentConv(g,W),y=this.recurrentConv(y,U);let G=this.recurrentActivation.apply(J(u,m)),X=this.recurrentActivation.apply(J(c,f)),j=J(B(X,s),B(G,this.activation.apply(J(p,g)))),te=B(this.recurrentActivation.apply(J(d,y)),this.activation.apply(j));return[te,te,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=WB(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=Et(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Za(r,n,this.dataFormat):r}recurrentConv(e,t){return Et(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};hm.className="ConvLSTM2DCell";re.registerClass(hm);var hx=class extends FI{constructor(e){let t=new hm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};hx.className="ConvLSTM2D";re.registerClass(hx);var mm=class extends je{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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Re(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Gc(()=>kk(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};mm.className="Dropout";re.registerClass(mm);var mx=class extends mm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};mx.className="SpatialDropout1D";re.registerClass(mx);var fx=class extends je{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,Kt(this.units,"units"),this.activation=ls(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ut(e.kernelConstraint),this.biasConstraint=Ut(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=pt(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=pt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Re(e),a=ck(this.activation.getClassName()),r;return a!=null?r=Qa(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Qa(n,this.kernel.read()),this.bias!=null&&(r=Za(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:os(this.activation),useBias:this.useBias,kernelInitializer:At(this.kernelInitializer),biasInitializer:At(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};fx.className="Dense";re.registerClass(fx);var gx=class extends je{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=pt(e);for(let t of e.slice(1))if(t==null)throw new V(`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],rs(e,1)]}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Re(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return pz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};gx.className="Flatten";re.registerClass(gx);var yx=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.activation=ls(e.activation)}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Re(e);return this.activation.apply(n)})}getConfig(){let e={activation:os(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};yx.className="Activation";re.registerClass(yx);var bx=class extends je{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=Re(e),uz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};bx.className="RepeatVector";re.registerClass(bx);var xx=class extends je{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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new V("Can only specifiy one unknown dimension.");else r*=l}let i=rs(e);if(s!==null){if(r===0||i%r!=0)throw new V(n);a[s]=i/r}else if(i!==r)throw new V(n);return a}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=Re(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};xx.className="Reshape";re.registerClass(xx);var vx=class extends je{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=_a(1,e.dims.length+1);if(!I.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Yt({ndim:this.dims.length+1})]}computeOutputShape(e){e=pt(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ve(Re(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};vx.className="Permute";re.registerClass(vx);var wx=class extends je{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=Re(e),a=-1;return Ic(Ai(n,this.maskValue),a)}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Re(e),a=-1,r=!0,s=Ic(Ai(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};wx.className="Masking";re.registerClass(wx);var kx=class extends je{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(gt(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=wt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=kt(e.embeddingsRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.embeddingsConstraint=Ut(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=Re(e),Ai(e,Ge(e))):null)}computeOutputShape(e){if(e=pt(e),this.inputLength==null)return[...e,this.outputDim];let t=gt(this.inputLength);if(t.length!==e.length-1)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Re(e);return n.dtype!=="int32"&&(n=Bc(n,"int32")),wk(this.embeddings.read(),n.as1D()).reshape(pt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:At(this.embeddingsInitializer),embeddingsRegularizer:dt(this.embeddingsRegularizer),activityRegularizer:dt(this.activityRegularizer),embeddingsConstraint:Vt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};kx.className="Embedding";re.registerClass(kx);var Wi=class extends je{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new V("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[pt(e)]),e=e,e.length<2)throw new V(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=as(t),t.length>1)throw new V(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&as(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return M(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ss(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Vc(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,c=u[0],p=u.slice(1).concat([c]),d=o.reshape([c].concat(rs(u.slice(1))));d=Ve(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let u=_a(1,l).concat([0]);n.push(Ve(o,u)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=Ve(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(_a(0,i-1));s=Ve(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=as(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return M(()=>{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Rn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=da(n,t[a]);return n})}},Ix=class extends Wi{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=J(t,e[n]);return t})}};Ix.className="Add";re.registerClass(Ix);var Nx=class extends Wi{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=B(t,e[n]);return t})}};Nx.className="Multiply";re.registerClass(Nx);var Tx=class extends Wi{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=J(t,e[n]);return B(1/e.length,t)})}};Tx.className="Average";re.registerClass(Tx);var Sx=class extends Wi{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=qa(t,e[n]);return t})}};Sx.className="Maximum";re.registerClass(Sx);var Cx=class extends Wi{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ll(t,e[n]);return t})}};Cx.className="Minimum";re.registerClass(Cx);var Ax=class extends Wi{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 V("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(I.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return M(()=>wb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new V("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new V("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new V(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return M(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(On(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(Rn(t[s],-1)):a.push(t[s]);let r=Je(a,this.axis);return eh(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="Concatenate";re.registerClass(Ax);function ep(e,t){for(;e<0;)e+=t;return e}function BB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("batchDot is not implemented for tensors of 4D or higher rank yet");if(I.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),I.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 $e("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return M(()=>{let i;if(a>r){i=a-r;let l=[];for(let u=0;u<i;++u)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=e.matMul(t,l,u)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let u=[];for(let c=l;c<l+i;++c)u.push(c);o=o.squeeze(u)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var Fx=class extends Wi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){I.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 $e("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new V(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>ep(r,e[s].shape.length)):a=[ep(this.axes,t.shape.length),ep(this.axes,n.shape.length)],this.normalize&&(t=Qh(t,a[0]),n=Qh(n,a[1])),BB(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[ep(this.axes,e.length),ep(this.axes,t.length)],n}computeOutputShape(e){I.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 $e("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Fx.className="Dot";re.registerClass(Fx);var _x=class extends je{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=Re(e);return Gc(()=>zh(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};_x.className="GaussianNoise";re.registerClass(_x);var Ex=class extends je{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=Re(e);return this.rate>0&&this.rate<1?Gc(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(zh(n.shape,1,a))},()=>n,t.training||!1):n})}};Ex.className="GaussianDropout";re.registerClass(Ex);var $x=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Re(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return M(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Gc(()=>{let a=Re(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=es(zl(n),this.rate);o=Bc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>Re(e),t.training||!1)}return e})}};$x.className="AlphaDropout";re.registerClass($x);function tp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=r1(e,t,n,a,r,s);else if(e.rank===3)i=s1(e,t,n,a,r,s);else if(e.rank===4)i=i1(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function VB(e,t,n,a,r=.001){return M(()=>{let s=dh(e,a),i=s.mean,o=s.variance;return[tp(e,i,o,n,t,r),i,o]})}function UB(e,t,n,a,r=.001){return M(()=>{let s=dh(e,a),i=s.mean,o=s.variance,l=[];for(let h of _a(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let u=i.reshape(l),c=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[tp(e,u,c,d,p,r),i,o]})}function GB(e,t,n,a,r=.001){return I.arraysEqual(a.slice().sort(),_a(0,e.rank-1))?VB(e,t,n,a,r):UB(e,t,n,a,r)}var Dx=class extends je{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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ut(e.betaConstraint),this.gammaConstraint=Ut(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=pt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Yt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return M(()=>{let n=t.training==null?!1:t.training,a=Re(e),r=a.shape,s=r.length,i=_a(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=$i(1,s);l[o]=r[o];let u=i.slice();u.sort();let c=!I.arraysEqual(u,_a(0,s).slice(0,s-1)),p=()=>{if(c){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),b=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return tp(a,g,y,b,x,this.epsilon)}else return tp(a,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 p();let[d,h,m]=GB(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{M(()=>{let x=1-b,v=g.read(),k=v.sub(y).mul(x);g.write(v.sub(k))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:At(this.betaInitializer),gammaInitializer:At(this.gammaInitializer),movingMeanInitializer:At(this.movingMeanInitializer),movingVarianceInitializer:At(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:Vt(this.betaConstraint),gammaConstraint:Vt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="BatchNormalization";re.registerClass(Dx);var Mx=class extends je{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=pt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==as(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Re(e),a=n.shape,r=a.length;return M(()=>{let s=!0,{mean:i,variance:o}=dh(n,this.axis,s),l=$i(1,r);for(let m of this.axis)l[m]=a[m];let u=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,c=u(this.gamma.read()),p=u(this.beta.read()),d=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(d.push(a[m]),h.push(1)):(d.push(1),h.push(a[m]));return i=i.tile(d),o=o.tile(d),c=c.tile(h),p=p.tile(h),tp(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:At(this.betaInitializer),gammaInitializer:At(this.gammaInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Mx.className="LayerNormalization";re.registerClass(Mx);function HB(e,t,n){return M(()=>{if(e.rank!==4)throw new V(`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 V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Aa()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ea(e,a)})}var Rx=class extends je{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Aa():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 V(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new V(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=pt(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(()=>HB(Re(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Rx.className="ZeroPadding2D";re.registerClass(Rx);function fm(e,t,n,a,r,s){return M(()=>{Mt(r),mk(s),ta(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Aa()),s==null&&(s="max"),e=ax(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=$t(e,t,n,o):i=Qn(e,t,n,o),r==="channelsFirst"&&(i=Ve(i,[0,3,1,2])),i})}function _I(e,t,n,a,r,s){return M(()=>{Mt(r),mk(s),ta(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Aa()),s==null&&(s="max"),e=NI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Hy(e,t,n,o):i=Ey(e,t,n,o),r==="channelsFirst"&&(i=Ve(i,[0,4,1,2,3])),i})}var EI=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(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 V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ta(this.padding),this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){e=pt(e);let t=Da(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=Vc(Re(e),2);let n=this.poolingFunction(Re(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ts(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Px=class extends EI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),fm(e,t,n,a,r,"max")}};Px.className="MaxPooling1D";re.registerClass(Px);var Ox=class extends EI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),fm(e,t,n,a,r,"avg")}};Ox.className="AveragePooling1D";re.registerClass(Ox);var $I=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`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];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),ta(this.padding),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Da(t,this.poolSize[0],this.padding,this.strides[0]),n=Da(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(Re(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}},Lx=class extends $I{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),fm(e,t,n,a,r,"max")}};Lx.className="MaxPooling2D";re.registerClass(Lx);var zx=class extends $I{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),fm(e,t,n,a,r,"avg")}};zx.className="AveragePooling2D";re.registerClass(zx);var DI=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`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];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),ta(this.padding),this.inputSpec=[new Yt({ndim:5})]}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Da(t,this.poolSize[0],this.padding,this.strides[0]),n=Da(n,this.poolSize[1],this.padding,this.strides[1]),a=Da(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return M(()=>(this.invokeCallHook(e,t),this.poolingFunction(Re(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}},Wx=class extends DI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),_I(e,t,n,a,r,"max")}};Wx.className="MaxPooling3D";re.registerClass(Wx);var Bx=class extends DI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),ta(a),_I(e,t,n,a,r,"avg")}};Bx.className="AveragePooling3D";re.registerClass(Bx);var MI=class extends je{constructor(e){super(e);this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},Vx=class extends MI{constructor(e){super(e||{})}call(e,t){return M(()=>{let n=Re(e);return Ct(n,1)})}};Vx.className="GlobalAveragePooling1D";re.registerClass(Vx);var Ux=class extends MI{constructor(e){super(e||{})}call(e,t){return M(()=>{let n=Re(e);return Zn(n,1)})}};Ux.className="GlobalMaxPooling1D";re.registerClass(Ux);var RI=class extends je{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Gx=class extends RI{call(e,t){return M(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};Gx.className="GlobalAveragePooling2D";re.registerClass(Gx);var Hx=class extends RI{call(e,t){return M(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?Zn(n,[1,2]):Zn(n,[2,3])})}};Hx.className="GlobalMaxPooling2D";re.registerClass(Hx);var PI=class extends je{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=$a(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},jx=class extends PI{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=pt(e),e.length<3)throw new V(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=pt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return M(()=>(e=Re(e),AI((n,a)=>[Re(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};jx.className="TimeDistributed";re.registerClass(jx);function jB(e){Mi(rz,"BidirectionalMergeMode",e)}var qB="concat",qx=class extends PI{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=$a(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=$a(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?qB:e.mergeMode,jB(this.mergeMode),e.weights)throw new $e("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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):Cn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=CI(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new V("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(c=>new Yt({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(a!=null)throw new $e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Ea;for(let l of s)if(l instanceof Ea!==o)throw new V("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return M(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Ln(r,1));let 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          tensor.shape[0], but sum of lengths is
        ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];M(()=>{t=H(t,[1,n,r]);for(let o=0;o<e.length;++o){let l=o===0?0:a[o-1],u=[0,l,0],c=[1,e[o],r];s[o]=H(Be(t,u,c),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},ap=class{constructor(e,t,n,a=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);ga(t,r.shape,"TensorList shape mismatch: "),qt(r)}),this.idTensor=ce(0),this.maxNumElements=a,qt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new ap([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);ga(e,this.elementShape,"TensorList shape mismatch: ");let a=np(this.elementShape,this.tensors,e);return M(()=>{let r=this.tensors.map(s=>H(s,a));return Dt(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=np(this.elementShape,this.tensors,e),a=this.tensors.pop();return ga(a.shape,e,"TensorList shape mismatch: "),H(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(ga(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");qt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);ga(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=np(this.elementShape,this.tensors,t);return H(this.tensors[e],a)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);ga(this.elementShape,t.shape,"TensorList shape mismatch: "),qt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);ga(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=np(this.elementShape,this.tensors,n);return e.length===0?Yn([],[0].concat(a)):M(()=>{let r=e.map(s=>H(this.tensors[s],a));return Dt(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);ga(this.elementShape,t,"TensorList shape mismatch: ");let n=np(this.elementShape,this.tensors,t);return this.size()===0?Yn([],[0].concat(n)):M(()=>{let a=this.tensors.map(r=>H(r,n));return Je(a,0)})}};function z4(e,t,n){let a=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);ga(r,t,"TensorList shape mismatch: ");let s=ct(e);return new ap(s,t,a)}function W4(e,t,n){return new ap([],e,t,n)}function B4(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r}  vs. ${a})`);let s=new ap([],n,e.dtype,a),i=ct(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function V4(e,t,n){let a=0,r=t.map(c=>(a+=c,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let p=u.map(h=>h.id);c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let a=N("pred",e,t,n);return[Ir(a)]}case"Switch":{let a=N("pred",e,t,n),r=N("data",e,t,n);return r.kept||(r=Ir(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>Fn(r,t,n)!==void 0);if(a){let r=Fn(a,t,n);return[Ir(r)]}return}case"Enter":{let a=N("frameName",e,t,n),r=N("tensor",e,t,n);return n.enterFrame(a),[Ir(r)]}case"Exit":{let a=N("tensor",e,t,n);return n.exitFrame(),[Ir(a)]}case"NextIteration":{let a=N("tensor",e,t,n);return n.nextIteration(),[Ir(a)]}case"TensorArrayV3":{let a=N("size",e,t,n),r=N("dtype",e,t,n),s=N("elementShape",e,t,n),i=N("dynamicSize",e,t,n),o=N("clearAfterRead",e,t,n),l=N("identicalElementShapes",e,t,n),u=N("name",e,t,n),c=new L4(u,r,a,s,l,i,o);return n.addTensorArray(c),[c.idTensor,ce(1)]}case"TensorArrayWriteV3":{let a=N("tensorArrayId",e,t,n),r=N("index",e,t,n),s=N("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=N("tensorArrayId",e,t,n),r=N("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=N("tensorArrayId",e,t,n),r=N("indices",e,t,n),s=N("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=N("tensorArrayId",e,t,n),r=N("indices",e,t,n),s=N("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=N("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=N("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=N("tensorArrayId",e,t,n),r=N("tensor",e,t,n),s=N("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=N("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[ce(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=N("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=N("tensorListId",e,t,n),r=N("index",e,t,n),s=N("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let a=N("tensorListId",e,t,n),r=N("index",e,t,n),s=N("elementShape",e,t,n),i=N("elementDType",e,t,n);return[n.getTensorList(a.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let a=N("indices",e,t,n),r=N("tensor",e,t,n),s=N("elementShape",e,t,n),i=N("numElements",e,t,n),o=B4(r,a,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let a=N("elementShape",e,t,n),r=N("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=N(s,e,t,n),o=W4(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=N("tensorListId",e,t,n),r=N("indices",e,t,n),s=N("elementShape",e,t,n),i=N("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=N("tensorListId",e,t,n),r=N("elementShape",e,t,n),s=N("elementDType",e,t,n),i=N("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=N("tensor",e,t,n),r=N("elementShape",e,t,n),s=N("elementDType",e,t,n),i=z4(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=N("tensorListId",e,t,n),r=n.getTensorList(a.id),s=N("dtype",e,t,n),i=N("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=N("tensorListId",e,t,n),r=N("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let a=N("tensorListId",e,t,n),r=N("elementShape",e,t,n),s=N("elementDType",e,t,n);return[n.getTensorList(a.id).popBack(r,s)]}case"TensorListSplit":{let a=N("tensor",e,t,n),r=N("elementShape",e,t,n),s=N("lengths",e,t,n),i=V4(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function hN(e,t,n){let[a,r]=N("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=a==="fusedbatchnorm",l=N("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let u=N("strides",e,t,n),c=bm(e,t,n),p=N("dataFormat",e,t,n).toUpperCase(),d=N("dilations",e,t,n),[h,m]=N("args",e,t,n),f=N("leakyreluAlpha",e,t,n);return{stride:u,pad:c,dataFormat:p,dilations:d,biasArg:h,preluArg:m,activationFunc:r,leakyreluAlpha:f}}var G4=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=N("stride",e,t,n),r=N("pad",e,t,n),s=N("dataFormat",e,t,n).toUpperCase(),i=N("dilation",e,t,n);return[nh(N("x",e,t,n),N("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=N("strides",e,t,n),r=bm(e,t,n),s=N("dataFormat",e,t,n).toUpperCase(),i=N("dilations",e,t,n);return[Et(N("x",e,t,n),N("filter",e,t,n),[a[1],a[2]],r,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:c}=hN(e,t,n);return[ns.conv2d({x:N("x",e,t,n),filter:N("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:c}=hN(e,t,n);return[ns.depthwiseConv2d({x:N("x",e,t,n),filter:N("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let a=N("outputShape",e,t,n),r=N("strides",e,t,n),s=bm(e,t,n);return[ah(N("x",e,t,n),N("filter",e,t,n),a,[r[1],r[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let a=N("strides",e,t,n),r=bm(e,t,n),s=N("dilations",e,t,n),i=N("dataFormat",e,t,n).toUpperCase();return[Qr(N("input",e,t,n),N("filter",e,t,n),[a[1],a[2]],r,i,[s[1],s[2]])]}case"Conv3D":{let a=N("strides",e,t,n),r=N("pad",e,t,n),s=N("dataFormat",e,t,n).toUpperCase(),i=N("dilations",e,t,n);return[My(N("x",e,t,n),N("filter",e,t,n),[a[1],a[2],a[3]],r,s,[i[1],i[2],i[3]])]}case"AvgPool":{let a=N("strides",e,t,n),r=N("pad",e,t,n),s=N("kernelSize",e,t,n);return[Qn(N("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPool":{let a=N("strides",e,t,n),r=N("pad",e,t,n),s=N("kernelSize",e,t,n);return[$t(N("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPoolWithArgmax":{let 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oV(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(c=>Bn(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{a.