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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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,I,T)=>{(v==null||I==null||T==null)&&(v=y.sourceLayer,I=y.nodeIndex,T=y.tensorIndex);let _=v.inboundNodes[I];if(x.indexOf(_)!==-1)throw new Ga(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(b.indexOf(_)!==-1)return;this.containerNodes.add(nr.nodeKey(v,I)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(_)===-1&&x.push(_);let A=_.inboundLayers.length;for(let F=0;F<A;F++){let P=_.inputTensors[F],$=_.inboundLayers[F],S=_.nodeIndices[F],M=_.tensorIndices[F];o(P,b,x,$,S,M)}for(b.push(_);x.indexOf(_)>=0;)x.splice(x.indexOf(_),1);i.push(_)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let p=i.slice().reverse();for(let y of p){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 I=y.inboundLayers[v],T=y.nodeIndices[v],_=I.inboundNodes[T],A=t[_.id]==null?0:t[_.id];t[_.id]=Math.max(b+1,A),n[_.id]=_}}let d={};for(let y in t){let b=t[y];b in d||(d[b]=[]),d[b].push(n[y])}let c={};for(let y in a){let b=a[y];b in c||(c[b]=[]),c[b].push(r[y])}let h=Object.keys(c).map(y=>parseInt(y,10)).sort(ch);this.layers=[];for(let y of h){let b=c[y];b.sort((x,v)=>{let I=s[x.id],T=s[v.id];return I<T?-1:I>T?1:0});for(let x of b)x instanceof nr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=c,h=Object.keys(d).map(y=>parseInt(y,10)).sort(ch);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of d[y]){let x=b.outboundLayer;if(x!=null){for(let v of b.inputTensors)if(m.indexOf(v)===-1)throw new Ga(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of b.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(x=>x===y).length;if(b!==1)throw new Ga(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Lf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new H("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new H(`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 H(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new H(`${s.length} of ${a} weights are not set: ${s}`)}fw(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Nw}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=tx(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return O(()=>{e=xt(e);let n=new Zs;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Wp(this.outputs,n,t)})}computeMask(e,t){return O(()=>{e=xt(e);let n;return t==null?n=hi(null,e.length):n=xt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Uh(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";n[u]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(ch);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(u.id)!==-1)continue;let p=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,x=n[b];p.push(x)}let d=u.computeOutputShape(Ln(p)),c=Uh(d),h=u.inboundNodes.indexOf(l);for(let m=0;m<c.length;m++){let f=`${u.name}_${h}_${m}`;n[f]=c[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],p=`${o.name}_${l}_${u}`;s.push(p)}for(let i=0;i<s.length;i++){let o=s[i];rr(o in n),r.push(n[o])}return Ln(r)}runInternalGraph(e,t){t==null&&(t=hi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],p=t[o];n[l.id]=[u,p]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(ch);for(let o of a){let l=this.nodesByDepth[o];for(let u of l){let p=u.outboundLayer,d=u.inputTensors,c=u.outputTensors,h=new Array;for(let m of d)m.id in n&&h.push(n[m.id]);if(h.length===d.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=xt(p.call(x,m)),b=xt(p.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=xt(p.call(f,m)),b=xt(p.computeMask(f,g));if(p.activityRegularizer)throw new Pe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<c.length;++x){let v=c[x],I=y[x],T=b[x];n[v.id]=[I,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){rr(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 nr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=nr.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 H(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new H("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new H(`No such layer: ${e}`)}calculateLosses(){return O(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=nr.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 p=0;p<s.inboundNodes.length;p++){let d=s.inboundNodes[p],c=nr.nodeKey(s,p),h={};if(this.containerNodes.has(c)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let m=[];for(let f=0;f<d.inboundLayers.length;f++){let g=d.inboundLayers[f],y=d.nodeIndices[f],b=d.tensorIndices[f],x=nr.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=nr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let p=this.inputLayersTensorIndices[s];a.push([i.name,u,p])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=nr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let p=this.outputLayersTensorIndices[s];r.push([i.name,u,p])}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],I=x[1],T=x[2];if(b=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let _=r[v];if(_.inboundNodes.length<=I){i(f,g);return}let A=_.inboundNodes[I];y.push(A.outputTensors[T])}y.length>0&&f.apply(Ln(y),b)}function l(f){let g=f.name,y=qa(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let u=t.name,p=t.layers;for(let f of p)l(f);for(;!R4(s);)for(let f of p){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 d=[],c=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];rr(g in r);let x=r[g].inboundNodes[y].outputTensors;d.push(x[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];rr(g in r);let x=r[g].inboundNodes[y].outputTensors;c.push(x[b])}return new e({inputs:d,outputs:c,name:u})}get stateful(){if(this._stateful)throw new H("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){O(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function iU(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: ${JSON.stringify(e)}`)}function m2(e,t){return iU(e,t,"classWeight")}async function f2(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=O(()=>{if(e.shape.length===1)return or(e);if(e.shape.length===2){if(e.shape[1]>1)return ui(e,1);if(e.shape[1]===1)return W(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await r.data());Me(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])}),Ke(i,"float32")}else return null}function oU(e,t){return B(e,t)}var lU=32;function g2(e,t){let n,a,r=t;n=r.xs,a=r.ys,w.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 ${t}`);let s=$k("input",e.inputNames,n),i=$k("output",e.outputNames,a),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function $k(e,t,n){if(n instanceof Ae)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new H(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function uU(e){if(e.length===3)throw new Pe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function pU(e,t,n){let a=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!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}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,s,i;if(r)if(Fk(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=uU(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;r?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let p=i2(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=o2(p,d,n.epochs,null,null,cU(t,n),null,r,u);c.setModel(e),e.history=h,await c.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await c.onEpochBegin(m);let y=0,b=0;for(a||(f=await t.iterator());!a||y<n.batchesPerEpoch;){let x=await f.next();if(a&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Xs(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=Nn.getSaveHandlers(e);if(i.length===0)throw new H(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new H(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new H("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Nn.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:vU,generatedBy:`TensorFlow.js tfjs-layers v${Nw}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Nn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=Nn.concatenateArrayBuffers([n.data,o])}return 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Dw.className="ThresholdedReLU";se.registerClass(Dw);var Rw=class extends Ye{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Cw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=ze(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}};Rw.className="Softmax";se.registerClass(Rw);function ul(e,t,n){if(typeof e=="number")return hi(e,t);if(e.length!==t)throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!H4(r))throw new H(`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 Ka(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 sr(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+rs([n-t,0]);else if(a==="same")e=e*t;else throw new H(`Unsupport padding mode: ${a}.`);return e}function Mw(e,t){return O(()=>(Pt(t),t==="channelsFirst"?$e(e,[0,2,3,1]):e))}function R2(e,t){return O(()=>(Pt(t),t==="channelsFirst"?$e(e,[0,2,3,4,1]):e))}function $U(e,t,n,a=1,r="valid",s,i=1){return O(()=>{if(s==null&&(s=Ya()),Pt(s),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=$e(e,[0,2,1])),r==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=qm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Qa(o,n)),o})}function Lk(e,t,n,a=[1,1],r="valid",s,i,o=null){return O(()=>{if(s==null&&(s=Ya()),Pt(s),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Mw(e,s);if(r==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=bl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=$e(l,[0,3,1,2])),l})}function FU(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ya()),Pt(s),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=R2(e,s);if(r==="causal")throw new Pe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=vv(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Qa(o,n)),s==="channelsFirst"&&(o=$e(o,[0,4,1,2,3])),o})}var Pw=class extends Ye{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Pw.verifyArgs(t),this.rank=e,en(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Pe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=ul(t.kernelSize,e,"kernelSize"),this.strides=ul(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ya(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Pt(this.dataFormat),this.activation=is(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=St(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Xt(t.biasConstraint),this.biasRegularizer=Nt(t.biasRegularizer),this.activityRegularizer=Nt(t.activityRegularizer),this.dilationRate=ul(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new H(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new H(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new H(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(rr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!iw(e.kernelSize,"number",1,3))throw new H(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ss(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),biasConstraint:Kt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},sd=class extends Pw{constructor(e,t){super(e,t),this.kernel=null,sd.verifyArgs(t),this.filters=t.filters,en(this.filters,"filters"),this.kernelInitializer=St(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Xt(t.kernelConstraint),this.kernelRegularizer=Nt(t.kernelRegularizer)}build(e){e=it(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return O(()=>{e=ze(e);let n,a=this.bias==null?null:this.bias.read(),r=VN(this.activation.getClassName());if(r!=null&&this.rank===2)n=Lk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=$U(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Lk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=FU(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Pe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=it(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=Ka(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:Et(this.kernelInitializer),kernelRegularizer:ht(this.kernelRegularizer),kernelConstraint:Kt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new H(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},id=class extends sd{constructor(e){super(2,e),id.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!iw(e.kernelSize,"number",1,2))throw new H(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};id.className="Conv2D";se.registerClass(id);var od=class extends sd{constructor(e){super(3,e),od.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new H(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};od.className="Conv3D";se.registerClass(od);var Ow=class extends id{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=it(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=ze(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let 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],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=sr(o,d,u,this.padding),m=sr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=$e(n,[0,2,3,1]));let g=Km(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=$e(g,[0,3,1,2])),this.bias!=null&&(g=Qa(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=it(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]=sr(t[a],o,s,this.padding),t[r]=sr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ow.className="Conv2DTranspose";se.registerClass(Ow);var Lw=class extends od{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=it(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=ze(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=sr(l,m,d,this.padding),b=sr(u,f,c,this.padding),x=sr(p,g,h,this.padding),v=[r,y,b,x,this.filters];this.dataFormat!=="channelsLast"&&(n=$e(n,[0,2,3,4,1]));let I=wv(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=$e(I,[0,4,1,2,3])),this.bias!==null&&(I=Qa(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=it(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=sr(t[a],u,i,this.padding),t[r]=sr(t[r],p,o,this.padding),t[s]=sr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Lw.className="Conv3DTranspose";se.registerClass(Lw);var M2=class extends sd{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new H(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Xt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Xt(t.pointwiseConstraint)}build(e){if(e=it(e),e.length<this.rank+2)throw new H(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{e=ze(e);let n;if(this.rank===1)throw new Pe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=$e(e,[0,2,3,1])),n=ks(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Qa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=$e(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=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.pointwiseRegularizer=ht(this.pointwiseRegularizer),e.depthwiseConstraint=Kt(this.depthwiseConstraint),e.pointwiseConstraint=Kt(this.pointwiseConstraint),e}};M2.className="SeparableConv";var zw=class extends M2{constructor(e){super(2,e)}};zw.className="SeparableConv2D";se.registerClass(zw);var Wf=class extends sd{constructor(e){super(1,e),Wf.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"&&!iw(e.kernelSize,"number",1,1))throw new H(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Wf.className="Conv1D";se.registerClass(Wf);var Bw=class extends Ye{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=ze(e),this.dataFormat==="channelsLast"){let n=hh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return hh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=hh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return hh(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Bw.className="Cropping2D";se.registerClass(Bw);var Ww=class extends Ye{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,V4(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=ze(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=$e(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Sr.resizeNearestNeighbor(n,[r,s]):Sr.resizeBilinear(n,[r,s]);return $e(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Sr.resizeNearestNeighbor(n,[r,s]):Sr.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};Ww.className="UpSampling2D";se.registerClass(Ww);function DU(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ya()),Pt(r);let i=Mw(e,r);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=xs(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=$e(i,[0,3,1,2])),i})}var Vw=class extends Pw{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Xt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=it(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],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 O(()=>{e=ze(e);let n=DU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Qa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=it(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=Ka(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ka(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=Et(this.depthwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.depthwiseConstraint=Kt(this.depthwiseRegularizer),e}};Vw.className="DepthwiseConv2D";se.registerClass(Vw);function P2(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new H("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 O2(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new H(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Xa(2,l));if(t=$e(t,u),s!=null)throw new Pe("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=le(le(r,"bool"),"float32"),r.rank===l-1&&(r=mn(r,-1)),r=$e(r,u)),a&&(t=ma(t,0),r!=null&&(r=ma(r,0)));let p=[],d,c=n,h=t.shape[0],m=mt(t),f;r!=null&&(f=mt(r));for(let y=0;y<h;++y){let b=m[y],x=O(()=>e(b,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[y],T=ce(ta(I),I),_=Z(B(x[0],I),B(c[0],T)),A=c.map((F,P)=>Z(B(x[1][P],I),B(F,T)));return{output:_,newStates:A}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Rt(p,1)),[d,g,c]})}var fr=class extends Ye{constructor(e){super(e);let t;if(e.cell==null)throw new H("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Gf({cells:e.cell}):t=e.cell,t.stateSize==null)throw new H("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Xa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Zb(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 O(()=>{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){if(this.numConstants!=null)throw new Pe("Constants support is not implemented in RNN yet.");Zb(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new zt({shape:[t,null,...n]});let a=[e[0]].concat(e.slice(2));this.cell.build(a);let r;if(Array.isArray(this.cell.stateSize)?r=this.cell.stateSize:r=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),r))throw new H(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=r.map(s=>new zt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_=[It([n,this.cell.stateSize])];else if(e==null)Me(this.states_),this.keptStates!=null&&(Me(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_[0]=It([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Me(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(!w.arraysEqual(r.shape,i))throw new H(`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=P2(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 zt({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 Ha){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return O(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=ze(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 H(`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=O2((c,h)=>{let m=this.cell.call([c].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],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return O(()=>{let t=It(e.shape);return t=be(t,[1,2]),t=ed(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Yb(t,[1,n]):t):this.cell.stateSize>1?[Yb(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()===fr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=qa(a,n);return new e(Object.assign(t,{cell:r}))}};fr.className="RNN";se.registerClass(fr);var ld=class extends Ye{},Vf=class extends ld{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=is(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=xl([1,rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=xl([1,rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=it(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 O(()=>{if(e=e,e.length!==2)throw new H(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>ta(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>ta(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=lr(B(e,s),this.kernel.read()):r=lr(e,this.kernel.read()),this.bias!=null&&(r=Qa(r,this.bias.read())),i!=null&&(n=B(n,i));let o=Z(r,lr(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:ss(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),recurrentConstraint:Kt(this.recurrentConstraint),biasConstraint:Kt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Vf.className="SimpleRNNCell";se.registerClass(Vf);var Uw=class extends fr{constructor(e){e.cell=new Vf(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Me(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Me(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)}};Uw.className="SimpleRNN";se.registerClass(Uw);var Uf=class extends ld{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,en(this.units,"units"),this.activation=is(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=is(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=xl([1,rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=xl([1,rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=it(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 O(()=>{if(e=e,e.length!==2)throw new H(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>ta(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>ta(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=B(e,r[0]));let u=lr(e,this.kernel.read());this.useBias&&(u=Qa(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,s[0]));let p=this.recurrentKernel.read(),[d,c]=Wn(p,[2*this.units,this.units],p.rank-1),h=lr(a,d),[m,f,g]=Wn(u,3,u.rank-1),[y,b]=Wn(h,2,h.rank-1);i=this.recurrentActivation.apply(Z(m,y)),o=this.recurrentActivation.apply(Z(f,b));let x=lr(B(o,a),c);l=this.activation.apply(Z(g,x));let v=Z(B(i,a),B(Z(1,vt(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ss(this.activation),recurrentActivation:ss(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),recurrentConstraint:Kt(this.recurrentConstraint),biasConstraint:Kt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Uf.className="GRUCell";se.registerClass(Uf);var Gw=class extends fr{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 Uf(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Me(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Me(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)}};Gw.className="GRU";se.registerClass(Gw);var ud=class extends ld{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=is(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=is(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=xl([1,rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=xl([1,rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=it(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 Aa{apply(i,o){let l=r.apply([s]),u=new $f().apply([s]),p=r.apply([s*2]);return kk(kk(l,u),p)}},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 O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>ta(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>ta(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0<this.dropout&&this.dropout<1&&(e=B(e,s[0]));let d=lr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,i[0])),d=Z(d,lr(a,this.recurrentKernel.read())),this.useBias&&(d=Qa(d,this.bias.read()));let[c,h,m,f]=Wn(d,4,d.rank-1);o=this.recurrentActivation.apply(c),l=this.recurrentActivation.apply(h),u=Z(B(l,r),B(o,this.activation.apply(m))),p=this.recurrentActivation.apply(f);let g=B(p,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ss(this.activation),recurrentActivation:ss(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),recurrentConstraint:Kt(this.recurrentConstraint),biasConstraint:Kt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};ud.className="LSTMCell";se.registerClass(ud);var Hw=class extends fr{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 ud(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Me(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Me(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)}};Hw.className="LSTM";se.registerClass(Hw);var Gf=class extends ld{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 O(()=>{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){Zb(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{ti(`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(qa(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 Qb(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]])}fw(t)}};Gf.className="StackedRNNCells";se.registerClass(Gf);function os(e){let{ones:t,rate:n,training:a=!1,count:r=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),n):XN(t(),n),o=()=>nd(i,t,a);return!r||r<=1?Qt(o().clone()):Array(r).fill(void 0).map(o).map(l=>Qt(l.clone()))}var RU=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},L2=class extends fr{constructor(e){if(e.unroll)throw new Pe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Pe("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new zt({ndim:5})]}call(e,t){return O(()=>{if(this.cell.dropoutMask!=null&&(Me(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Me(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new H("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,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 O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new vr("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 H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_=[It(r)];else if(e==null)Me(this.states_),this.keptStates!=null&&(Me(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_[0]=It(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Me(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!w.arraysEqual(i.shape,o))throw new H(`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],p=Ka(l,a[0],r,s[0],i[0]),d=Ka(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};L2.className="ConvRNN2D";var Hf=class extends ud{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,en(this.filters,"filters"),this.kernelSize=ul(n,2,"kernelSize"),this.kernelSize.forEach(o=>en(o,"kernelSize")),this.strides=ul(a||1,2,"strides"),this.strides.forEach(o=>en(o,"strides")),this.padding=r||"valid",ya(this.padding),this.dataFormat=s||"channelsLast",Pt(this.dataFormat),this.dilationRate=ul(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>en(o,"dilationRate"))}build(e){var t;e=it(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[n]}`);let 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 Aa{apply(p,d){let c=l.apply([u]),h=Zn([u]),m=l.apply([u*2]);return ow([c,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 O(()=>{if(e.length!==3)throw new H(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=os({ones:()=>ta(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(te,re,Q)=>!re||!re[Q]?te:B(re[Q],te),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=os({ones:()=>ta(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,v,I,T]=Wn(this.kernel.read(),i,b),[_,A,F,P]=this.useBias?Wn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,_,this.padding),p=this.inputConv(p,v,A,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,T,P,this.padding);let[$,S,M,U]=Wn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),y=this.recurrentConv(y,U);let j=this.recurrentActivation.apply(Z(u,m)),q=this.recurrentActivation.apply(Z(p,f)),K=Z(B(q,s),B(j,this.activation.apply(Z(d,g)))),Y=B(this.recurrentActivation.apply(Z(c,y)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=RU(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=Dt(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Qa(r,n,this.dataFormat):r}recurrentConv(e,t){return Dt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Hf.className="ConvLSTM2DCell";se.registerClass(Hf);var jw=class extends L2{constructor(e){let t=new Hf(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};jw.className="ConvLSTM2D";se.registerClass(jw);var jf=class extends Ye{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 O(()=>{this.invokeCallHook(e,t);let n=ze(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return nd(()=>XN(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()}};jf.className="Dropout";se.registerClass(jf);var qw=class extends jf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};qw.className="SpatialDropout1D";se.registerClass(qw);var Kw=class extends Ye{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,en(this.units,"units"),this.activation=is(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Xt(e.kernelConstraint),this.biasConstraint=Xt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=it(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=it(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=ze(e),a=VN(this.activation.getClassName()),r;return a!=null?r=lr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=lr(n,this.kernel.read()),this.bias!=null&&(r=Qa(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ss(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Kt(this.kernelConstraint),biasConstraint:Kt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Kw.className="Dense";se.registerClass(Kw);var Xw=class extends Ye{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=it(e);for(let t of e.slice(1))if(t==null)throw new H(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new H(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({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 O(()=>{let n=t.training==null?!1:t.training,a=ze(e),r=a.shape,s=r.length,i=Xa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=hi(1,s);l[o]=r[o];let u=i.slice();u.sort();let p=!w.arraysEqual(u,Xa(0,s).slice(0,s-1)),d=()=>{if(p){let 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e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),movingMeanInitializer:Et(this.movingMeanInitializer),movingVarianceInitializer:Et(this.movingVarianceInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer),betaConstraint:Kt(this.betaConstraint),gammaConstraint:Kt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};d0.className="BatchNormalization";se.registerClass(d0);var h0=class extends Ye{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=St(e.betaInitializer||"zeros"),this.gammaInitializer=St(e.gammaInitializer||"ones"),this.betaRegularizer=Nt(e.betaRegularizer),this.gammaRegularizer=Nt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=it(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!==Qr(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=ze(e),a=n.shape,r=a.length;return O(()=>{let{mean:s,variance:i}=jc(n,this.axis,!0),o=hi(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],c=[];for(let h=0;h<r;++h)this.axis.indexOf(h)!==-1?(d.push(a[h]),c.push(1)):(d.push(1),c.push(a[h]));return s=zn(s,d),i=zn(i,d),u!=null&&(u=zn(u,c)),p!=null&&(p=zn(p,c)),uc(n,s,i,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};h0.className="LayerNormalization";se.registerClass(h0);function zU(e,t,n){return O(()=>{if(e.rank!==4)throw new H(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ya()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. 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length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=it(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 O(()=>zU(ze(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};m0.className="ZeroPadding2D";se.registerClass(m0);function qf(e,t,n,a,r,s){return O(()=>{Pt(r),GN(s),ya(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Ya()),s==null&&(s="max"),e=Mw(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Mt(e,t,n,o):i=fa(e,t,n,o),r==="channelsFirst"&&(i=$e(i,[0,3,1,2])),i})}function z2(e,t,n,a,r,s){return O(()=>{Pt(r),GN(s),ya(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ya()),s==null&&(s="max"),e=R2(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Ov(e,t,n,o):i=uv(e,t,n,o),r==="channelsFirst"&&(i=$e(i,[0,4,1,2,3])),i})}var B2=class extends Ye{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 H(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(en(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new H(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ya(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=it(e);let t=Ka(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return O(()=>{this.invokeCallHook(e,t),e=ed(ze(e),2);let n=this.poolingFunction(ze(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Is(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},f0=class extends B2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),ya(a),qf(e,t,n,a,r,"max")}};f0.className="MaxPooling1D";se.registerClass(f0);var g0=class extends B2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),ya(a),qf(e,t,n,a,r,"avg")}};g0.className="AveragePooling1D";se.registerClass(g0);var W2=class extends Ye{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 H(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),ya(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=it(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ka(t,this.poolSize[0],this.padding,this.strides[0]),n=Ka(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 O(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(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}},y0=class extends W2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),ya(a),qf(e,t,n,a,r,"max")}};y0.className="MaxPooling2D";se.registerClass(y0);var b0=class extends W2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),ya(a),qf(e,t,n,a,r,"avg")}};b0.className="AveragePooling2D";se.registerClass(b0);var V2=class extends Ye{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 H(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),ya(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=it(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=Ka(t,this.poolSize[0],this.padding,this.strides[0]),n=Ka(n,this.poolSize[1],this.padding,this.strides[1]),a=Ka(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 O(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(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}},x0=class extends V2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),ya(a),z2(e,t,n,a,r,"max")}};x0.className="MaxPooling3D";se.registerClass(x0);var v0=class extends V2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),ya(a),z2(e,t,n,a,r,"avg")}};v0.className="AveragePooling3D";se.registerClass(v0);var U2=class extends Ye{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Pe}},w0=class extends U2{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=ze(e);return _t(n,1)})}};w0.className="GlobalAveragePooling1D";se.registerClass(w0);var k0=class extends U2{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=ze(e);return Sa(n,1)})}};k0.className="GlobalMaxPooling1D";se.registerClass(k0);var G2=class extends Ye{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Pe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},I0=class extends G2{call(e,t){return O(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?_t(n,[1,2]):_t(n,[2,3])})}};I0.className="GlobalAveragePooling2D";se.registerClass(I0);var S0=class extends G2{call(e,t){return O(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?Sa(n,[1,2]):Sa(n,[2,3])})}};S0.className="GlobalMaxPooling2D";se.registerClass(S0);var H2=class extends Ye{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=qa(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},N0=class extends H2{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=it(e),e.length<3)throw new H(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=it(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 O(()=>(e=ze(e),O2((n,a)=>[ze(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};N0.className="TimeDistributed";se.registerClass(N0);function BU(e){ko(W4,"BidirectionalMergeMode",e)}var WU="concat",T0=class extends H2{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=qa(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=qa(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?WU:e.mergeMode,BU(this.mergeMode),e.weights)throw new Pe("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()):Ln(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=P2(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 H("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(p=>new 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a=k("elementShape",e,t,n),r=k("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,n),o=e.op==="TensorListReserve"?-1:i,l=qH(a,r,i,o);return n.addTensorList(l),[l.idTensor]}case"TensorListGather":{let a=k("tensorListId",e,t,n),r=k("indices",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=k("tensorListId",e,t,n),r=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=k("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=k("tensor",e,t,n),r=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=jH(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let a=k("tensorListId",e,t,n),r=n.getTensorList(a.id),s=k("dtype",e,t,n),i=k("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=k("tensorListId",e,t,n),r=k("tensor",e,t,n),s=n.getTensorList(a.id);return 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r=k("outputShape",e,t,n),s=k("strides",e,t,n),i=Ih(e,t,n);return[a.conv2dTranspose(k("x",e,t,n),k("filter",e,t,n),r,[s[1],s[2]],i)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=k("strides",e,t,n),s=Ih(e,t,n),i=k("dilations",e,t,n),o=k("dataFormat",e,t,n).toUpperCase();return[a.depthwiseConv2d(k("input",e,t,n),k("filter",e,t,n),[r[1],r[2]],s,o,[i[1],i[2]])]}case"Conv3D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("dataFormat",e,t,n).toUpperCase(),o=k("dilations",e,t,n);return[a.conv3d(k("x",e,t,n),k("filter",e,t,n),[r[1],r[2],r[3]],s,i,[o[1],o[2],o[3]])]}case"AvgPool":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.avgPool(k("x",e,t,n),[i[1],i[2]],[r[1],r[2]],s)]}case"MaxPool":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.maxPool(k("x",e,t,n),[i[1],i[2]],[r[1],r[2]],s)]}case"MaxPoolWithArgmax":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n),o=k("includeBatchInIndex",e,t,n),{result:l,indexes:u}=a.maxPoolWithArgmax(k("x",e,t,n),[i[1],i[2]],[r[1],r[2]],s,o);return[l,u]}case"AvgPool3D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.avgPool3d(k("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"MaxPool3D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.maxPool3d(k("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"Dilation2D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("dilations",e,t,n),o=r[1],l=r[2],u=i[1],p=i[2];return[a.dilation2d(k("x",e,t,n),k("filter",e,t,n),[o,l],s,[u,p],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ZH=(e,t,n,a=un)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,n),s=k("dtype",e,t,n),i=k("value",e,t,n);return[a.fill(r,i,s)]}case"LinSpace":{let r=k("start",e,t,n),s=k("stop",e,t,n),i=k("num",e,t,n);return[a.linspace(r,s,i)]}case"Multinomial":{let r=k("logits",e,t,n),s=k("numSamples",e,t,n),i=k("seed",e,t,n);return[a.multinomial(r,s,i)]}case"OneHot":{let r=k("indices",e,t,n),s=k("depth",e,t,n),i=k("onValue",e,t,n),o=k("offValue",e,t,n);return[a.oneHot(r,s,i,o)]}case"Ones":return[a.ones(k("shape",e,t,n),k("dtype",e,t,n))];case"OnesLike":return[a.onesLike(k("x",e,t,n))];case"RandomStandardNormal":return[a.randomStandardNormal(k("shape",e,t,n),k("dtype",e,t,n),k("seed",e,t,n))];case"RandomUniform":return[a.randomUniform(k("shape",e,t,n),k("minval",e,t,n),k("maxval",e,t,n),k("dtype",e,t,n))];case"Range":{let r=k("start",e,t,n),s=k("stop",e,t,n),i=k("step",e,t,n);return[a.range(r,s,i,k("dtype",e,t,n))]}case"TruncatedNormal":{let r=k("shape",e,t,n),s=k("mean",e,t,n),i=k("stdDev",e,t,n),o=k("seed",e,t,n);return[a.truncatedNormal(r,s,i,k("dtype",e,t,n),o)]}case"Zeros":return[a.zeros(k("shape",e,t,n),k("dtype",e,t,n))];case"ZerosLike":return[a.zerosLike(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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s=r.cast(k("condition",e,t,n),"bool"),i=[await r.whereAsync(s)];return s.dispose(),i}case"ListDiff":return r.setdiff1dAsync(k("x",e,t,n),k("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},e6=(e,t,n,a=un)=>{switch(e.op){case"LowerBound":{let r=k("sortedSequence",e,t,n),s=k("values",e,t,n);return[a.lowerBound(r,s)]}case"TopKV2":{let r=k("x",e,t,n),s=k("k",e,t,n),i=k("sorted",e,t,n),o=a.topk(r,s,i);return[o.values,o.indices]}case"UpperBound":{let r=k("sortedSequence",e,t,n),s=k("values",e,t,n);return[a.upperBound(r,s)]}case"Unique":{let r=k("x",e,t,n),s=a.unique(r);return[s.values,s.indices]}case"UniqueV2":{let r=k("x",e,t,n),s=k("axis",e,t,n),i=a.unique(r,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},t6=(e,t,n,a=un)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let r=k("default",e,t,n);return[In(e.name,t,n)||r];case"Placeholder":return[In(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let p=k("x",e,t,n);return[kr(p)]}case"IdentityN":return k("x",e,t,n).map(p=>kr(p));case"Snapshot":let s=k("x",e,t,n);return[kr(s)];case"Shape":return[a.tensor1d(k("x",e,t,n).shape,"int32")];case"ShapeN":return k("x",e,t,n).map(p=>a.tensor1d(p.shape));case"Size":return[a.scalar(k("x",e,t,n).size,"int32")];case"Rank":return[a.scalar(k("x",e,t,n).rank,"int32")];case"NoOp":return[a.scalar(1)];case"Print":let i=k("x",e,t,n),o=k("data",e,t,n),l=k("message",e,t,n),u=k("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(l);for(let p=0;p<o.length;p++)console.log(Array.prototype.slice.call(o[p].dataSync()).slice(0,u));return[i];default:throw TypeError(`Node type ${e.op} is not implemented`)}},n6=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=ke(0),this.tensorMap=new Map,Qt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),O(()=>{let a=mt(t),r=n.length,s=a.length;w.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i<r;i++){let o=n[i],l=a[i];Qt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return O(()=>{let a=[];for(let r=0;r<n.length;r++){let s=n[r],i=this.findWithDefault(s,t);a.push(i)}return Rt(a)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},a6=async(e,t,n,a)=>{switch(e.op){case"HashTable":case"HashTableV2":{let r=k("keyDType",e,t,n),s=k("valueDType",e,t,n),i=new n6(r,s);return a.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let r=k("tableHandle",e,t,n,a),s=k("keys",e,t,n),i=k("values",e,t,n);return[await a.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let r=k("tableHandle",e,t,n,a),s=k("keys",e,t,n),i=k("defaultValue",e,t,n);return[await a.getHashTableById(r.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=k("tableHandle",e,t,n,a);return[a.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},r6=(e,t,n,a=un)=>{switch(e.op){case"ResizeBilinear":{let r=k("images",e,t,n),s=k("size",e,t,n),i=k("alignCorners",e,t,n),o=k("halfPixelCenters",e,t,n);return[a.image.resizeBilinear(r,[s[0],s[1]],i,o)]}case"ResizeNearestNeighbor":{let r=k("images",e,t,n),s=k("size",e,t,n),i=k("alignCorners",e,t,n),o=k("halfPixelCenters",e,t,n);return[a.image.resizeNearestNeighbor(r,[s[0],s[1]],i,o)]}case"CropAndResize":{let r=k("image",e,t,n),s=k("boxes",e,t,n),i=k("boxInd",e,t,n),o=k("cropSize",e,t,n),l=k("method",e,t,n),u=k("extrapolationValue",e,t,n);return[a.image.cropAndResize(r,s,i,o,l,u)]}case"ImageProjectiveTransformV3":{let r=k("images",e,t,n),s=k("transforms",e,t,n),i=k("outputShape",e,t,n),o=k("fillValue",e,t,n),l=k("interpolation",e,t,n),u=k("fillMode",e,t,n);return[a.image.transform(r,s,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},s6=(e,t,n,a=un)=>{switch(e.op){case"Equal":return[a.equal(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[a.notEqual(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[a.greater(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[a.greaterEqual(k("a",e,t,n),k("b",e,t,n))];case"Less":return[a.less(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[a.lessEqual(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[a.logicalAnd(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[a.logicalNot(k("a",e,t,n))];case"LogicalOr":return[a.logicalOr(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[a.where(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},i6=(e,t,n,a=un)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[a.matMul(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Einsum":return[a.einsum(k("equation",e,t,n),...k("tensors",e,t,n))];case"Transpose":return[a.transpose(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,n),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,n),u=k("leakyreluAlpha",e,t,n);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,d]=k("args",e,t,n);return[a.fused.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:p,activation:s,preluActivationWeights:d,leakyreluAlpha:u})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},o6=(e,t,n,a=un)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];case"SparseToDense":return[a.sparseToDense(k("sparseIndices",e,t,n),k("outputShape",e,t,n),k("sparseValues",e,t,n),k("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},l6=(e,t,n,a=un)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.sum(k("x",e,t,n),o,l)]}case"All":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.all(k("x",e,t,n),o,l)]}case"Any":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.any(k("x",e,t,n),o,l)]}case"ArgMax":{let o=k("axis",e,t,n);return[a.argMax(k("x",e,t,n),o)]}case"ArgMin":{let o=k("axis",e,t,n);return[a.argMin(k("x",e,t,n),o)]}case"Prod":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.prod(k("x",e,t,n),o,l)]}case"Cumprod":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumprod(k("x",e,t,n),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumsum(k("x",e,t,n),o,l,u)]}case"Bincount":let r=k("x",e,t,n),s=k("weights",e,t,n),i=k("size",e,t,n);return[a.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,n),l=k("weights",e,t,n),u=k("size",e,t,n),p=k("binaryOutput",e,t,n);return[a.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},u6=(e,t,n,a=un)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o<r.length;o++)r[o]&&s.push(o);let i=k("x",e,t,n);return[a.reverse(i,s)]}case"ReverseV2":{let r=k("axis",e,t,n),s=k("x",e,t,n);return[a.reverse(s,r)]}case"Slice":{let r=k("begin",e,t,n),s=k("size",e,t,n);return[a.slice(k("x",e,t,n),r,s)]}case"StridedSlice":{let r=k("begin",e,t,n),s=k("end",e,t,n),i=k("strides",e,t,n),o=k("beginMask",e,t,n),l=k("endMask",e,t,n),u=k("ellipsisMask",e,t,n),p=k("newAxisMask",e,t,n),d=k("shrinkAxisMask",e,t,n),c=k("x",e,t,n);return[a.stridedSlice(c,r,s,i,o,l,u,p,d)]}case"Pack":return O(()=>{let r=k("axis",e,t,n),s=k("tensors",e,t,n),i=s[0].shape,o=a.squeeze(s[0]).shape,l=s.map(u=>{let p=w.arraysEqual(u.shape,i);if(!p&&!w.arraysEqual(a.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:a.reshape(u,i)});return[a.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return a.unstack(s,r)}case"Tile":{let r=k("reps",e,t,n);return[a.tile(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),i=k("x",e,t,n);return a.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("shape",e,t,n);return[a.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),i=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[a.sparseToDense(r,i,s,i.dtype===o.dtype?o:a.cast(o,i.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},p6=(e,t,n,a=un)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},c6=(e,t,n,a=un)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},d6=(e,t,n,a=un)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},h6=(e,t,n,a=un)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Hk(e,t,n,a,r=O){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>UH(i,o,l));case"basic_math":return r(()=>GH(i,o,l));case"control":return YH(i,o,l);case"convolution":return r(()=>JH(i,o,l));case"creation":return r(()=>ZH(i,o,l));case"dynamic":return QH(i,o,l);case"evaluation":return r(()=>e6(i,o,l));case"image":return r(()=>r6(i,o,l));case"graph":return r(()=>t6(i,o,l));case"logical":return r(()=>s6(i,o,l));case"matrices":return r(()=>i6(i,o,l));case"normalization":return r(()=>o6(i,o,l));case"reduction":return r(()=>l6(i,o,l));case"slice_join":return r(()=>u6(i,o,l));case"sparse":return r(()=>p6(i,o,l));case"spectral":return r(()=>c6(i,o,l));case"string":return r(()=>d6(i,o,l));case"transformation":return r(()=>h6(i,o,l));case"hash_table":return a6(i,o,l,a);case"custom":let u=tT(i.op);if(u&&u.customExecutor)return u.customExecutor(new VH(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var jk=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function qk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>Yn(c)[0]),p=[];a!=null&&(p=a.map(c=>Yn(c.name)[0]));let d=[...t];for(;d.length>0;){let c=d.pop();if((IT(c)||b6(c)||x6(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&p.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function m6(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(p=>Yn(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{a.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{a.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{a.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(d=>{!l.has(d.name)&&a.has(d.name)&&d.inputs.every(c=>l.has(c.name))&&s.push(d)})}return u}var f6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],g6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],y6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function IT(e){return f6.indexOf(e.op)>=0}function b6(e){return g6.indexOf(e.op)>=0}function x6(e){return y6.indexOf(e.op)>=0}var gx=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new gx(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=qk(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return m6(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(p=>this.graph.nodes[Yn(p)[0]]),r=t.map(p=>Yn(p)[0]),s=r.map(p=>this.graph.nodes[p]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return O(()=>{let p=new jk(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Yn(m),y=[];y[g]=e[m],d[f]=y});let c=this.getFrozenTensorIds(d),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!d[f.name]){let g=Hk(f,d,p,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. 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u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=ir(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!In(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!In(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Yn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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this.upstream.next()}},W6=class extends nn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},V6=class extends nn{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},U6=class extends nn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Me(e.value)}}},G6=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ja.getTensorsInContainer(e.value),n=this.transform(e.value),a=ja.getTensorsInContainer(n);for(let r of t)ja.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},H6=class extends nn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Kk=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ja.getTensorsInContainer(e.value),n=await this.transform(e.value),a=ja.getTensorsInContainer(n);for(let r of t)ja.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},M0=class extends nn{constructor(){super(),this.outputQueue=new D0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},j6=class extends M0{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ja.getTensorsInContainer(e.value),n=this.transform(e.value),a=ja.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)ja.isTensorInList(r,a)||r.dispose();return!0}},AT=class extends nn{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}},Jr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Jr||(Jr={}));var q6=class extends nn{constructor(e,t=Jr.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 nn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await CT(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Jr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Jr.SHORTEST:return{value:null,done:!0};case Jr.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},$T=class extends nn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new _T(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()}},K6=class extends $T{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=_6.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Uu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Xn(async()=>(await n.iterator()).columnMajorBatch(e,t,J6),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Xn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Xn(async()=>(await t.iterator()).filter(a=>O(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Xn(async()=>(await t.iterator()).map(n=>O(()=>e(n))),this.size)}mapAsync(e){let t=this;return Xn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Xn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Xn(async()=>{let a=R0(async()=>({value:await t.iterator(),done:!1}));return M6(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Xn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=C6.alea(t||w.now().toString());return Xn(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,Xn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Uu.MAX_BUFFER_SIZE=1e4;function Xn(e,t=null){return new class extends Uu{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function X6(e){return Xn(async()=>ET(e),e.length)}function Y6(e){if(!Il(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 Xn(async()=>{let n=await CT(e,a=>{if(a instanceof Uu)return{value:a.iterator(),recurse:!1};if(Il(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return P6(n,Jr.SHORTEST)},t)}function J6(e){if(e===null)return null;let t=e[0];return $6(t)?{value:Z6(e),recurse:!1}:{value:null,recurse:!0}}function Z6(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ae?Rt(e):Bn(e)}var FT=class extends Uu{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))}},gh='"',Lp=Symbol("out"),Xk=Symbol("field"),yh=Symbol("quote"),Eb=Symbol("quoteafterquote"),Yk=Symbol("quoteinquote"),DT=class extends Uu{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 FT(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(w.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=Lp;for(let i=0;i<r;i++)switch(s){case Lp:switch(e.charAt(i)){case gh:a=i+1,s=yh;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Lp;break;default:s=Xk,a=i;break}break;case Xk:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=Lp,a=i+1;break;default:}break;case yh:switch(e.charAt(i)){case gh:s=Eb;break;default:}break;case Eb:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=Lp,a=i+1;break;case gh:s=yh;break;default:s=Yk;break}break;case Yk:switch(e.charAt(i)){case gh:s=yh;break;default:}break;default:}if(s===Eb?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}},RT=class extends nn{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!X().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new RT(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]===-1/0&&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(w.sizeFromShape(t));return n.set(e,n.length-e.length),Bn(n,t)}},MT=class extends nn{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([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=Na([s,r,o,i],[1,4])}else this.cropBox=Na([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!X().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new MT(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=bo.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return O(()=>{let t=mn(le(e,"float32"),0),n;n=Sr.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},PT=class{},OT=class extends nn{split(e){return new Q6(this,e)}},Q6=class extends OT{constructor(e,t){super(),this.upstream=e,this.impl=new ej(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ej=class extends M0{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},tj=class extends nn{decodeUTF8(){return new nj(this)}},nj=class extends OT{constructor(e){super(),this.upstream=e,this.impl=new aj(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},aj=class extends M0{constructor(e){if(super(),this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=TI();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},LT=class extends tj{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(X().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof 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TC({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return NC({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return mC({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function bt(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=w.sizeFromShape(r.shape),o=w.inferFromImplicitShape(s,i),l=w.sizeFromShape(o);w.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. 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x=i?[g,p,c]:[g,c,p],v=o?[y,h,d]:[y,d,h],I=bt({inputs:{x:r},backend:n,attrs:{shape:x}}),T=bt({inputs:{x:s},backend:n,attrs:{shape:v}}),_=i?I.shape[1]:I.shape[2],A=i?I.shape[2]:I.shape[1],F=o?T.shape[1]:T.shape[2],P=Math.max(g,y),$=n.data.get(I.dataId).values,S=n.data.get(T.dataId).values,M=w.computeStrides(I.shape),U=w.computeStrides(T.shape),[j,q,K]=i?[M[0],1,M[1]]:[M[0],M[1],1],[Y,te,re]=o?[1,U[1],U[0]]:[U[1],1,U[0]],Q=A*F,ie=Le([P,A,F],I.dtype),ae=ie.values,oe=n.blockSize;for(let ue=0;ue<P;ue++)for(let we=0;we<A;we+=oe)for(let ye=0;ye<F;ye+=oe)for(let Ie=0;Ie<_;Ie+=oe){let Ee=Math.min(we+oe,A),De=Math.min(ye+oe,F),We=Math.min(Ie+oe,_);for(let je=we;je<Ee;je++)for(let st=ye;st<De;st++){let et=0;for(let tt=Ie;tt<We;tt++){let Te=Math.min(ue,g-1)*j,ft=Math.min(ue,y-1)*re,dt=$[Te+je*q+tt*K],yn=S[tt*Y+st*te+ft];et+=dt*yn}ae[ue*Q+(je*F+st)]+=et}}return n.disposeIntermediateTensorInfo(I),n.disposeIntermediateTensorInfo(T),n.makeTensorInfo(b,ie.dtype,ie.values)}var xq={kernelName:Ii,backendName:"cpu",kernelFunc:EC};function vq(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c,h,m,f=[];c=EC({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:u},backend:n}),i&&(h=Sl({inputs:{a:c,b:i},backend:n}),f.push(c),c=h),p&&(m=nm(n,c,p,o,d),f.push(c),c=m);for(let g of f)n.disposeIntermediateTensorInfo(g);return c}var wq={kernelName:ri,backendName:"cpu",kernelFunc:vq},kq=ot(Al,e=>Math.acos(e)),Iq={kernelName:Al,backendName:"cpu",kernelFunc:kq},Sq=ot($l,e=>Math.acosh(e)),Nq={kernelName:$l,backendName:"cpu",kernelFunc:Sq};function Tq(e){let{inputs:t,backend:n}=e,a=t;xe(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=Le(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let l=r[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var Cq={kernelName:vi,backendName:"cpu",kernelFunc:Tq};function _q(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;xe(r,"all");let o=w.parseAxisParam(s,r.shape),l=o,u=C.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Gn({inputs:{x:r},backend:n,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("all",l,p.shape.length);let[d,c]=C.computeOutAndReduceShapes(p.shape,l),h=w.sizeFromShape(c),m=w.makeZerosTypedArray(w.sizeFromShape(d),p.dtype),f=n.data.get(p.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,x=f[b];for(let v=0;v<h;++v){let I=f[b+v];x=x&&I}m[y]=x}u!=null&&n.disposeIntermediateTensorInfo(p);let g=n.makeTensorInfo(d,p.dtype,m);if(i){let y=C.expandShapeToKeepDim(d,o),b=bt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var Eq={kernelName:Fl,backendName:"cpu",kernelFunc:_q};function Aq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;xe(r,"any");let o=w.parseAxisParam(s,r.shape),l=o,u=C.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Gn({inputs:{x:r},backend:n,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("any",l,p.shape.length);let[d,c]=C.computeOutAndReduceShapes(p.shape,l),h=w.sizeFromShape(c),m=w.makeZerosTypedArray(w.sizeFromShape(d),p.dtype),f=n.data.get(p.dataId).values;for(let y=0;y<m.length;++y){let b=y*h,x=f[b];for(let v=0;v<h;++v){let I=f[b+v];x=x||I}m[y]=x}u!=null&&n.disposeIntermediateTensorInfo(p);let g=n.makeTensorInfo(d,p.dtype,m);if(i){let y=C.expandShapeToKeepDim(d,o),b=bt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),b}return g}var $q={kernelName:Dl,backendName:"cpu",kernelFunc:Aq};function Fq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;xe(r,"argMax");let i=w.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Gn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[p,d]=C.computeOutAndReduceShapes(l.shape,i),c=w.sizeFromShape(p),h=w.makeZerosTypedArray(c,"int32"),m=w.sizeFromShape(d),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],x=0;for(let v=0;v<m;++v){let I=f[y+v];I>b&&(b=I,x=v)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(p,"int32",h)}var Dq={kernelName:wi,backendName:"cpu",kernelFunc:Fq};function Rq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;xe(r,"argMin");let i=w.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Gn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,d]=C.computeOutAndReduceShapes(l.shape,i),c=w.sizeFromShape(p),h=w.makeZerosTypedArray(c,"int32"),m=w.sizeFromShape(d),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],x=0;for(let v=0;v<m;++v){let I=f[y+v];I<b&&(b=I,x=v)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(p,"int32",h)}var Mq={kernelName:gc,backendName:"cpu",kernelFunc:Rq},Pq=ot(Rl,e=>Math.asin(e)),Oq={kernelName:Rl,backendName:"cpu",kernelFunc:Pq},Lq=ot(Ml,e=>Math.asinh(e)),zq={kernelName:Ml,backendName:"cpu",kernelFunc:Lq},Bq=ot(Pl,e=>Math.atan(e)),Wq={kernelName:Pl,backendName:"cpu",kernelFunc:Bq},Vq=Vt((e,t)=>Math.atan2(e,t)),Uq=an(Ll,Vq),Gq={kernelName:Ll,backendName:"cpu",kernelFunc:Uq},Hq=ot(Ol,e=>Math.atanh(e)),jq={kernelName:Ol,backendName:"cpu",kernelFunc:Hq};function K0(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,d=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Le(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],b=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let v=0;v<r.batchSize;++v){let I=v*y,T=v*a[0];for(let _=0;_<r.inChannels;++_)for(let A=0;A<r.outHeight;++A){let 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j=U-_,q=f.get(g,S,U,y);q>P&&(P=q,r?$=s?((g*a.inHeight+S)*a.inWidth+U)*a.inChannels+y:(S*a.inWidth+U)*a.inChannels+y:$=M*c+j)}}i.set($,g,b,T,y)}}return i}function $C(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,d=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Le(r.outShape,n),v=x.values,I=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],_=r.outShape[3]*r.outShape[4],A=r.outShape[4];for(let F=0;F<r.batchSize;++F){let P=F*I,$=F*a[0];for(let S=0;S<r.inChannels;++S)for(let M=0;M<r.outDepth;++M){let U=M*i-f,j=U;for(;j<0;)j+=u;let q=Math.min(r.inDepth,c+U),K=P+M*T;for(let Y=0;Y<r.outHeight;++Y){let te=Y*o-g,re=te;for(;re<0;)re+=p;let Q=Math.min(r.inHeight,h+te),ie=K+Y*_;for(let ae=0;ae<r.outWidth;++ae){let oe=ae*l-y,ue=oe;for(;ue<0;)ue+=d;let we=Math.min(r.inWidth,m+oe),ye=ie+ae*A,Ie=b,Ee=0,De=0;for(let je=j;je<q;je+=u){let st=$+je*a[1];for(let et=re;et<Q;et+=p){let tt=st+et*a[2];for(let Te=ue;Te<we;Te+=d){let ft=tt+Te*a[3],dt=e[ft+S];if(s==="max"&&dt>Ie?Ie=dt:s==="avg"&&(Ee+=dt,De++),isNaN(Ie))break}if(isNaN(Ie))break}if(isNaN(Ie))break}let We=ye+S;v[We]=s==="avg"?Ee/De:Ie}}}}return x}function qq(e,t){let n=Le(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,d=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let b=y*a-c,x=b;for(;x<0;)x+=i;let v=Math.min(t.inDepth,u+b);for(let I=0;I<t.outHeight;++I){let T=I*r-h,_=T;for(;_<0;)_+=o;let A=Math.min(t.inHeight,p+T);for(let F=0;F<t.outWidth;++F){let P=F*s-m,$=P;for(;$<0;)$+=l;let S=Math.min(t.inWidth,d+P),M=Number.NEGATIVE_INFINITY,U=-1;for(let j=x;j<v;j+=i){let q=j-b;for(let K=_;K<A;K+=o){let Y=K-T;for(let te=$;te<S;te+=l){let re=te-P,Q=e.get(f,j,K,te,g);Q>=M&&(M=Q,U=q*p*d+Y*p+re)}}}n.set(U,f,y,I,F,g)}}}return n}function Kq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;xe(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l),d;if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))d=cr({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=w.computeStrides(r.shape),m=K0(c,r.shape,r.dtype,h,p,"avg");d=n.makeTensorInfo(p.outShape,r.dtype,m.values)}return d}var Xq={kernelName:ki,backendName:"cpu",kernelFunc:Kq};function Yq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;xe(r,"avgPool3d");let p=C.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.data.get(r.dataId).values,c=$C(d,r.shape,r.dtype,w.computeStrides(r.shape),p,"avg");return n.makeTensorInfo(c.shape,"float32",c.values)}var Jq={kernelName:yc,backendName:"cpu",kernelFunc:Yq};function Zq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;xe([r,s],"avgPool3DGrad");let p=C.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,y=p.dilationDepth,b=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,T=p.effectiveFilterWidth,_=v-1-p.padInfo.front,A=T-1-p.padInfo.left,F=I-1-p.padInfo.top,P=Le(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M<p.batchSize;++M)for(let U=0;U<p.inChannels;++U)for(let j=0;j<p.inDepth;++j)for(let q=0;q<p.inHeight;++q)for(let K=0;K<p.inWidth;++K){let Y=j-_,te=q-F,re=K-A,Q=0;for(let ie=0;ie<v;ie+=y){let ae=(Y+ie)/d;if(!(ae<0||ae>=p.outDepth||Math.floor(ae)!==ae))for(let oe=0;oe<I;oe+=b){let ue=(te+oe)/c;if(!(ue<0||ue>=p.outHeight||Math.floor(ue)!==ue))for(let we=0;we<T;we+=x){let ye=(re+we)/h;ye<0||ye>=p.outWidth||Math.floor(ye)!==ye||(Q+=S.get(M,ae,ue,ye,U))}}}P.set(Q*$,M,j,q,K,U)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var Qq={kernelName:mm,backendName:"cpu",kernelFunc:Zq};function e5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;xe([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=C.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,b=p.effectiveFilterWidth,x=b-1-p.padInfo.left,v=y-1-p.padInfo.top,I=Le(i.shape,"float32"),T=1/(h*m),_=n.data.get(r.dataId).values,A=Le(r.shape,"float32",_);for(let F=0;F<p.batchSize;++F)for(let P=0;P<p.inChannels;++P)for(let $=0;$<p.inHeight;++$)for(let S=0;S<p.inWidth;++S){let M=$-v,U=S-x,j=0;for(let q=0;q<y;q+=f){let K=(M+q)/d;if(!(K<0||K>=p.outHeight||Math.floor(K)!==K))for(let Y=0;Y<b;Y+=g){let te=(U+Y)/c;te<0||te>=p.outWidth||Math.floor(te)!==te||(j+=A.get(F,K,te,P))}}I.set(j*T,F,$,S,P)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var t5={kernelName:hm,backendName:"cpu",kernelFunc:e5};function n5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),xe([r,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=a;u==null&&(u=.001);let p=n.data.get(r.dataId).values,d=n.data.get(o.dataId).values,c=n.data.get(l.dataId).values,h=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(p.length),g=m.length,y=h.length,b=c.length,x=d.length,v=0,I=0,T=0,_=0;for(let A=0;A<p.length;++A)f[A]=m[v++]+(p[A]-d[I++])*h[T++]/Math.sqrt(c[_++]+u),v>=g&&(v=0),I>=x&&(I=0),T>=y&&(T=0),_>=b&&(_=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var a5={kernelName:Oi,backendName:"cpu",kernelFunc:n5};function r5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;xe([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),d=C.getSliceBeginCoords(i,s.length),c=C.getSliceSize(p,i,s.length),h=bt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Gn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=bt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=fi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var s5={kernelName:zl,backendName:"cpu",kernelFunc:r5};function i5(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=L0(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var o5={kernelName:fm,backendName:"cpu",kernelFunc:i5};function l5(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var u5={kernelName:gm,backendName:"cpu",kernelFunc:l5},p5=ot(hs,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),c5={kernelName:hs,backendName:"cpu",kernelFunc:p5},d5=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.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 p=o[u],d=l[u];a[u]=Math.hypot(p,d)}return n.makeOutput(a,t.shape,"float32")},h5={kernelName:bc,backendName:"cpu",kernelFunc:d5};function Nl(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 m5={kernelName:Em,backendName:"cpu",kernelFunc:Nl};function Tl(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=C.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>w.sizeFromShape(f.shape)>0);if(o.length===1)return cr({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(v=>mi({inputs:{input:v},backend:n})),g=o.map(v=>Nl({inputs:{input:v},backend:n})),y=Tl({inputs:f,backend:n,attrs:{axis:s}}),b=Tl({inputs:g,backend:n,attrs:{axis:s}}),x=Jn({inputs:{real:y,imag:b},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),x}let u=o.map(f=>{let g=w.sizeFromShape(f.shape.slice(s));return bt({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=z0(p,i,t[0].dtype,d),h=C.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var f5={kernelName:Bl,backendName:"cpu",kernelFunc:Tl};function FC(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;xe([r,s],"conv2d");let d=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,y=c.padInfo.left,b=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new 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y5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a;xe([r,s],"conv2dBackpropFilter");let d=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),{strideHeight:h,strideWidth:m,filterHeight:f,filterWidth:g}=c,y=c.dataFormat==="channelsLast",b=new jt(c.filterShape,"float32"),x=c.padInfo.left,v=c.padInfo.top,I=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,_=new jt(r.shape,r.dtype,I),A=new jt(s.shape,s.dtype,T);for(let F=0;F<f;++F){let P=Math.max(0,Math.ceil((v-F)/h)),$=Math.min(c.outHeight,(c.inHeight+v-F)/h);for(let S=0;S<g;++S){let M=Math.max(0,Math.ceil((x-S)/m)),U=Math.min(c.outWidth,(c.inWidth+x-S)/m);for(let j=0;j<c.inChannels;++j)for(let q=0;q<c.outChannels;++q){let K=0;for(let Y=0;Y<c.batchSize;++Y)for(let te=P;te<$;++te){let re=F+te*h-v;for(let Q=M;Q<U;++Q){let ie=S+Q*m-x;y?K+=_.get(Y,re,ie,j)*A.get(Y,te,Q,q):K+=_.get(Y,j,re,ie)*A.get(Y,q,te,Q)}}b.set(K,F,S,j,q)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var b5={kernelName:bm,backendName:"cpu",kernelFunc:y5};function x5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a;xe([r,s],"conv2dBackpropInput");let d=w.computeStrides(s.shape),c=w.computeStrides(r.shape),h=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(i,s.shape,o,1,l,p,!1,h),f=new jt(m.inShape,"float32"),g=f.values,y=n.data.get(r.dataId).values,b=n.data.get(s.dataId).values,[x,v,I]=d,{batchSize:T,filterHeight:_,filterWidth:A,inChannels:F,inHeight:P,inWidth:$,outChannels:S,outHeight:M,outWidth:U,strideHeight:j,strideWidth:q}=m;h=m.dataFormat;let K=_-1-m.padInfo.top,Y=A-1-m.padInfo.left,te=h==="channelsLast",re=f.strides[0],Q=te?f.strides[1]:f.strides[2],ie=te?f.strides[2]:1,ae=te?1:f.strides[1],oe=c[0],ue=te?c[1]:c[2],we=te?c[2]:1,ye=te?1:c[1];for(let Ie=0;Ie<T;++Ie)for(let Ee=0;Ee<F;++Ee)for(let De=0;De<P;++De){let We=De-K,je=Math.max(0,Math.ceil(We/j)),st=Math.min(M,(_+We)/j);for(let et=0;et<$;++et){let tt=et-Y,Te=Math.max(0,Math.ceil(tt/q)),ft=Math.min(U,(A+tt)/q),dt=0;for(let Yt=je;Yt<st;++Yt){let Rn=Yt*j-We;for(let Ut=Te;Ut<ft;++Ut){let Jt=Ut*q-tt,Ma=oe*Ie+ue*Yt+we*Ut,Mn=x*(_-1-Rn)+v*(A-1-Jt)+I*Ee;for(let Gt=0;Gt<S;++Gt){let sa=y[Ma+ye*Gt],ia=b[Mn+Gt];dt+=sa*ia}}}let yn=re*Ie+Q*De+ie*et+ae*Ee;g[yn]=dt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var v5={kernelName:Ci,backendName:"cpu",kernelFunc:x5};function w5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;xe([r,s],"conv3d");let u=C.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:p,filterHeight:d,filterWidth:c,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,b=g.left,x=g.top,v=new jt(u.outShape,r.dtype),I=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,_=v.values,A=w.computeStrides(r.shape),F=w.computeStrides(s.shape);for(let P=0;P<u.batchSize;++P){let 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u=w.computeStrides(r.shape),p=w.computeStrides(s.shape),d=C.computeConv3DInfo(r.shape,l,i,1,o),c=d.strideDepth,h=d.strideHeight,m=d.strideWidth,f=d.filterDepth,g=d.filterHeight,y=d.filterWidth,b=new jt(d.filterShape,"float32"),x=b.values,[v,I,T,_]=b.strides,A=n.data.get(s.dataId).values,[F,P,$,S]=p,M=n.data.get(r.dataId).values,[U,j,q,K]=u,Y=d.padInfo.front,te=d.padInfo.left,re=d.padInfo.top;for(let Q=0;Q<f;++Q){let ie=Math.max(0,Math.ceil((Y-Q)/c)),ae=Math.min(d.outDepth,(d.inDepth+Y-Q)/c),oe=Q*v;for(let ue=0;ue<g;++ue){let we=Math.max(0,Math.ceil((re-ue)/h)),ye=Math.min(d.outHeight,(d.inHeight+re-ue)/h),Ie=ue*I+oe;for(let Ee=0;Ee<y;++Ee){let De=Math.max(0,Math.ceil((te-Ee)/m)),We=Math.min(d.outWidth,(d.inWidth+te-Ee)/m),je=Ee*T+Ie;for(let st=0;st<d.inChannels;++st){let et=st*_+je;for(let tt=0;tt<d.outChannels;++tt){let Te=0;for(let ft=0;ft<d.batchSize;++ft){let dt=ft*U,yn=ft*F;for(let Yt=ie;Yt<ae;++Yt){let Rn=(Q+Yt*c-Y)*j+dt,Ut=Yt*P+yn;for(let Jt=we;Jt<ye;++Jt){let 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st=je-ye,et=Math.max(0,Math.ceil(st/oe)),tt=Math.min(Q,(M+st)/oe);for(let Te=0;Te<Y;++Te){let ft=Te-Ie,dt=Math.max(0,Math.ceil(ft/ue)),yn=Math.min(ie,(U+ft)/ue);for(let Yt=0;Yt<te;++Yt){let Rn=Yt-Ee,Ut=Math.max(0,Math.ceil(Rn/we)),Jt=Math.min(ae,(j+Rn)/we),Ma=0;for(let Mn=et;Mn<tt;++Mn){let Gt=Mn*oe-st;for(let sa=dt;sa<yn;++sa){let ia=sa*ue-ft;for(let Wr=Ut;Wr<Jt;++Wr){let Ps=Wr*we-Rn,_d=x*De+v*Mn+I*sa+T*Wr,Vr=A*(M-1-Gt)+F*(U-1-ia)+P*(j-1-Ps)+$*We;for(let xr=0;xr<re;++xr){let kp=b[_d+xr],Bo=_[Vr+xr];Ma+=kp*Bo}}}}h[m*De+f*je+g*Te+y*Yt+We]=Ma}}}return n.makeTensorInfo(c.shape,c.dtype,c.values)}var T5={kernelName:vm,backendName:"cpu",kernelFunc:N5},C5=ot(_i,e=>Math.cos(e)),_5={kernelName:_i,backendName:"cpu",kernelFunc:C5},E5=ot(Ei,e=>Math.cosh(e)),A5={kernelName:Ei,backendName:"cpu",kernelFunc:E5};function $5(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),T=w.computeStrides(y.shape);for(let _=0;_<m;_++){let A=_*4,F=b[A],P=b[A+1],$=b[A+2],S=b[A+3],M=x[_];if(M>=p)continue;let U=f>1?($-F)*(d-1)/(f-1):0,j=g>1?(S-P)*(c-1)/(g-1):0;for(let q=0;q<f;q++){let K=f>1?F*(d-1)+q*U:.5*(F+$)*(d-1);if(K<0||K>d-1){for(let Y=0;Y<g;Y++)for(let te=0;te<h;te++){let re=te+Y*T[2]+q*T[1]+_*T[0];y.values[re]=u}continue}if(l==="bilinear"){let Y=Math.floor(K),te=Math.ceil(K),re=K-Y;for(let Q=0;Q<g;Q++){let ie=g>1?P*(c-1)+Q*j:.5*(P+S)*(c-1);if(ie<0||ie>c-1){for(let we=0;we<h;we++){let ye=we+Q*T[2]+q*T[1]+_*T[0];y.values[ye]=u}continue}let ae=Math.floor(ie),oe=Math.ceil(ie),ue=ie-ae;for(let we=0;we<h;we++){let ye=we+ae*I[2]+Y*I[1]+M*I[0],Ie=v[ye];ye=we+oe*I[2]+Y*I[1]+M*I[0];let Ee=v[ye];ye=we+ae*I[2]+te*I[1]+M*I[0];let De=v[ye];ye=we+oe*I[2]+te*I[1]+M*I[0];let We=v[ye],je=Ie+(Ee-Ie)*ue,st=De+(We-De)*ue;ye=we+Q*T[2]+q*T[1]+_*T[0],y.values[ye]=je+(st-je)*re}}}else for(let Y=0;Y<g;++Y){let te=g>1?P*(c-1)+Y*j:.5*(P+S)*(c-1);if(te<0||te>c-1){for(let ie=0;ie<h;ie++){let ae=ie+Y*T[2]+q*T[1]+_*T[0];y.values[ae]=u}continue}let re=Math.round(te),Q=Math.round(K);for(let ie=0;ie<h;ie++){let ae=ie+re*I[2]+Q*I[1]+M*I[0],oe=ie+Y*T[2]+q*T[1]+_*T[0];y.values[oe]=v[ae]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var F5={kernelName:Vl,backendName:"cpu",kernelFunc:$5};function D5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;xe(r,"cumprod");let l=C.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=Gn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=C.getInnerMostAxes(1,r.shape.length)[0];if(p!==u.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${p}`);let d=ha(u.dtype,"int32"),c=w.makeOnesTypedArray(w.sizeFromShape(u.shape),d),h=n.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let x=f(y,b);if(b===0)c[x]=i?1:h[x];else{let v=f(y,b-1);c[x]=i?h[v]*c[v]:h[x]*c[v]}}let g=n.makeTensorInfo(u.shape,d,c);if(l!=null){let y=C.getUndoAxesPermutation(l),b=Gn({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),b}return g}var R5={kernelName:Wl,backendName:"cpu",kernelFunc:D5};function M5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;xe(r,"cumsum");let l=C.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=Gn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=C.getInnerMostAxes(1,r.shape.length)[0];if(p!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${p}`);let d=ha(u.dtype,"int32"),c=w.makeZerosTypedArray(w.sizeFromShape(u.shape),d),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)c[x]=i?0:h[x];else{let v=f(y,b-1);c[x]=i?h[v]+c[v]:h[x]+c[v]}}let g=n.makeTensorInfo(u.shape,d,c);if(l!=null){let y=C.getUndoAxesPermutation(l),b=Gn({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),b}return g}var P5={kernelName:Ai,backendName:"cpu",kernelFunc:M5};function O5(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,p=L0(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=HT(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in 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V5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a;xe([r,s],"depthwiseConv2dNativeBackpropFilter");let d=C.computeConv2DInfo(r.shape,p,i,o,l,u,!0),{strideHeight:c,strideWidth:h,filterHeight:m,filterWidth:f}=d,g=new jt(d.filterShape,"float32"),y=d.padInfo.left,b=d.padInfo.top,x=d.outChannels/d.inChannels,v=n.data.get(r.dataId).values,I=new jt(r.shape,r.dtype,v),T=n.data.get(s.dataId).values,_=new jt(s.shape,s.dtype,T);for(let A=0;A<m;++A){let F=Math.max(0,Math.ceil((b-A)/c)),P=Math.min(d.outHeight,(d.inHeight+b-A)/c);for(let $=0;$<f;++$){let S=Math.max(0,Math.ceil((y-$)/h)),M=Math.min(d.outWidth,(d.inWidth+y-$)/h);for(let U=0;U<d.outChannels;++U){let j=Math.trunc(U/x),q=U%x,K=0;for(let Y=0;Y<d.batchSize;++Y)for(let te=F;te<P;++te){let re=A+te*c-b;for(let Q=S;Q<M;++Q){let ie=$+Q*h-y;K+=I.get(Y,re,ie,j)*_.get(Y,te,Q,U)}}g.set(K,A,$,j,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var U5={kernelName:km,backendName:"cpu",kernelFunc:V5};function G5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a;xe([r,s],"depthwiseConv2DNativeBackpropInput");let d=w.computeStrides(r.shape),c=w.computeStrides(s.shape),h=C.computeConv2DInfo(p,s.shape,i,o,l,u,!0),m=new jt(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,x=n.data.get(r.dataId).values,[v,I,T]=d,_=n.data.get(s.dataId).values,[A,F,P]=c,{batchSize:$,filterHeight:S,filterWidth:M,inChannels:U,inHeight:j,inWidth:q,outChannels:K,outHeight:Y,outWidth:te,strideHeight:re,strideWidth:Q}=h,ie=S-1-h.padInfo.top,ae=M-1-h.padInfo.left,oe=K/U;for(let ue=0;ue<$;++ue)for(let we=0;we<U;++we)for(let ye=0;ye<j;++ye){let Ie=ye-ie,Ee=Math.max(0,Math.ceil(Ie/re)),De=Math.min(Y,(S+Ie)/re);for(let We=0;We<q;++We){let je=We-ae,st=Math.max(0,Math.ceil(je/Q)),et=Math.min(te,(M+je)/Q),tt=0;for(let Te=Ee;Te<De;++Te){let ft=Te*re-Ie;for(let dt=st;dt<et;++dt){let 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q5={kernelName:Sm,backendName:"cpu",kernelFunc:j5},K5={kernelName:vc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:T,filterWidth:_,dilationHeight:A,dilationWidth:F,outShape:P}=C.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(P),S=P.length,M=w.getArrayFromDType(a.dtype,$);for(let U=0;U<h;++U)for(let j=0;j<y;++j){let q=j*v-x.top;for(let K=0;K<b;++K){let Y=K*I-x.left;for(let te=0;te<g;++te){let re=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<T;++ie){let ae=q+ie*A;if(ae>=0&&ae<m)for(let oe=0;oe<_;++oe){let ue=Y+oe*F;if(ue>=0&&ue<f){let we=w.locToIndex([U,ae,ue,te],p,w.computeStrides(a.shape)),ye=w.locToIndex([ie,oe,te],c,w.computeStrides(r.shape)),Ie=u[we]+d[ye];Ie>re&&(re=Ie)}}}let Q=w.locToIndex([U,j,K,te],S,w.computeStrides(P));M[Q]=re}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),P,a.dtype),shape:P,dtype:a.dtype}}},X5={kernelName:Mh,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,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:T,dilationHeight:_,dilationWidth:A,outShape:F}=C.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Mh}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S<c;++S)for(let M=0;M<g;++M){let U=M*x-b.top;for(let j=0;j<y;++j){let q=j*v-b.left;for(let K=0;K<f;++K){let Y=Number.MIN_SAFE_INTEGER,te=0,re=0;for(let Q=0;Q<I;++Q){let ie=U+Q*_;if(ie>=0&&ie<h)for(let ae=0;ae<T;++ae){let oe=q+ae*A;if(oe>=0&&oe<m){let ue=p[S][ie][oe][K]+d[Q][ae][K];ue>Y&&(Y=ue,te=Q,re=ae)}}}$[te][re][K]+=P[S][M][j][K]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Y5={kernelName:Rh,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,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:T,dilationHeight:_,dilationWidth:A,outShape:F}=C.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Rh}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S<c;++S)for(let M=0;M<g;++M){let U=M*x-b.top;for(let j=0;j<y;++j){let q=j*v-b.left;for(let K=0;K<f;++K){let Y=Number.MIN_SAFE_INTEGER,te=U<0?0:U,re=q<0?0:q;for(let Q=0;Q<I;++Q){let ie=U+Q*_;if(ie>=0&&ie<h)for(let ae=0;ae<T;++ae){let oe=q+ae*A;if(oe>=0&&oe<m){let ue=p[S][ie][oe][K]+d[Q][ae][K];ue>Y&&(Y=ue,te=ie,re=oe)}}}$[S][te][re][K]+=P[S][M][j][K]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function pd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;xe(r,"sum");let o;r.dtype==="bool"?o=ls({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=cr({inputs:{x:r},backend:n});let l=o.shape.length,u=w.parseAxisParam(s,o.shape),p=C.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Gn({inputs:{x:o},backend:n,attrs:{perm:p}}),d=C.getInnerMostAxes(d.length,l)),C.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=C.computeOutAndReduceShapes(c.shape,d),f=C.upcastType(c.dtype,"int32"),g=em(n,h,f),y=w.sizeFromShape(m),b=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let v=0;v<b.length;++v){let I=v*y,T=0;for(let _=0;_<y;++_)T+=x[I+_];b[v]=T}if(i){let v=C.expandShapeToKeepDim(g.shape,u),I=g;g=bt({inputs:{x:g},backend:n,attrs:{shape:v}}),n.disposeIntermediateTensorInfo(I)}return n.disposeIntermediateTensorInfo(o),p!=null&&n.disposeIntermediateTensorInfo(c),g}var J5={kernelName:co,backendName:"cpu",kernelFunc:pd};function Z5(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(r,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=C.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f<d;++f){for(let g of p[f]){let{permutationIndices:y,expandDims:b}=C.getEinsumPermutation(h,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Gn({inputs:{x:s[g]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let I=0;I<b.length;++I)v.splice(b[I],0,1);w.arraysEqual(x.shape,v)||(x=bt({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),c===null?c=x:(c=Kf({inputs:{a:x,b:c},backend:n}),m.push(c))}f<d-1&&(u[f]>=0&&(c=pd({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Q5={kernelName:Nm,backendName:"cpu",kernelFunc:Z5};function eK(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;xe([a,r],"eluGrad");let s=new 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a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=w.sizeFromShape(u),d=w.getTypedArrayFromDType("float32",p),c=w.getTypedArrayFromDType("float32",p);for(let g=0;g<r;g++){let y=fi({inputs:{x:o},backend:n,attrs:{begin:[g,0],size:[1,s]}}),b=fi({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,s]}}),x=Jn({inputs:{real:y,imag:b},backend:n}),{real:v,imag:I}=dK(x,t,n),T=C.mergeRealAndImagArrays(v,I);for(let _=0;_<s;_++){let A=C.getComplexWithIndex(T,_);d[g*s+_]=A.real,c[g*s+_]=A.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(x)}let h=n.makeTensorInfo(u,"float32",d),m=n.makeTensorInfo(u,"float32",c),f=Jn({inputs:{real:h,imag:m},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}function dK(e,t,n){let a=w.sizeFromShape(e.shape),r=n.data.get(e.dataId),s=n.data.get(r.complexTensorInfos.real.dataId).values,i=n.data.get(r.complexTensorInfos.imag.dataId).values;if(hK(a)){let o=bx(s,i,a,t,n),l=[e.shape[0],e.shape[1]];if(t){let u=n.makeTensorInfo(l,"float32",o.real),p=n.makeTensorInfo(l,"float32",o.imag),d=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),c=cr({inputs:{x:d},backend:n}),h=yx.kernelFunc({inputs:{a:u,b:d},backend:n}),m=yx.kernelFunc({inputs:{a:p,b:c},backend:n}),f=n.data.get(h.dataId).values,g=n.data.get(m.dataId).values;return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),{real:f,imag:g}}return o}else{let o=C.mergeRealAndImagArrays(s,i),l=mK(o,a,t);return C.splitRealAndImagArrays(l)}}function hK(e){return(e&e-1)===0}function bx(e,t,n,a,r){if(n===1)return{real:e,imag:t};let 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      bool isnan_custom(float val) {
        uint floatToUint = floatBitsToUint(val);
        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
      }