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{a.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{a.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&s.push(p)})}return u}var lV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],uV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],cV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function gN(e){return lV.indexOf(e.op)>=0}function sV(e){return uV.indexOf(e.op)>=0}function iV(e){return cV.indexOf(e.op)>=0}var lv=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new 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in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Bn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Bn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},pV=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 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t=jt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(jt.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 a=jt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new lv(uN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=uN.Instance.transformGraph(e.modelInitializer);this.initializer=new lv(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=jt.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 Fe)&&!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,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function mV(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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a=0;a<n;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};uv.INITIAL_CAPACITY=32;function _N(e){return new _V(e)}function cv(e){return new EV(e)}function $V(e,t){return new EN(e,t)}function MV(e,t=cs.FAIL){return new DV(e,t)}var Jt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new BV(this,e)}filter(e){return new zV(this,e)}map(e){return new WV(this,e)}mapAsync(e){return new $N(this,e)}serialMapAsync(e){return new $N(this,e).serial()}flatmap(e){return new VV(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new LV(this,e,t)}columnMajorBatch(e,t=!0,n=CN){return this.rowMajorBatch(e,t).map(a=>TV(a,n))}concatenate(e,t){return new EN(_N([this,e]),t)}take(e){return e<0||e==null?this:new OV(this,e)}skip(e){return e<0||e==null?this:new PV(this,e)}prefetch(e){return new DN(this,e)}shuffle(e,t){return new UV(this,e,t)}serial(){return new RV(this)}},_V=class extends Jt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:FV(e),done:!1}}},EV=class extends Jt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},RV=class extends Jt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},PV=class extends Jt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ee(e.value)}return this.upstream.next()}},OV=class extends Jt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},LV=class extends Jt{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},zV=class extends Jt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},WV=class extends Jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ia.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ia.getTensorsInContainer(n);for(let r of t)Ia.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},BV=class extends Jt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},$N=class extends Jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ia.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ia.getTensorsInContainer(n);for(let r of t)Ia.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},pv=class extends Jt{constructor(){super();this.outputQueue=new uv,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},VV=class extends pv{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ia.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ia.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ia.isTensorInList(r,a)||r.dispose();return!0}},EN=class extends Jt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},cs;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(cs||(cs={}));var DV=class extends Jt{constructor(e,t=cs.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof Jt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await AN(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case cs.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case cs.SHORTEST:return{value:null,done:!0};case cs.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},DN=class extends Jt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new FN(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},UV=class extends DN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=IV.alea(n||I.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Jl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;I.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Vn(async()=>(await n.iterator()).columnMajorBatch(e,t,GV),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Vn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Vn(async()=>(await t.iterator()).filter(a=>M(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Vn(async()=>(await t.iterator()).map(n=>M(()=>e(n))),this.size)}mapAsync(e){let t=this;return Vn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Vn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Vn(async()=>{let a=cv(async()=>({value:await t.iterator(),done:!1}));return $V(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Vn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=kV.alea(t||I.now().toString());return Vn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Vn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Jl.MAX_BUFFER_SIZE=1e4;function Vn(e,t=null){return new class extends Jl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function fV(e){return Vn(async()=>_N(e),e.length)}function gV(e){if(!Ql(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Vn(async()=>{let n=await AN(e,a=>{if(a instanceof Jl)return{value:a.iterator(),recurse:!1};if(Ql(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return MV(n,cs.SHORTEST)},t)}function GV(e){if(e===null)return null;let t=e[0];return CV(t)?{value:HV(e),recurse:!1}:{value:null,recurse:!0}}function HV(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Fe?Dt(e):Yn(e)}var wN=class extends Jl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},vm='"',rp=Symbol("out"),MN=Symbol("field"),wm=Symbol("quote"),dv=Symbol("quoteafterquote"),RN=Symbol("quoteinquote"),kN=class extends Jl{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 wN(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(I.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&&I.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((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(I.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=rp;for(let i=0;i<r;i++)switch(s){case rp:switch(e.charAt(i)){case vm:a=i+1,s=wm;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=rp;break;default:s=MN,a=i;break}break;case MN:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=rp,a=i+1;break;default:}break;case wm:switch(e.charAt(i)){case vm:s=dv;break;default:}break;case dv:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=rp,a=i+1;break;case vm:s=wm;break;default:s=RN;break}break;case RN:switch(e.charAt(i)){case vm:s=wm;break;default:}break;default:}if(s===dv?n.push(e.substring(a,r-1)):n.push(e.substring(a)),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}},PN=class extends Jt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Z().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new PN(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(I.sizeFromShape(t));return n.set(e,n.length-e.length),Yn(n,t)}},ON=class extends Jt{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=Qe([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Sa([s,r,o,i],[1,4])}else this.cropBox=Sa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Z().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new ON(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&I.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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============================
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de=ee*l-y,oe=de;for(;oe<0;)oe+=p;let ge=Math.min(r.inWidth,m+de),fe=ie+ee*A,we=b,Ne=0,Ae=0;for(let _e=G;_e<X;_e+=u){let nt=R+_e*a[1];for(let at=se;at<ne;at+=c){let ot=nt+at*a[2];for(let Ke=oe;Ke<ge;Ke+=p){let ft=ot+Ke*a[3],We=e[ft+L];if(s==="max"&&We>we?we=We:s==="avg"&&(Ne+=We,Ae++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Me=fe+L;v[Me]=s==="avg"?Ne/Ae:we}}}}return x}function IG(e,t){let n=Le(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let b=y*a-d,x=b;for(;x<0;)x+=i;let v=Math.min(t.inDepth,u+b);for(let k=0;k<t.outHeight;++k){let w=k*r-h,S=w;for(;S<0;)S+=o;let A=Math.min(t.inHeight,c+w);for(let F=0;F<t.outWidth;++F){let D=F*s-m,R=D;for(;R<0;)R+=l;let 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c=E.computePool3DInfo(s.shape,i,o,1,l,u),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,m=c.filterDepth,f=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,b=c.dilationHeight,x=c.dilationWidth,v=c.effectiveFilterDepth,k=c.effectiveFilterHeight,w=c.effectiveFilterWidth,S=v-1-c.padInfo.front,A=w-1-c.padInfo.left,F=k-1-c.padInfo.top,D=Le(s.shape,"float32"),R=1/(m*f*g),L=n.bufferSync(r);for(let W=0;W<c.batchSize;++W)for(let U=0;U<c.inChannels;++U)for(let G=0;G<c.inDepth;++G)for(let X=0;X<c.inHeight;++X)for(let j=0;j<c.inWidth;++j){let te=G-S,Q=X-F,se=j-A,ne=0;for(let ie=0;ie<v;ie+=y){let ee=(te+ie)/p;if(!(ee<0||ee>=c.outDepth||Math.floor(ee)!==ee))for(let de=0;de<k;de+=b){let oe=(Q+de)/d;if(!(oe<0||oe>=c.outHeight||Math.floor(oe)!==oe))for(let ge=0;ge<w;ge+=x){let fe=(se+ge)/h;fe<0||fe>=c.outWidth||Math.floor(fe)!==fe||(ne+=L.get(W,ee,oe,fe,U))}}}D.set(ne*R,W,G,X,j,U)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var FG={kernelName:dd,backendName:"cpu",kernelFunc:AG};function 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n.makeTensorInfo(r.shape,r.dtype,f)}var DG={kernelName:zs,backendName:"cpu",kernelFunc:$G};function MG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ve([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=E.getReshaped(r.shape,s,o),u=E.getPermuted(l.length,s.length),c=E.getReshapedPermuted(r.shape,s,o),p=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(c,i,s.length),h=It({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ya({inputs:{x:h},backend:n,attrs:{perm:u}}),f=It({inputs:{x:m},backend:n,attrs:{shape:c}}),g=Vi({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var RG={kernelName:Ju,backendName:"cpu",kernelFunc:MG};function PG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=mv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var OG={kernelName:hd,backendName:"cpu",kernelFunc:PG},LG=it(Vr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),zG={kernelName:Vr,backendName:"cpu",kernelFunc:LG},WG=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(I.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let c=o[u],p=l[u];a[u]=Math.hypot(c,p)}return n.makeOutput(a,t.shape,"float32")},BG={kernelName:Qu,backendName:"cpu",kernelFunc:WG};function tu(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var VG={kernelName:Cd,backendName:"cpu",kernelFunc:tu};function nu(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=I.parseAxisParam(r,t[0].shape)[0],i=E.computeOutShape(t.map(f=>f.shape),s);if(I.sizeFromShape(i)===0)return 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XG={kernelName:Es,backendName:"cpu",kernelFunc:qG};function KG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;ve([r,s],"conv3d");let u=E.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,b=g.left,x=g.top,v=new Lt(u.outShape,r.dtype),k=n.data.get(r.dataId).values,w=n.data.get(s.dataId).values,S=v.values,A=I.computeStrides(r.shape),F=I.computeStrides(s.shape);for(let D=0;D<u.batchSize;++D){let R=D*A[0],L=D*v.strides[0];for(let W=0;W<u.outDepth;++W){let U=L+W*v.strides[1],G=W*u.strideDepth-y;for(let X=0;X<c;++X){let j=G+X*h;if(j<0||j>=u.inDepth)continue;let te=X*F[0],Q=R+j*A[1];for(let se=0;se<u.outHeight;++se){let ne=U+se*v.strides[2],ie=se*u.strideHeight-x;for(let ee=0;ee<p;++ee){let de=ie+ee*m;if(de<0||de>=u.inHeight)continue;let oe=te+ee*F[1],ge=Q+de*A[2];for(let fe=0;fe<u.outWidth;++fe){let 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ie=Math.max(0,Math.ceil((te-ne)/d)),ee=Math.min(p.outDepth,(p.inDepth+te-ne)/d),de=ne*v;for(let oe=0;oe<g;++oe){let ge=Math.max(0,Math.ceil((se-oe)/h)),fe=Math.min(p.outHeight,(p.inHeight+se-oe)/h),we=oe*k+de;for(let Ne=0;Ne<y;++Ne){let Ae=Math.max(0,Math.ceil((Q-Ne)/m)),Me=Math.min(p.outWidth,(p.inWidth+Q-Ne)/m),_e=Ne*w+we;for(let nt=0;nt<p.inChannels;++nt){let at=nt*S+_e;for(let ot=0;ot<p.outChannels;++ot){let Ke=0;for(let ft=0;ft<p.batchSize;++ft){let We=ft*U,vn=ft*F;for(let Nt=ie;Nt<ee;++Nt){let qn=(ne+Nt*d-te)*G+We,en=Nt*D+vn;for(let wn=ge;wn<fe;++wn){let Xn=(oe+wn*h-se)*X+qn,Mn=wn*R+en;for(let cn=Ae;cn<Me;++cn){let tn=(Ne+cn*m-Q)*j+Xn,Wa=cn*L+Mn;Ke+=W[tn+nt]*A[Wa+ot]}}}}x[at+ot]=Ke}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var QG={kernelName:gd,backendName:"cpu",kernelFunc:JG};function ZG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;ve([r],"conv3dBackpropInputV2");let u=I.computeStrides(r.shape),c=I.computeStrides(s.shape),p=E.computeConv3DInfo(l,s.shape,o,1,i),d=new Lt(p.inShape,"float32"),h=d.values,[m,f,g,y]=d.strides,b=n.data.get(r.dataId).values,[x,v,k,w]=u,S=n.data.get(s.dataId).values,[A,F,D,R]=c,{batchSize:L,filterDepth:W,filterHeight:U,filterWidth:G,inChannels:X,inDepth:j,inHeight:te,inWidth:Q,outChannels:se,outDepth:ne,outHeight:ie,outWidth:ee,strideDepth:de,strideHeight:oe,strideWidth:ge}=p,fe=W-1-p.padInfo.front,we=U-1-p.padInfo.top,Ne=G-1-p.padInfo.left;for(let Ae=0;Ae<L;++Ae)for(let Me=0;Me<X;++Me)for(let _e=0;_e<j;++_e){let nt=_e-fe,at=Math.max(0,Math.ceil(nt/de)),ot=Math.min(ne,(W+nt)/de);for(let Ke=0;Ke<te;++Ke){let ft=Ke-we,We=Math.max(0,Math.ceil(ft/oe)),vn=Math.min(ie,(U+ft)/oe);for(let Nt=0;Nt<Q;++Nt){let qn=Nt-Ne,en=Math.max(0,Math.ceil(qn/ge)),wn=Math.min(ee,(G+qn)/ge),Xn=0;for(let Mn=at;Mn<ot;++Mn){let cn=Mn*de-nt;for(let tn=We;tn<vn;++tn){let Wa=tn*oe-ft;for(let sa=en;sa<wn;++sa){let 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te=0;te<g;te++)for(let Q=0;Q<h;Q++){let se=Q+te*w[2]+X*w[1]+S*w[0];y.values[se]=u}continue}if(l==="bilinear"){let te=Math.floor(j),Q=Math.ceil(j),se=j-te;for(let ne=0;ne<g;ne++){let ie=g>1?D*(d-1)+ne*G:.5*(D+L)*(d-1);if(ie<0||ie>d-1){for(let ge=0;ge<h;ge++){let fe=ge+ne*w[2]+X*w[1]+S*w[0];y.values[fe]=u}continue}let ee=Math.floor(ie),de=Math.ceil(ie),oe=ie-ee;for(let ge=0;ge<h;ge++){let fe=ge+ee*k[2]+te*k[1]+W*k[0],we=v[fe];fe=ge+de*k[2]+te*k[1]+W*k[0];let Ne=v[fe];fe=ge+ee*k[2]+Q*k[1]+W*k[0];let Ae=v[fe];fe=ge+de*k[2]+Q*k[1]+W*k[0];let Me=v[fe],_e=we+(Ne-we)*oe,nt=Ae+(Me-Ae)*oe;fe=ge+ne*w[2]+X*w[1]+S*w[0],y.values[fe]=_e+(nt-_e)*se}}}else for(let te=0;te<g;++te){let Q=g>1?D*(d-1)+te*G:.5*(D+L)*(d-1);if(Q<0||Q>d-1){for(let ie=0;ie<h;ie++){let ee=ie+te*w[2]+X*w[1]+S*w[0];y.values[ee]=u}continue}let se=Math.round(Q),ne=Math.round(j);for(let ie=0;ie<h;ie++){let ee=ie+se*k[2]+ne*k[1]+W*k[0],de=ie+te*w[2]+X*w[1]+S*w[0];y.values[de]=v[ee]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var iH={kernelName:Po,backendName:"cpu",kernelFunc:sH};function oH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;ve(r,"cumsum");let l=E.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=ya({inputs:{x:r},backend:n,attrs:{perm:l}}));let c=E.getInnerMostAxes(1,r.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let p=ua(u.dtype,"int32"),d=I.makeZerosTypedArray(I.sizeFromShape(u.shape),p),h=n.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let x=f(y,b);if(b===0)d[x]=i?0:h[x];else{let v=f(y,b-1);d[x]=i?h[v]+d[v]:h[x]+d[v]}}let g=n.makeTensorInfo(u.shape,p,d);if(l!=null){let y=E.getUndoAxesPermutation(l),b=ya({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),b}return g}var lH={kernelName:Ds,backendName:"cpu",kernelFunc:oH};function uH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,c=mv(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),c=HN(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var cH={kernelName:bd,backendName:"cpu",kernelFunc:uH};function pH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;I.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${i} and dilations '${d}'`);let h=E.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,x=b.left,v=b.top,k=h.outChannels/h.inChannels,w=new Lt(h.outShape,r.dtype),S=n.data.get(r.dataId).values,A=n.data.get(s.dataId).values,F=w.values;for(let D=0;D<h.batchSize;++D){let R=D*c[0],L=D*w.strides[0];for(let W=0;W<h.outHeight;++W){let U=L+W*w.strides[1],G=W*h.strideHeight-x;for(let X=0;X<m;++X){let j=G+X*g;if(j<0||j>=h.inHeight)continue;let te=X*p[0],Q=R+j*c[1];for(let se=0;se<h.outWidth;++se){let ne=U+se*w.strides[2],ie=se*h.strideWidth-v;for(let ee=0;ee<f;++ee){let de=ie+ee*y;if(de<0||de>=h.inWidth)continue;let oe=te+ee*p[1],ge=Q+de*h.inChannels,fe=ne,we=oe;for(let Ne=0;Ne<h.inChannels;++Ne){let Ae=S[ge+Ne];for(let Me=0;Me<k;++Me)F[fe+Me]+=Ae*A[we+Me];fe+=k,we+=k}}}}}}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var hH={kernelName:Ms,backendName:"cpu",kernelFunc:N2};function mH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=a;ve([r,s],"depthwiseConv2dNativeBackpropFilter");let p=E.computeConv2DInfo(r.shape,c,i,o,l,u,!0),{strideHeight:d,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new Lt(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,x=p.outChannels/p.inChannels,v=n.data.get(r.dataId).values,k=new Lt(r.shape,r.dtype,v),w=n.data.get(s.dataId).values,S=new Lt(s.shape,s.dtype,w);for(let A=0;A<m;++A){let F=Math.max(0,Math.ceil((b-A)/d)),D=Math.min(p.outHeight,(p.inHeight+b-A)/d);for(let R=0;R<f;++R){let L=Math.max(0,Math.ceil((y-R)/h)),W=Math.min(p.outWidth,(p.inWidth+y-R)/h);for(let U=0;U<p.outChannels;++U){let G=Math.trunc(U/x),X=U%x,j=0;for(let te=0;te<p.batchSize;++te)for(let Q=F;Q<D;++Q){let se=A+Q*d-b;for(let ne=L;ne<W;++ne){let ie=R+ne*h-y;j+=k.get(te,se,ie,G)*S.get(te,Q,ne,U)}}g.set(j,A,R,G,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var fH={kernelName:xd,backendName:"cpu",kernelFunc:mH};function gH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=a;ve([r,s],"depthwiseConv2DNativeBackpropInput");let p=I.computeStrides(r.shape),d=I.computeStrides(s.shape),h=E.computeConv2DInfo(c,s.shape,i,o,l,u,!0),m=new Lt(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,x=n.data.get(r.dataId).values,[v,k,w]=p,S=n.data.get(s.dataId).values,[A,F,D]=d,{batchSize:R,filterHeight:L,filterWidth:W,inChannels:U,inHeight:G,inWidth:X,outChannels:j,outHeight:te,outWidth:Q,strideHeight:se,strideWidth:ne}=h,ie=L-1-h.padInfo.top,ee=W-1-h.padInfo.left,de=j/U;for(let oe=0;oe<R;++oe)for(let ge=0;ge<U;++ge)for(let fe=0;fe<G;++fe){let we=fe-ie,Ne=Math.max(0,Math.ceil(we/se)),Ae=Math.min(te,(L+we)/se);for(let Me=0;Me<X;++Me){let _e=Me-ee,nt=Math.max(0,Math.ceil(_e/ne)),at=Math.min(Q,(W+_e)/ne),ot=0;for(let Ke=Ne;Ke<Ae;++Ke){let ft=Ke*se-we;for(let We=nt;We<at;++We){let vn=We*ne-_e,Nt=v*oe+k*Ke+w*We,qn=A*(L-1-ft)+F*(W-1-vn)+D*ge;for(let en=0;en<de;++en){let wn=ge*de+en,Xn=x[Nt+wn],Mn=S[qn+en];ot+=Xn*Mn}}}f[g*oe+y*fe+b*Me+ge]=ot}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var yH={kernelName:vd,backendName:"cpu",kernelFunc:gH};function bH(e){let{inputs:t,backend:n}=e,{x:a}=t,r=I.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=Le([r,r],a.dtype),o=i.values;for(let u=0;u<s.length;u++)o[u*r+u]=s[u];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var xH={kernelName:wd,backendName:"cpu",kernelFunc:bH},vH={kernelName:ec,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,c=a.shape.length,p=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:v,strideWidth:k,filterHeight:w,filterWidth:S,dilationHeight:A,dilationWidth:F,outShape:D}=E.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),R=I.sizeFromShape(D),L=D.length,W=I.getArrayFromDType(a.dtype,R);for(let U=0;U<h;++U)for(let G=0;G<y;++G){let X=G*v-x.top;for(let j=0;j<b;++j){let te=j*k-x.left;for(let Q=0;Q<g;++Q){let se=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<w;++ie){let ee=X+ie*A;if(ee>=0&&ee<m)for(let de=0;de<S;++de){let oe=te+de*F;if(oe>=0&&oe<f){let ge=I.locToIndex([U,ee,oe,Q],c,I.computeStrides(a.shape)),fe=I.locToIndex([ie,de,Q],d,I.computeStrides(r.shape)),we=u[ge]+p[fe];we>se&&(se=we)}}}let ne=I.locToIndex([U,G,j,Q],L,I.computeStrides(D));W[ne]=se}}}return{dataId:l.write(I.toTypedArray(W,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},wH={kernelName:Id,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,c=I.toNestedArray(a.shape,u.data.get(a.dataId).values),p=I.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:v,filterHeight:k,filterWidth:w,dilationHeight:S,dilationWidth:A,outShape:F}=E.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);I.assert(s.rank===F.length,()=>`Error in ${Id}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=I.toNestedArray(F,u.data.get(s.dataId).values),R=I.makeZerosNestedTypedArray(r.shape,r.dtype);for(let L=0;L<d;++L)for(let W=0;W<g;++W){let U=W*x-b.top;for(let G=0;G<y;++G){let X=G*v-b.left;for(let j=0;j<f;++j){let 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t.makeTensorInfo([o.length],s,o)}var kj={kernelName:oc,backendName:"cpu",kernelFunc:wj},Ij=it(ul,e=>1/e),Nj={kernelName:ul,backendName:"cpu",kernelFunc:Ij};function Tj(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ve(r,"resizeBilinear");let l=I.computeStrides(r.shape),[u,c]=o,[p,d,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(I.sizeFromShape([p,u,c,m])),y=[s&&u>1?d-1:d,s&&c>1?h-1:h],b=[s&&u>1?u-1:u,s&&c>1?c-1:c],x=0,v=y[0]/b[0],k=y[1]/b[1];for(let w=0;w<p;w++)for(let S=0;S<u;S++){let A;i?A=v*(S+.5)-.5:A=v*S;let F=Math.max(0,Math.floor(A)),D=A-F,R=Math.min(d-1,Math.ceil(A)),L=w*l[0]+F*l[1],W=w*l[0]+R*l[1];for(let U=0;U<c;U++){let G;i?G=k*(U+.5)-.5:G=k*U;let X=Math.max(0,Math.floor(G)),j=G-X,te=Math.min(h-1,Math.ceil(G)),Q=L+X*l[2],se=W+X*l[2],ne=L+te*l[2],ie=W+te*l[2];for(let ee=0;ee<m;ee++){let de=f[Q+ee],oe=f[se+ee],ge=f[ne+ee],fe=f[ie+ee],we=de+(ge-de)*j,Ne=oe+(fe-oe)*j,Ae=we+(Ne-we)*D;g[x++]=Ae}}}return n.makeTensorInfo([p,u,c,m],"float32",g)}var Sj={kernelName:ni,backendName:"cpu",kernelFunc:Tj};function Cj(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;ve([s,r],"resizeBilinearGrad");let o=I.computeStrides(r.shape),[l,u,c,p]=r.shape,[,d,h]=s.shape,m=new Float32Array(l*u*c*p),f=[i&&d>1?u-1:u,i&&h>1?c-1:c],g=[i&&d>1?d-1:d,i&&h>1?h-1:h],y=f[0]/g[0],b=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let k=0;k<l;k++){let w=k*o[0];for(let S=0;S<d;S++){let A=S*y,F=Math.floor(A),D=Math.min(Math.ceil(A),u-1),R=w+F*o[1],L=w+D*o[1],W=A-F,U=1-W;for(let G=0;G<h;G++){let X=G*b,j=Math.floor(X),te=Math.min(Math.ceil(X),c-1),Q=X-j,se=1-Q,ne=R+j*o[2],ie=R+te*o[2],ee=L+j*o[2],de=L+te*o[2],oe=U*se,ge=U*Q,fe=W*se,we=W*Q;for(let Ne=0;Ne<p;Ne++){let Ae=x[v++];m[ne+Ne]+=Ae*oe,m[ie+Ne]+=Ae*ge,m[ee+Ne]+=Ae*fe,m[de+Ne]+=Ae*we}}}}return n.makeTensorInfo([l,c,u,p],"float32",m)}var Aj={kernelName:Pd,backendName:"cpu",kernelFunc:Cj};function 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        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
      }