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

      #define isnan(value) isnan_custom(value)
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      #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)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
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        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 No(e,t,n="index"){let a=w.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 Xf(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function P7(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function O7(e,t,n="index"){let a=e.map((s,i)=>i),r=P7(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function Q0(e){let t=w.computeStrides(e).map(n=>n.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}function e1(){return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`}var d_=`
  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;
  }
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`),s=e.map(c=>z7(c,t,n.packedInputs,n.enableShapeUniforms)).join(`
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`)}function qu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return oY(e,t);case 1:return uY(e,t);case 2:return cY(e,t);case 3:return hY(e,t);case 4:return fY(e,t);case 5:return gY(e);case 6:return yY(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function m_(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return iY(e);case 1:return lY(e,t);case 2:return pY(e,t);case 3:return dY(e,t);default:return mY(e,t)}}function z7(e,t,n=!1,a){let r="";n?r+=m_(e,a):r+=qu(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=bY(e,t):r+=xY(e,t)),r}function B7(e,t,n){switch(e.length){case 0:return f_();case 1:return Y7(e,t,n);case 2:return rY(e,t,n);case 3:return Z7(e,t,n);default:return eY(e,t,n)}}function W7(e,t,n){switch(e.length){case 0:return f_();case 1:return J7(e,t,n);case 2:return sY(e,t,n);case 3:return Q7(e,t,n);case 4:return tY(e,t,n);case 5:return nY(e,t);case 6:return aY(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function V7(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function U7(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function G7(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function H7(e){return`${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);
    }

    ${j7}
    ${q7}
    ${K7}
  `}var j7=`
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);
}
`,q7=`
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);
}
`,K7=`
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);
}
`,X7=`
  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 f_(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function Y7(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${a[1]}.0);
      }
    `:a[1]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${a[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));
      return 2 * (resTexRC.x * ${a[1]} + resTexRC.y);
    }
  `}function J7(e,t,n){return t[0]===1?n?`
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?n?`
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function Z7(e,t,n){if(n)return`
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec3(b, r, c);
    }
  `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));
      int index = resTexRC.x * ${a[1]} + resTexRC.y;

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

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

      return ivec3(b, r, c);
    }
  `}function Q7(e,t,n){if(n)return`
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${Xf(["r","c","d"],e)}
    return ivec3(r, c, d);
  }
`;let a=No(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${a}
      return ivec3(r, c, d);
    }
  `}function eY(e,t,n){if(n)return`
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatchN = texelsInBatch * outShape[1];

      int b2 = index / texelsInBatchN;
      index -= b2 * texelsInBatchN;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec4(b2, b, r, c);
    }
  `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u<e.length-1;u++)i*=e[e.length-u-1],o=`
      int b${u} = index / ${i};
      index -= b${u} * ${i};
    `+o,l=`b${u}, `+l;return`
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));
      int index = resTexRC.x * ${a[1]} + resTexRC.y;

      ${o}

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

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

      return ivec${e.length}(${l});
    }
  `}function tY(e,t,n){if(n)return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${Xf(["r","c","d","d2"],e)}
      return ivec4(r, c, d, d2);
    }
  `;let a=No(["r","c","d","d2"],e);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${a}
      return ivec4(r, c, d, d2);
    }
  `}function nY(e,t){let n=No(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function aY(e,t){let n=No(["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 rY(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${a[0]}, ${a[1]}));
      }
    `;let r=Math.ceil(e[1]/2);return n?`
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));

      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));

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

      return ivec2(r, c);
    }
  `}function sY(e,t,n){return w.arraysEqual(e,t)?n?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:e[1]===1?n?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:e[0]===1?n?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:n?`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `}function To(e){return`offset${e}`}function iY(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=An();return`
    vec4 ${n}() {
      return ${a.texture2D}(${t}, halfCR);
    }
  `}function oY(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return`
      float ${a}() {
        return sampleTexture(${n}, halfCR);
      }
    `;let i=To(n);if(t)return`
    float ${a}() {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
      return sampleTexture(${n}, uv);
    }
  `;let[o,l]=e.shapeInfo.texShape;return`
    float ${a}() {
      vec2 uv = uvFromFlat(${o}, ${l}, ${i});
      return sampleTexture(${n}, uv);
    }
  `}function lY(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=An();if(t)return`
    vec4 ${a}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${s.texture2D}(${n}, uv);
    }
  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
    vec4 ${a}(int index) {
      vec2 uv = packedUVfrom1D(
        ${i[0]}, ${i[1]}, index);
      return ${s.texture2D}(${n}, uv);
    }
  `}function uY(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
      float ${a}(int index) {
        ${Ku(e)}
      }
    `;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return`
      float ${a}(int index) {
        return sampleTexture(${n}, halfCR);
      }
    `;let o=To(n);return i===1?t?`
      float ${a}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${a}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
        return sampleTexture(${n}, uv);
      }
    `:s===1?t?`
      float ${a}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${a}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
        return sampleTexture(${n}, uv);
      }
    `:t?`
    float ${a}(int index) {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
      return sampleTexture(${n}, uv);
    }
  `:`
    float ${a}(int index) {
      vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
      return sampleTexture(${n}, uv);
    }
  `}function pY(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=An();if(s!=null&&w.arraysEqual(n,s))return t?`
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);

        return ${l.texture2D}(${a}, uv);
      }
    `:`
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);

        return ${l.texture2D}(${a}, uv);
      }
    `;if(t)return`
    vec4 ${r}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${a}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${l.texture2D}(${a}, uv);
    }
  `;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],p=Math.ceil(n[1]/2);return`
    vec4 ${r}(int row, int col) {
      vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col);
      return ${l.texture2D}(${a}, uv);
    }
  `}function cY(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(n,s)){if(t)return`
      float ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `;let c=s[0],h=s[1];return`
    float ${r}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${c}.0);
      return sampleTexture(${a}, uv);
    }
  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),l=i;if(l.length<n.length){let c=Xu(e,l),h=["row","col"];return`
      ${qu(c,t)}
      float ${r}(int row, int col) {
        return ${r}(${Yu(h,o)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${Ku(e)}
      }
    `;let u=s[0],p=s[1],d=To(a);return p===1?t?`
      float ${r}(int row, int col) {
        float index = dot(vec3(row, col, ${d}), vec3(${a}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${a}TexShape[0]));
        return sampleTexture(${a}, uv);
      }
    `:`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
      return sampleTexture(${a}, uv);
    }
  `:u===1?t?`
      float ${r}(int row, int col) {
        float index = dot(vec3(row, col, ${d}), vec3(${a}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${a}TexShape[1]), 0.5);
        return sampleTexture(${a}, uv);
      }
    `:`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${p}.0, 0.5);
      return sampleTexture(${a}, uv);
    }
  `:t?`
      float ${r}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${a}Shape[1] + col + ${d};
        vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
        return sampleTexture(${a}, uv);
      }
    `:`
  float ${r}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${n[1]} + col + ${d};
    vec2 uv = uvFromFlat(${u}, ${p}, index);
    return sampleTexture(${a}, uv);
  }
`}function dY(e,t){let n=e.shapeInfo.logicalShape,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)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=Xu(e,c),f=["b","row","col"];return`
        ${m_(m,t)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${Yu(f,h)});
        }
      `}let o=An();if(t)return`
    vec4 ${r}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${a}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${a}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${o.texture2D}(${a}, uv);
    }
  `;let l=i[0],u=i[1],p=Math.ceil(n[2]/2),d=p*Math.ceil(n[1]/2);return`
    vec4 ${r}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${l}, ${u}, ${d}, ${p}, b, row, col);
      return ${o.texture2D}(${a}, uv);
    }
  `}function hY(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=w.squeezeShape(n),u=o;if(u.length<n.length){let f=Xu(e,u),g=["row","col","depth"];return`
        ${qu(f,t)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${Yu(g,l)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${s}, ${i}, 1)));
        ${Ku(e)}
      }
    `;let p=e.shapeInfo.texShape,d=p[0],c=p[1],h=e.shapeInfo.flatOffset;if(c===s&&h==null)return t?`
      float ${r}(int row, int col, int depth) {
        int stride1 = ${a}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
        float ${r}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${i}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${c}.0, ${d}.0);
          return sampleTexture(${a}, uv);
        }
      `;if(c===i&&h==null)return t?`
      float ${r}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${a}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
    float ${r}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}.0, ${d}.0);
      return sampleTexture(${a}, uv);
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    float ${r}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
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      int stride1 = ${a}Shape[2];
      int index = row * ${s} + col * ${i} + depth + ${m};
      vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
      return sampleTexture(${a}, uv);
    }
    `:`
      float ${r}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s} + col * ${i} + depth + ${m};
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      int texC = index - texR * packedTexShape[1];
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      int texC = index - texR * ${p};
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    }
  `}function fY(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[3],i=n[2]*s,o=n[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(n);if(l.length<n.length){let b=Xu(e,l),x=["row","col","depth","depth2"];return`
      ${qu(b,t)}
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        return ${r}(${Yu(x,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${o}, ${i}, ${s}, 1)));
        ${Ku(e)}
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        ${m}
        ${f}
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    `:`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = float(row);
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                vec3(${i}, ${s}, 1));
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      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${a}Shape[1] * ${a}Shape[2], ${a}Shape[2], 1));
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    `:`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
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    `;let y=To(a);return t?`
    float ${r}(int row, int col, int depth, int depth2) {
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      ${f}
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      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${i} +
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      float ${a}(int row, int col, int depth, int depth2, int depth3) {
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          depth3;
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      }
    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,c=d[0],h=d[1];if(h===o&&p==null)return`
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
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                         vec4(${i}, ${s}, ${r}, 1));
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    `;if(h===r&&p==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));
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    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} +
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      ${qu(g)}
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                    int depth2, int depth3, int depth4) {
        return ${a}(${Yu(y,s)});
      }
    `}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,p=t[1]*u;if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${p}, ${u}, ${l}, ${o})) +
          dot(
            vec2(depth3, depth4),
            vec2(${i}, 1)));
        ${Ku(e)}
      }
    `;let d=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],m=c[1];if(m===p&&d==null)return`
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
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          vec4(${u}, ${l}, ${o}, ${i})) +
               float(depth4);
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    `;if(m===i&&d==null)return`
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
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               ${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=To(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 * ${p} + col * ${u} + depth * ${l} +
          depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
      vec2 uv = uvFromFlat(${h}, ${m}, index);
      return sampleTexture(${n}, uv);
    }
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    for (int i = 0; i < ${n}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
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      return vec4(outputValue.xy, outputValue.xy);
    `;else if(m&&!f)i===1?h=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
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        return vec4(outputValue.x);
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    vec4 ${r}() {
      ${l} coords = getOutputCoords();
      ${p}
      vec4 outputValue = get${a}(${c});
      ${h}
    }
  `}function xY(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&&w.arraysEqual(i,s))return`
      float ${r}() {
        return sampleTexture(${n}, resultUV);
      }
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`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),`
    float ${r}() {
      ${u} coords = getOutputCoords();
      ${c}
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    }
  `}function ut(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function t1(e,t,n){let{newShape:a,keptDims:r}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!w.arraysEqual(t,n)&&a.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function Xu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Yu(e,t){return t.map(n=>e[n]).join(", ")}function vY(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=L7(r,i,t),l=HC(e.gl,o),u=e.createProgram(l);return X().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},g_(e,t,u))}function g_(e,t,n){let a={},r={},s={},i=[],o,l,u,p=null,d=null;d=e.getUniformLocation(n,"NAN",!1),X().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(n,"INFINITY",!1));let c=!1;for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h];a[m]=e.getUniformLocation(n,m,c),a[`offset${m}`]=e.getUniformLocation(n,`offset${m}`,c),t.enableShapeUniforms&&(r[`${m}Shape`]=e.getUniformLocation(n,`${m}Shape`,c),s[`${m}TexShape`]=e.getUniformLocation(n,`${m}TexShape`,c))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,"outShape",c),u=e.getUniformLocation(n,"outShapeStrides",c),l=e.getUniformLocation(n,"outTexShape",c)),t.customUniforms&&t.customUniforms.forEach((h,m)=>{i[m]=e.getUniformLocation(n,h.name,c)}),{uniformLocations:a,customUniformLocations:i,infLoc:p,nanLoc:d,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function Qk(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function wY(e,t,n,a,r){t.program.enableShapeUniforms||(Qk(t.inShapeInfos,n),Qk([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let p=t.program.variableNames[u],d=t.uniformLocations[p],c=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],m=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:f}=t1(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(h,new Int32Array(f));break;case 2:e.gl.uniform2iv(h,new Int32Array(f));break;case 3:e.gl.uniform3iv(h,new Int32Array(f));break;case 4:e.gl.uniform4iv(h,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(d,f)}return}l.texData.slice!=null&&c!=null&&e.gl.uniform1i(c,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,d,u)}});let o=t.outShapeLocation;if(o)switch(a.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(a.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(a.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(a.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(a.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(a.shape);switch(a.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,a.texData.texShape[0],a.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(p,d);else if(l.type==="vec2")e.gl.uniform2fv(p,d);else if(l.type==="vec3")e.gl.uniform3fv(p,d);else if(l.type==="vec4")e.gl.uniform4fv(p,d);else if(l.type==="int")e.gl.uniform1iv(p,d);else if(l.type==="ivec2")e.gl.uniform2iv(p,d);else if(l.type==="ivec3")e.gl.uniform3iv(p,d);else if(l.type==="ivec4")e.gl.uniform4iv(p,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function kY(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=t1(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=w.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,b=C.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&w.arraysEqual(l,n.texData.texShape),v=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${y}_${b}_${g}_${c}_${h}_${m}_${v}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,s}function qn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var IY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=pc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=An();this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Xf(["r","c","d"],e):No(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }

        ${t.output} = result;
      }
    `}},SY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=pc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=An();this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Xf(["r","c","d"],e):No(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }

        ${t.output} = result;
      }
    `}},NY=class{constructor(e){this.variableNames=["A"],this.outTexUsage=pa.DOWNLOAD;let t=An();this.outputShape=e,this.userCode=`
      ${d_}

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

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},CY=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=An();this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let a="result";t&&(a="floor(result * 255. + 0.5)"),this.userCode=`
      ${this.enableShapeUniforms?e1():Q0(e)}

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

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

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

        int r = flatIndex / texShape[1];
        int c = imod(flatIndex, texShape[1]);
        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
        vec4 values = ${n.texture2D}(A, uv);

        float result;

        if(offset == 0) {
          result = values[0];
        } else if(offset == 1) {
          result = values[1];
        } else if(offset == 2) {
          result = values[2];
        } else {
          result = values[3];
        }

        ${n.output} = vec4(${a}, 0., 0., 0.);
      }
    `}},_Y=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=An();this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let a="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;a+=`
          localCoords = coords;
          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
          localCoords[2] += ${i};
          if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
            localCoords[1] += ${s};

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

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

            int r = flatIndex / texShape[1];
            int c = imod(flatIndex, texShape[1]);
            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
            values = ${n.texture2D}(A, uv);

            if (offset == 0) {
              result[${o}] = values[0];
            } else if (offset == 1) {
              result[${o}] = values[1];
            } else if (offset == 2) {
              result[${o}] = values[2];
            } else {
              result[${o}] = values[3];
            }
          }
        }
        `}this.userCode=`
        ${this.enableShapeUniforms?e1():Q0(e)}

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

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

          ${a}

          ${n.output} = ${r};
        }
    `}},y_={};Fe(y_,{bindVertexProgramAttributeStreams:()=>T_,createBufferFromOutputTexture:()=>E_,createFloat16MatrixTexture:()=>k_,createFloat16PackedMatrixTexture:()=>N_,createFloat32MatrixTexture:()=>w_,createIndexBuffer:()=>v_,createPackedMatrixTexture:()=>S_,createUnsignedBytesMatrixTexture:()=>I_,createVertexBuffer:()=>x_,createVertexShader:()=>b_,downloadByteEncodedFloatMatrixFromOutputTexture:()=>$_,downloadFloat32MatrixFromBuffer:()=>A_,downloadMatrixFromPackedOutputTexture:()=>D_,downloadPackedMatrixFromBuffer:()=>F_,getInternalFormatForFloat16MatrixTexture:()=>a1,getInternalFormatForFloat16PackedMatrixTexture:()=>i1,getInternalFormatForFloat32MatrixTexture:()=>n1,getInternalFormatForPackedMatrixTexture:()=>s1,getInternalFormatForUnsignedBytesMatrixTexture:()=>r1,uploadDenseMatrixToTexture:()=>C_,uploadPixelDataToTexture:()=>__});function b_(e){let t=An(),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 GC(e,n)}function x_(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 KC(e,t)}function v_(e){let t=new Uint16Array([0,1,2,2,1,3]);return XC(e,t)}function dd(e,t,n,a,r,s){JC(t,n);let i=YC(e),o=e.TEXTURE_2D;return ge(e,()=>e.bindTexture(o,i)),ge(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ge(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),X().getNumber("WEBGL_VERSION")===1?ge(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)):ge(e,()=>e.texStorage2D(o,1,a,t,n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function n1(e){return e.internalFormatFloat}function w_(e,t,n,a){let[r,s]=cd(t,n);return dd(e,r,s,n1(a),a.textureFormatFloat,e.FLOAT)}function a1(e){return e.internalFormatHalfFloat}function k_(e,t,n,a){let[r,s]=cd(t,n);return dd(e,r,s,a1(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function r1(e){return e.downloadTextureFormat}function I_(e,t,n,a){let[r,s]=cd(t,n);return dd(e,r,s,r1(a),e.RGBA,e.UNSIGNED_BYTE)}function s1(e){return e.internalFormatPackedFloat}function S_(e,t,n,a){let[r,s]=Hu(t,n);return dd(e,r,s,s1(a),e.RGBA,e.FLOAT)}function i1(e){return e.internalFormatPackedHalfFloat}function N_(e,t,n,a){let[r,s]=Hu(t,n);return dd(e,r,s,i1(a),e.RGBA,a.textureTypeHalfFloat)}function T_(e,t,n){return ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),xx(e,t,"clipSpacePos",n,3,20,0)&&xx(e,t,"uv",n,2,20,12)}function C_(e,t,n,a,r,s){ge(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),X().getNumber("WEBGL_VERSION")===2?ge(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,a,e.RGBA,o,i)):ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function __(e,t,n){ge(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?X().getNumber("WEBGL_VERSION")===2?ge(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):X().getNumber("WEBGL_VERSION")===2?ge(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function E_(e,t,n,a){let r=e.createBuffer();ge(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return ge(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ge(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ge(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function A_(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 $_(e,t,n,a){let[r,s]=cd(t,n),i=4,o=new Uint8Array(S7(t*n,i));return ge(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function F_(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(N7(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 D_(e,t,n){let a=new Float32Array(t*n*4);return ge(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var Eh=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=X().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,WC(t,e)):this.gl=Ja(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),X().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Hp(this.gl,r),ca(this.gl,s))this.textureHalfFloatExtension=Hp(this.gl,s);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),ca(this.gl,a))this.colorBufferHalfFloatExtension=Hp(this.gl,a);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",ca(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ca(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=x_(this.gl),this.indexBuffer=v_(this.gl),this.framebuffer=ZC(this.gl),this.textureConfig=J0(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),N_(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),S_(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(vx(this.gl,this.framebuffer),this.outputTexture=null),ge(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>$_(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return F_(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return A_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=E_(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(X().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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ge(t,()=>t.attachShader(n,this.vertexShader)),ge(t,()=>t.attachShader(n,e)),qC(t,n),this.debug&&Sh(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=T_(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ge(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Sh(this.gl,this.program),ge(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?e_(this.gl,e,t):t_(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ge(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(),n_(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Hu(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&&Sh(this.gl,this.program),jp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ge(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ge(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Hp(this.gl,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,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(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,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=EY(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Nh(this.gl,e,this.framebuffer),this.debug&&jp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Nh(this.gl,this.outputTexture,this.framebuffer),this.debug&&jp(this.gl)):vx(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;Nh(a,e,this.framebuffer),this.debug&&jp(a),this.outputTexture=e,ge(a,()=>a.viewport(0,0,t,n)),ge(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),ge(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 EY(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:AY,bincountImpl:R_,bincountReduceImpl:$Y,ceilImpl:FY,concatImpl:DY,equalImpl:RY,expImpl:MY,expm1Impl:PY,floorImpl:OY,gatherNdImpl:LY,gatherV2Impl:zY,greaterImpl:BY,greaterEqualImpl:WY,lessImpl:VY,lessEqualImpl:UY,linSpaceImpl:GY,logImpl:HY,maxImpl:jY,maximumImpl:qY,minimumImpl:KY,multiplyImpl:XY,negImpl:YY,notEqualImpl:JY,prodImpl:ZY,rangeImpl:QY,rsqrtImpl:eJ,scatterImpl:tJ,sigmoidImpl:nJ,simpleAbsImpl:M_,sliceImpl:aJ,sparseFillEmptyRowsImpl:rJ,sparseReshapeImpl:sJ,sparseSegmentReductionImpl:P_,sqrtImpl:iJ,stridedSliceImpl:oJ,stringNGramsImpl:lJ,stringSplitImpl:uJ,stringToHashBucketFastImpl:pJ,subImpl:cJ,tileImpl:dJ,topKImpl:hJ,transposeImpl:o1,uniqueImpl:mJ}=VT;function O_(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Sn(e,t){return t===1?[e]:O_(e,t)}function fJ(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var gJ=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=qn(this.outputShape.length),this.rank===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let t=Sn("rc",this.rank),n=ut(this.rank),a=this.getOutOfBoundsCondition(t),r=this.getSetup(t),s=this.getOutput(t);this.userCode=`
        void main() {
          ${n} rc = getOutputCoords();

          if(${a}) {
            setOutput(vec4(0));
          } else {
            ${r}

            setOutput(vec4(${s}));
          }
        }
      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let a=0;a<=1;a++){let r=`${n===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let s=2;s<this.rank;s++)r=`${e[e.length-1-s]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],a=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
      int r = ${t[0]};
      int c = ${t[1]};
      int rp1 = r + 1;
      int cp1 = c + 1;

      bool cEdge = cp1 >= ${n};
      bool rEdge = rp1 >= ${a};
    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
            cEdge ? 0. : getA(${t[1]}),
            rEdge ? 0. : getA(${t[2]}),
            rEdge || cEdge ? 0. : getA(${t[3]})`}},L_=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
        ${r}
        ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
          int flatIndex = getFlatIndex(thisRC);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${a}] =
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        ${a>0?"}":""}
      `}this.userCode=`
      ${yJ(t,this.enableShapeUniforms)}
      ${this.enableShapeUniforms?e1():Q0(e)}

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

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
        int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};

        ${n}

        setOutput(result);
      }
    `}};function yJ(e,t){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t?O7(["r","c","d"],"inputShape"):No(["r","c","d"],e)}
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  `}var bJ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=tI(t,n),r=nI(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=eI(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===on.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===on.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===on.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===on.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===on.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=tI(n,a),s=nI(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=eI(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function xJ(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function eI(e,t,n,a,r){let s=vJ(t,a),i;if(r){let[l,u]=Hu(e[0],e[1]);i=l*u}else{let[l,u]=cd(e[0],e[1]);i=l*u}let o=xJ(n,s);return i*o}function vJ(e,t){switch(e){case on.PACKED_2X2_FLOAT32:return s1(t);case on.PACKED_2X2_FLOAT16:return i1(t);case on.UNPACKED_FLOAT32:return n1(t);case on.UNPACKED_FLOAT16:return a1(t);case on.PACKED_4X1_UNSIGNED_BYTE:return r1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function wJ(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?on.PACKED_2X2_FLOAT32:on.UNPACKED_FLOAT32:e?on.PACKED_2X2_FLOAT16:on.UNPACKED_FLOAT16}function tI(e,t){if(e===pa.UPLOAD)return on.PACKED_2X2_FLOAT32;if(e===pa.RENDER||e==null)return wJ(t);if(e===pa.DOWNLOAD||e===pa.PIXELS)return on.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function nI(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Nr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length),this.userCode=`
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        ${t}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
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  return (x < 0.0) ? 0.0 : min(6.0, x);
`,el="return x;",TJ="return 1.0 / (1.0 + exp(-1.0 * x));",CJ="return x;",_J=`
  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;
`,EJ=`
  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
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  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;
`,AJ=`
  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;
`,$J="return 1.0 / (1.0 + exp(-1.0 * x));",Qs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length),this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
      }
    `}},FJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let t=e.length,n=Sn("rc",t),a=ut(t),r=fJ(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
      void main() {
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        vec4 packedInput = getA(${r});

        setOutput(getChannel(packedInput, ${i}));
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Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:pa.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:pa.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let d;o?d=new Qs(i,el):d=new Nr(i,el);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=w.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=C.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Qs(a,el):h=new Nr(a,el);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...bh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=C.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ge(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ar().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new Qs(r,el):c=new Nr(r,el);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=ar().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>w.decodeString(a));return Le(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!VC(n))throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),c=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture.texture,...bh(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let s=X().getBool("WEBGL_PACK")&&a===!0,i=s?Th(t):t,o=s?new TY(i):new NY(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=OJ){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return DJ(e.shape,t)}packedUnaryOp(e,t,n){let a=new Qs(e.shape,t),r=this.compileAndRun(a,[e],n);return ar().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=M_(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,aI,e.dtype);let t=new Nr(e.shape,aI),n=this.compileAndRun(t,[e]);return ar().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(s=>w.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){return ar().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new FJ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new gJ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[gi(e.shape),...yi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[gi(t),...yi(t)],s=new L_(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=w.sizeFromShape(r),c=t[0]*t[1]*4;w.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Th(r),o;a?o=new SY(i):o=new IY(i);let l=!0,u=[t!=null?t:bh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===pc.DENSE){let g=s!=null?s:bh(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!cc(y.shape,g.shape)){let b=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),b.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=kY(e,u,p),c=this.getAndSaveBinary(d,()=>vY(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),X().get("ENGINE_COMPILE_ONLY")||wY(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=X().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=w.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?RJ:MJ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let p=t.texShape;if(p==null&&(p=s_(n,o),t.texShape=p),r!=null){let d=Th(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=Hu(p[0],p[1])),o?c=new _Y(d,f):c=new CY(d,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,a),b=this.texData.get(y.dataId);f?b.usage=pa.PIXELS:b.usage=pa.UPLOAD,b.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let x=[[m,h]],v=!0,I=this.runWebGLProgram(c,[y],a,x,v),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=w.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=BJ(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await aw(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Z0(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=g_(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}};Yf.nextDataId=0;function BJ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var WJ="3.19.0";function z_(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}Pc.isBrowser()&&Gm("webgl",()=>new Yf,2);var VJ={forceHalfFloat:z_},B_=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,Cl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=qn(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},Jf=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`,hd=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=qn(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(s=`
          ${ut(r)} coords = getOutputCoords();
        `,r===1)this.enableShapeUniforms?s+=`
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `:s+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=Sn("coords",r);this.enableShapeUniforms?s+=`
            bool nextRowOutOfBounds =
              (${i[r-2]} + 1) >= outShape[${r} - 2];
            bool nextColOutOfBounds =
              (${i[r-1]} + 1) >= outShape[${r} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `:s+=`
            bool nextRowOutOfBounds =
              (${i[r-2]} + 1) >= ${this.outputShape[r-2]};
            bool nextColOutOfBounds =
              (${i[r-1]} + 1) >= ${this.outputShape[r-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${s}

        setOutput(result);
      }
    `}};function na(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var UJ={kernelName:zi,backendName:"webgl",kernelFunc:na};function Ns(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=na({inputs:{x:a},backend:n}),l=na({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var GJ={kernelName:ym,backendName:"webgl",kernelFunc:Ns},W_="return (a < 0.) ? b * a : a;",V_=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function HJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hd(V_,r.shape,i.shape):new Cl(W_,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var jJ={kernelName:Bi,backendName:"webgl",kernelFunc:HJ},U_="return (a < 0.) ? b * a : a;",G_=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function qJ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hd(G_,a.shape,r.shape):new Cl(U_,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var KJ={kernelName:Qi,backendName:"webgl",kernelFunc:qJ},Ju="if (isnan(x)) return x;",XJ=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,YJ=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;function Je({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Qs(i.shape,t):p=new Nr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function pn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,I]=x,T={dataId:v.dataId,dtype:v.dtype,shape:l.shape},_={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A=new Cl(e,l.shape,u.shape);return p.runWebGLProgram(A,[T,_],ha(v.dtype,I.dtype))}),b=Ns({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),b}let d=s||ha(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?C.fromUint8ToStringArray(m):m,y=l.dtype==="string"?C.fromUint8ToStringArray(f):f,[b,x]=r(l.shape,u.shape,g,y,d),v=p.makeTensorInfo(x,d),I=p.texData.get(v.dataId);return I.values=b,v}let c=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new hd(t,l.shape,u.shape,n):h=new Cl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Zf(e,t=!1){if(e==="linear")return t?CJ:kJ;if(e==="relu")return t?EJ:SJ;if(e==="elu")return t?_J:IJ;if(e==="relu6")return t?AJ:NJ;if(e==="prelu")return t?G_:U_;if(e==="leakyrelu")return t?V_:W_;if(e==="sigmoid")return t?$J:TJ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var H_=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=qn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${i}
        }`:l?f=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${i}
        }`:f=`vec4 activation(vec4 x) {
          ${i}
        }`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let 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}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${p}.0;