      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan_custom(val.x),
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      }

      #define isnan(value) isnan_custom(value)
    `,l="",u=`
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

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        return ivec4(floor(value + vec4(0.5)));
      }
    `):(e="",t="attribute",n="varying",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
      #define isnan(value) isnan_custom(value)
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        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `,l=`
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `,u=`
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Gi(e,t,n="index"){let a=I.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Pv(e){let t=I.computeStrides(e).map(n=>n.toString());return`
  int getFlatIndex(ivec3 coords) {
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  }
`}var L2=`
  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;
  }
`,v5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=op.DENSE;let t=up(e),n=mn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${Gi(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx *
          vec2(${t[0]}, ${t[1]}));
        int index = 4 * (resTexRC.x * ${t[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);
        }

        ${n.output} = result;
      }
    `}},w5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=op.DENSE;let t=up(e),n=mn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${Gi(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx *
          vec2(${t[0]}, ${t[1]}));
        int index = 4 * (resTexRC.x * ${t[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));
        }

        ${n.output} = result;
      }
    `}},k5=class{constructor(e){this.variableNames=["A"],this.outTexUsage=na.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
      ${L2}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}},I5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=na.DOWNLOAD;let t=mn();this.outputShape=e,this.userCode=`
      ${L2}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},N5=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=mn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
      ${Pv(e)}

      void main() {
        ivec3 coords = getOutputCoords();

        int flatIndex = getFlatIndex(coords);
        int offset = imod(flatIndex, 4);

        flatIndex = idiv(flatIndex, 4, 1.);

        int r = flatIndex / ${s};
        int c = imod(flatIndex, ${s});
        vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
        vec4 values = ${a.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];
        }

        ${a.output} = vec4(${i}, 0., 0., 0.);
      }
    `}},T5=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=mn(),[r,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=`
          localCoords = coords;
          if(localCoords[2] + ${u} < ${e[2]}) {
            localCoords[2] += ${u};
            if(localCoords[1] + ${l} < ${e[1]}) {
              localCoords[1] += ${l};

              flatIndex = getFlatIndex(localCoords);
              offset = imod(flatIndex, 4);

              flatIndex = idiv(flatIndex, 4, 1.);

              r = flatIndex / ${s};
              c = imod(flatIndex, ${s});
              uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
              values = ${a.texture2D}(A, uv);

              if(offset == 0) {
                result[${c}] = values[0];
              } else if(offset == 1) {
                result[${c}] = values[1];
              } else if(offset == 2) {
                result[${c}] = values[2];
              } else {
                result[${c}] = values[3];
              }
            }
          }
        `}this.userCode=`
      ${Pv(e)}

      void main() {
        ivec3 coords = getOutputCoords();

        vec4 result = vec4(0.);
        int flatIndex, r, c, offset;
        ivec3 localCoords;
        vec2 uv;
        vec4 values;

        ${i}

        ${a.output} = ${o};
      }
    `}};function S5(e){let t=mn(),n=`${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 Kq(e,n)}function C5(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 t5(e,t)}function A5(e){let t=new Uint16Array([0,1,2,2,1,3]);return n5(e,t)}function pp(e,t,n,a,r,s){r5(t,n);let i=a5(e),o=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(o,i)),Ie(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ie(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function z2(e){return e.internalFormatFloat}function F5(e,t,n,a){let[r,s]=lp(t,n);return pp(e,r,s,z2(a),a.textureFormatFloat,e.FLOAT)}function W2(e){return e.internalFormatHalfFloat}function _5(e,t,n,a){let[r,s]=lp(t,n);return pp(e,r,s,W2(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function B2(e){return e.downloadTextureFormat}function E5(e,t,n,a){let[r,s]=lp(t,n);return pp(e,r,s,B2(a),e.RGBA,e.UNSIGNED_BYTE)}function V2(e){return e.internalFormatPackedFloat}function $5(e,t,n,a){let[r,s]=au(t,n);return pp(e,r,s,V2(a),e.RGBA,e.FLOAT)}function U2(e){return e.internalFormatPackedHalfFloat}function D5(e,t,n,a){let[r,s]=au(t,n);return pp(e,r,s,U2(a),e.RGBA,a.textureTypeHalfFloat)}function M5(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),R2(e,t,"clipSpacePos",n,3,s,a)&&R2(e,t,"uv",n,2,s,r)}function R5(e,t,n,a,r,s){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function P5(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function O5(e,t,n,a){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function L5(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function z5(e,t,n,a){let[r,s]=lp(t,n),i=4,o=new Uint8Array(Vq(t*n,i));return Ie(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function W5(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(Uq(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function B5(e,t,n){let a=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var U5=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Z().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,zq(t,e)):this.gl=ar(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(Z().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Cm(this.gl,r),ba(this.gl,s))this.textureHalfFloatExtension=Cm(this.gl,s);else if(Z().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),ba(this.gl,a))this.colorBufferHalfFloatExtension=Cm(this.gl,a);else if(Z().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",ba(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ba(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=C5(this.gl),this.indexBuffer=A5(this.gl),this.framebuffer=s5(this.gl),this.textureConfig=Fv(this.gl,this.textureHalfFloatExtension)}get debug(){return Z().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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;Ie(e,()=>e.finish()),Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.deleteFramebuffer(this.framebuffer)),Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ie(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),F5(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),_5(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),E5(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),P5(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),R5(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),D5(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),$5(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(P2(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>z5(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return W5(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return L5(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=O5(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Z().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>B5(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Jq(t,e),a=S5(t),r=Zq(t);return Ie(t,()=>t.attachShader(r,a)),Ie(t,()=>t.attachShader(r,n)),e5(t,r),this.debug&&_v(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=M5(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&_v(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?l5(this.gl,e,t):u5(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(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(),c5(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=au(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&_v(this.gl,this.program),Am(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Cm(this.gl,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await I.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Z().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,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=V5(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)&&I.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ev(this.gl,e,this.framebuffer),this.debug&&Am(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ev(this.gl,this.outputTexture,this.framebuffer),this.debug&&Am(this.gl)):P2(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Ev(a,e,this.framebuffer),this.debug&&Am(a),this.outputTexture=e,Ie(a,()=>a.viewport(0,0,t,n)),Ie(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,n,a))}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 V5(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:G2}=E;function Q5(e,t,n,a){let r=[];e.forEach(h=>{let m=I.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
`),i=e.map(h=>G5(h,t,a)).join(`
`),o=t.texShape,l=mn(),u=q5(l),c,p,d=Y5(l);return t.isPacked?(c=H5(t.logicalShape,o),p=K5(l)):(c=j5(t.logicalShape,o),p=X5(l)),a&&(d+=J5),[d,u,p,s,c,i,n].join(`
`)}function iu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return Z5(e);case 1:return e8(e);case 2:return t8(e);case 3:return n8(e);case 4:return a8(e);case 5:return r8(e);case 6:return s8(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function H2(e){switch(e.shapeInfo.logicalShape.length){case 0:return i8(e);case 1:return o8(e);case 2:return l8(e);case 3:return u8(e);default:return c8(e)}}function G5(e,t,n=!1){let a="";n?a+=H2(e):a+=iu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=p8(e,t):a+=d8(e,t)),a}function H5(e,t){switch(e.length){case 0:return j2();case 1:return h8(e,t);case 2:return g8(e,t);case 3:return m8(e,t);default:return f8(e,t)}}function j5(e,t){switch(e.length){case 0:return j2();case 1:return y8(e,t);case 2:return k8(e,t);case 3:return b8(e,t);case 4:return x8(e,t);case 5:return v8(e,t);case 6:return w8(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function q5(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function X5(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function K5(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function Y5(e){return`${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);
    }

    ${I8}
    ${N8}
    ${T8}
  `}var I8=`
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);
}
`,N8=`
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);
}
`,T8=`
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);
}
`,J5=`
  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 j2(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function h8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${n[1]}.0);
      }
    `:n[1]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${n[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
    }
  `}function y8(e,t){return t[0]===1?`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function m8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;

      int b = index / ${r};
      index -= b * ${r};

      int r = 2 * (index / ${a});
      int c = imod(index, ${a}) * 2;

      return ivec3(b, r, c);
    }
  `}function b8(e,t){let n=Gi(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${n}
      return ivec3(r, c, d);
    }
  `}function f8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
      int b${l} = index / ${s};
      index -= b${l} * ${s};
    `+i,o=`b${l}, `+o;return`
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;

      ${i}

      int b = index / ${r};
      index -= b * ${r};

      int r = 2 * (index / ${a});
      int c = imod(index, ${a}) * 2;

      return ivec${e.length}(${o});
    }
  `}function x8(e,t){let n=Gi(["r","c","d","d2"],e);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${n}
      return ivec4(r, c, d, d2);
    }
  `}function v8(e,t){let n=Gi(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function w8(e,t){let n=Gi(["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 g8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(I.arraysEqual(e,t))return`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
      }
    `;let a=Math.ceil(e[1]/2);return`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));

      int index = resTexRC.x * ${n[1]} + resTexRC.y;
      int r = 2 * (index / ${a});
      int c = imod(index, ${a}) * 2;

      return ivec2(r, c);
    }
  `}function k8(e,t){return I.arraysEqual(e,t)?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:e[1]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:e[0]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `}function Hi(e){return`offset${e}`}function i8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=mn();return`
    vec4 ${n}() {
      return ${a.texture2D}(${t}, halfCR);
    }
  `}function Z5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
      float ${n}() {
        return sampleTexture(${t}, halfCR);
      }
    `;let[s,i]=e.shapeInfo.texShape,o=Hi(t);return`
    float ${n}() {
      vec2 uv = uvFromFlat(${s}, ${i}, ${o});
      return sampleTexture(${t}, uv);
    }
  `}function o8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=mn();return`
    vec4 ${n}(int index) {
      vec2 uv = packedUVfrom1D(
        ${r[0]}, ${r[1]}, index);
      return ${s.texture2D}(${t}, uv);
    }
  `}function e8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
      float ${n}(int index) {
        ${ou(e)}
      }
    `;let a=e.shapeInfo.texShape,r=a[0],s=a[1];if(s===1&&r===1)return`
      float ${n}(int index) {
        return sampleTexture(${t}, halfCR);
      }
    `;let i=Hi(t);return s===1?`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
        return sampleTexture(${t}, uv);
      }
    `:r===1?`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
        return sampleTexture(${t}, uv);
      }
    `:`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
      return sampleTexture(${t}, uv);
    }
  `}function l8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=mn();if(r!=null&&I.arraysEqual(t,r))return`
      vec4 ${a}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);

        return ${o.texture2D}(${n}, uv);
      }
    `;let l=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],u=Math.ceil(t[1]/2);return`
    vec4 ${a}(int row, int col) {
      vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
      return ${o.texture2D}(${n}, uv);
    }
  `}function t8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&I.arraysEqual(t,r)){let p=r[0],d=r[1];return`
    float ${a}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${p}.0);
      return sampleTexture(${n}, uv);
    }
  `}let{newShape:s,keptDims:i}=I.squeezeShape(t),o=s;if(o.length<t.length){let p=lu(e,o),d=["row","col"];return`
      ${iu(p)}
      float ${a}(int row, int col) {
        return ${a}(${uu(d,i)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
        ${ou(e)}
      }
    `;let l=r[0],u=r[1],c=Hi(n);return u===1?`
    float ${a}(int row, int col) {
      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
      return sampleTexture(${n}, uv);
    }
  `:l===1?`
    float ${a}(int row, int col) {
      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
      return sampleTexture(${n}, uv);
    }
  `:`
  float ${a}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${t[1]} + col + ${c};
    vec2 uv = uvFromFlat(${l}, ${u}, index);
    return sampleTexture(${n}, uv);
  }
`}function u8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),d=[1,2],h=lu(e,p),m=["b","row","col"];return`
        ${H2(h)}
        vec4 ${a}(int b, int row, int col) {
          return ${a}(${uu(m,d)});
        }
      `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),u=l*Math.ceil(t[1]/2),c=mn();return`
    vec4 ${a}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${i}, ${o}, ${u}, ${l}, b, row, col);
      return ${c.texture2D}(${n}, uv);
    }
  `}function n8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=I.squeezeShape(t),l=i;if(l.length<t.length){let m=lu(e,l),f=["row","col","depth"];return`
        ${iu(m)}
        float ${a}(int row, int col, int depth) {
          return ${a}(${uu(f,o)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${r}, ${s}, 1)));
        ${ou(e)}
      }
    `;let u=e.shapeInfo.texShape,c=u[0],p=u[1],d=e.shapeInfo.flatOffset;if(p===r&&d==null)return`
        float ${a}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${s}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${p}.0, ${c}.0);
          return sampleTexture(${n}, uv);
        }
      `;if(p===s&&d==null)return`
    float ${a}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
      return sampleTexture(${n}, uv);
    }
  `;let h=Hi(n);return`
      float ${a}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${r} + col * ${s} + depth + ${h};
        vec2 uv = uvFromFlat(${c}, ${p}, index);
        return sampleTexture(${n}, uv);
      }
  `}function c8(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],u=Math.ceil(t[n-1]/2),c=u*Math.ceil(t[n-2]/2),p="int b, int row, int col",d=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,c*=t[n-m-1],d=`b${m} * ${c} + `+d;let h=mn();return`
    vec4 ${r}(${p}) {
      int index = ${d};
      int texR = index / ${l};
      int texC = index - texR * ${l};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
      return ${h.texture2D}(${a}, uv);
    }
  `}function a8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=I.squeezeShape(t);if(o.length<t.length){let m=lu(e,o),f=["row","col","depth","depth2"];return`
      ${iu(m)}
      float ${a}(int row, int col, int depth, int depth2) {
        return ${a}(${uu(f,l)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${i}, ${s}, ${r}, 1)));
        ${ou(e)}
      }
    `;let u=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,p=c[0],d=c[1];if(d===i&&u==null)return`
      float ${a}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${s}, ${r}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(d===r&&u==null)return`
      float ${a}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${d}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;let h=Hi(n);return`
    float ${a}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${s} +
          depth * ${r} + depth2;
      vec2 uv = uvFromFlat(${p}, ${d}, index + ${h});
      return sampleTexture(${n}, uv);
    }
  `}function r8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=I.squeezeShape(t);if(l.length<t.length){let f=lu(e,l),g=["row","col","depth","depth2","depth3"];return`
      ${iu(f)}
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        return ${a}(${uu(g,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${o}, ${i}, ${s}, ${r})) +
          depth3;
        ${ou(e)}
      }
    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${i}, ${s}, ${r}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(h===r&&c==null)return`
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${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, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;let m=Hi(n);return`
    float ${a}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${i} + depth * ${s} +
          depth2 * ${r} + depth3 + ${m};
      vec2 uv = uvFromFlat(${d}, ${h}, index);
      return sampleTexture(${n}, uv);
    }
  `}function s8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=I.squeezeShape(t);if(r.length<t.length){let g=lu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${iu(g)}
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${a}(${uu(y,s)});
      }
    `}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${u}, ${l}, ${o})) +
          dot(
            vec2(depth3, depth4),
            vec2(${i}, 1)));
        ${ou(e)}
      }
    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],m=d[1];if(m===c&&p==null)return`
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${u}, ${l}, ${o}, ${i})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${m}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(m===i&&p==null)return`
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${m}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;let f=Hi(n);return`
    float ${a}(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 * ${c} + col * ${u} + depth * ${l} +
          depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
      vec2 uv = uvFromFlat(${h}, ${m}, index);
      return sampleTexture(${n}, uv);
    }
  `}function ou(e){let t=e.name,n=I.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
    for (int i = 0; i < ${n}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function p8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=G2(e.shapeInfo.logicalShape,t.logicalShape),l=ht(i),u=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(g=>`coords.${p[g+u]} = 0;`).join(`
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+u]}`).join(", ");let h="return outputValue;",m=I.sizeFromShape(e.shapeInfo.logicalShape)===1,f=I.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(m&&!f)i===1?h=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:h=`
        return vec4(outputValue.x);
      `;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${r}() {
      ${l} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${a}(${d});
      ${h}
    }
  `}function d8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&I.arraysEqual(i,s))return`
      float ${r}() {
        return sampleTexture(${n}, resultUV);
      }
    `;let u=ht(l),c=G2(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(f=>`coords.${h[f+p]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
    float ${r}() {
      ${u} coords = getOutputCoords();
      ${d}
      return get${a}(${m});
    }
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        }`:f=`vec4 activation(vec4 x) {
          ${i}
        }`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${f}

      const float sharedDimension = ${c}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${c}; i++) {
          int batchA = ${b};
          int batchB = ${x};
          vec4 a = getMatrixA(batchA, ${p});
          vec4 b = getMatrixB(batchB, ${d});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${h[0]} * ${m[0]});
          result += (${h[1]} * ${m[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${y}

        ${g}

        setOutput(result);
      }
    `}},uT={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},cT=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${e}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}},pT="return a * b;";function dT(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=E.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new cT(uT.REAL,a.shape,r.shape),c=new cT(uT.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),m=hs({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,c]=G8(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(c,s),d=n.texData.get(p.dataId);return d.values=u,p}let i;return Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new dp(pT,a.shape,r.shape):i=new pu(pT,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var LX={kernelName:Ys,backendName:"webgl",kernelFunc:dT};function zX(e,t,n){let a=[ru(e.shape),...su(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[ru(t),...su(t)],i=new J2(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ye(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=I.sizeFromShape(r.shape),l=I.inferFromImplicitShape(s,o),u=I.sizeFromShape(l);I.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!_m(r.shape,l)&&!(c.texture!==null&&_m(c.shape,l))?zX(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var WX={kernelName:cl,backendName:"webgl",kernelFunc:ye},hT=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${I.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${u}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};

        float sumValue = 0.0;

        for (int i = 0; i < ${i}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${l}
        }

        int inIdx = inOffset + ${i};
        if (${o===1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);

          ${l}
        } else if (${o===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);

          ${l}
        } else if (${o===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},BX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,p=`
      if (${t==="sum"}) {
        sumValue += dot(values, ones);
      } else if (${t==="prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${o}(values, minMaxValue);
      }
    `,d="vec4";t==="all"?(i="1.0",p=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,d="bvec4"):t==="any"&&(i="0.0",p=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,d="bvec4");let h="";r%n>0&&(h=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return initializationValue;
        }
      `),this.userCode=`
      const float initializationValue = ${i};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${h}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};

        vec4 minMaxValue = vec4(${i});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;

        for (int i = 0; i < ${u}; i += 4) {
          int inIdx = inOffset + i;
          ${d} values = ${d}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${p}
        }

        int inIdx = inOffset + ${u};
        if (${c===1}) {
          ${d} values = ${d}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${p}
        } else if (${c===2}) {
          ${d} values = ${d}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${p}
        } else if (${c===3}) {
          ${d} values = ${d}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${p}
        }
        setOutput(${l});
      }
    `}};function VX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function ji(e,t,n,a){let r=VX(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],c,p;n==="mean"?c=i===0?new hT({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new hT({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):c=new BX({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),p=s,s=a.runWebGLProgram(c,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var GX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=ht(this.rank),r=UX(t);this.userCode=`
    void main() {
      ${a} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `}};function UX(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var HX=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ht(this.rank),r=Y2("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
    void main() {
      ${a} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${o}) {
        result[1] = ${l};
      }
      --${r[this.rank-1]};
      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${l};
        if(${o}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function Mm(e,t,n){let a=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new HX(e.shape,t):new GX(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function jX(e,t,n,a){let r=t,s=e.shape.length,i=I.parseAxisParam(r,e.shape),o=i,l=E.getAxesPermutation(o,s),u=l!=null,c=e;u&&(c=Mm(e,l,a),o=E.getInnerMostAxes(o.length,s)),E.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=E.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=E.expandShapeToKeepDim(p,i));let m=I.sizeFromShape(d),f=I.sizeFromShape(e.shape)/m,g=ye({inputs:{x:c},attrs:{shape:[f,m]},backend:a}),y=jd(e.dtype),b=ji(g,y,"sum",a),x=ye({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),u&&a.disposeIntermediateTensorInfo(c),x}function Wv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return jX(r,s,i,n)}var qX={kernelName:ci,backendName:"webgl",kernelFunc:Wv};function _n(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let c=0;c<l.length;c++)l[c]=r.shape[s[c]];let u;if(i.shouldExecuteOnCPU([r])){let c=i.texData.get(r.dataId).values,p=Ov(c,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(u.dataId);d.values=p}else u=Mm(r,s,i);return u}var XX={kernelName:fi,backendName:"webgl",kernelFunc:_n},mT=1e3;function Rm({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=a?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],m=a?t.shape[c-2]:t.shape[c-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=I.sizeFromShape(f),b=I.sizeFromShape(g),x=y===b||y===1||b===1;I.assert(u>=2&&c>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let v=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);I.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let k=n?[y,p,h]:[y,h,p],w=a?[b,m,d]:[b,d,m],S=ye({inputs:{x:e},backend:r,attrs:{shape:k}}),A=ye({inputs:{x:t},backend:r,attrs:{shape:w}}),F=[S,A],D=Math.max(y,b),R=n?S.shape[1]:S.shape[2],L=s!=null,W=i!=null,U=l==="leakyrelu",G=l!=null?Dm(l,!0):null,X=L||W||U||G!=null,j;if((h===1||m===1)&&R>mT&&X===!1){let Q=S,se=A;n&&(Q=_n({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),F.push(Q)),a&&(se=_n({inputs:{x:A},backend:r,attrs:{perm:[0,2,1]}}),F.push(se));let ne=m!==1,ie=m===1,ee=Q;ne&&(ee=ye({inputs:{x:Q},backend:r,attrs:{shape:[D,R,1]}}),F.push(ee));let de=m===1?2:1,oe=se;ie&&(oe=ye({inputs:{x:se},backend:r,attrs:{shape:[D,1,R]}}),F.push(oe));let ge=dT({inputs:{a:ee,b:oe},backend:r});j=Wv({inputs:{x:ge},backend:r,attrs:{axis:de,keepDims:!0}}),F.push(ge)}else{let Q=ua(e.dtype,t.dtype),se=new lT(k,w,[D,h,m],n,a,L,G,W,U),ne=[S,A];if(s!=null&&ne.push(s),W&&ne.push(i),U){let ie=r.makeTensorInfo([],"float32",I.createScalarValue(o,"float32"));ne.push(ie),F.push(ie)}j=r.runWebGLProgram(se,ne,Q)}let te=ye({inputs:{x:j},backend:r,attrs:{shape:v}});F.push(j);for(let Q of F)r.disposeIntermediateTensorInfo(Q);return te}function KX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=a;return Rm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var YX={kernelName:gi,backendName:"webgl",kernelFunc:KX},fT="return abs(x);";function JX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=K2(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new cu(a.shape,fT):r=new ds(a.shape,fT),n.runWebGLProgram(r,[a],a.dtype)}var QX={kernelName:So,backendName:"webgl",kernelFunc:JX},ZX=Ra+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,eK=Xe({opSnippet:ZX}),tK={kernelName:Co,backendName:"webgl",kernelFunc:eK},nK=Ra+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,aK=Xe({opSnippet:nK}),rK={kernelName:Ao,backendName:"webgl",kernelFunc:aK},gT="return a + b;",sK=sn({opSnippet:gT,packedOpSnippet:gT,supportsComplex:!0,cpuKernelImpl:F8}),iK={kernelName:Br,backendName:"webgl",kernelFunc:sK},oK=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        float result = ${a};
        setOutput(result);
      }
    `}},lK=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        vec4 result = ${a};
        setOutput(result);
      }
    `}};function Pm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Gn({inputs:{x:a[0]},backend:n});if(a.length>Z().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Pm({inputs:a.slice(0,o),backend:n}),u=Pm({inputs:a.slice(o),backend:n});return Pm({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ua(o,l)),s=a.map(o=>o.shape),i=Z().getBool("WEBGL_PACK")?new lK(a[0].shape,s):new oK(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var uK={kernelName:Ns,backendName:"webgl",kernelFunc:Pm};function cK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=I.parseAxisParam(s,r.shape),u=l,c=E.getAxesPermutation(u,o),p=r;c!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:c}}),u=E.getInnerMostAxes(u.length,o)),E.assertAxesAreInnerMostDims("all",u,o);let[d,h]=E.computeOutAndReduceShapes(p.shape,u),m=I.sizeFromShape(h),f=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=ji(f,f.dtype,"all",n),y;if(i){let b=E.expandShapeToKeepDim(d,l);y=ye({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ye({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var pK={kernelName:ud,backendName:"webgl",kernelFunc:cK};function dK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=I.parseAxisParam(s,r.shape),u=l,c=E.getAxesPermutation(u,o),p=r;c!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:c}}),u=E.getInnerMostAxes(u.length,o)),E.assertAxesAreInnerMostDims("any",u,o);let[d,h]=E.computeOutAndReduceShapes(p.shape,u),m=I.sizeFromShape(h),f=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=ji(f,f.dtype,"any",n),y;if(i){let b=E.expandShapeToKeepDim(d,l);y=ye({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ye({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var hK={kernelName:cd,backendName:"webgl",kernelFunc:dK},mK=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${a};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${a}; i++) {
          int inIdx = ${o};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}},fK=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,I.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ht(o),u=fn("coords",o),c,p;if(s===1){p=o+1;let S=ht(p);c=`
        ${S} sourceLocR = ${S}(${u.join()}, 0);
        ++${u[o-1]};
        ${S} sourceLocG = ${S}(${u.join()}, 0);
        ++${u[o-2]};
        ${S} sourceLocA = ${S}(${u.join()}, 0);
        --${u[o-1]};
        ${S} sourceLocB = ${S}(${u.join()}, 0);
        --${u[o-2]};`}else p=o,c=`
        ${l} sourceLocR = coords;
        ++${u[o-1]};
        ${l} sourceLocG = coords;
        ++${u[o-2]};
        ${l} sourceLocA = coords;
        --${u[o-1]};
        ${l} sourceLocB = coords;
        --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],m=d.map(S=>"int "+S),f=fn("sourceLocR",p-1).concat("inIdx.r"),g=fn("sourceLocG",p-1).concat("inIdx.g"),y=fn("sourceLocB",p-1).concat("inIdx.b"),b=fn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
                             getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${b.join()})));`,k=`vec4(
            getAChannel(${f.join()}),
            hasNextCol ? getAChannel(${g.join()}) : 0.,
            hasNextRow ? getAChannel(${y.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,w=a?"":`
      float getBestIndicesAChannel(${m.join()}) {
        return getChannel(getBestIndicesA(${d.join()}),
                                          vec2(${d.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${m.join()}) {
        return getChannel(getA(${d.join()}),
                               vec2(${d.slice(-2).join()}));
      }
      ${w}
      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
        bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
        ${c}
        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
          sourceLocB${h}, sourceLocA${h}) * ${t};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${k};

        for (int i = 0; i < ${t}; i++) {
          inIdx = srcIdx;
          ${v}
          vec4 candidate = ${k};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function yT(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=E.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new mK(o,n,a==null),u=[t];a!=null&&u.push(a);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=yT(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function bT(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=E.computeOptimalWindowSize(s),o=new fK(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=bT(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function xT(e,t,n,a){let r=[n];if(E.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!Z().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=E.computeOutAndReduceShapes(t.shape,r),l=I.sizeFromShape(o),u=ye({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=yT(e,u,a);s.push(c);let p=ye({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return bT(e,t,a)}function gK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=I.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=_n({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=xT(n,l,i[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var yK={kernelName:Ts,backendName:"webgl",kernelFunc:gK};function bK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=I.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=_n({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=xT(n,l,i[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var xK={kernelName:Ku,backendName:"webgl",kernelFunc:bK},vK=Ra+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,wK=Xe({opSnippet:vK}),kK={kernelName:Fo,backendName:"webgl",kernelFunc:wK},IK=Ra+"return log(x + sqrt(x * x + 1.0));",NK=Xe({opSnippet:IK}),TK={kernelName:_o,backendName:"webgl",kernelFunc:NK},SK=Ra+`
  return atan(x);
`,CK=Xe({opSnippet:SK}),AK={kernelName:Eo,backendName:"webgl",kernelFunc:CK},FK=PX+`
  return atan(a, b);
`,_K=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+OX+`
  return result;
`,EK=sn({opSnippet:FK,packedOpSnippet:_K}),$K={kernelName:Do,backendName:"webgl",kernelFunc:EK},DK=Ra+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,MK=Xe({opSnippet:DK}),RK={kernelName:$o,backendName:"webgl",kernelFunc:MK},hp=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
        const ivec2 strides = ivec2(${i}, ${o});
        const ivec2 pads = ivec2(${d}, ${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 < ${c};
              wR += ${l}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${p};
                wC += ${u}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${S} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${a?r?f:g:`wR * ${p} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let b="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,k=s%4,w=`
      if (${m}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${b}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${i}, ${o});
      const ivec2 pads = ivec2(${d}, ${h});
      const float initializationValue = ${y};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${y});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${c};
            wR += ${l}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${v}; wC += 4) {
            int xC = xCCorner + wC * ${u};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              getValue(batch, xR, xC + 3 * ${u}, d)
            );

            ${w}
          }

          int xC = xCCorner + ${v};
          if (${k===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${w}
          } else if (${k===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              initializationValue,
              initializationValue
            );

            ${w}
          } else if (${k===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              initializationValue
            );

            ${w}
          }
        }
        setOutput(${x});
      }
    `}},Bv=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let F=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${i}, ${o}, ${l});
        const ivec3 pads = ivec3(${f}, ${g}, ${y});

        void main() {
          ivec5 coords = getOutputCoords();
          int batch = coords.x;
          int ch = coords.u;

          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;

          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;

          for (int wD = 0; wD < ${d};
              wD += ${u}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${e.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${h};
                wR += ${c}) {
              int xR = xRCorner + wR;

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int wC = 0; wC < ${m};
                  wC += ${p}) {
                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 ${F} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} +
                      wR * ${m} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let v="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / count");let w=Math.floor(s/4)*4,S=s%4,A=`
      if (${b}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${v}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${i}, ${o}, ${l});
      const ivec3 pads = ivec3(${f}, ${g}, ${y});
      const float initializationValue = ${x};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${x});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${d};
            wD += ${u}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${h};
            wR += ${c}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${w}; wC += 4) {
              int xC = xCCorner + wC * ${p};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${p}, ch),
                getValue(batch, xD, xR, xC + 2 * ${p}, ch),
                getValue(batch, xD, xR, xC + 3 * ${p}, ch)
              );

              ${A}
            }

            int xC = xCCorner + ${w};
            if (${S===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${A}
            } else if (${S===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${p}, ch),
                initializationValue,
                initializationValue
              );

              ${A}
            } else if (${S===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${p}, ch),
                getValue(batch, xD, xR, xC + 2 * ${p}, ch),
                initializationValue
              );

              ${A}
            }
          }
          setOutput(${k});
        }
      }
    `}};function PK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;cp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;I.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=E.computePool2DInfo(r.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&I.arraysEqual(c.inShape,c.outShape))return Gn({inputs:{x:r},backend:n});let p=new hp(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var OK={kernelName:Ss,backendName:"webgl",kernelFunc:PK};function LK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,c=[1,1,1],p=E.computePool3DInfo(r.shape,s,i,c,o,l,u),d=new Bv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var zK={kernelName:Yu,backendName:"webgl",kernelFunc:LK},WK=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
      const ivec2 pads = ivec2(${u}, ${c});
      const float avgMultiplier = float(${p});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${o};
            wR += ${s}) {
          float dyR = float(dyRCorner + wR) / ${a}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${l};
            wC+= ${i}) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}},BK=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,m=p-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
      const ivec3 pads = ivec3(${h}, ${m}, ${f});
      const float avgMultiplier = float(${g});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${c};
            wD += ${o}) {
          float dyD = float(dyDCorner + wD) / ${r}.0;

          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${p};
              wR += ${l}) {
            float dyR = float(dyRCorner + wR) / ${s}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${d};
                wC += ${u}) {
              float dyC = float(dyCCorner + wC) / ${i}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function VK(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=a,p=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,p,u,c),h=new BK(d);return n.runWebGLProgram(h,[r],i.dtype)}var UK={kernelName:dd,backendName:"webgl",kernelFunc:VK};function GK(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;cp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,c=E.computePool2DInfo(i.shape,o,l,1,u),p=new WK(c);return n.runWebGLProgram(p,[r],i.dtype)}var HK={kernelName:pd,backendName:"webgl",kernelFunc:GK};function jK(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Rm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var qK={kernelName:Cs,backendName:"webgl",kernelFunc:jK},XK=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${i};
        float scale = ${o};
        float inv = scale * inversesqrt(variance + float(${s}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}},KK=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${i};
        vec4 scale = ${o};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${s}));

        setOutput((x - mean) * inv + offset);
      }
    `}},YK=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;I.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),I.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),I.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],c=null;i!=null&&(c=i.shape,u.push(i));let p=null;o!=null&&(p=o.shape,u.push(o));let d=Z().getBool("WEBGL_PACK_NORMALIZATION")?new KK(a.shape,r.shape,s.shape,c,p,l):new XK(a.shape,r.shape,s.shape,c,p,l);return t.runWebGLProgram(d,u,u[0].dtype)},JK={kernelName:zs,backendName:"webgl",kernelFunc:YK},ZK=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank),n=`uniform int start[${this.rank}];`,a=QK(this.rank),r,s=e.map((i,o)=>`sourceLoc.${Vv[o]} = start[${o}] + coords.${Vv[o]};`);r=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${s.join(`
`)}
      `,this.userCode=`
      ${n}
      void main() {
        ${r}
        setOutput(getSource(${a}));
      }
    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Vv=["x","y","z","w","u","v"];function QK(e){if(e===1)return"sourceLoc";if(e<=6)return Vv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var e7=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ht(this.rank),n=fn("coords",this.rank),a=fn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
      result.x = ${s};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${a[this.rank-1]};
        result.y = ${s};
        --${a[this.rank-1]};
      }
    `,o=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${a[this.rank-2]};
        result.z = ${s};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${a[this.rank-1]};
          result.w = ${s};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${a[c]} = ${n[c]} + start[${c}];`).join(`
`);this.userCode=`
      uniform int start[${this.rank}];
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${i}
        ${o}
        setOutput(result);
      }
    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function t7(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=pn.computeFlatOffset(t,I.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function mp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=pn.parseSliceParams(r,s,i);if(pn.assertParamsValid(r,o,l),I.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=K8(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=n.texData.get(r.dataId),c=pn.isSliceContinous(r.shape,o,l);if(u||!c){let p=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new e7(l):new ZK(l),d=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),t7(r,o,l,n)}var n7={kernelName:ml,backendName:"webgl",kernelFunc:mp},a7=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;I.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,x)=>b*x),l=E.getReshaped(r.shape,s,o),u=E.getPermuted(l.length,s.length),c=E.getReshapedPermuted(r.shape,s,o),p=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(c,i,s.length),h=[],m=ye({inputs:{x:r},backend:n,attrs:{shape:l}}),f=_n({inputs:{x:m},backend:n,attrs:{perm:u}}),g=ye({inputs:{x:f},backend:n,attrs:{shape:c}}),y=mp({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},r7={kernelName:Ju,backendName:"webgl",kernelFunc:a7};function s7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=X2(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var i7={kernelName:hd,backendName:"webgl",kernelFunc:s7},o7="return float(a != b);",vT=sn({opSnippet:o7,dtype:"bool"}),l7={kernelName:nl,backendName:"webgl",kernelFunc:vT};function fp(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Gn({inputs:{x:r.complexTensorInfos.real},backend:n})}var u7={kernelName:Md,backendName:"webgl",kernelFunc:fp},c7="return float(int(x));";function p7(e,t){let n=new ds(e.shape,c7),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Uv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Gn({inputs:{x:r},backend:n});let i=vt(r.shape),o=Uv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=hs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=fp({inputs:{input:r},backend:n}),o=Uv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!I.hasEncodingLoss(r.dtype,s)){let i=Gn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return p7(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",I.getTypedArrayFromDType("bool",1)),o=vT({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var d7={kernelName:As,backendName:"webgl",kernelFunc:Uv},wT="return ceil(x);",h7=Xe({opSnippet:wT,packedOpSnippet:wT,cpuKernelImpl:E8}),m7={kernelName:Fs,backendName:"webgl",kernelFunc:h7},f7=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
      uniform float minVal;
      uniform float maxVal;

      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, minVal, maxVal));
      }
    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},g7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
      uniform float minVal;
      uniform float maxVal;

      void main() {
        vec4 value = getAAtOutCoords();

        if (any(isnan(value))) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function y7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;Z().getBool("WEBGL_PACK_CLIP")?o=new g7(r.shape):o=new f7(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var b7={kernelName:Vr,backendName:"webgl",kernelFunc:y7},x7=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 kT(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function v7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new x7(a.shape),i=[kT(a,r.complexTensorInfos.real),kT(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var w7={kernelName:Qu,backendName:"webgl",kernelFunc:v7},k7=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${n.join(`
        `)}
      }
    `}},I7=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ht(a),s=fn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],u=i.slice(-2),c=i.join(),p=`if (${l} < ${o[0]}) {
        return getChannel(
            getT0(${c}), vec2(${u.join()}));
        }`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
        if (${l} < ${o[m]}  && ${l} >= ${o[m-1]}) {
          return getChannel(
            getT${m}(${Om(i,l,f)}),
            vec2(${Om(u,l,f)}));
        }`}let d=o.length,h=o[o.length-1];p+=`
        return getChannel(
          getT${d}(${Om(i,l,h)}),
          vec2(${Om(u,l,h)}));`,this.userCode=`
      float getValue(${i.map(m=>"int "+m)}) {
        ${p}
      }

      void main() {
        ${r} coords = getOutputCoords();
        vec4 result = vec4(getValue(${s}), 0., 0., 0.);

        ${s[a-1]} = ${s[a-1]} + 1;
        if (${s[a-1]} < ${n[a-1]}) {
          result.g = getValue(${s});
        }

        ${s[a-2]} = ${s[a-2]} + 1;
        if (${s[a-2]} < ${n[a-2]}) {
          result.a = getValue(${s});
        }

        ${s[a-1]} = ${s[a-1]} - 1;
        if (${s[a-2]} < ${n[a-2]} &&
            ${s[a-1]} < ${n[a-1]}) {
          result.b = getValue(${s});
        }
        setOutput(result);
      }
    `}};function Om(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Lm(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Gn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var N7={kernelName:Cd,backendName:"webgl",kernelFunc:Lm};function du(e,t,n){let a=e[0].dtype;if(a==="complex64"){let u=e.map(m=>fp({inputs:{input:m},backend:n})),c=e.map(m=>Lm({inputs:{input:m},backend:n})),p=du(u,t,n),d=du(c,t,n),h=hs({inputs:{real:p,imag:d},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),c.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(a==="string"){let{tensors2D:u,outShape:c}=IT(e,t,n),p=u.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=u[0].shape[0]===1,h=$8(p,c,a,d),m=E.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>Z().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=du(e.slice(0,u),t,n),p=du(e.slice(u),t,n),d=du([c,p],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),d}if(Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new I7(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,a)}let{tensors2D:r,outShape:s}=IT(e,t,n),i=new k7(r.map(u=>u.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=ye({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function IT(e,t,n){let a=E.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ye({inputs:{x:r},attrs:{shape:[-1,I.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function NT(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=I.parseAxisParam(r,t[0].shape)[0],i=E.computeOutShape(t.map(u=>u.shape),s);if(I.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>I.sizeFromShape(u.shape)>0);if(o.length===1)return Gn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return E.assertParamsConsistent(l,s),du(o,s,n)}var T7={kernelName:Mo,backendName:"webgl",kernelFunc:NT},TT=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,x="",v="";n&&(a?x=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?x=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:x=`
          float activation(float x) {
            ${n}
          }
        `,v="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${x}

      const ivec2 strides = ivec2(${o}, ${l});
      const ivec2 pads = ivec2(${s}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${b}];

        ivec2 xRCCorner =
            ivec2(coords[${g}], coords[${y}]) * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${p}; wR++) {
          int xR = xRCorner + wR * ${u};

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${d}; wC++) {
            int xC = xCCorner + wC * ${c};

            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 (${f}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }

            if (${m===1}) {

              if (${f}) {
                dotProd +=
                    getX(batch, xR, xC, ${h}) *
                    getW(wR, wC, ${h}, d2);
              } else {
                dotProd +=
                    getX(batch, ${h}, xR, xC) *
                    getW(wR, wC, ${h}, d2);
              }

            } else if (${m===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${h}, d2),
                getW(wR, wC, ${h} + 1, d2)
              );

              if (${f}) {
                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 (${m===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${h}, d2),
                getW(wR, wC, ${h} + 1, d2),
                getW(wR, wC, ${h} + 2, d2)
              );

              if (${f}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${h}),
                  getX(batch, xR, xC, ${h} + 1),
                  getX(batch, xR, xC, ${h} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${h}, xR, xC),
                  getX(batch, ${h} + 1, xR, xC),
                  getX(batch, ${h} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${k}
        ${v}
        setOutput(result);
      }
    `}},S7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${r}, ${s}, ${i});
      const ivec3 pads = ivec3(${t}, ${n}, ${a});