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

          // 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);
      }
    `}},rI={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},sI=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.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));
      }
    `}},iI="return a * b;";function l1(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=C.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new sI(rI.REAL,a.shape,r.shape),p=new sI(rI.IMAG,a.shape,r.shape),d=[{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}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Ns({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,p]=XY(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new hd(iI,a.shape,r.shape):i=new Cl(iI,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var JJ={kernelName:Xi,backendName:"webgl",kernelFunc:l1};function ZJ(e,t,n){let a=[gi(e.shape),...yi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[gi(t),...yi(t)],i=new L_(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function he(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!cc(r.shape,l)&&!(p.texture!==null&&cc(p.shape,l))?ZJ(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var QJ={kernelName:gu,backendName:"webgl",kernelFunc:he},oI=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 p=1/t;l=`sumValue += dot(values * ${w.isInt(p)?p.toPrecision(2):p}, 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);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},e9=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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,p=n%4,d=`
      if (${t==="sum"}) {
        sumValue += dot(values, ones);
      } else if (${t==="prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${o}(values, minMaxValue);
        if (${t==="min"} || ${t==="max"}) {
          minMaxValue = ${o}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `,c="vec4";t==="all"?(i="1.0",d=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,c="bvec4"):t==="any"&&(i="0.0",d=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,c="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;
          ${c} values = ${c}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${d}
        }

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

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

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

          ${d}
        }
        setOutput(${l});
      }
    `}};function t9(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=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Co(e,t,n,a){let r=t9(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],p,d;n==="mean"?p=i===0?new oI({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new oI({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):p=new e9({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),d=s,s=a.runWebGLProgram(p,[s],t),d.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(d)}return s}var n9=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=ut(this.rank),r=a9(t);this.userCode=`
    void main() {
      ${a} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `}};function a9(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 r9=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 ${this.rank} is not yet supported.`);let a=ut(this.rank),r=O_("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${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] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function Qf(e,t,n){let a=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new r9(e.shape,t):new n9(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function s9(e,t,n,a){let r=t,s=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Qf(e,l,a),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=C.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=C.expandShapeToKeepDim(d,i));let m=w.sizeFromShape(c),f=w.sizeFromShape(e.shape)/m,g=he({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),y=Um(e.dtype),b=Co(g,y,"sum",a),x=he({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),u&&a.disposeIntermediateTensorInfo(p),x}function eg(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return s9(r,s,i,n)}var i9={kernelName:co,backendName:"webgl",kernelFunc:eg};function Tn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let p=0;p<l.length;p++)l[p]=r.shape[s[p]];let u;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=o1(p,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let c=i.texData.get(u.dataId);c.values=d}else u=Qf(r,s,i);return u}var o9={kernelName:Tr,backendName:"webgl",kernelFunc:Tn},j_=1e3;function sm({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],c=a?t.shape[p-1]:t.shape[p-2],h=n?e.shape[u-1]:e.shape[u-2],m=a?t.shape[p-2]:t.shape[p-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(f),b=w.sizeFromShape(g),x=Du.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,m]);w.assert(d===c,()=>`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[y,d,h]:[y,h,d],I=a?[b,m,c]:[b,c,m],T=he({inputs:{x:e},backend:r,attrs:{shape:v}}),_=he({inputs:{x:t},backend:r,attrs:{shape:I}}),A=[T,_],F=Math.max(y,b),P=n?T.shape[1]:T.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",U=l!=null?Zf(l,!0):null,j=$||S||M||U!=null,q;if((h===1||m===1)&&P>j_&&j===!1){let Y=T,te=_;n&&(Y=Tn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),A.push(Y)),a&&(te=Tn({inputs:{x:_},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let re=m!==1,Q=m===1,ie=Y;re&&(ie=he({inputs:{x:Y},backend:r,attrs:{shape:[F,P,1]}}),A.push(ie));let ae=m===1?2:1,oe=te;Q&&(oe=he({inputs:{x:te},backend:r,attrs:{shape:[F,1,P]}}),A.push(oe));let ue=l1({inputs:{a:ie,b:oe},backend:r});q=eg({inputs:{x:ue},backend:r,attrs:{axis:ae,keepDims:!0}}),A.push(ue)}else{let Y=ha(e.dtype,t.dtype),te=new H_(v,I,[F,h,m],n,a,$,U,S,M),re=[T,_];if(s!=null&&re.push(s),S&&re.push(i),M){let Q=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));re.push(Q),A.push(Q)}q=r.runWebGLProgram(te,re,Y)}let K=he({inputs:{x:q},backend:r,attrs:{shape:x}});A.push(q);for(let Y of A)r.disposeIntermediateTensorInfo(Y);return K}function l9(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return sm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var u9={kernelName:ri,backendName:"webgl",kernelFunc:l9},lI="return abs(x);";function p9(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=M_(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Qs(a.shape,lI):r=new Nr(a.shape,lI),n.runWebGLProgram(r,[a],a.dtype)}var c9={kernelName:El,backendName:"webgl",kernelFunc:p9},d9=$a+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,h9=Je({opSnippet:d9}),m9={kernelName:Al,backendName:"webgl",kernelFunc:h9},f9=$a+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,g9=Je({opSnippet:f9}),y9={kernelName:$l,backendName:"webgl",kernelFunc:g9},uI="return a + b;",b9=pn({opSnippet:uI,packedOpSnippet:uI,supportsComplex:!0,cpuKernelImpl:AY}),x9={kernelName:ds,backendName:"webgl",kernelFunc:b9},v9=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 = ${a};
        setOutput(result);
      }
    `}},w9=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 Ah(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return na({inputs:{x:a[0]},backend:n});if(a.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Ah({inputs:a.slice(0,o),backend:n}),u=Ah({inputs:a.slice(o),backend:n});return Ah({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ha(o,l)),s=a.map(o=>o.shape),i=X().getBool("WEBGL_PACK")?new w9(a[0].shape,s):new v9(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var k9={kernelName:vi,backendName:"webgl",kernelFunc:Ah};function I9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),d=r;p!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[c,h]=C.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=he({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Co(f,f.dtype,"all",n),y;if(i){let b=C.expandShapeToKeepDim(c,l);y=he({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=he({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var S9={kernelName:Fl,backendName:"webgl",kernelFunc:I9};function N9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),d=r;p!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[c,h]=C.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=he({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Co(f,f.dtype,"any",n),y;if(i){let b=C.expandShapeToKeepDim(c,l);y=he({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=he({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var T9={kernelName:Dl,backendName:"webgl",kernelFunc:N9},C9=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 ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}},_9=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],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=ut(o),u=Sn("coords",o),p,d;if(s===1){d=o+1;let _=ut(d);p=`
        ${_} sourceLocR = ${_}(${u.join()}, 0);
        ++${u[o-1]};
        ${_} sourceLocG = ${_}(${u.join()}, 0);
        ++${u[o-2]};
        ${_} sourceLocA = ${_}(${u.join()}, 0);
        --${u[o-1]};
        ${_} sourceLocB = ${_}(${u.join()}, 0);
        --${u[o-2]};`}else d=o,p=`
        ${l} sourceLocR = coords;
        ++${u[o-1]};
        ${l} sourceLocG = coords;
        ++${u[o-2]};
        ${l} sourceLocA = coords;
        --${u[o-1]};
        ${l} sourceLocB = coords;
        --${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,d),h="."+c[d-1],m=c.map(_=>"int "+_),f=Sn("sourceLocR",d-1).concat("inIdx.r"),g=Sn("sourceLocG",d-1).concat("inIdx.g"),y=Sn("sourceLocB",d-1).concat("inIdx.b"),b=Sn("sourceLocA",d-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()})));`,I=`vec4(
            getAChannel(${f.join()}),
            hasNextCol ? getAChannel(${g.join()}) : 0.,
            hasNextRow ? getAChannel(${y.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,T=a?"":`
      float getBestIndicesAChannel(${m.join()}) {
        return getChannel(getBestIndicesA(${c.join()}),
                                          vec2(${c.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${m.join()}) {
        return getChannel(getA(${c.join()}),
                               vec2(${c.slice(-2).join()}));
      }
      ${T}
      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
        bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
        ${p}
        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
          sourceLocB${h}, sourceLocA${h}) * ${t};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${I};

        for (int i = 0; i < ${t}; i++) {
          inIdx = srcIdx;
          ${v}
          vec4 candidate = ${I};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${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 q_(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=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new C9(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=q_(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function K_(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=C.computeOptimalWindowSize(s),o=new _9(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 p=K_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function X_(e,t,n,a){let r=[n];if(C.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=C.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(p),c=he({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=q_(e,c,a);s.push(h);let m=he({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return K_(e,t,a)}function E9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Tn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=X_(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var A9={kernelName:wi,backendName:"webgl",kernelFunc:E9};function $9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Tn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=X_(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var F9={kernelName:gc,backendName:"webgl",kernelFunc:$9},D9=$a+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,R9=Je({opSnippet:D9}),M9={kernelName:Rl,backendName:"webgl",kernelFunc:R9},P9=$a+"return log(x + sqrt(x * x + 1.0));",O9=Je({opSnippet:P9}),L9={kernelName:Ml,backendName:"webgl",kernelFunc:O9},z9=$a+`
  return atan(x);
`,B9=Je({opSnippet:z9}),W9={kernelName:Pl,backendName:"webgl",kernelFunc:B9},V9=XJ+`
  return atan(a, b);
`,U9=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+YJ+`
  return result;
`,G9=pn({opSnippet:V9,packedOpSnippet:U9}),H9={kernelName:Ll,backendName:"webgl",kernelFunc:G9},j9=$a+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,q9=Je({opSnippet:j9}),K9={kernelName:Ol,backendName:"webgl",kernelFunc:q9},dc=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,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=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) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let _=">=";this.userCode=`
        const ivec2 strides = ivec2(${i}, ${o});
        const ivec2 pads = ivec2(${c}, ${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 < ${p};
              wR += ${l}) {
            int xR = xRCorner + wR;

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

            for (int wC = 0; wC < ${d};
                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 ${_} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${a?r?f:g:`wR * ${d} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let b="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,I=s%4,T=`
      if (${m}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${b}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${i}, ${o});
      const ivec2 pads = ivec2(${c}, ${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 < ${p};
            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)
            );

            ${T}
          }

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

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

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

            ${T}
          }
        }
        setOutput(${x});
      }
    `}},u1=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,p=e.dilationHeight,d=e.dilationWidth,c=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 < ${c};
              wD += ${u}) {
            int xD = xDCorner + wD;

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

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

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

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

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

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${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 * ${m} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let v="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / count");let T=Math.floor(s/4)*4,_=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 < ${c};
            wD += ${u}) {
          int xD = xDCorner + wD;

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

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

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

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

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

              ${A}
            }

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

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

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

              ${A}
            }
          }
          setOutput(${I});
        }
      }
    `}};function X9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ju(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new dc(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var Y9={kernelName:ki,backendName:"webgl",kernelFunc:X9};function J9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=C.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new u1(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Z9={kernelName:yc,backendName:"webgl",kernelFunc:J9},Q9=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,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
      const ivec2 pads = ivec2(${u}, ${p});
      const float avgMultiplier = float(${d});

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

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

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${o};
            wR += ${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);
      }
    `}},eZ=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,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-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 < ${p};
            wD += ${o}) {
          float dyD = float(dyDCorner + wD) / ${r}.0;

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

          for (int wR = 0; wR < ${d};
              wR += ${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 < ${c};
                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 tZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=C.computePool3DInfo(i.shape,o,l,d,u,p),h=new eZ(c);return n.runWebGLProgram(h,[r],i.dtype)}var nZ={kernelName:mm,backendName:"webgl",kernelFunc:tZ};function aZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ju([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=C.computePool2DInfo(i.shape,o,l,1,u),d=new Q9(p);return n.runWebGLProgram(d,[r],i.dtype)}var rZ={kernelName:hm,backendName:"webgl",kernelFunc:aZ};function sZ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return sm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var iZ={kernelName:Ii,backendName:"webgl",kernelFunc:sZ},oZ=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(C.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)));
      }
    `}},lZ=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(C.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);
      }
    `}},uZ=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=X().getBool("WEBGL_PACK_NORMALIZATION")?new lZ(a.shape,r.shape,s.shape,p,d,l):new oZ(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},pZ={kernelName:Oi,backendName:"webgl",kernelFunc:uZ},cZ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=dZ(this.rank),a,r=e.map((s,i)=>`sourceLoc.${Ix[i]} = start[${i}] + coords.${Ix[i]};`);a=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${r.join(`
`)}
      `,this.userCode=`
      void main() {
        ${a}
        setOutput(getSource(${n}));
      }
    `}},Ix=["x","y","z","w","u","v"];function dZ(e){if(e===1)return"sourceLoc";if(e<=6)return Ix.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var hZ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ut(this.rank),n=Sn("coords",this.rank),a=Sn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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,p)=>`start[${p}]`).join()});`:e.map((u,p)=>`${a[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${i}
        ${o}
        setOutput(result);
      }
    `}};function mZ(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=qt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Zu(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=qt.parseSliceParams(r,s,i);if(qt.assertParamsValid(r,o,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=aJ(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=qt.isSliceContinous(r.shape,o,l);if(u||!p){let d=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hZ(l):new cZ(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),mZ(r,o,l,n)}var fZ={kernelName:vu,backendName:"webgl",kernelFunc:Zu},gZ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;w.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=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),d=C.getSliceBeginCoords(i,s.length),c=C.getSliceSize(p,i,s.length),h=[],m=he({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Tn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=he({inputs:{x:f},backend:n,attrs:{shape:p}}),y=Zu({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},yZ={kernelName:zl,backendName:"webgl",kernelFunc:gZ};function bZ(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=R_(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var xZ={kernelName:fm,backendName:"webgl",kernelFunc:bZ};function vZ(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var wZ={kernelName:gm,backendName:"webgl",kernelFunc:vZ},kZ="return float(a != b);",Y_=pn({opSnippet:kZ,cpuKernelImpl:JY,dtype:"bool"}),IZ={kernelName:uu,backendName:"webgl",kernelFunc:Y_};function md(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.real},backend:n})}var SZ={kernelName:Pm,backendName:"webgl",kernelFunc:md},NZ="return float(int(x));";function TZ(e,t){let n=new Nr(e.shape,NZ),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Sx(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return na({inputs:{x:r},backend:n});let i=It(r.shape),o=Sx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Ns({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=md({inputs:{input:r},backend:n}),o=Sx({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,s)){let i=na({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return TZ(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=Y_({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 CZ={kernelName:Si,backendName:"webgl",kernelFunc:Sx},pI="return ceil(x);",_Z=Je({opSnippet:pI,packedOpSnippet:pI,cpuKernelImpl:FY}),EZ={kernelName:Ni,backendName:"webgl",kernelFunc:_Z},AZ=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`

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

        setOutput(clamp(value, minVal, maxVal));
      }
    `}},$Z=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
      void main() {
        vec4 value = getAAtOutCoords();

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

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}};function FZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;X().getBool("WEBGL_PACK_CLIP")?o=new $Z(r.shape):o=new AZ(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var DZ={kernelName:hs,backendName:"webgl",kernelFunc:FZ},RZ=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 cI(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function MZ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new RZ(a.shape),i=[cI(a,r.complexTensorInfos.real),cI(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var PZ={kernelName:bc,backendName:"webgl",kernelFunc:MZ},OZ=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let 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;

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

      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 vh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function tg(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.imag},backend:n})}var zZ={kernelName:Em,backendName:"webgl",kernelFunc:tg};function qp(e,t,n){let a=e[0].dtype;if(a==="complex64"){let d=e.map(g=>md({inputs:{input:g},backend:n})),c=e.map(g=>tg({inputs:{input:g},backend:n})),h=qp(d,t,n),m=qp(c,t,n),f=Ns({inputs:{real:h,imag:m},backend:n});return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let d=e.map(b=>{let x=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,x]}})}),c=d.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),h=C.computeOutShape(d.map(b=>b.shape),1),m=d[0].shape[0]===1,f=DY(c,h,a,m),g=C.computeOutShape(e.map(b=>b.shape),t),y=n.makeTensorInfo(g,a,f);return d.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}let s=X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(e.length>s){let d=[];for(let h=0;h<e.length;h+=s){let m=e.slice(h,h+s);d.push(qp(m,t,n))}let c=qp(d,t,n);for(let h of d)n.disposeIntermediateTensorInfo(h);return c}if(X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new LZ(e.map(c=>c.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:i,outShape:o}=BZ(e,t,n),l=new OZ(i.map(d=>d.shape)),u=n.runWebGLProgram(l,i,a);i.forEach(d=>n.disposeIntermediateTensorInfo(d));let p=he({inputs:{x:u},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(u),p}function BZ(e,t,n){let a=C.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>he({inputs:{x:r},attrs:{shape:[-1,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function J_(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return na({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),qp(o,s,n)}var WZ={kernelName:Bl,backendName:"webgl",kernelFunc:J_},Z_=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,p=e.dilationWidth,d=e.filterHeight,c=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 I=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 < ${d}; wR++) {
          int xR = xRCorner + wR * ${u};

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

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

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

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

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

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

            for (int wC = 0; wC < ${c}; 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);
      }
    `}},UZ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let{dataFormat:n}=t,a=An(),r=n==="channelsLast",s=r?1:2,i=r?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,l="";for(let u=0;u<=1;u++)for(let p=0;p<=1;p++)l+=`
          blockIndex = rc.z + ${p};
          pos = rc.y + ${u};

          ${o}
            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);

            if(d0 < inputShape[${s}] && d0 >= 0) {
              // Use custom imod instead mod. On Intel GPU, mod may generate
              // unexpected value.
              // https://github.com/tensorflow/tfjs/issues/5447
              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
                  inChannels);

              if(d1 < inputShape[${i}] && d1 >= 0) {

                ch = imod(pos, inChannels);

                if (${r}) {
                  innerDims = vec2(d1, ch);
                  result[${u*2+p}] = getChannel(
                    getA(rc.x, d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${u*2+p}] = getChannel(
                    getA(rc.x, ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;this.userCode=`
      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0);

        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
        vec2 innerDims;

        ${l}

        ${a.output} = result;
      }
    `}};function im(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function Q_({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),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let b=im(s.shape,h);b!=null&&(s=he({inputs:{x:s},backend:a,attrs:{shape:b}}),y.push(s))}if(r!=null){let b=im(r.shape,h);b!=null&&(r=he({inputs:{x:r},backend:a,attrs:{shape:b}}),y.push(r))}if(!((d===1||c===1)&&p>j_)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(cc(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=he({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(I);let T=sm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=a.texData.get(T.dataId);w.assert(_.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,_.shape=n.outShape,g=na({inputs:{x:T},backend:a}),g.shape=n.outShape,y.push(T)}else{let b=n.outHeight*n.outWidth,x=he({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,b,n.inChannels]:[n.batchSize,n.inChannels,b]}}),v=he({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=sm({a:h?x:v,b:h?v:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=he({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),y.push(x),y.push(v),y.push(I)}for(let b of y)a.disposeIntermediateTensorInfo(b);return g}function eE({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,y=[n.batchSize,f,g],b=!0,x=!1,v=[];if(s!=null){let Y=im(s.shape,m);Y!=null&&(s=he({inputs:{x:s},backend:a,attrs:{shape:Y}}),v.push(s))}if(r!=null){let Y=im(r.shape,m);Y!=null&&(r=he({inputs:{x:r},backend:a,attrs:{shape:Y}}),v.push(r))}let I=he({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(I);let T=new UZ(y,n),_=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=a.runWebGLProgram(T,[e],"float32",_),F=he({inputs:{x:A},backend:a,attrs:{shape:y}});v.push(A),v.push(F);let P=r!=null,$=s!=null,S=o==="leakyrelu",M=o?Zf(o,!0):null,U=new H_(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],b,x,P,M,$,S),j=m?[F,I]:[I,F];if(r&&j.push(r),$&&j.push(s),S){let Y=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));j.push(Y),v.push(Y)}let q=a.runWebGLProgram(U,j,"float32"),K=he({inputs:{x:q},backend:a,attrs:{shape:n.outShape}});v.push(q);for(let Y of v)a.disposeIntermediateTensorInfo(Y);return K}function GZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=Q_({x:r,filter:s,convInfo:c,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL"))h=eE({x:r,filter:s,convInfo:c,backend:n});else{let f=new Z_(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=he({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var HZ={kernelName:Ti,backendName:"webgl",kernelFunc:GZ},jZ=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);
      }
    `}},qZ=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,p=s?3:1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${o});

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

        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);
      }
    `}},KZ=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);
      }
    `}},XZ=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 YZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new jZ(c);return n.runWebGLProgram(h,[r,s],"float32")}var JZ={kernelName:bm,backendName:"webgl",kernelFunc:YZ};function ZZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=C.convertConv2DDataFormat(u),c=C.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d),h=new qZ(c);return n.runWebGLProgram(h,[r,s],"float32")}var QZ={kernelName:Ci,backendName:"webgl",kernelFunc:ZZ};function eQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=C.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new VZ(u);return n.runWebGLProgram(p,[r,s],"float32")}var tQ={kernelName:xc,backendName:"webgl",kernelFunc:eQ};function nQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=C.computeConv3DInfo(r.shape,l,i,1,o),p=new KZ(u);return n.runWebGLProgram(p,[r,s],"float32")}var aQ={kernelName:xm,backendName:"webgl",kernelFunc:nQ};function rQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=C.computeConv3DInfo(l,s.shape,o,1,i),p=new XZ(u);return n.runWebGLProgram(p,[r,s],"float32")}var sQ={kernelName:vm,backendName:"webgl",kernelFunc:rQ},iQ=Ju+`
  return cos(x);
`,oQ=Je({opSnippet:iQ}),lQ={kernelName:_i,backendName:"webgl",kernelFunc:oQ},uQ=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,pQ=Je({opSnippet:uQ}),cQ={kernelName:Ei,backendName:"webgl",kernelFunc:pQ},dQ=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,v]=d>1?[`${(o-1)/(d-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(${c} == 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);
        }
      }
    `}},hQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new dQ(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},mQ={kernelName:Vl,backendName:"webgl",kernelFunc:hQ},hc;(function(e){e.Prod="*",e.Sum="+"})(hc||(hc={}));var dI=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===hc.Prod?"1.0":"0.0",i=n?s:`getX(${hI(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${ut(r)} coords = getOutputCoords();
        int end = ${mI(r,"coords",this.op)};
        float val = ${i};
        int pow2 = int(pow(2.0, index));
        if (${l}) {
          int idx = ${u};
          ${mI(r,"coords",this.op)} = idx;
          val ${this.op}= getX(${hI(r,"coords",this.op)});
        }
        setOutput(val);
      }
    `}};function hI(e,t,n){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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function mI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function tE(e,t,n,a,r,s){let i=t.shape.length,o=C.getAxesPermutation([a],i),l=t;o!=null&&(l=Tn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=na({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new dI(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new dI(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=C.getUndoAxesPermutation(o),h=Tn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function fQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return tE(hc.Prod,r,n,s,i,o)}var gQ={kernelName:Wl,backendName:"webgl",kernelFunc:fQ};function yQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return tE(hc.Sum,r,n,s,i,o)}var bQ={kernelName:Ai,backendName:"webgl",kernelFunc:yQ};function xQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=R_(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=$Y(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var vQ={kernelName:wm,backendName:"webgl",kernelFunc:xQ},wQ=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 kQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new wQ(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var IQ={kernelName:Ul,backendName:"webgl",kernelFunc:kQ},nE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=qn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?l=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:l=`
          float activation(float x) {
            ${n}
          }
        `,u="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${l}

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

        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
        for (int wR = 0; wR < ${s}; wR++) {
          int xR = xRCorner + wR * dilations[0];

          if (xR < 0 || xR >= inDims[0]) {
            continue;
          }

          for (int wC = 0; wC < ${i}; wC++) {
            int xC = xCCorner + wC * dilations[1];

            if (xC < 0 || xC >= inDims[1]) {
              continue;
            }

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

        float result = dotProd;
        ${p}
        ${u}
        setOutput(result);
      }
    `}},aE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=qn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<p;g++)c+=`
          vec4 xTexelC${g*2};
          int xTexelC${g*2}Ready;
          vec4 xTexelC${g*2+1};
          int xTexelC${g*2+1}Ready;
          vec4 xC${g};`;c+=`
    for (int r = 0; r < ${u}; r++) {
      `;for(let g=0;g<p;g++)c+=`
          xTexelC${g*2} = vec4(0.0);
          xTexelC${g*2}Ready = 0;
          xTexelC${g*2+1} = vec4(0.0);
          xTexelC${g*2+1}Ready = 0;
          xC${g} = vec4(0.0);`;c+=`
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;for(let g=0;g<(d+1)/2;g++){let y=g*2;if(c+=`
          xC = xCCorner + ${y*l};
          `,o===1){if(y<p&&(i%2===1?(c+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }
              `,l===1&&y>0?c+=`
                xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
                `:c+=`
                  xCOffset = xC + 1 - 2;