      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 < ${c}; wF++) {
          int xF = xFCorner + wF * ${o};

          if (xF < 0 || xF >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${p}; wR++) {
            int xR = xRCorner + wR * ${l};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${d}; wC++) {
              int xC = xCCorner + wC * ${u};

              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 (${m===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${h}) *
                  getW(wF, wR, wC, ${h}, d2);
              } else if (${m===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, ${h} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${m===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);
      }
    `}},C7=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:p}=n,{left:d,top:h}=o,m=r*a,f=mn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,x="";for(let v=0;v<=1;v++)for(let k=0;k<=1;k++)x+=`
          blockIndex = rc.y + ${k};
          pos = rc.x + ${v};

          if(blockIndex < ${e[1]} && pos < ${e[0]}) {
            offsetY = int(blockIndex / (${l})) * ${i} - ${h};
            d0 = offsetY + ${c} * (pos / ${m});

            if(d0 < ${t[y]} && d0 >= 0) {

              offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
              d1 = offsetX + ${u} * (int(mod(float(pos), ${m}.) / ${r}.));

              if(d1 < ${t[b]} && d1 >= 0) {

                ch = int(mod(float(pos), ${r}.));

                if (${g}) {
                  innerDims = vec2(d1, ch);
                  result[${v*2+k}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${v*2+k}] = 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;

        ${x}

        ${f.output} = result;
      }
    `}};function ST({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),c=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],b=(p===1||d===1)&&c>mT,x=l[2]%2!=0&&!!u.isPacked;if(b||!Z().getBool("WEBGL_LAZILY_UNPACK")||!Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],k=ye({inputs:{x:e},backend:a,attrs:{shape:[1,v,n.inChannels]}}),w=ye({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Rm({a:k,b:w,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ye({inputs:{x:S},backend:a,attrs:{shape:n.outShape}}),y.push(k),y.push(w),y.push(S)}else{let v=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),k={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,I.assert(_m(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let S=ye({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let A=Rm({a:k,b:S,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),F=a.texData.get(A.dataId);I.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,F.shape=n.outShape,g=Gn({inputs:{x:A},backend:a}),g.shape=n.outShape,y.push(A)}for(let v of y)a.disposeIntermediateTensorInfo(v);return g}function CT({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,m=h==="channelsLast",f=l*u*c,g=d*p,y=[f,g],b=!0,x=!1,v=[],k=ye({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),w=ye({inputs:{x:t},backend:a,attrs:{shape:[1,f,I.sizeFromShape(t.shape)/f]}});v.push(k),v.push(w);let S=new C7(y,k.shape,n),A=a.runWebGLProgram(S,[k],"float32"),F=ye({inputs:{x:A},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push(A),v.push(F);let D=r!=null,R=s!=null,L=o==="leakyrelu",W=o?Dm(o,!0):null,U=new lT(F.shape,w.shape,[1,g,n.outChannels],b,x,D,W,R,L),G=[F,w];if(r&&G.push(r),R&&G.push(s),L){let Q=a.makeTensorInfo([],"float32",I.createScalarValue(i,"float32"));G.push(Q),v.push(Q)}let X=a.runWebGLProgram(U,G,"float32"),j=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],te=ye({inputs:{x:X},backend:a,attrs:{shape:j}});v.push(X);for(let Q of v)a.disposeIntermediateTensorInfo(Q);return te}function A7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=a,p=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=ST({x:r,filter:s,convInfo:d,backend:n});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=CT({x:r,filter:s,convInfo:d,backend:n});else{let f=new TT(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ye({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var F7={kernelName:_s,backendName:"webgl",kernelFunc:A7},_7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${a};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${r};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              if (${s}) {
                float dyValue = getDy(b, yR, yC, d2);
                float xValue = getX(b, xR, xC, d1);
                dotProd += (xValue * dyValue);
              } else {
                float dyValue = getDy(b, d2, yR, yC);
                float xValue = getX(b, d1, xR, xC);
                dotProd += (xValue * dyValue);
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}},E7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${o});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${c}];

        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${a}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${t} - 1 - wR;

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${n} - 1 - wC;

            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {

              if (${s}) {
                float xValue = getDy(batch, idyR, idyC, d2);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              } else {
                float xValue = getDy(batch, d2, idyR, idyC);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}},$7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yF = 0; yF < ${e.outDepth}; yF++) {
            int xF = wF + yF * ${t} - ${r};

            if (xF < 0 || xF >= ${e.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${e.outHeight}; yR++) {
              int xR = wR + yR * ${n} - ${s};

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${e.outWidth}; yC++) {
                int xC = wC + yC * ${a} - ${i};

                if (xC < 0 || xC >= ${e.inWidth}) {
                  continue;
                }

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},D7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${o}, ${l}, ${u});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyFCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        float dotProd = 0.0;
        for (int wF = 0; wF < ${t}; wF++) {
          float dyF = float(dyFCorner + wF) / ${r}.0;

          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
            continue;
          }
          int idyF = int(dyF);

          int wFPerm = ${t} - 1 - wF;

          for (int wR = 0; wR < ${n}; wR++) {
            float dyR = float(dyRCorner + wR) / ${s}.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 < ${a}; wC++) {
              float dyC = float(dyCCorner + wC) / ${i}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              int wCPerm = ${a} - 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 M7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=a,p=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(r.shape,c,i,1,o,u,!1,p),h=new _7(d);return n.runWebGLProgram(h,[r,s],"float32")}var R7={kernelName:fd,backendName:"webgl",kernelFunc:M7};function P7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=a,p=E.convertConv2DDataFormat(u),d=E.computeConv2DInfo(i,s.shape,o,1,l,c,!1,p),h=new E7(d);return n.runWebGLProgram(h,[r,s],"float32")}var O7={kernelName:Es,backendName:"webgl",kernelFunc:P7};function L7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=E.computeConv3DInfo(r.shape,s.shape,i,l,o),c=new S7(u);return n.runWebGLProgram(c,[r,s],"float32")}var z7={kernelName:Zu,backendName:"webgl",kernelFunc:L7};function W7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=E.computeConv3DInfo(r.shape,l,i,1,o),c=new $7(u);return n.runWebGLProgram(c,[r,s],"float32")}var B7={kernelName:gd,backendName:"webgl",kernelFunc:W7};function V7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=E.computeConv3DInfo(l,s.shape,o,1,i),c=new D7(u);return n.runWebGLProgram(c,[r,s],"float32")}var U7={kernelName:yd,backendName:"webgl",kernelFunc:V7},G7=oT+`
  return cos(x);
`,H7=Xe({opSnippet:G7}),j7={kernelName:$s,backendName:"webgl",kernelFunc:H7},q7=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,X7=Xe({opSnippet:q7}),K7={kernelName:Ro,backendName:"webgl",kernelFunc:X7},Y7=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,p]=n;this.outputShape=[u,c,p,l];let d=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,v]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
      const float height_ratio = float(${f});
      const float width_ratio = float(${b});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

        // get box vals
        float y1 = getBoxes(b,0);
        float x1 = getBoxes(b,1);
        float y2 = getBoxes(b,2);
        float x2 = getBoxes(b,3);

        // get image in batch index
        int bInd = round(getBoxInd(b));
        if(bInd < 0 || bInd >= ${s}) {
          return;
        }

        float height_scale = ${g};
        float width_scale = ${x};

        float in_y = ${y};
        if( in_y < 0.0 || in_y > ${h} ) {
          setOutput(float(${r}));
          return;
        }
        float in_x = ${v};
        if( in_x < 0.0 || in_x > ${m} ) {
          setOutput(float(${r}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${d} == 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);
        }
      }
    `}},J7=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,c=new Y7(r.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[r,s,i],"float32")},Q7={kernelName:Po,backendName:"webgl",kernelFunc:J7},_T=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${AT(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
      uniform float index;
      void main() {
        ${ht(a)} coords = getOutputCoords();
        int end = ${FT(a,"coords")};
        float val = ${r};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${o};
          ${FT(a,"coords")} = idx;
          val += getX(${AT(a,"coords")});
        }
        setOutput(val);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function AT(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 ${e} is not yet supported`)}function FT(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function Z7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=E.getAxesPermutation([s],l),c=r;u!=null&&(c=_n({inputs:{x:r},backend:n,attrs:{perm:u}}));let p=E.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=c.shape[p],h=Gn({inputs:{x:c},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new _T(c.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new _T(c.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=E.getUndoAxesPermutation(u),f=_n({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),f}return h}var eY={kernelName:Ds,backendName:"webgl",kernelFunc:Z7};function tY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),c=X2(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),c=_8(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var nY={kernelName:bd,backendName:"webgl",kernelFunc:tY},aY=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 rY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;I.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=u*s,h=c/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=new aY(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var sY={kernelName:Oo,backendName:"webgl",kernelFunc:rY},ET=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?g=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:g=`
          float activation(float x) {
            ${n}
          }
        `,y="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${g}

      const ivec2 strides = ivec2(${u}, ${c});
      const ivec2 pads = ivec2(${o}, ${l});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${f};
        int q = d2 - d1 * ${f};

        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 < ${h}; wR++) {
          int xR = xRCorner + wR * ${p};

          if (xR < 0 || xR >= ${s}) {
            continue;
          }

          for (int wC = 0; wC < ${m}; wC++) {
            int xC = xCCorner + wC * ${d};

            if (xC < 0 || xC >= ${i}) {
              continue;
            }

            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }

        float result = dotProd;
        ${b}
        ${y}
        setOutput(result);
      }
    `}},$T=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let v=0;v<h;v++)for(let k=0;k<m;k++)g+=`
          vec4 xTexelR${v}C${k*2} = vec4(0.);
          vec4 wR${v}C${k} = vec4(0.);
          vec4 xR${v}C${k} = vec4(0.);`;for(let v=0;v<h;v++)for(let k=0;k<f;k++){let w=k*2;if(g+=`
          xR = xRCorner + ${v*p};
          xC = xCCorner + ${w*d};
        `,c===1){if(w<m&&(l%2==1?g+=`
                xCOffset = xC + 1;
                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
                  xTexelR${v}C${w} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${i}) {
                    xTexelR${v}C${w}.zw = vec2(0.);
                  }
                } else {
                  xTexelR${v}C${w} = vec4(0.);
                }

                xCOffset = xC + 1 - 2;
                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
                  vec4 previous = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${i}) {
                    previous.zw = vec2(0.);
                  }

                  xR${v}C${w} = vec4(previous.zw, xTexelR${v}C${w}.xy);
                } else {
                  xR${v}C${w} = vec4(0, 0, xTexelR${v}C${w}.xy);
                }
              `:g+=`
                if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
                  xTexelR${v}C${w} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${v}C${w} = vec4(0.);
                }

                xR${v}C${w} = xTexelR${v}C${w};
              `,w+1<m)){let S=l%2==0?I.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(g+=`
                  xCOffset = xC + ${l%2} + ${S};

                  if(xR >= 0 && xR < ${s} &&
                    xCOffset >= 0 && xCOffset < ${i}) {
                    xTexelR${v}C${w+2} = getX(batch, xR, xCOffset, d1);
                  }
                `,d>1&&(g+=`
                    xCOffset -= 2;
                    if(xR >= 0 && xR < ${s} &&
                      xCOffset >= 0 && xCOffset < ${i}) {
                      xTexelR${v}C${w} = getX(batch, xR, xCOffset, d1);
                    } else {
                      xTexelR${v}C${w} = vec4(0.);
                    }
                  `),g+=`
                  xR${v}C${w+1} = vec4(
                    xTexelR${v}C${w}.zw, xTexelR${v}C${w+2}.xy);
                `):g+=`
                  xCOffset = xC + ${S};

                  if(xR >= 0 && xR < ${s} &&
                    xCOffset >= 0 && xCOffset < ${i}) {
                    xTexelR${v}C${w+2} = getX(batch, xR, xCOffset, d1);
                  }

                  xR${v}C${w+1} = xTexelR${v}C${w+2};
                `}}else w<m&&(g+=`
              if(xR >= 0 && xR < ${s}) {
            `,l%2==1?(g+=`
                xCOffset = xC + 1 - ${c};
                if(xCOffset >= 0 && xCOffset < ${i}) {
                  xTexelR${v}C${w} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${v}C${w} = vec4(0.);
                }

                if(xC + 1 >= 0 && xC + 1 < ${i}) {
                  xTexelR${v}C${w+2} = getX(batch, xR, xC + 1, d1);
                } else {
                  xTexelR${v}C${w+2} = vec4(0.);
                }

                xR${v}C${w} = vec4(
                  xTexelR${v}C${w}.zw, xTexelR${v}C${w+2}.zw);
              `,w+1<m&&(g+=`
                  vec4 final = vec4(0.);
                  xCOffset = xC + 1 + ${c};
                  if(xCOffset >= 0 && xCOffset < ${i}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xR${v}C${w+1} = vec4(xTexelR${v}C${w+2}.xy, final.xy);
                `)):(g+=`
                if(xC >= 0 && xC < ${i}) {
                  xTexelR${v}C${w} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${v}C${w} = vec4(0.);
                }

                xCOffset = xC + ${c};
                if(xCOffset >= 0 && xCOffset < ${i}) {
                  xTexelR${v}C${w+2} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${v}C${w+2} = vec4(0.);
                }

                xR${v}C${w} = vec4(
                  xTexelR${v}C${w}.xy, xTexelR${v}C${w+2}.xy);
              `,w+1<m&&(g+=`
                  xR${v}C${w+1} = vec4(
                    xTexelR${v}C${w}.zw, xTexelR${v}C${w+2}.zw);
                `)),g+="}");w<m&&(g+=`
            vec4 wTexelR${v}C${w} = getW(${v}, ${w}, d1, q);
            wR${v}C${w} = vec4(wTexelR${v}C${w}.xz, wTexelR${v}C${w}.xz);
          `,w+1<m&&(g+=`
              vec4 wTexelR${v}C${w+1} = getW(${v}, ${w+1}, d1, q);
              wR${v}C${w+1} =
                vec4(wTexelR${v}C${w+1}.xz, wTexelR${v}C${w+1}.xz);`))}for(let v=0;v<h;v++)for(let k=0;k<m;k++)g+=`dotProd += xR${v}C${k} * wR${v}C${k};`;let y="",b="";n&&(a?y=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?y=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:y=`vec4 activation(vec4 x) {
          ${n}
        }`,b="result = activation(result);");let x=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${y}

      const ivec2 strides = ivec2(${u}, ${c});
      const ivec2 pads = ivec2(${o}, ${l});

      void main() {

        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2;
        int q = 0;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        vec4 dotProd = vec4(0.);

        ${g}

        vec4 result = dotProd;
        ${x}
        ${b}
        setOutput(result);
      }
    `}};function iY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,c=l;c==null&&(c=[1,1]),I.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=E.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),d;return Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new $T(p):d=new ET(p),n.runWebGLProgram(d,[r,s],"float32")}var oY={kernelName:Ms,backendName:"webgl",kernelFunc:iY},lY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${s} + dm;

        float dotProd = 0.0;

        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${a};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${r};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `}},uY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${s}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        float dotProd = 0.0;

        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${a}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${t} - 1 - wR;

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${n} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${o}; dm++) {
              int d2 = d1 * ${o} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function cY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=a,p=E.computeConv2DInfo(r.shape,c,i,o,l,u,!0),d=new lY(p);return n.runWebGLProgram(d,[r,s],"float32")}var pY={kernelName:xd,backendName:"webgl",kernelFunc:cY};function dY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=a,p=E.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new uY(p);return n.runWebGLProgram(d,[r,s],"float32")}var hY={kernelName:vd,backendName:"webgl",kernelFunc:dY},mY=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function fY(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=I.sizeFromShape(a.shape),i=ye({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new mY(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ye({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var gY={kernelName:wd,backendName:"webgl",kernelFunc:fY},yY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:p}=a;this.userCode=`
      const ivec2 strides = ivec2(${r}, ${s});
      const ivec2 pads = ivec2(${c}, ${p});
      const float neg_infinity = -3.4e38;

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.w;
        ivec2 outTopLeftCorner =
            coords.yz * strides - pads;
        int hBeg = outTopLeftCorner.x;
        int wBeg = outTopLeftCorner.y;

        float curVal = neg_infinity;
        for (int h = 0; h < ${i}; h++) {
          int hIn = hBeg + h * ${l};

          if (hIn >= 0 && hIn < ${t}) {
            for (int w = 0; w < ${o}; w++) {
              int wIn = wBeg + w * ${u};

              if (wIn >= 0 && wIn < ${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 bY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=E.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),c,p=new yY(u);c=n.runWebGLProgram(p,[r,s],"float32");let d=ye({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var xY={kernelName:ec,backendName:"webgl",kernelFunc:bY},vY="return (x >= 0.0) ? x : (exp(x) - 1.0);",wY=`
  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;
`,kY=Xe({opSnippet:vY,packedOpSnippet:wY}),IY={kernelName:Lo,backendName:"webgl",kernelFunc:kY},NY="return (b >= 1.0) ? a : a * (b + 1.0);",TY=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,SY=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new dp(TY,a.shape,r.shape):new pu(NY,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},CY={kernelName:Nd,backendName:"webgl",kernelFunc:SY},AY=`
  return vec4(equal(a, b));
`,FY="return float(a == b);",_Y=sn({opSnippet:FY,packedOpSnippet:AY,dtype:"bool"}),EY={kernelName:Wo,backendName:"webgl",kernelFunc:_Y},$Y=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${E.ERF_P};
  float a1 = ${E.ERF_A1};
  float a2 = ${E.ERF_A2};
  float a3 = ${E.ERF_A3};
  float a4 = ${E.ERF_A4};
  float a5 = ${E.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,DY=Xe({opSnippet:$Y}),MY={kernelName:zo,backendName:"webgl",kernelFunc:DY},DT="return exp(x);",MT=Xe({opSnippet:DT,packedOpSnippet:DT,cpuKernelImpl:D8}),RY={kernelName:Ps,backendName:"webgl",kernelFunc:MT};function Gv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(I.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ye({inputs:{x:s},backend:a,attrs:{shape:o}})}var PY={kernelName:Bo,backendName:"webgl",kernelFunc:Gv},RT="return exp(x) - 1.0;",OY=Xe({opSnippet:RT,packedOpSnippet:RT,cpuKernelImpl:M8}),LY={kernelName:Vo,backendName:"webgl",kernelFunc:OY},PT=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
      const float exponentMultiplier = ${r};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${i}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${a});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${a}; i++) {
          // x = (-2|2 * PI / N) * index * i;
          float x = exponentMultiplierTimesIndexRatio * float(i);
          float expR = cos(x);
          float expI = sin(x);
          float real = getReal(batch, i);
          float imag = getImag(batch, i);