                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
                    previous = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      previous.zw = vec2(0.0);
                    }

                    xC${y} = vec4(previous.zw, xTexelC${y}.xy);
                  } else {
                    xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
                  }
                  `):c+=`
                if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }

                xC${y} = xTexelC${y};
                `,y+1<p)){let b=i%2===0?w.nearestLargerEven(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(c+=`
                  xCOffset = xC + imod(pads[1], 2) + ${b};

                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                    xTexelC${y+1} = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      xTexelC${y+1}.zw = vec2(0.0);
                    }
                    xTexelC${y+1}Ready = 1;
                  }
                  `,l>1&&(c+=`
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
                      xTexelC${y} = getX(batch, xR, xCOffset, d1);
                      xTexelC${y}Ready = 1;
                    }
                    `),c+=`
                  xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
                  `):b===1?c+=`
                    xC${y+1} = xTexelC${y};
                    `:c+=`
                    xCOffset = xC + ${b};

                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                      xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= inDims[1]) {
                        xTexelC${y+1}.zw = vec2(0.0);
                      }
                      xTexelC${y+1}Ready = 1;
                    }

                    xC${y+1} = xTexelC${y+1};
                    `}}else y<p&&(i%2===1?(c+=`
                xCOffset = xC + 1 - strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xCOffset, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }

                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
                  xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xC + 2 >= inDims[1]) {
                    xTexelC${y+1}.zw = vec2(0.0);
                  }
                  xTexelC${y+1}Ready = 1;
                }

                xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
              `,y+1<p&&(c+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + strides[1];
                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
                `)):(c+=`
                if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }

                xCOffset = xC + strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                  xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${y+1}.zw = vec2(0.);
                  }
                  xTexelC${y+1}Ready = 1;
                }

                xC${y} = vec4(
                  xTexelC${y}.xy, xTexelC${y+1}.xy);
              `,y+1<p&&(c+=`
                  xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
                `)));y<p&&(c+=`
            wTexel = getW(r, ${y}, d1, q);
            dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
          `,y+1<p&&(c+=`
              wTexel = getW(r, ${y+1}, d1, q);
              dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
            `))}c+=`
    }
  `,c+=`
      }
    `;let h="",m="";n&&(a?h=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?h=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:h=`vec4 activation(vec4 x) {
          ${n}
        }`,m="result = activation(result);");let f=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${h}

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

        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);

        ${c}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${f}
        ${m}
        setOutput(result);
      }
    `}};function SQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,p=l;p==null&&(p=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=C.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;X().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new aE(d):c=new nE(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var NQ={kernelName:$i,backendName:"webgl",kernelFunc:SQ},TQ=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);
      }
    `}},CQ=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 _Q(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=C.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new TQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var EQ={kernelName:km,backendName:"webgl",kernelFunc:_Q};function AQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=C.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new CQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var $Q={kernelName:Im,backendName:"webgl",kernelFunc:AQ},FQ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function DQ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=he({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new FQ(s),l=n.runWebGLProgram(o,[i],i.dtype),u=he({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var RQ={kernelName:Sm,backendName:"webgl",kernelFunc:DQ},MQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=`
      const ivec2 strides = ivec2(${r}, ${s});
      const ivec2 pads = ivec2(${p}, ${d});
      const float neg_infinity = -3.4e38;

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

        float curVal = neg_infinity;
        for (int h = 0; h < ${i}; h++) {
          int hIn = hBeg + h * ${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 PQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new MQ(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=he({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var OQ={kernelName:vc,backendName:"webgl",kernelFunc:PQ};function LQ(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(r,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=C.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f<d;++f){for(let g of p[f]){let{permutationIndices:y,expandDims:b}=C.getEinsumPermutation(h,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Tn({inputs:{x:s[g]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let I=0;I<b.length;++I)v.splice(b[I],0,1);w.arraysEqual(x.shape,v)||(x=he({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),c===null?c=x:(c=l1({inputs:{a:x,b:c},backend:n}),m.push(c))}f<d-1&&(u[f]>=0&&(c=eg({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var zQ={kernelName:Nm,backendName:"webgl",kernelFunc:LQ},BQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",WQ=`
  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;
`,VQ=Je({opSnippet:BQ,packedOpSnippet:WQ}),UQ={kernelName:Di,backendName:"webgl",kernelFunc:VQ},GQ="return (b >= 1.0) ? a : a * (b + 1.0);",HQ=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,jQ=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hd(HQ,a.shape,r.shape):new Cl(GQ,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},qQ={kernelName:Tm,backendName:"webgl",kernelFunc:jQ},KQ=`
  return vec4(equal(a, b));
`,XQ="return float(a == b);",YQ=pn({opSnippet:XQ,packedOpSnippet:KQ,dtype:"bool",cpuKernelImpl:RY}),JQ={kernelName:Hl,backendName:"webgl",kernelFunc:YQ},ZQ=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${C.ERF_P};
  float a1 = ${C.ERF_A1};
  float a2 = ${C.ERF_A2};
  float a3 = ${C.ERF_A3};
  float a4 = ${C.ERF_A4};
  float a5 = ${C.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,QQ=Je({opSnippet:ZQ}),eee={kernelName:Gl,backendName:"webgl",kernelFunc:QQ},tee=Ju+`
  return exp(x);
`,nee=`
  vec4 result = exp(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`,rE=Je({opSnippet:tee,packedOpSnippet:nee,cpuKernelImpl:MY,dtype:"float32"}),aee={kernelName:Ri,backendName:"webgl",kernelFunc:rE};function Nx(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&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),he({inputs:{x:s},backend:a,attrs:{shape:o}})}var ree={kernelName:jl,backendName:"webgl",kernelFunc:Nx},fI="return exp(x) - 1.0;",see=Je({opSnippet:fI,packedOpSnippet:fI,cpuKernelImpl:PY}),iee={kernelName:ql,backendName:"webgl",kernelFunc:see},gI=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 sE(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new gI("real",l,t),p=new gI("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Ns({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=he({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function oee(e){let{inputs:t,backend:n}=e,{input:a}=t;return sE(a,!1,n)}var lee={kernelName:Cm,backendName:"webgl",kernelFunc:oee},uee=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}};function fd(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new uee(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var pee={kernelName:wc,backendName:"webgl",kernelFunc:fd},cee=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${t} - x - 1;
          float outputValue;
          if(coordX >= 0 && coordX < ${t}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}},dee={kernelName:Kl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new cee(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},yI="return floor(x);",hee=Je({opSnippet:yI,packedOpSnippet:yI,cpuKernelImpl:OY}),mee={kernelName:Mi,backendName:"webgl",kernelFunc:hee},fee=`
  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;
  }
`,gee=`
  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);
`,yee=pn({opSnippet:fee,packedOpSnippet:gee,dtype:"int32"}),bee={kernelName:Pi,backendName:"webgl",kernelFunc:yee},xee=class{constructor(e){this.variableNames=["A"];let t=An(),[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));
      }
    `}},vee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=An(),[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);
          }
        }

        ${t.output} = result;
      }
    `}},wee={kernelName:Ph,backendName:"webgl",kernelFunc:kee},tl;function kee(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],d=[u,l,s];(o||i)&&(tl==null&&(tl=document.createElement("canvas").getContext("2d")),tl.canvas.width=l,tl.canvas.height=u,tl.drawImage(r,0,0,l,u),r=tl.canvas);let c=n.makeTensorInfo(p,"int32");n.texData.get(c.dataId).usage=pa.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=X().getBool("WEBGL_PACK")?new vee(d):new xee(d),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function Iee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=C.convertConv2DDataFormat(p),g=C.computeConv2DInfo(r.shape,s.shape,l,d,u,c,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=Q_({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(X().getBool("WEBGL_CONV_IM2COL"))y=eE({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,I=o!=null,T=h==="leakyrelu",_=h?Zf(h,!1):null,A=new Z_(g,v,_,I,T),F=[r,s],P=($,S)=>{if(S==="NCHW"&&$.shape.length===1&&$.shape[0]!==1){let M=he({inputs:{x:$},backend:n,attrs:{shape:[$.shape[0],1,1]}});return b.push(M),M}return $};if(v&&F.push(P(i,p)),I&&F.push(P(o,p)),T){let $=n.makeTensorInfo([],"float32",w.createScalarValue(m,"float32"));F.push($),b.push($)}y=n.runWebGLProgram(A,F,"float32")}let x=he({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var See={kernelName:si,backendName:"webgl",kernelFunc:Iee};function Nee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:d,activation:c,leakyreluAlpha:h}=a,m=[],f=p;f==null&&(f=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=C.computeConv2DInfo(r.shape,s.shape,l,f,u,d,!0),y=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=c?Zf(c,y):null,x=[r,s],v=i!=null,I=o!=null,T=c==="leakyrelu";if(v&&x.push(i),I&&x.push(o),T){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));x.push(P),m.push(P)}let _;y?_=new aE(g,v,b,I,T):_=new nE(g,v,b,I,T);let A=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=n.runWebGLProgram(_,x,"float32",A);return m.forEach(P=>n.disposeIntermediateTensorInfo(P)),F}var Tee={kernelName:ii,backendName:"webgl",kernelFunc:Nee},Cee=class{constructor(e,t,n,a){this.sliceDim=e,this.strides=t,this.paramsShape=a,this.variableNames=["x","indices"],this.outputShape=n;let r=ut(t.length),s=ut(n.length),i=this.sliceDim>1?"strides[j]":"strides",o=ut(a.length),l=a.length>1?"paramsShape[j]":"paramsShape";this.userCode=`
        ${r} strides = ${r}(${this.strides});
        ${o} paramsShape = ${o}(${this.paramsShape});
         void main() {
          ${s} coords = getOutputCoords();
          int flattenIndex = 0;
          bool out_of_bounds = false;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            out_of_bounds = out_of_bounds || index < 0;
            out_of_bounds = out_of_bounds || index >= ${l};
            flattenIndex += index * ${i};
          }
          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
        }
      `}};function _ee(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=w.sizeFromShape(a.shape),[l,u,p,d]=C.prepareAndValidate(a,r),c=he({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape)/p,p]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let y=n.readSync(r.dataId),b=n.bufferSync(a),x=LY(y,b,a.dtype,u,i,p,d,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new Cee(i,d,[u,p],a.shape),f=n.runWebGLProgram(m,[h,c],h.dtype),g=he({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var Eee={kernelName:Yl,backendName:"webgl",kernelFunc:_ee},Aee=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),a=$ee(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${a}));
      }
    `}};function $ee(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("index"):a.push(`${n[r]}`);return a.join()}function iE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let b=n.readSync(s.dataId),x=r.shape[l];for(let v=0;v<b.length;++v){let I=b[v];w.assert(I<=x-1&&I>=0,()=>`GatherV2: the index value ${I} is not in [0, ${x-1}]`)}}let u=C.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=w.sizeFromShape(s.shape),d=[],c=he({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,p/u.batchSize]}});d.push(c),d.push(h);let m=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),x=n.bufferSync(c),v=zY(x,b,m);return d.forEach(I=>n.disposeIntermediateTensorInfo(I)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new Aee(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);d.push(g);let y=he({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var Fee={kernelName:Xl,backendName:"webgl",kernelFunc:iE},Dee="return float(a > b);",Ree=`
  return vec4(greaterThan(a, b));
`,Mee=pn({opSnippet:Dee,packedOpSnippet:Ree,cpuKernelImpl:BY,dtype:"bool"}),Pee={kernelName:Jl,backendName:"webgl",kernelFunc:Mee},Oee="return float(a >= b);",Lee=`
  return vec4(greaterThanEqual(a, b));
`,zee=pn({opSnippet:Oee,packedOpSnippet:Lee,dtype:"bool",cpuKernelImpl:WY}),Bee={kernelName:Li,backendName:"webgl",kernelFunc:zee};function Wee(e){let{inputs:t,backend:n}=e,{input:a}=t;return sE(a,!0,n)}var Vee={kernelName:_m,backendName:"webgl",kernelFunc:Wee},Uee="return float(!isnan(x) && !isinf(x));",Gee=Je({opSnippet:Uee,dtype:"bool"}),Hee={kernelName:Zl,backendName:"webgl",kernelFunc:Gee},jee="return float(isinf(x));",qee=Je({opSnippet:jee,dtype:"bool"}),Kee={kernelName:Ql,backendName:"webgl",kernelFunc:qee},Xee="return float(isnan(x));",Yee=Je({opSnippet:Xee,dtype:"bool"}),Jee={kernelName:eu,backendName:"webgl",kernelFunc:Yee},Zee="return float(a < b);",Qee=`
  return vec4(lessThan(a, b));
`,ete=pn({opSnippet:Zee,packedOpSnippet:Qee,cpuKernelImpl:VY,dtype:"bool"}),tte={kernelName:tu,backendName:"webgl",kernelFunc:ete},nte="return float(a <= b);",ate=`
  return vec4(lessThanEqual(a, b));
`,rte=pn({opSnippet:nte,packedOpSnippet:ate,cpuKernelImpl:UY,dtype:"bool"}),ste={kernelName:nu,backendName:"webgl",kernelFunc:rte};function ite(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=GY(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var ote={kernelName:Am,backendName:"webgl",kernelFunc:ite},lte=Ju+`
  return x < 0.0 ? 0./0. : log(x);
`,ute=`
  vec4 result = log(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
  return result;
`,pte=Je({opSnippet:lte,packedOpSnippet:ute,cpuKernelImpl:HY}),cte={kernelName:Wi,backendName:"webgl",kernelFunc:pte},dte=Ju+`
  return log(1.0 + x);
`,hte=Je({opSnippet:dte}),mte={kernelName:au,backendName:"webgl",kernelFunc:hte},fte="return float(a >= 1.0 && b >= 1.0);",gte=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,yte=pn({opSnippet:fte,packedOpSnippet:gte,dtype:"bool"}),bte={kernelName:ru,backendName:"webgl",kernelFunc:yte},xte="return float(!(x >= 1.0));",vte=Je({opSnippet:xte}),wte={kernelName:su,backendName:"webgl",kernelFunc:vte},kte="return float(a >= 1.0 || b >= 1.0);",Ite=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,Ste=pn({opSnippet:kte,packedOpSnippet:Ite,dtype:"bool"}),Nte={kernelName:iu,backendName:"webgl",kernelFunc:Ste},Tte=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 * ${o};
        setOutput(val);
      }
    `}},Cte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
      }
    `}},_te=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=X().getBool("WEBGL_PACK_NORMALIZATION")?new Cte(r.shape,s,i,o,l):new Tte(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Ete={kernelName:kc,backendName:"webgl",kernelFunc:_te},Ate=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 * ${r});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},$te=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new Ate(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},Fte={kernelName:$m,backendName:"webgl",kernelFunc:$te};function Dte(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=he({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Co(i,e.dtype,"max",a),l=he({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function oE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[p[T]];let v=o1(b,r.shape,r.dtype,p,x);h=n.makeTensorInfo(x,r.dtype);let I=n.texData.get(h.dataId);I.values=v}else h=Qf(r,p,n);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(h.shape,u),g=m;i&&(g=C.expandShapeToKeepDim(m,l));let y;if(c){let b=n.texData.get(h.dataId).values,x=jY(b,w.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=Dte(h,f,g,n);return d&&n.disposeIntermediateTensorInfo(h),y}var Rte={kernelName:Vi,backendName:"webgl",kernelFunc:oE},Mte=B_+`
  return max(a, b);
`,Pte=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Jf+`
  return result;
`,Ote=pn({opSnippet:Mte,packedOpSnippet:Pte,cpuKernelImpl:qY}),Lte={kernelName:Ui,backendName:"webgl",kernelFunc:Ote};function zte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ju(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new dc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Bte={kernelName:Gi,backendName:"webgl",kernelFunc:zte};function Wte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=C.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new u1(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Vte={kernelName:Ic,backendName:"webgl",kernelFunc:Wte},Ute=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);
      }
    `}},Gte=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,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=`
      const ivec3 pads = ivec3(${p}, ${d}, ${c});

      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 Hte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=C.computePool3DInfo(i.shape,o,l,d,u,p),h=new u1(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new Gte(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var jte={kernelName:Dm,backendName:"webgl",kernelFunc:Hte};function qte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ju([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=C.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new dc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Ute(c),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var Kte={kernelName:Fm,backendName:"webgl",kernelFunc:qte};function Xte(e,t,n,a){let r=new dc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new dc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Yte={kernelName:Rm,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=C.computePool2DInfo(a.shape,r,s,u,i),[d,c]=Xte(a,o,p,l);return[d,c]}};function Jte(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=he({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Co(i,"float32","mean",a),l=he({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Zte={kernelName:Hi,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=w.parseAxisParam(s,a.shape),u=l,p=C.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let _=0;_<v.length;_++)v[_]=a.shape[p[_]];let I=o1(x,a.shape,a.dtype,p,v);m=i.makeTensorInfo(v,a.dtype);let T=i.texData.get(m.dataId);T.values=I}else m=Qf(a,p,i);h.push(m),u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=C.computeOutAndReduceShapes(m.shape,u),y=f;r&&(y=C.expandShapeToKeepDim(f,l));let b=Jte(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return b}};function Qte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),d=r;p!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,r.shape.length)),C.assertAxesAreInnerMostDims("min",u,o);let[c,h]=C.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=he({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Co(f,f.dtype,"min",n),y;if(i){let b=C.expandShapeToKeepDim(c,l);y=he({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=he({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var ene={kernelName:ji,backendName:"webgl",kernelFunc:Qte},tne=B_+`
  return min(a, b);
`,nne=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Jf+`
  return result;
`,ane=pn({opSnippet:tne,packedOpSnippet:nne,cpuKernelImpl:KY}),rne={kernelName:qi,backendName:"webgl",kernelFunc:ane},sne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let a=e.length,r=ut(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).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}));
      }
    `}},ine=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=ut(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=Sn("rc",a),l=Sn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=`
        ${r} source = rc;
        if (source < start) {
          source = start * 2 - source - ${d};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${d};
        }
        source -= start;
      `;c=`
        ${r} rc = outputLoc;
        ${h}
        result[0] = getChannel(getX(${l.join()}), ${p});
        ${o[a-1]} += 1;
        if(${u}) {
          ${h}
          result[1] = getChannel(getX(${l.join()}), ${p});
        }
      `}else{let h=`
        ${r} source = rc;
        ${r} lt = ${r}(lessThan(source, start));
        ${r} gte = ${r}(greaterThanEqual(source, end));
        ${r} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${d}) +
                gte * ((end - 1) * 2 - source + ${d});
        source -= start;
      `;c=`
        ${r} rc = outputLoc;
        ${h}
        result[0] = getChannel(getX(${l.join()}), ${p});
        ${o[a-1]} += 1;
        if(${u}) {
          ${h}
          result[1] = getChannel(getX(${l.join()}), ${p});
        }
        rc = outputLoc;
        ${o[a-2]} += 1;
        if(${o[a-2]} < ${this.outputShape[a-2]}) {
          ${h}
          result[2] = getChannel(getX(${l.join()}), ${p});
          ${o[a-1]} += 1;
          if(${u}) {
            ${h}
            result[3] = getChannel(getX(${l.join()}), ${p});
          }
        }
      `}this.userCode=`
      const ${r} start = ${r}(${s});
      const ${r} end = ${r}(${i});

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${c}
        setOutput(result);
      }
    `}},one=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ine(a.shape,r,s):new sne(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},lne={kernelName:Ki,backendName:"webgl",kernelFunc:one},une=`if (b == 0.0) return NAN;
  return mod(a, b);`,pne=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+Jf+`
  return result;
`,cne=pn({opSnippet:une,packedOpSnippet:pne}),dne={kernelName:ou,backendName:"webgl",kernelFunc:cne},hne=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

        float r = random(seed);
        float cdf = 0.0;

        for (int i = 0; i < ${t-1}; i++) {
          cdf += getProbs(batch, i);

          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutput(float(${t-1}));
      }
    `}},mne=`
if (a == b) {
  return 1.0;
};
return a / b;`,fne=`
  // 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;
`,lE=pn({opSnippet:mne,packedOpSnippet:fne,checkOutOfBounds:!0}),gne={kernelName:Fi,backendName:"webgl",kernelFunc:lE},bI="return a - b;",uE=pn({opSnippet:bI,packedOpSnippet:bI,supportsComplex:!0,cpuKernelImpl:cJ}),yne={kernelName:fo,backendName:"webgl",kernelFunc:uE};function pE(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=oE({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=he({inputs:{x:o},backend:n,attrs:{shape:l}}),p=uE({inputs:{a:r,b:u},backend:n}),d=rE({inputs:{x:p},backend:n}),c=eg({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=he({inputs:{x:c},backend:n,attrs:{shape:l}}),m=lE({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var bne={kernelName:ho,backendName:"webgl",kernelFunc:pE};function xne(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:pE({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new hne(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var vne={kernelName:Mm,backendName:"webgl",kernelFunc:xne},wne=$a+`
  return -x;
`,kne=`
  vec4 result = -x;
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;function Ine(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=YY(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Qs(a.shape,kne):r=new Nr(a.shape,wne),n.runWebGLProgram(r,[a],a.dtype)}var Sne={kernelName:lu,backendName:"webgl",kernelFunc:Ine},Nne=mr.nonMaxSuppressionV3Impl;function Tne(e){C.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),p=n.readSync(s.dataId),{selectedIndices:d}=Nne(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Cne={kernelName:pu,backendName:"webgl",kernelFunc:Tne},_ne=mr.nonMaxSuppressionV4Impl;function Ene(e){C.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,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=_ne(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Ane={kernelName:cu,backendName:"webgl",kernelFunc:Ene},$ne=mr.nonMaxSuppressionV5Impl;function Fne(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=$ne(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Dne={kernelName:du,backendName:"webgl",kernelFunc:Fne},Rne=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)));
      }
    `}},Mne=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=w.sizeFromShape(r.shape),u=new Rne(l,s,i,o),p=he({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[p],r.dtype);n.disposeIntermediateTensorInfo(p);let c=[...r.shape,s],h=he({inputs:{x:d},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(d),h},Pne={kernelName:Yi,backendName:"webgl",kernelFunc:Mne};function om(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=md({inputs:{input:a},backend:n}),s=om({inputs:{x:r},backend:n}),i=tg({inputs:{input:a},backend:n}),o=om({inputs:{x:i},backend:n}),l=Ns({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return fd({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var One={kernelName:$u,backendName:"webgl",kernelFunc:om};function cE(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=md({inputs:{input:a},backend:n}),s=cE({inputs:{x:r},backend:n}),i=tg({inputs:{input:a},backend:n}),o=om({inputs:{x:i},backend:n}),l=Ns({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return fd({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var Lne={kernelName:hu,backendName:"webgl",kernelFunc:cE};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Nx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=Nx({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=J_({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Bne={kernelName:mu,backendName:"webgl",kernelFunc:zne},Wne=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ut(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};

        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});

      void main() {
        ${r} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${r} coords = outC - start;
          setOutput(getX(${o}));
        }
      }
    `}},Vne=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ut(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Sn("rc",a),l=Sn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${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}) {`],c=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+=`
        ${d[m]}
        if (${c}) {
          result[${m}] = float(value);
        } else {
          ${r} source = rc - start;
          result[${m}] = getChannel(getX(${l.join()}), ${p});
        }
      `;h+=a===1?"} ":"}}",this.userCode=`
      const ${r} start = ${r}(${s});
      const ${r} end = ${r}(${i});

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `}},dE=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return fd({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Vne(r.shape,s,i):new Wne(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},Une={kernelName:Ji,backendName:"webgl",kernelFunc:dE},Gne=`
  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);
`,Hne=`
  // 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));
  `+Jf+`
  return result;
`,jne=pn({opSnippet:Gne,packedOpSnippet:Hne}),qne={kernelName:Zi,backendName:"webgl",kernelFunc:jne};function Kne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.parseAxisParam(s,r.shape),p=u,d=C.getAxesPermutation(p,o),c=r;d!=null&&(c=Tn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=C.getInnerMostAxes(p.length,o),l.push(c)),C.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=ZY(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(c.shape,p),g=w.sizeFromShape(f),y=he({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),b=Um(r.dtype),x=Co(y,b,"prod",n);h=he({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=C.expandShapeToKeepDim(h.shape,u);h=he({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Xne={kernelName:eo,backendName:"webgl",kernelFunc:Kne},hE=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=QY(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Yne={kernelName:Sc,backendName:"webgl",kernelFunc:hE},Jne="return 1.0 / x;",Zne=Je({opSnippet:Jne}),Qne={kernelName:fu,backendName:"webgl",kernelFunc:Zne},eae=$a+`
  return (x < 0.0) ? 0.0 : x;
`,tae=`
  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;
`,nae=Je({opSnippet:eae,packedOpSnippet:tae}),aae={kernelName:to,backendName:"webgl",kernelFunc:nae},rae=$a+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,sae=`
  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;
`,iae=Je({opSnippet:rae,packedOpSnippet:sae}),oae={kernelName:ro,backendName:"webgl",kernelFunc:iae},lae=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],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/p[0]},
          ${u[1]/p[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);

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

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

        // Compute the four integer indices.
        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
        ivec2 sourceCeilRC = ivec2(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

        float top = topLeft + (topRight - topLeft) * fracRC.y;
        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
        float newValue = top + (bottom - top) * fracRC.x;

        setOutput(newValue);
      }
    `}},uae=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],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/p[0]},
          ${u[1]/p[1]},
          ${u[1]/p[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                     ${o}.0);

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

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

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

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

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${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 pae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new uae(r.shape,l,u,s,i):new lae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var cae={kernelName:ao,backendName:"webgl",kernelFunc:pae},dae=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],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*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(${u});
        const float widthScale = float(${p});

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

        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 hae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new dae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var mae={kernelName:Lm,backendName:"webgl",kernelFunc:hae},fae=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],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/p[0]},
          ${u[1]/p[1]});
      const vec2 inputShapeRC = vec2(${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 = ${c};

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

        setOutput(newValue);
      }
    `}},gae=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],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/p[0]},
          ${u[1]/p[1]},
          ${u[1]/p[1]});
      const vec3 inputShapeRC = vec3(${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 = ${c};

        // Compute the coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));

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

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

        setOutput(newValue);
      }
    `}};function yae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new gae(r.shape,l,u,s,i):new fae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var bae={kernelName:no,backendName:"webgl",kernelFunc:yae},xae=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],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*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(${u});
        const float widthScale = float(${p});

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

        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 vae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new xae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var wae={kernelName:Om,backendName:"webgl",kernelFunc:vae},kae=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=ut(n);this.userCode=`
      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${r}));
      }
    `}},Iae=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-${n} tensor is not yet supported`);this.outputShape=e;let a=Sn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${r}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${i} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${o(a.slice())};
          if(${r}){
            result.g = ${l(a.slice())};
          }
          if(${s}) {
            result.b = ${u(a.slice())};
            if(${r}) {
              result.a = ${p(a.slice())};
            }
          }
          setOutput(result);
        }
    `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((y,b)=>c(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Sae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return na({inputs:{x:r},backend:n});let l=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Iae(r.shape,o):new kae(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Nae={kernelName:so,backendName:"webgl",kernelFunc:Sae},Tae=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];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=`
        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 < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}},Cae={kernelName:Fu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new Tae(a.shape,s),[u,p]=C.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},_ae=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,Eae=Je({opSnippet:_ae}),Aae={kernelName:io,backendName:"webgl",kernelFunc:Eae},$ae="return inversesqrt(x);",Fae=Je({opSnippet:$ae,cpuKernelImpl:eJ}),Dae={kernelName:oo,backendName:"webgl",kernelFunc:Fae},mE=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(r.length),l=ut(s.length),u="";n===1?u="i":n===2&&(u="i, j");let p=`getIndices(${u})`,d="";a===1?d="i":a===2&&(d="i, coords[1]");let c=`getUpdates(${d})`,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 < ${t}; j++) {
              int index = round(${p});
              flattenedIndex += index * ${h};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${c};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function Rae(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=C.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=he({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new mE(l,o,h.shape.length,m.shape.length,p,c),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=he({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var Mae={kernelName:yu,backendName:"webgl",kernelFunc:Rae},Pae=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=X().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=`
       int findBound(int batch, float value) {
         int left = 0;
         int right = numInputs;
         int mid;
         ${i}
           mid = (left + right) / 2;
           if (getSortedSequence(batch, mid) ${o} value) {
             left = mid + 1;
           } else {
             right = mid;
           }
         }
         return right;
       }

       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int valueIndex = coords[1];

         float value = getValues(batch, valueIndex);

         setOutput(float(findBound(batch, value)));
       }
     `}};function Oae(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a,o=new Pae(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,s],"int32",l)}var Lae={kernelName:zm,backendName:"webgl",kernelFunc:Oae},zae=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=ut(n);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        float cVal = getC(${a});
        if (cVal >= 1.0) {
          setOutput(getA(${r}));
        } else {
          setOutput(getB(${r}));
        }
      }
    `}};function Bae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new zae(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ha(r.dtype,s.dtype))}var Wae={kernelName:bu,backendName:"webgl",kernelFunc:Bae},Vae=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${C.SELU_SCALEALPHA};
  float scale = ${C.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Uae=Je({opSnippet:Vae}),Gae={kernelName:xu,backendName:"webgl",kernelFunc:Uae},Hae=Ju+`
  return 1.0 / (1.0 + exp(-1.0 * x));
`,jae=`
  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`,qae=Je({opSnippet:Hae,packedOpSnippet:jae,cpuKernelImpl:nJ}),Kae={kernelName:uo,backendName:"webgl",kernelFunc:qae},Xae=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,Yae=Je({opSnippet:Xae}),Jae={kernelName:ku,backendName:"webgl",kernelFunc:Yae},Zae=Ju+`
  return sin(x);
`,Qae=Je({opSnippet:Zae}),ere={kernelName:lo,backendName:"webgl",kernelFunc:Qae},tre=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,nre=Je({opSnippet:tre}),are={kernelName:wu,backendName:"webgl",kernelFunc:nre},rre=`
  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;
`,sre=Je({opSnippet:rre}),ire={kernelName:Iu,backendName:"webgl",kernelFunc:sre},ore=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let u=[],p=dE({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=C.getReshaped(p.shape,s,o,!1),c=C.getPermuted(d.length,s.length,!1),h=C.getReshapedPermuted(p.shape,s,o,!1),m=he({inputs:{x:p},backend:n,attrs:{shape:d}}),f=Tn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=he({inputs:{x:f},backend:n,attrs:{shape:h}});return u.push(p),u.push(m),u.push(f),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},lre={kernelName:Su,backendName:"webgl",kernelFunc:ore};function ure(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=n.readSync(i.dataId)[0],[d,c,h,m,f]=rJ(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var pre={kernelName:Nc,backendName:"webgl",kernelFunc:ure};function cre(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=sJ(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var dre={kernelName:Tu,backendName:"webgl",kernelFunc:cre};function hre(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=P_(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var mre={kernelName:Tc,backendName:"webgl",kernelFunc:hre};function fre(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=P_(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var gre={kernelName:Cc,backendName:"webgl",kernelFunc:fre};function yre(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=C.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=n.bufferSync(r),b=n.bufferSync(s),x=w.decodeString(n.readSync(i.dataId)[0]),v=tJ(y,b,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,v.dtype,v.values)}let m=new mE(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var bre={kernelName:Bm,backendName:"webgl",kernelFunc:yre};function xre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=C.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=Zu({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var vre={kernelName:Nu,backendName:"webgl",kernelFunc:xre},xI="return sqrt(x);",wre=Je({opSnippet:xI,packedOpSnippet:xI,cpuKernelImpl:iJ}),kre={kernelName:po,backendName:"webgl",kernelFunc:wre},Ire="return x * x;",Sre=Je({opSnippet:Ire}),Nre={kernelName:_c,backendName:"webgl",kernelFunc:Sre},vI="return (a - b) * (a - b);",Tre=pn({opSnippet:vI,packedOpSnippet:vI}),Cre={kernelName:mo,backendName:"webgl",kernelFunc:Tre};function _re({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=$a+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new Nr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Ere={kernelName:fs,backendName:"webgl",kernelFunc:_re},Are=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ut(n.length),s=ut(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
      ${r} begin = ${r}(${e});
      ${r} strides = ${r}(${t});

      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}};function $re(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:v}=qt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=he({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||y){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let _=qt.computeOutShape(b,x,v),A=Zu({inputs:{x:r},backend:n,attrs:{begin:b,size:_}});I=he({inputs:{x:A},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(A)}else if(n.shouldExecuteOnCPU([r])){let _=n.readSync(r.dataId),A=Le(r.shape,r.dtype,_),F=oJ(h,A,v,b);I=n.makeTensorInfo(m,r.dtype,F.values)}else{let _=new Are(b,v,h);I=n.runWebGLProgram(_,[r],r.dtype)}let T=he({inputs:{x:I},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(I),T}var Fre={kernelName:Cu,backendName:"webgl",kernelFunc:$re};function Dre(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=lJ(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var Rre={kernelName:Ec,backendName:"webgl",kernelFunc:Dre};function Mre(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=uJ(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Pre={kernelName:Ac,backendName:"webgl",kernelFunc:Mre};function Ore(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=pJ(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var Lre={kernelName:$c,backendName:"webgl",kernelFunc:Ore},zre="return tan(x);",Bre=Je({opSnippet:zre}),Wre={kernelName:go,backendName:"webgl",kernelFunc:Bre},Vre=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Ure=Je({opSnippet:Vre}),Gre={kernelName:yo,backendName:"webgl",kernelFunc:Ure},Hre=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=ut(this.rank),r=jre(e);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function jre(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 fE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>w.decodeString(d)):o,u=Le(r.shape,r.dtype,l),p=dJ(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Hre(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var qre={kernelName:ms,backendName:"webgl",kernelFunc:fE},Kre=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // We compare elements pair-wise within a group of size 2 * inc.
         // The comparing rule for each group alternates between ascending
         // and descending. Within each group, we compare each pair at
         // positions i and i+inc. To decide whether an element at position i
         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
         // inc, it is in the first half of the group, we denote it as x0,
         // otherwise we denote it as x1.
         // For example, as shown in the Bitonic top K paper referenced above,
         // Figure5(a) shows that element[1] is in the
         // second half of the group when group size is 2, but it is in the
         // first half of the group when group size is 4.

         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
         int i = isFirstInPair ? elemIdx : elemIdx - inc;

         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
         float x1 = i1 < n ? getX(batch, i1) : negativeInf;

         // Denotes which direction indices are in (ascending or descending).
         bool reverse = imod(elemIdx, 2 * dir) >= dir;
         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
         if (reverse == isGreater) { // Elements in opposite order of direction
           int iTemp = i0;
           i0 = i1;
           i1 = iTemp;
         }
         if (isFirstInPair) {
            setOutput(float(i0));
         } else {
            setOutput(float(i1));
         }
       }
     `}},Xre=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
    void main() {
         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // The output size is half of the previous size.
         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
         // we only need to output the indices at positions |, the indices at
         // positions _ can be thrown away, see Figure5(b) After Phase 2
         // (Merge phase) in the Bitonic Top K paper referenced above.
         // For example, the paper shows we only need to output the orange bars.
         // The output sequence should look like this | | | | | | | |.
         // Because the sequence is halved, to map the output index back
         // to the previous sequence to find the corresponding value,
         // we need to double the index. When we double the index,
         // we basically interpolate a position, so 2i looks like
         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
         // of each 2k positions by - elemIdx % k. E.g. for output at
         // index 4,5,6,7, we want to get the corresponding element at
         // original index 8,9,10,11, for output at index 8,9,10,11,
         // we want to get the corresponding element at original index
         // 16,17,18,19, so on and so forth.

         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));

         float x0 = getX(batch, i0);
         float x1 = i1 < n ? getX(batch, i1) : x0;

         setOutput(x0 >= x1 ? float(i0) : float(i1));
       }
     `}};function Gs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function wI(e){let t=1;for(;t<e;)t*=2;return t}function Yre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,p=u[u.length-1];if(n.shouldExecuteOnCPU([r])||p<o||s>l){let F=n.readSync(r.dataId),[P,$]=hJ(F,u,r.dtype,s,i);return[n.makeTensorInfo(P.shape,P.dtype,P.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,fd({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=w.sizeFromShape(u)/p,f=he({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Gs(n,h);let g=wI(s),y=wI(p),b=null,x=()=>b===null?[f,f]:[f,b],v=(F,P,$)=>{let S=x(),M=new Kre($),U=[[p],[b===null?1:0],[Number.NEGATIVE_INFINITY],[F],[P]],j=b;b=n.runWebGLProgram(M,S,"int32",U),Gs(n,j)};for(let F=1;F<g;F*=2){let P=F*2;for(let $=F;$>=1;$/=2)v(P,$,[m,y])}for(let F=y;F>g;F/=2){let P=x(),$=new Xre([m,F/2]),S=[[p],[b===null?1:0],[g]],M=b;b=n.runWebGLProgram($,P,"int32",S),Gs(n,M);let U=g/2,j=U*2;for(let q=U;q>=1;q/=2)v(j,q,b.shape)}let I=b;b=Zu({inputs:{x:b},backend:n,attrs:{begin:0,size:[m,s]}}),Gs(n,I);let T=iE({inputs:{x:f,indices:b},backend:n,attrs:{axis:1,batchDims:1}});Gs(n,f);let _=u.slice(0,-1);_.push(s),I=b,b=he({inputs:{x:b},attrs:{shape:_},backend:n}),Gs(n,I);let A=T;return T=he({inputs:{x:T},attrs:{shape:_},backend:n}),Gs(n,A),[T,b]}var Jre={kernelName:_u,backendName:"webgl",kernelFunc:Yre},Zre=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 Qre(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],y=new Zre(d,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var ese={kernelName:Eu,backendName:"webgl",kernelFunc:Qre};function tse(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;ju(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}=mJ(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var nse={kernelName:Wm,backendName:"webgl",kernelFunc:tse};function ase(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),p=0;for(let f=0;f<o;f++)f!==s&&(u[p++]=i.shape[f]);let d=[],c=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++){c[s]=f;let g=Zu({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),y=he({inputs:{x:g},backend:n,attrs:{shape:u}});m[f]=y,d.push(g)}return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var rse={kernelName:Au,backendName:"webgl",kernelFunc:ase},sse=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,p=n%4,d=`
        sumValue += dot(values, segFilter);
    `,c="";r%n>0&&(c=`
        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) {
        ${c}
        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
          );

          ${d}
        }

        int inIdx = inOffset + ${u};
        if (${p===1}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

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

          ${d}
        } else if (${p===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

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

          ${d}
        } else if (${p===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