          result +=
              unaryOpComplex(real, expR, imag, expI) / ${s};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function OT(e,t,n){let a=n.texData.get(e.dataId),r=I.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ye({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new PT("real",l,t),c=new PT("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),m=hs({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let f=ye({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function zY(e){let{inputs:t,backend:n}=e,{input:a}=t;return OT(a,!1,n)}var WY={kernelName:Td,backendName:"webgl",kernelFunc:zY},BY=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
      uniform float value;
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Hv(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||I.inferDtype(r),s==="string"){let i=I.getArrayFromDType(s,I.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new BY(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var VY={kernelName:tc,backendName:"webgl",kernelFunc:Hv},UY=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;
          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);
        }
    `}},GY={kernelName:Uo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new UY(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},LT="return floor(x);",HY=Xe({opSnippet:LT,packedOpSnippet:LT,cpuKernelImpl:R8}),jY={kernelName:Os,backendName:"webgl",kernelFunc:HY},qY=`
  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;
  }
`,XY=`
  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);
`,KY=sn({opSnippet:qY,packedOpSnippet:XY,dtype:"int32"}),YY={kernelName:Ls,backendName:"webgl",kernelFunc:KY},JY=class{constructor(e){this.variableNames=["A"];let t=mn(),[n,a]=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(${a}.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));
      }
    `}},QY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=mn(),[n,a]=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(${a}.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;
      }
    `}},e9={kernelName:Wd,backendName:"webgl",kernelFunc:ZY},hu;function ZY(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],p=[u,l,s];(o||i)&&(hu==null&&(hu=document.createElement("canvas").getContext("2d")),hu.canvas.width=l,hu.canvas.height=u,hu.drawImage(r,0,0,l,u),r=hu.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=na.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=Z().getBool("WEBGL_PACK")?new QY(p):new JY(p),m=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),m}function t9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=E.convertConv2DDataFormat(c),g=E.computeConv2DInfo(r.shape,s.shape,l,p,u,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=ST({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=CT({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,k=o!=null,w=h==="leakyrelu",S=h?Dm(h,!1):null,A=new TT(g,v,S,k,w),F=[r,s];if(i&&F.push(i),o&&F.push(o),w){let D=n.makeTensorInfo([],"float32",I.createScalarValue(m,"float32"));F.push(D),b.push(D)}y=n.runWebGLProgram(A,F,"float32")}let x=ye({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var n9={kernelName:yi,backendName:"webgl",kernelFunc:t9};function a9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=a,m=[],f=c;f==null&&(f=[1,1]),I.assert(E.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=E.computeConv2DInfo(r.shape,s.shape,l,f,u,p,!0),y=Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?Dm(d,y):null,x=[r,s],v=i!=null,k=o!=null,w=d==="leakyrelu";if(v&&x.push(i),k&&x.push(o),w){let F=n.makeTensorInfo([],"float32",I.createScalarValue(h,"float32"));x.push(F),m.push(F)}let S;y?S=new $T(g,v,b,k,w):S=new ET(g,v,b,k,w);let A=n.runWebGLProgram(S,x,"float32");return m.forEach(F=>n.disposeIntermediateTensorInfo(F)),A}var r9={kernelName:bi,backendName:"webgl",kernelFunc:a9},s9=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ht(t.length),r=ht(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${a} strides = ${a}(${this.strides});
         void main() {
          ${r} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${s};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}};function i9(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,u,c]=E.prepareAndValidate(a,r),p=ye({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=ye({inputs:{x:a},backend:n,attrs:{shape:[I.sizeFromShape(a.shape)/u,u]}}),h=new s9(i,c,[l,u]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=ye({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var o9={kernelName:Ho,backendName:"webgl",kernelFunc:i9},u9=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ht(this.rank),a=l9(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${a}));
      }
    `}};function l9(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function c9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=I.parseAxisParam(i,r.shape)[0],u=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=I.sizeFromShape(s.shape),p=[],d=ye({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=ye({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(d),p.push(h);let m=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),x=n.bufferSync(d),v=P8(x,b,m);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new u9(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=ye({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var p9={kernelName:Go,backendName:"webgl",kernelFunc:c9},d9="return float(a > b);",h9=`
  return vec4(greaterThan(a, b));
`,m9=sn({opSnippet:d9,packedOpSnippet:h9,cpuKernelImpl:O8,dtype:"bool"}),f9={kernelName:jo,backendName:"webgl",kernelFunc:m9},g9="return float(a >= b);",y9=`
  return vec4(greaterThanEqual(a, b));
`,b9=sn({opSnippet:g9,packedOpSnippet:y9,dtype:"bool"}),x9={kernelName:Ws,backendName:"webgl",kernelFunc:b9};function v9(e){let{inputs:t,backend:n}=e,{input:a}=t;return OT(a,!0,n)}var w9={kernelName:Sd,backendName:"webgl",kernelFunc:v9},k9="return float(!isnan(x) && !isinf(x));",I9=Xe({opSnippet:k9,dtype:"bool"}),N9={kernelName:qo,backendName:"webgl",kernelFunc:I9},T9="return float(isinf(x));",S9=Xe({opSnippet:T9,dtype:"bool"}),C9={kernelName:Xo,backendName:"webgl",kernelFunc:S9},A9="return float(isnan(x));",F9=Xe({opSnippet:A9,dtype:"bool"}),_9={kernelName:Ko,backendName:"webgl",kernelFunc:F9},E9="return float(a < b);",$9=`
  return vec4(lessThan(a, b));
`,D9=sn({opSnippet:E9,packedOpSnippet:$9,cpuKernelImpl:L8,dtype:"bool"}),M9={kernelName:Yo,backendName:"webgl",kernelFunc:D9},R9="return float(a <= b);",P9=`
  return vec4(lessThanEqual(a, b));
`,O9=sn({opSnippet:R9,packedOpSnippet:P9,dtype:"bool"}),L9={kernelName:Jo,backendName:"webgl",kernelFunc:O9};function z9(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=z8(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var W9={kernelName:Ad,backendName:"webgl",kernelFunc:z9},B9=`if (x < 0.0) return NAN;
  return log(x);`,V9=`
  vec4 result = log(x);
  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
  result.r = isNaN.r == 1.0 ? NAN : result.r;
  result.g = isNaN.g == 1.0 ? NAN : result.g;
  result.b = isNaN.b == 1.0 ? NAN : result.b;
  result.a = isNaN.a == 1.0 ? NAN : result.a;

  return result;
`,U9=Xe({opSnippet:B9,packedOpSnippet:V9,cpuKernelImpl:W8}),G9={kernelName:Us,backendName:"webgl",kernelFunc:U9},H9="return log(1.0 + x);",j9=Xe({opSnippet:H9}),q9={kernelName:Qo,backendName:"webgl",kernelFunc:j9},X9="return float(a >= 1.0 && b >= 1.0);",K9=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,Y9=sn({opSnippet:X9,packedOpSnippet:K9,dtype:"bool"}),J9={kernelName:Zo,backendName:"webgl",kernelFunc:Y9},Q9="return float(!(x >= 1.0));",Z9=Xe({opSnippet:Q9}),eJ={kernelName:nc,backendName:"webgl",kernelFunc:Z9},tJ="return float(a >= 1.0 || b >= 1.0);",nJ=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,aJ=sn({opSnippet:tJ,packedOpSnippet:nJ,dtype:"bool"}),rJ={kernelName:ac,backendName:"webgl",kernelFunc:aJ},sJ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${s}; j <= ${s}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${i}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${o};
        setOutput(val);
      }
    `}},iJ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

        bool hasNextCol = d < ${this.outputShape[3]};
        bool hasNextRow = c < ${this.outputShape[2]};

        vec4 sum = vec4(0.);
        vec4 xFragAtOutputCoords = getX(b, r, c, d);

        vec4 xAtOutputCoords = vec4(
          getChannel(xFragAtOutputCoords, vec2(c, d)),
          hasNextCol ?
            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
          hasNextRow ?
            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
          (hasNextRow && hasNextCol) ?
            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
        );

        int firstChannel = d - ${s};
        vec2 cache = vec2(0.);
        if(firstChannel >= 0){
          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
            if(hasNextRow){
              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
            }
        }

        ivec2 depth = ivec2(d, d + 1);
        for (int j = - ${s}; j <= ${s}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));

          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${o};
        setOutput(result);
      }
    `}},oJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=Z().getBool("WEBGL_PACK_NORMALIZATION")?new iJ(r.shape,s,i,o,l):new sJ(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},lJ={kernelName:rc,backendName:"webgl",kernelFunc:oJ},uJ=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];

        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${t})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${t} + 1)));

          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};

          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }

          norm = float(${a}) * norm + float(${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(${a})
                * float(${r})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${r});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},cJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=a,p=new uJ(r.shape,o,l,u,c);return n.runWebGLProgram(p,[r,s,i],r.dtype)},pJ={kernelName:Fd,backendName:"webgl",kernelFunc:cJ};function dJ(e,t,n,a){let r=I.sizeFromShape(t),s=I.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=ji(i,e.dtype,"max",a),l=ye({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function zT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=I.parseAxisParam(s,r.shape),u=l,c=E.getAxesPermutation(u,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let w=0;w<x.length;w++)x[w]=r.shape[c[w]];let v=Ov(b,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let k=n.texData.get(h.dataId);k.values=v}else h=Mm(r,c,n);u=E.getInnerMostAxes(u.length,o)}E.assertAxesAreInnerMostDims("max",u,o);let[m,f]=E.computeOutAndReduceShapes(h.shape,u),g=m;i&&(g=E.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,x=B8(b,I.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=dJ(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var hJ={kernelName:Gs,backendName:"webgl",kernelFunc:zT},mJ=nT+`
  return max(a, b);
`,fJ=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+$m+`
  return result;
`,gJ=sn({opSnippet:mJ,packedOpSnippet:fJ,cpuKernelImpl:V8}),yJ={kernelName:Hs,backendName:"webgl",kernelFunc:gJ};function bJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;cp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;I.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=E.computePool2DInfo(r.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&I.arraysEqual(c.inShape,c.outShape))return Gn({inputs:{x:r},backend:n});let p=new hp(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var xJ={kernelName:js,backendName:"webgl",kernelFunc:bJ};function vJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,c=[1,1,1],p=E.computePool3DInfo(r.shape,s,i,c,o,u,l),d=new Bv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var wJ={kernelName:sc,backendName:"webgl",kernelFunc:vJ},kJ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${o});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${r};
          wR += ${a}) {
          float dyR = float(dyRCorner + wR) / ${t}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${s}; wC++) {
            float dyC = float(dyCCorner + wC) / ${n}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));

            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${s} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}},IJ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=`
      const ivec3 pads = ivec3(${c}, ${p}, ${d});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${o};
           wD += ${r}) {
          float dyD = float(dyDCorner + wD) / ${t}.0;

          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${l};
              wR += ${s}) {
            float dyR = float(dyRCorner + wR) / ${n}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${u};
                wC += ${i}) {
              float dyC = float(dyCCorner + wC) / ${a}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${h} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${l} * ${u} +
                  wR * ${u} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function NJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=a,p=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,p,u,c),h=new Bv(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new IJ(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var TJ={kernelName:Ed,backendName:"webgl",kernelFunc:NJ};function SJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;cp([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=a,d=E.computePool2DInfo(o.shape,l,u,1,c,p),h=!0,m=new hp(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new kJ(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var CJ={kernelName:_d,backendName:"webgl",kernelFunc:SJ};function AJ(e,t,n,a){let r=new hp(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new hp(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var FJ={kernelName:$d,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;I.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];I.assert(E.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=E.computePool2DInfo(a.shape,r,s,u,i),[p,d]=AJ(a,o,c,l);return[p,d]}};function _J(e,t,n,a){let r=I.sizeFromShape(t),s=I.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=ji(i,"float32","mean",a),l=ye({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var EJ={kernelName:qs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=I.parseAxisParam(s,a.shape),u=l,c=E.getAxesPermutation(u,o),p=c!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let S=0;S<v.length;S++)v[S]=a.shape[c[S]];let k=Ov(x,a.shape,a.dtype,c,v);m=i.makeTensorInfo(v,a.dtype);let w=i.texData.get(m.dataId);w.values=k}else m=Mm(a,c,i);h.push(m),u=E.getInnerMostAxes(u.length,o)}E.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=E.computeOutAndReduceShapes(m.shape,u),y=f;r&&(y=E.expandShapeToKeepDim(f,l));let b=_J(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return b}};function $J(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=I.parseAxisParam(s,r.shape),u=l,c=E.getAxesPermutation(u,o),p=r;c!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:c}}),u=E.getInnerMostAxes(u.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",u,o);let[d,h]=E.computeOutAndReduceShapes(p.shape,u),m=I.sizeFromShape(h),f=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=ji(f,f.dtype,"min",n),y;if(i){let b=E.expandShapeToKeepDim(d,l);y=ye({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ye({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var DJ={kernelName:Xs,backendName:"webgl",kernelFunc:$J},MJ=nT+`
  return min(a, b);
`,RJ=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+$m+`
  return result;
`,PJ=sn({opSnippet:MJ,packedOpSnippet:RJ,cpuKernelImpl:U8}),OJ={kernelName:Ks,backendName:"webgl",kernelFunc:PJ},LJ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let a=e.length,r=ht(a),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
        int start = ${s};
        int end = ${i};

        void main() {
          int outC = getOutputCoords();
          if (outC < start) {
            outC = start * 2 - outC - ${l};
          } else if(outC >= end) {
            outC = (end - 1) * 2 - outC + ${l};
          }
          setOutput(getX(outC - start));
        }
      `;return}this.userCode=`
      ${r} start = ${r}(${s});
      ${r} end = ${r}(${i});

      void main() {
        ${r} outC = getOutputCoords();
        for (int i = 0; i < ${a}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${l};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
          }
        }
        ${r} coords = outC - start;
        setOutput(getX(${o}));
      }
    `}},zJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ht(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=fn("rc",a),l=fn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,c=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(a===1){let h=`
        ${r} source = rc;
        if (source < start) {
          source = start * 2 - source - ${p};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${p};
        }
        source -= start;
      `;d=`
        ${r} rc = outputLoc;
        ${h}
        result[0] = getChannel(getX(${l.join()}), ${c});
        ${o[a-1]} += 1;
        if(${u}) {
          ${h}
          result[1] = getChannel(getX(${l.join()}), ${c});
        }
      `}else{let h=`
        ${r} source = rc;
        ${r} lt = ${r}(lessThan(source, start));
        ${r} gte = ${r}(greaterThanEqual(source, end));
        ${r} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${p}) +
                gte * ((end - 1) * 2 - source + ${p});
        source -= start;
      `;d=`
        ${r} rc = outputLoc;
        ${h}
        result[0] = getChannel(getX(${l.join()}), ${c});
        ${o[a-1]} += 1;
        if(${u}) {
          ${h}
          result[1] = getChannel(getX(${l.join()}), ${c});
        }
        rc = outputLoc;
        ${o[a-2]} += 1;
        if(${o[a-2]} < ${this.outputShape[a-2]}) {
          ${h}
          result[2] = getChannel(getX(${l.join()}), ${c});
          ${o[a-1]} += 1;
          if(${u}) {
            ${h}
            result[3] = getChannel(getX(${l.join()}), ${c});
          }
        }
      `}this.userCode=`
      const ${r} start = ${r}(${s});
      const ${r} end = ${r}(${i});

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `}},WJ=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zJ(a.shape,r,s):new LJ(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},BJ={kernelName:ic,backendName:"webgl",kernelFunc:WJ},VJ=`if (b == 0.0) return NAN;
  return mod(a, b);`,UJ=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+$m+`
  return result;
`,GJ=sn({opSnippet:VJ,packedOpSnippet:UJ}),HJ={kernelName:el,backendName:"webgl",kernelFunc:GJ},jJ=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
      uniform float seed;

      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}));
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},qJ=`
if (a == b) {
  return 1.0;
};
return a / b;`,XJ=`
  // 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;
`,WT=sn({opSnippet:qJ,packedOpSnippet:XJ,checkOutOfBounds:!0}),KJ={kernelName:Rs,backendName:"webgl",kernelFunc:WT},BT="return a - b;",VT=sn({opSnippet:BT,packedOpSnippet:BT,supportsComplex:!0,cpuKernelImpl:J8}),YJ={kernelName:hi,backendName:"webgl",kernelFunc:VT};function UT(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=I.parseAxisParam([s],r.shape),o=zT({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=E.expandShapeToKeepDim(o.shape,i),u=ye({inputs:{x:o},backend:n,attrs:{shape:l}}),c=VT({inputs:{a:r,b:u},backend:n}),p=MT({inputs:{x:c},backend:n}),d=Wv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=ye({inputs:{x:d},backend:n,attrs:{shape:l}}),m=WT({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}var JJ={kernelName:pi,backendName:"webgl",kernelFunc:UT};function QJ(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:UT({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new jJ(u,c,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var ZJ={kernelName:Dd,backendName:"webgl",kernelFunc:QJ},GT="return -x;";function eQ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=H8(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new cu(a.shape,GT):r=new ds(a.shape,GT),n.runWebGLProgram(r,[a],a.dtype)}var tQ={kernelName:tl,backendName:"webgl",kernelFunc:eQ},nQ=Ya.nonMaxSuppressionV3Impl;function aQ(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),c=n.readSync(s.dataId),{selectedIndices:p}=nQ(u,c,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var rQ={kernelName:al,backendName:"webgl",kernelFunc:aQ},sQ=Ya.nonMaxSuppressionV4Impl;function iQ(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,c=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=sQ(c,p,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var oQ={kernelName:rl,backendName:"webgl",kernelFunc:iQ},lQ=Ya.nonMaxSuppressionV5Impl;function uQ(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,c=n.readSync(r.dataId),p=n.readSync(s.dataId),d=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=lQ(c,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var cQ={kernelName:sl,backendName:"webgl",kernelFunc:uQ},pQ=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${a}), float(${n}),
                      float(index == coords.y)));
      }
    `}},dQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=I.sizeFromShape(r.shape),u=new pQ(l,s,i,o),c=ye({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(u,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,s],h=ye({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},hQ={kernelName:Js,backendName:"webgl",kernelFunc:dQ};function zm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=fp({inputs:{input:a},backend:n}),s=zm({inputs:{x:r},backend:n}),i=Lm({inputs:{input:a},backend:n}),o=zm({inputs:{x:i},backend:n}),l=hs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Hv({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var mQ={kernelName:Il,backendName:"webgl",kernelFunc:zm};function HT(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=fp({inputs:{input:a},backend:n}),s=HT({inputs:{x:r},backend:n}),i=Lm({inputs:{input:a},backend:n}),o=zm({inputs:{x:i},backend:n}),l=hs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Hv({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var fQ={kernelName:il,backendName:"webgl",kernelFunc:HT};function gQ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Gv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{I.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),I.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let p=Gv({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),u=NT({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var yQ={kernelName:ol,backendName:"webgl",kernelFunc:gQ},bQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ht(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
        int start = ${s};
        int end = ${i};
        uniform float value;

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${r} start = ${r}(${s});
      ${r} end = ${r}(${i});
      uniform float value;

      void main() {
        ${r} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${r} coords = outC - start;
          setOutput(getX(${o}));
        }
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},xQ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ht(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=fn("rc",a),l=fn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,c=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
       if(${u}) {
      `,a===1?"":`}
       rc = outputLoc;
       ${o[a-2]} += 1;
       if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":`  ${o[a-1]} += 1;
         if(${u}) {`],d=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
        ${p[m]}
        if (${d}) {
          result[${m}] = float(value);
        } else {
          ${r} source = rc - start;
          result[${m}] = getChannel(getX(${l.join()}), ${c});
        }
      `;h+=a===1?"} ":"}}",this.userCode=`
      const ${r} start = ${r}(${s});
      const ${r} end = ${r}(${i});
      uniform float value;

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},jT=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xQ(r.shape,s,i):new bQ(r.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,l)},vQ={kernelName:Qs,backendName:"webgl",kernelFunc:jT},wQ=`
  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);
`,kQ=`
  // 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));
  `+$m+`
  return result;
`,IQ=sn({opSnippet:wQ,packedOpSnippet:kQ}),NQ={kernelName:Zs,backendName:"webgl",kernelFunc:IQ};function TQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=I.parseAxisParam(s,r.shape),c=u,p=E.getAxesPermutation(c,o),d=r;p!=null&&(d=_n({inputs:{x:r},backend:n,attrs:{perm:p}}),c=E.getInnerMostAxes(c.length,o),l.push(d)),E.assertAxesAreInnerMostDims("prod",c,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=j8(d.shape,d.dtype,m,c);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=E.computeOutAndReduceShapes(d.shape,c),g=I.sizeFromShape(f),y=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=jd(r.dtype),x=ji(y,b,"prod",n);h=ye({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=E.expandShapeToKeepDim(h.shape,u);h=ye({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var SQ={kernelName:ll,backendName:"webgl",kernelFunc:TQ},qT=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=q8(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},CQ={kernelName:oc,backendName:"webgl",kernelFunc:qT},AQ="return 1.0 / x;",FQ=Xe({opSnippet:AQ}),_Q={kernelName:ul,backendName:"webgl",kernelFunc:FQ},EQ=Ra+`
  return (x < 0.0) ? 0.0 : x;
`,$Q=`
  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;
`,DQ=Xe({opSnippet:EQ,packedOpSnippet:$Q}),MQ={kernelName:ti,backendName:"webgl",kernelFunc:DQ},RQ=Ra+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,PQ=`
  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;
`,OQ=Xe({opSnippet:RQ,packedOpSnippet:PQ}),LQ={kernelName:ai,backendName:"webgl",kernelFunc:OQ},zQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],c=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/c[0]},
          ${u[1]/c[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${p};

        // Compute the 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);
      }
    `}},WQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],c=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/c[0]},
          ${u[1]/c[1]},
          ${u[1]/c[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                     ${o}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${p};

        // Compute the four integer indices.
        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${l-1};
        bool hasNextRow = coords.z < ${n-1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `}};function BQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,c=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new WQ(r.shape,l,u,s,i):new zQ(r.shape,l,u,s,i);return n.runWebGLProgram(c,[r],"float32")}var VQ={kernelName:ni,backendName:"webgl",kernelFunc:BQ},UQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${c});

        const float invHeightScale = float(${p});
        const float invWidthScale = float(${d});

        const int winHeight = int(${h});
        const int winWidth = int(${m});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${s}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${i}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${a-1}.0));
            float dxRLerp = dxR - float(topDxRIndex);
            float inverseDxRLerp = 1.0 - dxRLerp;

            float dxC = float(dyC) * widthScale;
            int leftDxCIndex = int(floor(dxC));
            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
            float dxCLerp = dxC - float(leftDxCIndex);
            float inverseDxCLerp = 1.0 - dxCLerp;

            if (r == topDxRIndex && c == leftDxCIndex) {
              // topLeft
              accumulator +=
                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
            }

            if (r == topDxRIndex && c == rightDxCIndex) {
              // topRight
              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
            }

            if (r == bottomDxRIndex && c == leftDxCIndex) {
              // bottomLeft
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
            }

            if (r == bottomDxRIndex && c == rightDxCIndex) {
              // bottomRight
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}};function GQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new UQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var HQ={kernelName:Pd,backendName:"webgl",kernelFunc:GQ},jQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],c=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/c[0]},
          ${u[1]/c[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${d};

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}};function qQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,c=new jQ(r.shape,l,u,s,i);return n.runWebGLProgram(c,[r],r.dtype)}var XQ={kernelName:lc,backendName:"webgl",kernelFunc:qQ},KQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${c});

        const float invHeightScale = float(${p});
        const float invWidthScale = float(${d});

        const int winHeight = int(${h});
        const int winWidth = int(${m});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${s}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${i}) {
              continue;
            }

            float sourceFracRow =
              float(${o[0]}) *
                (float(dyR) / float(${l[0]}));

            float sourceFracCol =
                float(${o[1]}) *
                  (float(dyC) / float(${l[1]}));

            int sourceNearestRow = int(min(
                float(int(${a}) - 1),
                ${n} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${r}) - 1),
                ${n} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));

            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}};function YQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new KQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var JQ={kernelName:Rd,backendName:"webgl",kernelFunc:YQ},QQ=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 a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=ht(n);this.userCode=`
      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${r}));
      }
    `}},ZQ=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-${n} tensor is not yet supported`);this.outputShape=e;let a=fn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ht(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${r}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${i} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${o(a.slice())};
          if(${r}){
            result.g = ${l(a.slice())};
          }
          if(${s}) {
            result.b = ${u(a.slice())};
            if(${r}) {
              result.a = ${c(a.slice())};
            }
          }
          setOutput(result);
        }
    `;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,b)=>d(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function eZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=I.parseAxisParam(s,r.shape);if(i===0)return Gn({inputs:{x:r},backend:n});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ZQ(r.shape,o):new QQ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var tZ={kernelName:ri,backendName:"webgl",kernelFunc:eZ},nZ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
        vec3 fill = vec3(${t.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        uniform vec4 params;
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${r}
          if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},aZ={kernelName:Nl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new nZ(a.shape,s),[u,c]=E.getImageCenter(i,a.shape[1],a.shape[2]),p=l.getCustomSetupFunc(u,c,Math.sin(r),Math.cos(r));return o.runWebGLProgram(l,[a],a.dtype,p)}},rZ=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,sZ=Xe({opSnippet:rZ}),iZ={kernelName:si,backendName:"webgl",kernelFunc:sZ},oZ="return inversesqrt(x);",lZ=Xe({opSnippet:oZ,cpuKernelImpl:X8}),uZ={kernelName:ii,backendName:"webgl",kernelFunc:lZ},XT=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ht(r.length),l=ht(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
        ${o} strides = ${o}(${r});

        void main() {
          ${l} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${e}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${t}; j++) {
              int index = round(${c});
              flattenedIndex += index * ${h};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${d};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function cZ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=E.calculateShapes(s,r,i),d=[p/u,u];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=ye({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ye({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new XT(l,o,h.shape.length,m.shape.length,c,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=ye({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var pZ={kernelName:pl,backendName:"webgl",kernelFunc:cZ},dZ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);a=o.join(),r=l.join()}let s=ht(n);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        float cVal = getC(${a});
        if (cVal >= 1.0) {
          setOutput(getA(${r}));
        } else {
          setOutput(getB(${r}));
        }
      }
    `}};function hZ(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new dZ(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ua(r.dtype,s.dtype))}var mZ={kernelName:dl,backendName:"webgl",kernelFunc:hZ},fZ=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${E.SELU_SCALEALPHA};
  float scale = ${E.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,gZ=Xe({opSnippet:fZ}),yZ={kernelName:hl,backendName:"webgl",kernelFunc:gZ},bZ="return 1.0 / (1.0 + exp(-1.0 * x));",xZ=Xe({opSnippet:bZ}),vZ={kernelName:li,backendName:"webgl",kernelFunc:xZ},wZ=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,kZ=Xe({opSnippet:wZ}),IZ={kernelName:gl,backendName:"webgl",kernelFunc:kZ},NZ=oT+`
  return sin(x);
`,TZ=Xe({opSnippet:NZ}),SZ={kernelName:oi,backendName:"webgl",kernelFunc:TZ},CZ=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,AZ=Xe({opSnippet:CZ}),FZ={kernelName:fl,backendName:"webgl",kernelFunc:AZ},_Z=`
  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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    return x > 0.0 ? 1.0 : float(${t.alpha});
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      ${r} begin = ${r}(${e});
      ${r} strides = ${r}(${t});

      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}};function YZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=a,{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:b}=pn.sliceInfo(r.shape,s,i,o,l,u,c,p,d),x=ye({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let w=mp({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=ye({inputs:{x:w},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(w)}else if(b.some(w=>w===0))v=n.makeTensorInfo(b,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let w=n.texData.get(x.dataId).values,S=Le(x.shape,x.dtype,w),A=Y8(b,S,f,m);v=n.makeTensorInfo(b,x.dtype,A.values)}else{let w=new KZ(m,f,b);v=n.runWebGLProgram(w,[x],x.dtype)}let k=ye({inputs:{x:v},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),k}var JZ={kernelName:xl,backendName:"webgl",kernelFunc:YZ},QZ="return tan(x);",ZZ=Xe({opSnippet:QZ}),eee={kernelName:vl,backendName:"webgl",kernelFunc:ZZ},tee=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,nee=Xe({opSnippet:tee}),aee={kernelName:mi,backendName:"webgl",kernelFunc:nee},see=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=ht(this.rank),r=ree(e);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function ree(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function YT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"){let o=n.readSync(r.dataId).map(c=>I.decodeString(c)),l=Le(r.shape,r.dtype,o),u=Q8(l,s);return n.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new see(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var iee={kernelName:Ur,backendName:"webgl",kernelFunc:YT};function oee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,u]=Z8(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var lee={kernelName:wl,backendName:"webgl",kernelFunc:oee},uee=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${o} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${r});
              }
              return outputValue;
            }

            void main() {
              ivec4 coords = getOutputCoords();
              float outputValue;
              int batch = coords[0];
              int x = coords[2];
              int y = coords[1];
              int channel = coords[3];
              float xf = float(x);
              float yf = float(y);
              float a1 = getTransforms(batch, 0);
              float a2 = getTransforms(batch, 1);
              float a3 = getTransforms(batch, 2);
              float b1 = getTransforms(batch, 3);
              float b2 = getTransforms(batch, 4);
              float b3 = getTransforms(batch, 5);
              float c1 = getTransforms(batch, 6);
              float c2 = getTransforms(batch, 7);
              float projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = float(${r});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t}));
                float mapY = mapCoord(inY, float(${e}));

                if (${i} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `}};function cee(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[c,p,d,h]=r.shape,[m,f]=u!=null?u:[p,d],g=[c,m,f,h],y=new uee(p,d,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var pee={kernelName:Ld,backendName:"webgl",kernelFunc:cee};function dee(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;cp(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=eX(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var hee={kernelName:zd,backendName:"webgl",kernelFunc:dee};function mee(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),c=0;for(let f=0;f<o;f++)f!==s&&(u[c++]=i.shape[f]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){d[s]=f;let g=mp({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),y=ye({inputs:{x:g},backend:n,attrs:{shape:u}});m[f]=y,p.push(g)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var fee={kernelName:kl,backendName:"webgl",kernelFunc:mee},gee=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,p=`
        sumValue += dot(values, segFilter);
    `,d="";r%n>0&&(d=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return initializationValue;
        }
      `);let h="";r%n>0&&(h=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${o};

      float getValue(int batch, int inIdx) {
        ${d}
        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(
          ${s})) * float(${n}));
        int currentSeg = int(mod(float(outIdx), float(${s})));

        float sumValue = 0.0;

        for (int i = 0; i < ${u}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
          );

          ${p}
        }

        int inIdx = inOffset + ${u};
        if (${c===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
          );

          ${p}
        } else if (${c===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
          );

          ${p}
        } else if (${c===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
          );

          ${p}
        }
        setOutput(${l});
      }
    `}};function yee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,c=E.getAxesPermutation([u],o),p=r;c!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(p),u=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(p.shape,u,i),h=I.sizeFromShape([p.shape[u]]),m=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=jd(r.dtype),g=(v,k,w,S,A)=>{let F=v.shape[0],D=v.shape[1],R=E.segment_util.segOpComputeOptimalWindowSize(D,A),L={windowSize:R,inSize:D,batchSize:F,numSegments:A},W=new gee(L,k),U=n.compileAndRun(W,[v,w],S);if(l.push(U),U.shape[1]===A)return U;let G=qT({backend:n,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),X=YT({inputs:{x:G},backend:n,attrs:{reps:[D/R]}});return l.push(G),l.push(X),g(U,k,X,S,A)},y=g(m,"unsortedSegmentSum",s,f,i),b=ye({inputs:{x:y},backend:n,attrs:{shape:d}}),x=b;if(c!=null){l.push(b);let v=E.getUndoAxesPermutation(c);x=_n({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var bee={kernelName:pc,backendName:"webgl",kernelFunc:yee},xee=[lJ,pJ,YX,QX,tK,rK,iK,uK,pK,hK,yK,xK,kK,TK,$K,AK,RK,zK,OK,UK,HK,qK,JK,r7,i7,d7,m7,b7,w7,EX,T7,R7,O7,F7,B7,U7,z7,j7,K7,Q7,eY,nY,sY,pY,hY,oY,gY,xY,IY,CY,EY,MY,RY,PY,LY,WY,VY,GY,jY,YY,e9,n9,r9,o9,p9,f9,x9,_X,w9,N7,N9,C9,_9,DX,M9,L9,W9,q9,G9,J9,eJ,rJ,hJ,wJ,xJ,TJ,CJ,FJ,yJ,EJ,DJ,OJ,BJ,HJ,ZJ,LX,tQ,rQ,oQ,cQ,l7,hQ,fQ,yQ,vQ,NQ,RX,SQ,CQ,u7,KJ,_Q,LQ,MQ,WX,VQ,HQ,XQ,JQ,tZ,aZ,iZ,uZ,pZ,mZ,yZ,vZ,IZ,SZ,FZ,n7,JJ,$Z,MZ,PZ,LZ,BZ,GZ,jZ,XZ,JZ,YJ,qX,eee,aee,iee,lee,pee,XX,hee,fee,bee,mQ];for(let e of xee)hc(e);var vee="3.3.0",wee={"tfjs-core":Q0,"tfjs-backend-cpu":BU,"tfjs-backend-webgl":FX,"tfjs-data":TN,"tfjs-layers":im,"tfjs-converter":xN,tfjs:vee},Hn;(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"})(Hn||(Hn={}));var gp;(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"})(gp||(gp={}));var JT;function kee(e){JT=e.wasm.cwrap(gi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Iee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=a,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let A=n.dataIdMap.get(i.dataId);if(A.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);m=A.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=gp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=u?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,b],r.dtype),k=n.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return JT(d,w,r.shape.length,h,S,s.shape.length,l,u,g,m,f,p||0,k),v}var Nee={kernelName:gi,backendName:"wasm",setupFunc:kee,kernelFunc:Iee};function En(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return I.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Tee=En(So);function gn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:c}=l,p=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,m=E.assertAndGetBroadcastShape(u.shape,c.shape),f=o.makeOutput(m,h);if(I.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),b=o.dataIdMap.get(f.dataId).id,x=()=>a(p,g,u.shape.length,d,y,c.shape.length,Hn[u.dtype],b);if(t&&u.dtype==="float32")return x(),f;let v=E.getBroadcastDims(u.shape,m),k=E.getBroadcastDims(c.shape,m),w=v.every((A,F)=>A===F),S=k.every((A,F)=>A===F);if(w&&S)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var See=!0,Cee=gn(Br,See),QT;function Aee(e){QT=e.wasm.cwrap(Ns,null,["array","number","number","number"])}function Fee(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(I.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return QT(s,r.length,Hn[a.dtype],i),a}var _ee={kernelName:Ns,backendName:"wasm",setupFunc:Aee,kernelFunc:Fee};function Wm(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Eee={kernelName:Bs,backendName:"wasm",kernelFunc:Wm},ZT;function $ee(e){ZT=e.wasm.cwrap(fi,null,["number","array","number","number","number","array","number"])}function Bm(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Mee(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Dee(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Wm({inputs:t,backend:n});return m.shape=o,m}let u=n.makeOutput(o,l.dtype),c=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(u.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return ZT(c,h,l.shape.length,Hn[l.dtype],p,d,s.length),u}function Dee(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function Mee(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Ree={kernelName:fi,backendName:"wasm",kernelFunc:Bm,setupFunc:$ee};function mu(e,t,n){let a=e.shape,r=e.shape.length,s=I.parseAxisParam(t,a),i=s,o=E.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let c=new Array(r);for(let d=0;d<c.length;d++)c[d]=a[o[d]];i=E.getInnerMostAxes(i.length,r),l=Bm({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var eS;function Pee(e){eS=e.wasm.cwrap(Ts,null,["number","number","number","number","number"])}function Oee(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:c,inputWasTransposed:p}=mu(s,r,t);if(p){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let d=l.shape.slice(0,-1),h=t.makeOutput(d,"int32"),m=t.dataIdMap.get(h.dataId).id,f=I.sizeFromShape(h.shape),g=l.shape[c[0]];return eS(o,Hn[l.dtype],f,g,m),p&&t.disposeData(u.dataId),h}var Lee={kernelName:Ts,backendName:"wasm",kernelFunc:Oee,setupFunc:Pee},tS;function zee(e){tS=e.wasm.cwrap(Ss,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Wee(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=E.computePool2DInfo(r.shape,i,o,1,l,u),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,g=c.padInfo.left,y=c.strideHeight,b=c.strideWidth,x=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let v=a.makeOutput(c.outShape,"float32"),k=a.dataIdMap.get(v.dataId).id;return tS(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,k),v}var Bee={kernelName:Ss,backendName:"wasm",setupFunc:zee,kernelFunc:Wee};function Pa(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=I.sizeFromShape(a.shape),i=I.inferFromImplicitShape(r,s);return I.assert(s===I.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var Vee={kernelName:cl,backendName:"wasm",kernelFunc:Pa},nS;function Uee(e){nS=e.wasm.cwrap(Cs,null,["number","array","number","number","array","number","number","number","number"])}function Gee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,c=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=I.sizeFromShape(m),y=I.sizeFromShape(f),b=g===y||g===1||y===1;I.assert(l>=2&&u>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);I.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[g,c,d]:[g,d,c],k=o?[y,h,p]:[y,p,h],w=Pa({inputs:{x:r},backend:n,attrs:{shape:v}}),S=Pa({inputs:{x:s},backend:n,attrs:{shape:k}}),A=n.dataIdMap.get(w.dataId).id,F=n.dataIdMap.get(S.dataId).id,D=i?w.shape[2]:w.shape[1],R=o?S.shape[1]:S.shape[2],L=Math.max(g,y),W=n.makeOutput([L,D,R],w.dtype),U=n.dataIdMap.get(W.dataId).id,G=new Uint8Array(new Int32Array(w.shape).buffer),X=new Uint8Array(new Int32Array(S.shape).buffer);return nS(A,G,w.shape.length,F,X,S.shape.length,i,o,U),n.disposeData(w.dataId),n.disposeData(S.dataId),W.shape=x,W}var Hee={kernelName:Cs,backendName:"wasm",setupFunc:Uee,kernelFunc:Gee};function Vm(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var jee={kernelName:As,backendName:"wasm",kernelFunc:Vm},qee=En(Fs),aS;function Xee(e){aS=e.wasm.cwrap(Vr,null,["number","number","number","number"])}function Kee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return aS(o,s,i,u),l}var Yee={kernelName:Vr,backendName:"wasm",setupFunc:Xee,kernelFunc:Kee};function rS(e){let{inputs:t,backend:n}=e,a=I.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>I.sizeFromShape(h.shape)>0);if(s.length===1)return Wm({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(I.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(E.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=I.sizeFromShape(x.shape.slice(a));return Pa({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=E.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=fv(m,r,t[0].dtype,f),y=E.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=E.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let l=I.sizeFromShape(s[0].shape.slice(0,a)),u=0,c=s.map(h=>{let m=I.sizeFromShape(h.shape.slice(a));return u+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<l;h++){let m=h*u;for(let f=0;f<p.length;f++){let g=c[f],y=h*g,b=p[f].subarray(y,y+g);d.set(b,m),m+=g}}return i}var Jee={kernelName:Mo,backendName:"wasm",kernelFunc:rS},sS;function Qee(e){sS=e.wasm.cwrap(_s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zee(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=E.convertConv2DDataFormat(d),m=E.computeConv2DInfo(r.shape,s.shape,l,u,c,p,!1,h),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,b=m.padInfo.right,x=m.padInfo.bottom,v=m.padInfo.left,k=m.dilationHeight,w=m.dilationWidth,S=m.strideHeight,A=m.strideWidth,F=m.inChannels,D=m.outChannels,R=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let L=a.makeOutput(m.outShape,"float32"),W=a.dataIdMap.get(L.dataId).id;return sS(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,x,v,R,k,w,S,A,F,D,W),L}var ete={kernelName:_s,backendName:"wasm",setupFunc:Qee,kernelFunc:Zee},iS;function tte(e){iS=e.wasm.cwrap(Es,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 nte(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:c}=a,p=1,d=E.convertConv2DDataFormat(l),h=E.computeConv2DInfo(c,s.shape,i,p,o,u,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:x,outChannels:v,outHeight:k,outWidth:w,strideHeight:S,strideWidth:A}=h,F=f-1-h.padInfo.top,D=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",L=I.computeStrides(h.inShape),W=I.computeStrides(r.shape),[U,G,X]=I.computeStrides(s.shape),j=L[0],te=R?L[1]:L[2],Q=R?L[2]:1,se=R?1:L[1],ne=W[0],ie=R?W[1]:W[2],ee=R?W[2]:1,de=R?1:W[1],oe=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(oe.dataId).id,fe=t.dataIdMap.get(r.dataId).id,we=t.dataIdMap.get(s.dataId).id;return iS(fe,we,m,f,g,b,x,y,k,w,v,S,A,F,D,U,G,X,j,te,Q,se,ne,ie,ee,de,ge),oe}var ate={kernelName:Es,backendName:"wasm",setupFunc:tte,kernelFunc:nte},rte=En($s),jv;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(jv||(jv={}));var oS;function ste(e){oS=e.wasm.cwrap(Po,null,["number","number","number","number","array","number","number","number","number","number"])}function ite(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:u}=n,c=l.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Vm({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(h,"float32"),v=t.dataIdMap.get(x.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return oS(g,y,b,c,k,p,d,jv[r],s,v),f!=null&&t.disposeData(f.dataId),x}var ote={kernelName:Po,backendName:"wasm",setupFunc:ste,kernelFunc:ite},lS;function lte(e){lS=e.wasm.cwrap(Ds,null,["number","number","number","number","number","number"])}function ute(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;I.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=E.getAxesPermutation([s],l),c=r;u!==null&&(c=Bm({inputs:{x:r},attrs:{perm:u},backend:n}));let p=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],m=n.dataIdMap.get(c.dataId).id,f=n.dataIdMap.get(d.dataId).id;lS(m,i?1:0,o?1:0,h,f,Hn[r.dtype]);let g=d;if(u!==null){let y=E.getUndoAxesPermutation(u);g=Bm({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var cte={kernelName:Ds,backendName:"wasm",setupFunc:lte,kernelFunc:ute},uS;function pte(e){uS=e.wasm.cwrap(Oo,null,["number","number","number","array","number","array","array","number","number"])}function dte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;I.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=u*s,h=c/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(I.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(I.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return uS(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var hte={kernelName:Oo,backendName:"wasm",setupFunc:pte,kernelFunc:dte},cS;function mte(e){cS=e.wasm.cwrap(Ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=E.computeConv2DInfo(r.shape,s.shape,l,d,c,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,k=h.dilationWidth,w=h.strideHeight,S=h.strideWidth,A=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=a.makeOutput(h.outShape,"float32"),L=a.dataIdMap.get(R.dataId).id;return cS(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,D,v,k,w,S,A,F,L),R}var gte={kernelName:Ms,backendName:"wasm",setupFunc:mte,kernelFunc:fte},yte=!1,bte=gn(Wo,yte,"bool"),xte=En(Ps);function qv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(I.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Pa({inputs:{x:r},backend:a,attrs:{shape:o}})}var vte={kernelName:Bo,backendName:"wasm",kernelFunc:qv};function wte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var kte={kernelName:tc,backendName:"wasm",kernelFunc:wte},pS;function Ite(e){pS=e.wasm.cwrap(Uo,null,["number","number","number","number","number","number"])}function Nte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,c]=a.shape;return pS(s,o,l,u,c,i),r}var Tte={kernelName:Uo,backendName:"wasm",kernelFunc:Nte,setupFunc:Ite},Ste=En(Os),Cte=!1,Ate=gn(Ls,Cte),dS;function Fte(e){dS=e.wasm.cwrap(zs,null,["number","number","number","number","number","number","number"])}function _te(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(I.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return dS(c,p,d,h,m,r,g),f}var Ete={kernelName:zs,backendName:"wasm",setupFunc:Fte,kernelFunc:_te},hS;function $te(e){hS=e.wasm.cwrap(yi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d),g=gp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return hS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var Mte={kernelName:yi,backendName:"wasm",setupFunc:$te,kernelFunc:Dte},mS;function Rte(e){mS=e.wasm.cwrap(bi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!0),g=gp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let k=f.filterHeight,w=f.filterWidth,S=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,R=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,G=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return mS(y,j,te,Q,b,k,w,v,S,A,F,D,X,R,L,W,U,G,x,g,ie,m||0,ne),se}var Ote={kernelName:bi,backendName:"wasm",setupFunc:Rte,kernelFunc:Pte},fS;function Lte(e){fS=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number","array","number"])}function zte(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=hy.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let c=r.shape,p=c[c.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return fS(d,Hn[a.dtype],h,i,p,o,m,f),u}var Wte={kernelName:Ho,backendName:"wasm",setupFunc:Lte,kernelFunc:zte},gS;function Bte(e){gS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Vte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=I.parseAxisParam(i,r.shape)[0],u=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Pa({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),p=I.sizeFromShape(s.shape),d=Pa({inputs:{x:s},attrs:{shape:[u.batchSize,p/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize],m=t.makeOutput(h,r.dtype);if(I.sizeFromShape(r.shape)===0)return m;let f=c.shape.length-1,g=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(I.computeStrides(c.shape)).buffer),v=new Uint8Array(new Int32Array(I.computeStrides(h)).buffer);return gS(g,Hn[r.dtype],x,f,y,u.batchSize,v,b),t.disposeData(c.dataId),t.disposeData(d.dataId),m.shape=u.outputShape,m}var Ute={kernelName:Go,backendName:"wasm",setupFunc:Bte,kernelFunc:Vte},Gte=!1,Hte=gn(jo,Gte,"bool"),jte=!1,qte=gn(Ws,jte,"bool"),yS;function Xte(e){yS=e.wasm.cwrap(Vs,null,["number","number","number"])}function Kte(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(I.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;yS(r,n,i)}return s}var Yte={kernelName:Vs,backendName:"wasm",setupFunc:Xte,kernelFunc:Kte},Jte=!1,Qte=gn(Yo,Jte,"bool"),Zte=!1,ene=gn(Jo,Zte,"bool"),tne=En(Us),nne=!1,ane=gn(Zo,nne,"bool"),bS;function rne(e){bS=e.wasm.cwrap(Gs,null,["number, number, number"])}function sne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:p,inputWasTransposed:d}=mu(i,r,t);if(d){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",c,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,c),g=I.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(I.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;bS(o,g,b)}if(d&&t.disposeData(u.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var ine={kernelName:Gs,backendName:"wasm",setupFunc:rne,kernelFunc:sne},one=!1,lne=gn(Hs,one),xS;function une(e){xS=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=E.computePool2DInfo(r.shape,i,o,1,l,u),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,g=c.padInfo.left,y=c.dilationHeight,b=c.dilationWidth,x=c.strideHeight,v=c.strideWidth,k=c.inChannels,w=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(c.outShape,"float32"),A=a.dataIdMap.get(S.dataId).id;return xS(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,k,w,A),S}var pne={kernelName:js,backendName:"wasm",setupFunc:une,kernelFunc:cne},vS;function dne(e){vS=e.wasm.cwrap(qs,null,["number, number, number"])}function hne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(u=c,l=v,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Vm({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(I.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;vS(l,y,v)}if(h&&t.disposeData(c.dataId),s){let v=E.expandShapeToKeepDim(x.shape,d);x.shape=v}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var mne={kernelName:qs,backendName:"wasm",setupFunc:dne,kernelFunc:hne},wS;function fne(e){wS=e.wasm.cwrap(Xs,null,["number, number, number"])}function gne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x)}let m=u.shape.length;E.assertAxesAreInnerMostDims("min",p,m);let[f,g]=E.computeOutAndReduceShapes(u.shape,p),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;wS(l,y,x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var yne={kernelName:Xs,backendName:"wasm",setupFunc:fne,kernelFunc:gne},bne=!1,xne=gn(Ks,bne),vne=!0,wne=gn(Ys,vne),kne=En(tl);function Xv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var kS;function Ine(e){kS=e.wasm.cwrap(al,"number",["number","number","number","number","number"])}function Nne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(l.dataId).id,p=kS(u,c,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=Xv(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Tne={kernelName:al,backendName:"wasm",setupFunc:Ine,kernelFunc:Nne},IS;function Sne(e){IS=e.wasm.cwrap(rl,"number",["number","number","number","number","number","bool"])}function Cne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=IS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Xv(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Ane={kernelName:rl,backendName:"wasm",setupFunc:Sne,kernelFunc:Cne},NS;function Fne(e){NS=e.wasm.cwrap(sl,"number",["number","number","number","number","number","number"])}function _ne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=NS(c,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=Xv(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Ene={kernelName:sl,backendName:"wasm",setupFunc:Fne,kernelFunc:_ne},$ne=!1,Dne=gn(nl,$ne,"bool"),TS;function Mne(e){TS=e.wasm.cwrap(Js,null,["number","number","number","number","number"])}function Rne(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,c=n.dataIdMap.get(r.dataId).id;return TS(c,s,i,o,u),l}var Pne={kernelName:Js,backendName:"wasm",setupFunc:Mne,kernelFunc:Rne};function One(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Lne={kernelName:il,backendName:"wasm",kernelFunc:One};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return qv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{I.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),I.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let p=qv({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),u=rS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),u}var Wne={kernelName:ol,backendName:"wasm",kernelFunc:zne},SS;function Bne(e){SS=e.wasm.cwrap(Qs,null,["number","array","number","number","array","array","number","number"])}function Vne(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return SS(i,u,t.shape.length,Hn[t.dtype],d,h,r,l),o}var Une={kernelName:Qs,backendName:"wasm",kernelFunc:Vne,setupFunc:Bne},Gne=!1,Hne=gn(Zs,Gne),CS;function jne(e){CS=e.wasm.cwrap(ei,null,["number","number","number"])}function qne(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return CS(s,i,l),o}var Xne={kernelName:ei,backendName:"wasm",setupFunc:jne,kernelFunc:qne},AS;function Kne(e){AS=e.wasm.cwrap(ll,null,["number","number","number","number"])}function Yne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=mu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==o&&(u=c,l=x,m=E.getInnerMostAxes(m.length,u.shape.length))}E.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=E.computeOutAndReduceShapes(u.shape,m),y=I.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(I.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;AS(l,y,Hn[b.dtype],x)}if(h&&t.disposeData(c.dataId),s){let x=E.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Jne={kernelName:ll,backendName:"wasm",setupFunc:Kne,kernelFunc:Yne},Qne=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=bv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Zne={kernelName:oc,backendName:"wasm",kernelFunc:Qne},eae=!0,tae=gn(Rs,eae),nae=En(ti),aae=En(ai),FS;function rae(e){FS=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number"])}function sae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[c,p,d,h]=r.shape,m=[c,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=Vm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(I.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return FS(y,c,p,d,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var iae={kernelName:ni,backendName:"wasm",setupFunc:rae,kernelFunc:sae},_S;function oae(e){_S=e.wasm.cwrap(ri,null,["number","array","number","array","number","number"])}function lae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=I.parseAxisParam(s,r.shape);if(r.shape.length===0)return Wm({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);_S(l,c,i.length,p,r.shape.length,u);let d=Pa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var uae={kernelName:ri,backendName:"wasm",kernelFunc:lae,setupFunc:oae},ES;function cae(e){ES=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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Zm=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function ku(e,t){return(n,a,r)=>{let s=Ca(e(3*3*n),[3,3,n,1]),i=Ca(e(n*a),[1,1,n,a]),o=Qe(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Zm(s,i,o)}}function Iu(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Zm(n,a,r)}}function jn(e,t){return(n,a,r)=>{let s=e[n];if(!qi(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function xn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function ef(e,t){let n=wu(e,t),a=ku(e,t);function r(i,o,l,u=!1){let c=u?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:c,conv1:p,conv2:d}}function 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Ap(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return JS(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),QS(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var yw=["neutral","happy","sad","angry","fearful","disgusted","surprised"],gs=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);yw.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return yw.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var rf=class extends Fp{constructor(t=new Cp){super("FaceExpressionNet",t)}forwardInput(t){return M(()=>Ta(this.runNet(t)))}async forward(t){return this.forwardInput(await mt(t))}async predictExpressions(t){let n=await mt(t),a=await this.forwardInput(n),r=await Promise.all(ct(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new gs(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function bw(e){return e.expressions instanceof gs}function sf(e,t){return{...e,...{expressions:t}}}function Ere(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof gs?s:bw(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),u=sr(s)?s.detection.box.bottomLeft:a||new De(0,0);new fs(l.map(p=>`${p.expression} (${Xi(p.probability)})`),u).draw(e)})}function no(e){return sr(e)&&e.landmarks instanceof ra&&e.unshiftedLandmarks instanceof ra&&e.alignedRect instanceof yt}function $re(e){let t=(o,l,u,c)=>Math.atan2(c-l,u-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>o<l._y?o:l._y,Infinity),i=r.reduce((o,l)=>o>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function Nu(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new 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a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return rC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=af(t);return this.faceFeatureExtractor.loadFromWeightMap(n),sC(a)}extractParams(t){let n=512*1+1+(512*2+2),a=t.slice(0,t.length-n),r=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var _p=class extends Fp{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return M(()=>{let i=(p,d)=>Dt([Sn([68],p,"float32"),Sn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return 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256}};function iC(e){let t=[],{extractDenseBlock3Params:n}=nf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return bn(e,t),{params:a,paramMappings:t}}function oC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=xn(e),{extractDenseBlock3Params:r}=ef(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var Iw=class extends on{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return M(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=La(a,[122.782,117.001,104.298]).div(ce(255)),i=Jm(s,n.dense0,!0);return i=Jm(i,n.dense1),i=Jm(i,n.dense2),i=Qn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await 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pC(e){let{extractWeights:t,getRemainingWeights:n}=xn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=Ore(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),u=s(9216,32,3,"conv32_3"),c=s(36864,64,3,"conv64_down",!0),p=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),k=M(()=>Ve(Sa(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:u,conv64_down:c,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:k},paramMappings:a}}function Lre(e,t){let n=jn(e,t);function 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or=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cw=class extends on{constructor(t){super("TinyYolov2");Sw(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=_r(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=_r(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=_r(a,n.conv6),a=_r(a,n.conv7),to(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Cu(to(t,n.conv0,"valid",!1)):Er(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Er(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Er(a,n.conv6):a,a=n.conv7?Er(a,n.conv7):a,to(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let r=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?La(r,this.config.meanRgb):r,r=r.div(ce(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await mt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new or(n),s=await mt(t),i=await this.forwardInput(s,a),o=M(()=>ct(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let c=u.map(g=>g.box),p=u.map(g=>g.score),d=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return nw(c.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new ms(p[g],d[g],h[g],c[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return AC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Cw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return CC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],c=this.config.anchors.length,[p,d,h]=M(()=>{let y=t.reshape([u,u,c,this.boxEncodingSize]),b=y.slice([0,0,0,0],[u,u,c,4]),x=y.slice([0,0,0,4],[u,u,c,1]),v=this.withClassScores?Ta(y.slice([0,0,0,5],[u,u,c,this.config.classes.length]),3):ce(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<u;y++)for(let b=0;b<u;b++)for(let x=0;x<c;x++){let v=vp(f[y][b][x][0]);if(!a||v>a){let k=(b+vp(g[y][b][x][0]))/u*o,w=(y+vp(g[y][b][x][1]))/u*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/u*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/u*l,F=k-S/2,D=w-A/2,R={row:y,col:b,anchor:x},{classScore:L,label:W}=this.withClassScores?await this.extractPredictedClass(h,R):{classScore:1,label:0};m.push({box:new gu(F,D,F+S,D+A),score:v,classScore:v*L,label:W,...R})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Au=Cw;Au.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Fu=class extends Au{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:wC,classes:["face"],...t?{anchors:IC,meanRgb:NC}:{anchors:kC,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(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?SC:TC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Zre(e,t=!0){let n=new Fu(t);return n.extractWeights(e),n}var mf=class extends or{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var wa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function so(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>no(l)?r(l):l.detection),i=a||(t instanceof Fe?await vu(t,s):await xu(t,s)),o=await n(i);return i.forEach(l=>l instanceof Fe&&l.dispose()),o}async function _u(e,t,n,a,r){return so([e],t,async s=>n(s[0]),a,r)}var FC=.4,_C=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],EC=[117.001,114.697,97.404];var Eu=class extends Au{constructor(){let t={withSeparableConvs:!0,iouThreshold:FC,classes:["face"],anchors:_C,meanRgb:EC,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(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Ze={ssdMobilenetv1:new ro,tinyFaceDetector:new Eu,tinyYolov2:new Fu,faceLandmark68Net:new Tu,faceLandmark68TinyNet:new lf,faceRecognitionNet:new Su,faceExpressionNet:new rf,ageGenderNet:new of},$C=(e,t)=>Ze.ssdMobilenetv1.locateFaces(e,t),ese=(e,t)=>Ze.tinyFaceDetector.locateFaces(e,t),tse=(e,t)=>Ze.tinyYolov2.locateFaces(e,t),DC=e=>Ze.faceLandmark68Net.detectLandmarks(e),nse=e=>Ze.faceLandmark68TinyNet.detectLandmarks(e),ase=e=>Ze.faceRecognitionNet.computeFaceDescriptor(e),rse=e=>Ze.faceExpressionNet.predictExpressions(e),sse=e=>Ze.ageGenderNet.predictAgeAndGender(e),MC=e=>Ze.ssdMobilenetv1.load(e),ise=e=>Ze.tinyFaceDetector.load(e),ose=e=>Ze.tinyYolov2.load(e),lse=e=>Ze.faceLandmark68Net.load(e),use=e=>Ze.faceLandmark68TinyNet.load(e),cse=e=>Ze.faceRecognitionNet.load(e),pse=e=>Ze.faceExpressionNet.load(e),dse=e=>Ze.ageGenderNet.load(e),hse=MC,mse=$C,fse=DC;var Aw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Mu=class extends Aw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>sf(a,n[r]))}withAgeAndGender(){return new $u(this,this.input)}},Ru=class extends Aw{async run(){let t=await this.parentTask;if(!t)return;let n=await _u(t,this.input,a=>Ze.faceExpressionNet.predictExpressions(a),this.extractedFaces);return sf(t,n)}withAgeAndGender(){return new Du(this,this.input)}},lo=class extends Mu{withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},uo=class extends Ru{withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var Fw=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},$u=class extends Fw{async run(){let t=await this.parentTask,n=await so(t,this.input,async a=>Promise.all(a.map(r=>Ze.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return pf(df(a,i,o),s)})}withFaceExpressions(){return new Mu(this,this.input)}},Du=class extends Fw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await _u(t,this.input,s=>Ze.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return pf(df(t,a,r),n)}withFaceExpressions(){return new Ru(this,this.input)}},io=class extends $u{withFaceExpressions(){return new lo(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},oo=class extends Du{withFaceExpressions(){return new uo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var ff=class extends wa{constructor(t,n){super();this.parentTask=t;this.input=n}},ys=class extends ff{async run(){let t=await this.parentTask;return(await so(t,this.input,a=>Promise.all(a.map(r=>Ze.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>cf(t[r],a))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}},bs=class extends ff{async run(){let t=await this.parentTask;if(!t)return;let n=await _u(t,this.input,a=>Ze.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return cf(t,n)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}};var gf=class extends wa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Ze.faceLandmark68TinyNet:Ze.faceLandmark68Net}},yf=class extends gf{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Fe?await vu(this.input,n):await xu(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Fe&&s.dispose()),t.map((s,i)=>Nu(s,r[i]))}withFaceExpressions(){return new lo(this,this.input)}withAgeAndGender(){return new io(this,this.input)}withFaceDescriptors(){return new ys(this,this.input)}},bf=class extends gf{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Fe?await vu(this.input,[n]):await xu(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Fe&&s.dispose()),Nu(t,r)}withFaceExpressions(){return new uo(this,this.input)}withAgeAndGender(){return new oo(this,this.input)}withFaceDescriptor(){return new bs(this,this.input)}};var xf=class extends wa{constructor(t,n=new va){super();this.input=t;this.options=n}},$p=class extends xf{async run(){let{input:t,options:n}=this,a;if(n instanceof mf)a=Ze.tinyFaceDetector.locateFaces(t,n);else if(n instanceof va)a=Ze.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof or)a=Ze.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>Yi({},a)))})}withFaceLandmarks(t=!1){return new yf(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Mu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new $u(this.runAndExtendWithFaceDetections(),this.input)}},vf=class extends xf{async run(){let t=await new $p(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Yi({},n):void 0)})}withFaceLandmarks(t=!1){return new bf(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ru(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Du(this.runAndExtendWithFaceDetection(),this.input)}};function gse(e,t=new va){return new vf(e,t)}function wf(e,t=new va){return new $p(e,t)}async function RC(e,t){return wf(e,new va(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function yse(e,t={}){return wf(e,new or(t)).withFaceLandmarks().withFaceDescriptors()}var bse=RC;function _w(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var kf=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Cr)return i;if(i instanceof Float32Array)return new Cr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Cr(s(),[i.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(a=>_w(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new wp(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new wp("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Cr.fromJSON(a));return new kf(n,t.distanceThreshold)}};function xse(e){let t=new Eu;return t.extractWeights(e),t}function PC(e,t){let{width:n,height:a}=new yn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>PC(r,{width:n,height:a}));if(no(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Nu(Yi(e,r),s)}return sr(e)?Yi(e,e.detection.forSize(n,a)):e instanceof ra||e instanceof yt?e.forSize(n,a):e}var vse=typeof process!="undefined",wse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",kse={faceapi:eC,node:vse,browser:wse};export{of as AgeGenderNet,gu as BoundingBox,ut as Box,wa as ComposableTask,ys as ComputeAllFaceDescriptorsTask,ff as ComputeFaceDescriptorsTaskBase,bs as ComputeSingleFaceDescriptorTask,yf as DetectAllFaceLandmarksTask,$p as DetectAllFacesTask,gf as DetectFaceLandmarksTaskBase,xf as DetectFacesTaskBase,bf as DetectSingleFaceLandmarksTask,vf as DetectSingleFaceTask,yn as Dimensions,yw as FACE_EXPRESSION_LABELS,yt as FaceDetection,vC as FaceDetectionNet,rf as FaceExpressionNet,gs as FaceExpressions,Tu as FaceLandmark68Net,lf as FaceLandmark68TinyNet,lC as FaceLandmarkNet,ra as FaceLandmarks,HS as FaceLandmarks5,bu as FaceLandmarks68,wp as FaceMatch,kf as FaceMatcher,Su as FaceRecognitionNet,Fr as Gender,kp as LabeledBox,Cr as LabeledFaceDescriptors,Ar as NetInput,on as NeuralNetwork,ms as ObjectDetection,De as Point,jS as PredictedBox,yu as Rect,ro as SsdMobilenetv1,va as SsdMobilenetv1Options,Eu as TinyFaceDetector,mf as TinyFaceDetectorOptions,Fu as TinyYolov2,or as TinyYolov2Options,bse as allFaces,RC as allFacesSsdMobilenetv1,yse as allFacesTinyYolov2,pw as awaitMediaLoaded,dw as bufferToImage,ase as computeFaceDescriptor,Zi as createCanvas,Tp as createCanvasFromMedia,Yre as createFaceDetectionNet,zre as createFaceRecognitionNet,xC as createSsdMobilenetv1,xse as createTinyFaceDetector,Zre as createTinyYolov2,wf as detectAllFaces,DC as detectFaceLandmarks,nse as detectFaceLandmarksTiny,fse as detectLandmarks,gse as detectSingleFace,ZS as draw,st as env,_w as euclideanDistance,pf as extendWithAge,cf as extendWithFaceDescriptor,Yi as extendWithFaceDetection,sf as extendWithFaceExpressions,Nu as extendWithFaceLandmarks,df as extendWithGender,vu as extractFaceTensors,xu as extractFaces,Are as fetchImage,fw as fetchJson,Fre as fetchNetWeights,eo as fetchOrThrow,$n as getContext2dOrThrow,Qi as getMediaDimensions,hw as imageTensorToCanvas,mw as imageToSquare,xre as inverseSigmoid,ew as iou,Km as isMediaElement,Np as isMediaLoaded,Wre as isWithAge,sr as isWithFaceDetection,bw as isWithFaceExpressions,no as isWithFaceLandmarks,Bre as isWithGender,dse as loadAgeGenderModel,hse as loadFaceDetectionModel,pse as loadFaceExpressionModel,lse as loadFaceLandmarkModel,use as loadFaceLandmarkTinyModel,cse as loadFaceRecognitionModel,MC as loadSsdMobilenetv1Model,ise as loadTinyFaceDetectorModel,ose as loadTinyYolov2Model,gw as loadWeightMap,mse as locateFaces,_re as matchDimensions,tw as minBbox,Ze as nets,nw as nonMaxSuppression,La as normalize,aw as padToSquare,sse as predictAgeAndGender,rse as recognizeFaceExpressions,PC as resizeResults,Ji as resolveInput,bre as shuffleArray,vp as sigmoid,$C as ssdMobilenetv1,Gw as tf,ese as tinyFaceDetector,tse as tinyYolov2,mt as toNetInput,GS as utils,Sw as validateConfig,kse as version};
/**
 * @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
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 * =============================================================================
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