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

          ${d}
        }
        setOutput(${l});
      }
    `}};function ise(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=C.getAxesPermutation([u],o),d=r;p!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=C.getInnerMostAxes(1,o)[0]);let c=C.segment_util.computeOutShape(d.shape,u,i),h=w.sizeFromShape([d.shape[u]]),m=he({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Um(r.dtype),g=(v,I,T,_,A)=>{let F=v.shape[0],P=v.shape[1],$=C.segment_util.segOpComputeOptimalWindowSize(P,A),S={windowSize:$,inSize:P,batchSize:F,numSegments:A},M=new sse(S,I),U=n.compileAndRun(M,[v,T],_);if(l.push(U),U.shape[1]===A)return U;let j=hE({backend:n,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),q=fE({inputs:{x:j},backend:n,attrs:{reps:[P/$]}});return l.push(j),l.push(q),g(U,I,q,_,A)},y=g(m,"unsortedSegmentSum",s,f,i),b=he({inputs:{x:y},backend:n,attrs:{shape:c}}),x=b;if(p!=null){l.push(b);let v=C.getUndoAxesPermutation(p);x=Tn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var ose={kernelName:Fc,backendName:"webgl",kernelFunc:ise},lse=[u9,c9,m9,y9,x9,k9,S9,T9,A9,F9,M9,L9,W9,H9,K9,Y9,Z9,nZ,rZ,iZ,pZ,yZ,xZ,wZ,CZ,EZ,DZ,GJ,PZ,WZ,HZ,JZ,QZ,tQ,aQ,sQ,lQ,cQ,mQ,gQ,bQ,vQ,IQ,NQ,EQ,$Q,RQ,OQ,zQ,UQ,qQ,JQ,eee,aee,ree,iee,lee,pee,dee,mee,bee,wee,See,Tee,Eee,Fee,Pee,Bee,UJ,Vee,zZ,Hee,Kee,Jee,jJ,tte,ste,ote,cte,mte,bte,wte,Nte,Ete,Fte,Rte,Lte,Bte,Vte,jte,Kte,Yte,Zte,ene,rne,lne,dne,vne,JJ,Sne,Cne,Ane,Dne,IZ,Pne,Lne,Bne,Une,qne,KJ,Xne,Yne,SZ,gne,Qne,aae,oae,QJ,cae,mae,bae,wae,Nae,Cae,Aae,Dae,Mae,Lae,Wae,Gae,Kae,Jae,ere,are,fZ,bne,ire,lre,pre,dre,mre,gre,bre,vre,kre,Nre,Cre,Ere,Fre,Rre,Pre,Lre,yne,i9,Wre,Gre,qre,Jre,ese,o9,nse,rse,ose,One];for(let e of lse)Dc(e);var $t;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})($t||($t={}));var mc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(mc||(mc={}));var gE;function use(e){gE=e.wasm.cwrap(ri,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function pse(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:p,leakyreluAlpha:d}=a,c=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 ${A.shape.length}.`);m=A.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=mc[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=C.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(w.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),b=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,y,p.shape.length,$t[u.dtype],b),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var hse=!0,mse=cn(ds,hse),yE;function fse(e){yE=e.wasm.cwrap(vi,null,["array","number","number","number"])}function gse(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return yE(s,r.length,$t[a.dtype],i),a}var yse={kernelName:vi,backendName:"wasm",setupFunc:fse,kernelFunc:gse};function ng(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 bse={kernelName:zi,backendName:"wasm",kernelFunc:ng},bE;function xse(e){bE=e.wasm.cwrap(Tr,null,["number","array","number","number","number","array","number"])}function us(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=wse(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=vse(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=ng({inputs:t,backend:n});return m.shape=o,m}let u=n.makeOutput(o,l.dtype),p=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(u.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return bE(p,h,l.shape.length,$t[l.dtype],d,c,s.length),u}function vse(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function wse(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 kse={kernelName:Tr,backendName:"wasm",kernelFunc:us,setupFunc:xse};function Ts(e,t,n){let a=e.shape,r=e.shape.length,s=w.parseAxisParam(t,a),i=s,o=C.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let c=0;c<p.length;c++)p[c]=a[o[c]];i=C.getInnerMostAxes(i.length,r),l=us({inputs:{x:e},attrs:{perm:o},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var xE;function Ise(e){xE=e.wasm.cwrap(Fl,null,["number, number, number"])}function Sse(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=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ts(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;C.assertAxesAreInnerMostDims("all",p,h);let[m,f]=C.computeOutAndReduceShapes(l.shape,p),g=w.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;xE(o,g,b)}if(c&&t.disposeData(u.dataId),s){let b=C.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Nse={kernelName:Fl,backendName:"wasm",setupFunc:Ise,kernelFunc:Sse},vE;function Tse(e){vE=e.wasm.cwrap(Dl,null,["number, number, number"])}function Cse(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=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ts(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;C.assertAxesAreInnerMostDims("any",p,h);let[m,f]=C.computeOutAndReduceShapes(l.shape,p),g=w.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;vE(o,g,b)}if(c&&t.disposeData(u.dataId),s){let b=C.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var _se={kernelName:Dl,backendName:"wasm",setupFunc:Tse,kernelFunc:Cse},wE;function Ese(e){wE=e.wasm.cwrap(wi,null,["number","number","number","number","number"])}function Ase(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:p,inputWasTransposed:d}=Ts(s,r,t);if(d){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let c=l.shape.slice(0,-1),h=t.makeOutput(c,"int32"),m=t.dataIdMap.get(h.dataId).id,f=w.sizeFromShape(h.shape),g=l.shape[p[0]];return wE(o,$t[l.dtype],f,g,m),d&&t.disposeData(u.dataId),h}var $se={kernelName:wi,backendName:"wasm",kernelFunc:Ase,setupFunc:Ese},kE;function Fse(e){kE=e.wasm.cwrap(ki,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dse(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=C.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.strideHeight,b=p.strideWidth,x=p.inChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);if(p.dilationWidth!==1||p.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${p.dilationHeight}, ${p.dilationWidth}].`);let v=a.makeOutput(p.outShape,"float32"),I=a.dataIdMap.get(v.dataId).id;return kE(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,y,b,x,I),v}var Rse={kernelName:ki,backendName:"wasm",setupFunc:Fse,kernelFunc:Dse};function Vn(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=w.sizeFromShape(a.shape),i=w.inferFromImplicitShape(r,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var Mse={kernelName:gu,backendName:"wasm",kernelFunc:Vn},IE;function Pse(e){IE=e.wasm.cwrap(Ii,null,["number","array","number","number","array","number","number","number","number"])}function Ose(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,p=i?r.shape[l-2]:r.shape[l-1],d=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=w.sizeFromShape(m),y=w.sizeFromShape(f),b=Du.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)).concat([c,h]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,p,c]:[g,c,p],v=o?[y,h,d]:[y,d,h],I=Vn({inputs:{x:r},backend:n,attrs:{shape:x}}),T=Vn({inputs:{x:s},backend:n,attrs:{shape:v}}),_=n.dataIdMap.get(I.dataId).id,A=n.dataIdMap.get(T.dataId).id,F=i?I.shape[2]:I.shape[1],P=o?T.shape[1]:T.shape[2],$=Math.max(g,y),S=n.makeOutput([$,F,P],I.dtype),M=n.dataIdMap.get(S.dataId).id,U=new Uint8Array(new Int32Array(I.shape).buffer),j=new Uint8Array(new Int32Array(T.shape).buffer);return IE(_,U,I.shape.length,A,j,T.shape.length,i,o,M),n.disposeData(I.dataId),n.disposeData(T.dataId),S.shape=b,S}var Lse={kernelName:Ii,backendName:"wasm",setupFunc:Pse,kernelFunc:Ose};function bi(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=qt.parseSliceParams(t,n,a),o=qt.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),p=w.computeStrides(t.shape),d=r.dataIdMap.get(u.dataId);if(o){let m=qt.computeFlatOffset(s,p);return t.dtype==="string"?d.stringBytes=l.slice(m,m+w.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+w.sizeFromShape(i))),u}if(t.dtype==="string"){let m=tm(l,s,i,t.shape,t.dtype);return d.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)zse(l,p[0],c,s,i);else if(h===3)Bse(l,p[0],p[1],c,s,i);else if(h===4)Wse(l,p[0],p[1],p[2],c,s,i);else{let m=tm(l,s,i,t.shape,t.dtype);c.set(m)}return u}function zse(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u<l;u++){let p=u*t+o;n.set(e.subarray(p,p+r[1]),s),s+=r[1]}}function Bse(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],u=r[2],p=o+s[0],d=l+s[1];for(let c=o;c<p;c++)for(let h=l;h<d;h++){let m=c*t+h*n+u;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function Wse(e,t,n,a,r,s,i){let o=0,l=s[0],u=s[1],p=s[2],d=l+i[0],c=u+i[1],h=p+i[2],m=s[3];for(let f=l;f<d;f++)for(let g=u;g<c;g++)for(let y=p;y<h;y++){let b=f*t+g*n+y*a+m;r.set(e.subarray(b,b+i[3]),o),o+=i[3]}}var Vse={kernelName:vu,backendName:"wasm",kernelFunc:bi};function Use(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a,o=s.reduce((y,b)=>y*b),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),d=C.getSliceBeginCoords(i,s.length),c=C.getSliceSize(p,i,s.length),h=Vn({inputs:{x:r},backend:n,attrs:{shape:l}}),m=us({inputs:{x:h},backend:n,attrs:{perm:u}}),f=Vn({inputs:{x:m},backend:n,attrs:{shape:p}}),g=bi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeData(h.dataId),n.disposeData(m.dataId),n.disposeData(h.dataId),g}var Gse={kernelName:zl,backendName:"wasm",kernelFunc:Use};function Qu(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 Hse={kernelName:Si,backendName:"wasm",kernelFunc:Qu},jse=rn(Ni),SE;function qse(e){SE=e.wasm.cwrap(hs,null,["number","number","number","number"])}function Kse(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 SE(o,s,i,u),l}var Xse={kernelName:hs,backendName:"wasm",setupFunc:qse,kernelFunc:Kse};function NE(e){let{inputs:t,backend:n}=e,a=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=C.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>w.sizeFromShape(h.shape)>0);if(s.length===1)return ng({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(C.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=w.sizeFromShape(x.shape.slice(a));return Vn({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=C.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=z0(m,r,t[0].dtype,f),y=C.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=C.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let l=w.sizeFromShape(s[0].shape.slice(0,a)),u=0,p=s.map(h=>{let m=w.sizeFromShape(h.shape.slice(a));return u+=m,m}),d=s.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(i);for(let h=0;h<l;h++){let m=h*u;for(let f=0;f<d.length;f++){let g=p[f],y=h*g,b=d[f].subarray(y,y+g);c.set(b,m),m+=g}}return i}var Yse={kernelName:Bl,backendName:"wasm",kernelFunc:NE},TE;function Jse(e){TE=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zse(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:p,dimRoundingMode:d,dataFormat:c}=n,h=C.convertConv2DDataFormat(c),m=C.computeConv2DInfo(r.shape,s.shape,l,u,p,d,!1,h),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,b=m.padInfo.right,x=m.padInfo.bottom,v=m.padInfo.left,I=m.dilationHeight,T=m.dilationWidth,_=m.strideHeight,A=m.strideWidth,F=m.inChannels,P=m.outChannels,$=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(m.outShape,"float32"),M=a.dataIdMap.get(S.dataId).id;return TE(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,x,v,$,I,T,_,A,F,P,M),S}var Qse={kernelName:Ti,backendName:"wasm",setupFunc:Jse,kernelFunc:Zse},CE;function eie(e){CE=e.wasm.cwrap(Ci,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 tie(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:p}=a,d=1,c=C.convertConv2DDataFormat(l),h=C.computeConv2DInfo(p,s.shape,i,d,o,u,!1,c),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:x,outChannels:v,outHeight:I,outWidth:T,strideHeight:_,strideWidth:A}=h,F=f-1-h.padInfo.top,P=g-1-h.padInfo.left,$=h.dataFormat==="channelsLast",S=w.computeStrides(h.inShape),M=w.computeStrides(r.shape),[U,j,q]=w.computeStrides(s.shape),K=S[0],Y=$?S[1]:S[2],te=$?S[2]:1,re=$?1:S[1],Q=M[0],ie=$?M[1]:M[2],ae=$?M[2]:1,oe=$?1:M[1],ue=t.makeOutput(h.inShape,"float32"),we=t.dataIdMap.get(ue.dataId).id,ye=t.dataIdMap.get(r.dataId).id,Ie=t.dataIdMap.get(s.dataId).id;return CE(ye,Ie,m,f,g,b,x,y,I,T,v,_,A,F,P,U,j,q,K,Y,te,re,Q,ie,ae,oe,we),ue}var nie={kernelName:Ci,backendName:"wasm",setupFunc:eie,kernelFunc:tie},aie=rn(_i),rie=rn(Ei),Tx;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Tx||(Tx={}));var _E;function sie(e){_E=e.wasm.cwrap(Vl,null,["number","number","number","number","array","number","number","number","number","number"])}function iie(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:u}=n,p=l.shape[0],[d,c]=i,h=[p,d,c,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Qu({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,I=new Uint8Array(new Int32Array(o.shape).buffer);return _E(g,y,b,p,I,d,c,Tx[r],s,v),f!=null&&t.disposeData(f.dataId),x}var oie={kernelName:Vl,backendName:"wasm",setupFunc:sie,kernelFunc:iie},EE;function lie(e){EE=e.wasm.cwrap(Wl,null,["number","number","number","number","number","number"])}function uie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),p=r;u!==null&&(p=us({inputs:{x:r},attrs:{perm:u},backend:n}));let d=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumprod",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;EE(m,i?1:0,o?1:0,h,f,$t[r.dtype]);let g=c;if(u!==null){let y=C.getUndoAxesPermutation(u);g=us({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var pie={kernelName:Wl,backendName:"wasm",setupFunc:lie,kernelFunc:uie},AE;function cie(e){AE=e.wasm.cwrap(Ai,null,["number","number","number","number","number","number"])}function die(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),p=r;u!==null&&(p=us({inputs:{x:r},attrs:{perm:u},backend:n}));let d=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;AE(m,i?1:0,o?1:0,h,f,$t[r.dtype]);let g=c;if(u!==null){let y=C.getUndoAxesPermutation(u);g=us({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var hie={kernelName:Ai,backendName:"wasm",setupFunc:cie,kernelFunc:die},$E;function mie(e){$E=e.wasm.cwrap(Ul,null,["number","number","number","array","number","array","array","number","number"])}function fie(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return $E(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var gie={kernelName:Ul,backendName:"wasm",setupFunc:mie,kernelFunc:fie},FE;function yie(e){FE=e.wasm.cwrap($i,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bie(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:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=C.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!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,I=h.dilationWidth,T=h.strideHeight,_=h.strideWidth,A=h.inChannels,F=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let $=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get($.dataId).id;return FE(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,P,v,I,T,_,A,F,S),$}var xie={kernelName:$i,backendName:"wasm",setupFunc:yie,kernelFunc:bie},vie=rn(Di),wie=!1,kie=cn(Hl,wie,"bool"),Iie=rn(Ri,"float32");function Cx(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&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Vn({inputs:{x:r},backend:a,attrs:{shape:o}})}var Sie={kernelName:jl,backendName:"wasm",kernelFunc:Cx};function DE(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Nie={kernelName:wc,backendName:"wasm",kernelFunc:DE},RE;function Tie(e){RE=e.wasm.cwrap(Kl,null,["number","number","number","number","number","number"])}function Cie(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return RE(s,o,l,u,p,i),r}var _ie={kernelName:Kl,backendName:"wasm",kernelFunc:Cie,setupFunc:Tie},Eie=rn(Mi),Aie=!1,$ie=cn(Pi,Aie),ME;function Fie(e){ME=e.wasm.cwrap(Oi,null,["number","number","number","number","number","number","number"])}function Die(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=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(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return ME(p,d,c,h,m,r,g),f}var Rie={kernelName:Oi,backendName:"wasm",setupFunc:Fie,kernelFunc:Die},PE;function Mie(e){PE=e.wasm.cwrap(si,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 Pie(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=mc[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 ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${x})`);v=ae.id}let I=f.filterHeight,T=f.filterWidth,_=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,P=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,U=f.strideWidth,j=f.inChannels,q=f.padInfo.type==="SAME"?1:0,K=f.batchSize,Y=f.inHeight,te=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let re=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(re.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return PE(y,K,Y,te,b,I,T,v,_,A,F,P,q,$,S,M,U,j,x,g,ie,m||0,Q),re}var Oie={kernelName:si,backendName:"wasm",setupFunc:Mie,kernelFunc:Pie},OE;function Lie(e){OE=e.wasm.cwrap(ii,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 zie(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=mc[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 ae=a.dataIdMap.get(i.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${x})`);v=ae.id}let I=f.filterHeight,T=f.filterWidth,_=f.padInfo.top,A=f.padInfo.right,F=f.padInfo.bottom,P=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,U=f.strideWidth,j=f.inChannels,q=f.padInfo.type==="SAME"?1:0,K=f.batchSize,Y=f.inHeight,te=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let re=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(re.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return OE(y,K,Y,te,b,I,T,v,_,A,F,P,q,$,S,M,U,j,x,g,ie,m||0,Q),re}var Bie={kernelName:ii,backendName:"wasm",setupFunc:Lie,kernelFunc:zie},LE;function Wie(e){LE=e.wasm.cwrap(Yl,null,["number","number","number","number","number","number","array","number"])}function Vie(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Kx.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=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 LE(c,$t[a.dtype],h,i,d,o,m,f),u}var Uie={kernelName:Yl,backendName:"wasm",setupFunc:Wie,kernelFunc:Vie},zE;function Gie(e){zE=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Hie(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let _=0;_<u.length;++_){let A=u[_];w.assert(A<=p-1&&A>=0,()=>`GatherV2: the index value ${A} is not in [0, ${p-1}]`)}let d=C.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Vn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=w.sizeFromShape(s.shape),m=Vn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let y=c.shape.length-1,b=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,v=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer);return zE(b,$t[r.dtype],I,y,x,d.batchSize,T,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var jie={kernelName:Xl,backendName:"wasm",setupFunc:Gie,kernelFunc:Hie},qie=!1,Kie=cn(Jl,qie,"bool"),Xie=!1,Yie=cn(Li,Xie,"bool"),BE;function Jie(e){BE=e.wasm.cwrap(Bi,null,["number","number","number","number"])}function Zie(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;BE(r,$t[t.dtype],n,i)}return s}var Qie={kernelName:Bi,backendName:"wasm",setupFunc:Jie,kernelFunc:Zie},eoe=!1,toe=cn(tu,eoe,"bool"),noe=!1,aoe=cn(nu,noe,"bool"),roe=rn(Wi),soe=!1,ioe=cn(ru,soe,"bool"),ooe=rn(su),loe=!1,uoe=cn(iu,loe,"bool"),poe=!1,coe=cn(BI,poe,"bool"),WE;function doe(e){WE=e.wasm.cwrap(Vi,null,["number","number","number","number"])}function hoe(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ts(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;C.assertAxesAreInnerMostDims("max",p,h);let[m,f]=C.computeOutAndReduceShapes(l.shape,p),g=w.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;WE(o,$t[i.dtype],g,b)}if(c&&t.disposeData(u.dataId),s){let b=C.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var moe={kernelName:Vi,backendName:"wasm",setupFunc:doe,kernelFunc:hoe},foe=!1,goe=cn(Ui,foe),VE;function yoe(e){VE=e.wasm.cwrap(Gi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function boe(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=C.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,b=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let _=a.makeOutput(p.outShape,"float32"),A=a.dataIdMap.get(_.dataId).id;return VE(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,y,b,x,v,I,T,A),_}var xoe={kernelName:Gi,backendName:"wasm",setupFunc:yoe,kernelFunc:boe},UE;function voe(e){UE=e.wasm.cwrap(Hi,null,["number, number, number"])}function woe(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:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ts(i,r,t),m=d;if(h){let v=t.dataIdMap.get(p.dataId).id;v!==o&&(u=p,l=v,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=w.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Qu({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;UE(l,y,v)}if(h&&t.disposeData(p.dataId),s){let v=C.expandShapeToKeepDim(x.shape,c);x.shape=v}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var koe={kernelName:Hi,backendName:"wasm",setupFunc:voe,kernelFunc:woe},GE;function Ioe(e){GE=e.wasm.cwrap(ji,null,["number","number","number","number"])}function Soe(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:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ts(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;C.assertAxesAreInnerMostDims("min",d,m);let[f,g]=C.computeOutAndReduceShapes(u.shape,d),y=w.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;GE(l,$t[i.dtype],y,x)}if(h&&t.disposeData(p.dataId),s){let x=C.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var Noe={kernelName:ji,backendName:"wasm",setupFunc:Ioe,kernelFunc:Soe},Toe=!1,Coe=cn(qi,Toe),_x;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(_x||(_x={}));var HE;function _oe(e){HE=e.wasm.cwrap(Ki,null,["number","array","number","number","array","array","number","number"])}function Eoe(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(d).buffer);return HE(i,u,t.shape.length,$t[t.dtype],c,h,_x[r],l),o}var Aoe={kernelName:Ki,backendName:"wasm",kernelFunc:Eoe,setupFunc:_oe},$oe=!0,Foe=cn(Xi,$oe),Doe=rn(lu);function p1(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 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Loe(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=qE(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=p1(t,c);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var zoe={kernelName:cu,backendName:"wasm",setupFunc:Ooe,kernelFunc:Loe},KE;function Boe(e){KE=e.wasm.cwrap(du,"number",["number","number","number","number","number","number"])}function Woe(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=KE(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=p1(t,c);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Voe={kernelName:du,backendName:"wasm",setupFunc:Boe,kernelFunc:Woe},Uoe=!1,Goe=cn(uu,Uoe,"bool"),XE;function Hoe(e){XE=e.wasm.cwrap(Yi,null,["number","number","number","number","number"])}function joe(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,p=n.dataIdMap.get(r.dataId).id;return XE(p,s,i,o,u),l}var qoe={kernelName:Yi,backendName:"wasm",setupFunc:Hoe,kernelFunc:joe};function Koe(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Xoe={kernelName:hu,backendName:"wasm",kernelFunc:Koe};function Yoe(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Cx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching 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nle(e){ZE=e.wasm.cwrap(Qi,null,["number","number","number"])}function ale(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=s,l=a,u=l;l.dtype!=="float32"&&(u=Qu({backend:n,inputs:{x:a},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(u.dataId).id);let p=n.makeOutput(a.shape,"float32"),d=n.dataIdMap.get(p.dataId).id;return ZE(o,i,d),l.dtype!=="float32"&&n.disposeData(u.dataId),p}var rle={kernelName:Qi,backendName:"wasm",setupFunc:nle,kernelFunc:ale},QE;function sle(e){QE=e.wasm.cwrap(eo,null,["number","number","number","number"])}function ile(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:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ts(i,r,t),m=d;if(h){let 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fle(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[p,d,c,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=Qu({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(w.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return eA(y,p,d,c,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var gle={kernelName:ao,backendName:"wasm",setupFunc:mle,kernelFunc:fle},tA;function yle(e){tA=e.wasm.cwrap(no,null,["number","number","number","number","number","number","number","number","number","number"])}function ble(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[p,d,c,h]=r.shape,m=[p,l,u,h],f=t.makeOutput(m,"float32");if(w.sizeFromShape(r.shape)===0)return f;let 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Ile(e){aA=e.wasm.cwrap(Fu,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Sle(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,p=n.dataIdMap.get(l.dataId).id,[d,c,h,m]=r.shape,[f,g]=C.getImageCenter(o,c,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],v=new Uint8Array(new Int32Array(x).buffer);return aA(u,d,c,h,m,s,f,g,v,x.length,p),l}var Nle={kernelName:Fu,backendName:"wasm",kernelFunc:Sle,setupFunc:Ile},Tle=rn(io),Cle=rn(oo),rA;function _le(e){rA=e.wasm.cwrap(yu,null,["number","number","number","number","number","number","array","number","number"])}function Ele(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(w.sizeFromShape(i)===0)return 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Mle(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return w.sizeFromShape(r.shape)===0||iA(a,s),r}var Ple={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Rle,kernelFunc:Mle},Ole=rn(lo),oA;function Lle(e){oA=e.wasm.cwrap(ho,null,["number","number","number","number"])}function zle(e){let{backend:t,inputs:{logits:n},attrs:{dim:a}}=e,r=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[a],l=w.sizeFromShape(n.shape)/o;return w.sizeFromShape(s.shape)===0||oA(r,i,o,l),s}var Ble={kernelName:ho,backendName:"wasm",setupFunc:Lle,kernelFunc:zle};function Wle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a,o=w.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<r.shape.length;++g)l.push([0,0]);let 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Gle(e){let{backend:t,inputs:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=n,o=a.shape[0],l=a.shape[1],u=t.readSync(s.dataId)[0],p=[o+u,l],d=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(i.dataId).id,m=t.makeOutput(p,a.dtype),f=t.dataIdMap.get(m.dataId).id,g=t.makeOutput(p.slice(0,1),r.dtype),y=t.dataIdMap.get(g.dataId).id,b=t.makeOutput([u],"bool"),x=t.dataIdMap.get(b.dataId).id,v=t.makeOutput([o],a.dtype),I=t.dataIdMap.get(v.dataId).id,T=t.makeOutput([4],"int32"),_=t.dataIdMap.get(T.dataId).id,A=lA(d,c,$t[r.dtype],o,u,l,h,f,y,x,I,_),F=t.readSync(T.dataId),P;switch(F[0]){case 1:{P=C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(F[1]);break}case 2:{P=C.getSparseFillEmptyRowsNegativeIndexErrorMessage(F[1],F[2]);break}case 3:P=C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(F[1],F[2],F[3]);break;default:P=""}if(t.disposeData(T.dataId),P)throw t.disposeData(m.dataId),t.disposeData(g.dataId),t.disposeData(b.dataId),t.disposeData(v.dataId),new Error(P);let $=m,S=g;return A!==p[0]&&($=bi({inputs:{x:m},attrs:{begin:0,size:[A,l]},backend:t}),S=bi({inputs:{x:g},attrs:{begin:0,size:A},backend:t}),t.disposeData(m.dataId),t.disposeData(g.dataId)),[$,S,b,v]}var Hle={kernelName:Nc,backendName:"wasm",setupFunc:Ule,kernelFunc:Gle},uA;function jle(e){uA=e.wasm.cwrap(Tu,null,["number","number","number","number","number","number","number"])}function qle(e){let{backend:t,inputs:n}=e,{inputIndices:a,inputShape:r,newShape:s}=n;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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this.width*this.height}get topLeft(){return new Oe(this.left,this.top)}get topRight(){return new Oe(this.right,this.top)}get bottomLeft(){return new Oe(this.left,this.bottom)}get bottomRight(){return new Oe(this.right,this.bottom)}round(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.round(s));return new ct({x:t,y:n,width:a,height:r})}floor(){let[t,n,a,r]=[this.x,this.y,this.width,this.height].map(s=>Math.floor(s));return new ct({x:t,y:n,width:a,height:r})}toSquare(){let{x:t,y:n,width:a,height:r}=this,s=Math.abs(a-r);return a<r&&(t-=s/2,a+=s),r<a&&(n-=s/2,r+=s),new ct({x:t,y:n,width:a,height:r})}rescale(t){let n=rg(t)?t.width:t,a=rg(t)?t.height:t;return new ct({x:this.x*n,y:this.y*a,width:this.width*n,height:this.height*a})}pad(t,n){let[a,r,s,i]=[this.x-t/2,this.y-n/2,this.width+t,this.height+n];return new ct({x:a,y:r,width:s,height:i})}clipAtImageBorders(t,n){let{x:a,y:r,right:s,bottom:i}=this,o=Math.max(a,0),l=Math.max(r,0),u=s-o,p=i-l,d=Math.min(u,t-o),c=Math.min(p,n-l);return new ct({x:o,y:l,width:d,height:c}).floor()}shift(t,n){let{width:a,height:r}=this,s=this.x+t,i=this.y+n;return new ct({x:s,y:i,width:a,height:r})}padAtBorders(t,n){let a=this.width+1,r=this.height+1,s=1,i=1,o=a,l=r,u=this.left,p=this.top,d=this.right,c=this.bottom;return d>n&&(o=-d+n+a,d=n),c>t&&(l=-c+t+r,c=t),u<1&&(l=2-u,u=1),p<1&&(l=2-p,p=1),{dy:i,edy:l,dx:s,edx:o,y:p,ey:c,x:u,ex:d,w:a,h:r}}calibrate(t){return new ct({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var tp=class extends ct{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var Cs=class{constructor(t,n,a,r,s){this._imageDims=new $n(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new ct(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new ct(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Cs(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var wt=class extends Cs{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new wt(a,r,s)}};function kA(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function IA(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>l<o?l:o,1/0),r=n.reduce((o,l)=>l<o?l:o,1/0),s=t.reduce((o,l)=>o<l?l:o,0),i=n.reduce((o,l)=>o<l?l:o,0);return new tp(a,r,s,i)}function SA(e,t,n,a=!0){let r=t.map((i,o)=>({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let u=0;u<o.length;u++){let p=o[u],d=e[i],c=e[p];l.push(kA(d,c,a))}r=r.filter((u,p)=>l[p]<=n)}return s}function yr(e,t){return O(()=>{let[n,a,r]=t,s=En([...e.shape.slice(0,3),1],n,"float32"),i=En([...e.shape.slice(0,3),1],a,"float32"),o=En([...e.shape.slice(0,3),1],r,"float32"),l=Qe([s,i,o],3);return ce(e,l)})}function NA(e,t=!1){return O(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=c=>{let h=e.shape.slice();return h[i]=c,En(h,0,"float32")},l=o(s),u=r-l.shape[i],d=[t&&u?o(u):null,e,l].filter(c=>!!c).map(c=>le(c,"float32"));return Qe(d,i)})}function eye(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function sg(e){return 1/(1+Math.exp(-e))}function nye(e){return Math.log(e/(1-e))}var np=class extends ct{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var ipe=.5,ope=.43,lpe=.45,xa=class{constructor(t,n,a=new Oe(0,0)){let{width:r,height:s}=n;this._imgDims=new $n(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new Oe(r,s)).add(a))}get shift(){return new Oe(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new Oe(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new Oe(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof wt?t.box.floor():new ct(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=d=>r.sub(d).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/lpe),l=Ao(t),u=Math.floor(Math.max(0,l.x-ipe*o)),p=Math.floor(Math.max(0,l.y-ope*o));return new np(u,p,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+p))}alignMinBbox(t){let n=IA(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var TA=class extends xa{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Ao([t[3],t[4]])]}};var ap=class extends xa{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Ao)}};var gd=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Eo(this.distance)})`:""}`}};var yd=class extends ct{constructor(n,a){super(n);this._label=a}static assertIsValidLabeledBox(n,a){if(ct.assertIsValidBox(n,a),!er(n.label))throw new Error(`${a} - expected property label (${n.label}) to be a number`)}get label(){return this._label}};var Mr=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new Mr(t.label,n)}};var CA=class extends yd{constructor(n,a,r,s){super(n,a);this._score=r,this._classScore=s}static assertIsValidPredictedBox(n,a){if(yd.assertIsValidLabeledBox(n,a),!ep(n.score)||!ep(n.classScore))throw new Error(`${a} - expected properties score (${n.score}) and (${n.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}};function Pr(e){return e.detection instanceof wt}function rp(e,t){return{...e,...{detection:t}}}function f1(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},s=()=>{if(n)return new n;throw new Error("createVideoElement - missing Video implementation for nodejs environment")},i=global.fetch,o=ig();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:a,createImageElement:r,createVideoElement:s,fetch:i,...o}}function y1(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var sn;function upe(){if(!sn)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return sn}function b1(e){sn=e}function x1(){return y1()?b1(f1()):bd()?b1(g1()):null}function ppe(e){if(sn||x1(),!sn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=sn.Canvas,Image:n=sn.Image}=e;sn.Canvas=t,sn.Image=n,sn.createCanvasElement=e.createCanvasElement||(()=>new t),sn.createImageElement=e.createImageElement||(()=>new n),sn.ImageData=e.ImageData||sn.ImageData,sn.Video=e.Video||sn.Video,sn.fetch=e.fetch||sn.fetch,sn.readFile=e.readFile||sn.readFile}var at={getEnv:upe,setEnv:b1,initialize:x1,createBrowserEnv:f1,createFileSystem:ig,createNodejsEnv:g1,monkeyPatch:ppe,isBrowser:y1,isNodejs:bd};x1();function sp(e){return!at.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function aa(e){let{Canvas:t,CanvasRenderingContext2D:n}=at.getEnv();if(e instanceof n)return e;let a=sp(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var v1=(r=>(r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT",r))(v1||{}),ip=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||"TOP_LEFT",this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},Or=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof Or?t.text:t,this.anchor=n,this.options=new ip(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a<r?r:a,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:a}=this.options,r=a==="BOTTOM_RIGHT"||a==="TOP_RIGHT",s=a==="BOTTOM_LEFT"||a==="BOTTOM_RIGHT",i=this.measureWidth(t),o=this.measureHeight(),l=r?this.anchor.x-i:this.anchor.x,u=s?this.anchor.y-o:this.anchor.y;if(n){let{width:p,height:d}=n,c=Math.max(Math.min(l,p-i),0),h=Math.max(Math.min(u,d-o),0);return{x:c,y:h}}return{x:l,y:u}}draw(t){let n=sp(t),a=aa(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let u=this.measureWidth(a),p=this.measureHeight();a.fillStyle=r;let d=this.getUpperLeft(a,n);a.fillRect(d.x,d.y,u,p),a.fillStyle=s,this.text.forEach((c,h)=>{let m=l+d.x,f=l+d.y+(h+1)*i;a.fillText(c,m,f)})}};var og=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new ip({...i,...s})}},xd=class{constructor(t,n={}){this.box=new ct(t),this.options=new og(n)}draw(t){let n=aa(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new Or([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function cpe(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof wt?a.score:Pr(a)?a.detection.score:void 0,s=a instanceof wt?a.box:Pr(a)?a.detection.box:new ct(a),i=r?`${Eo(r)}`:void 0;new xd(s,{label:i}).draw(e)})}function lg(e){let{Image:t,Video:n}=at.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function _A(e){return new Promise((t,n)=>{(e instanceof at.getEnv().Canvas||lg(e))&&t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function EA(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=at.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function op(e){let{Image:t,Video:n}=at.getEnv();return e instanceof t?new $n(e.naturalWidth,e.naturalHeight):e instanceof n?new $n(e.videoWidth,e.videoHeight):new $n(e.width,e.height)}function lp({width:e,height:t}){let{createCanvasElement:n}=at.getEnv(),a=n();return a.width=e,a.height=t,a}function ug(e,t){let{ImageData:n}=at.getEnv();if(!(e instanceof n)&&!lg(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||op(e),s=lp({width:a,height:r});return e instanceof n?aa(s).putImageData(e,0,0):aa(s).drawImage(e,0,0,a,r),s}async function AA(e,t){let n=t||at.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ba(e)?1:0),i=O(()=>e.as3D(a,r,s).toInt());return await bo.toPixels(i,n),i.dispose(),n}function w1(e){let{Image:t,Canvas:n,Video:a}=at.getEnv();return e instanceof t||e instanceof n||e instanceof a}function $A(e,t,n=!1){let{Image:a,Canvas:r}=at.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return lp({width:1,height:1});let s=op(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,u=lp({width:t,height:t}),p=e instanceof r?e:ug(e),d=Math.abs(o-l)/2,c=n&&o<l?d:0,h=n&&l<o?d:0;return p.width>0&&p.height>0&&aa(u).drawImage(p,c,h,o,l),u}var Lr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Rr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ba(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof at.getEnv().Canvas?a:ug(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return 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c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return Fn(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function HA(e,t,n){return Z(Dt(e,t.filters,n,"same"),t.bias)}function I1(e,t,n=!0){let a=n?Xe(e):e;return a=Kn(a,t.separable_conv0,[1,1]),a=Kn(Xe(a),t.separable_conv1,[1,1]),a=Mt(a,[3,3],[2,2],"same"),a=Z(a,HA(e,t.expansion_conv,[2,2])),a}function xpe(e,t){let n=Kn(Xe(e),t.separable_conv0,[1,1]);return n=Kn(Xe(n),t.separable_conv1,[1,1]),n=Kn(Xe(n),t.separable_conv2,[1,1]),n=Z(n,e),n}var vg=class extends dn{constructor(n){super("TinyXception");this._numMainBlocks=n}forwardInput(n){let{params:a}=this;if(!a)throw new Error("TinyXception - load model before inference");return O(()=>{let r=le(n.toBatchTensor(112,!0),"float32"),i=yr(r,[122.782,117.001,104.298]).div(255),o=Xe(HA(i,a.entry_flow.conv_in,[2,2]));return 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wg=class extends dn{constructor(n=new vg(2)){super("AgeGenderNet");this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return O(()=>{let r=n instanceof Lr?this.faceFeatureExtractor.forwardInput(n):n,s=fa(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=Sd(s,a.fc.age).as1D(),o=Sd(s,a.fc.gender);return{age:i,gender:o}})}forwardInput(n){return O(()=>{let{age:a,gender:r}=this.runNet(n);return{age:a,gender:Za(r)}})}async forward(n){return this.forwardInput(await kt(n))}async predictAgeAndGender(n){let a=await kt(n),r=await this.forwardInput(a),s=mt(r.age),i=mt(r.gender),o=s.map((u,p)=>({ageTensor:u,genderTensor:i[p]})),l=await Promise.all(o.map(async({ageTensor:u,genderTensor:p})=>{let d=u.dataSync()[0],c=p.dataSync()[0],h=c>.5,m=h?"male":"female",f=h?c:1-c;return u.dispose(),p.dispose(),{age:d,gender:m,genderProbability:f}}));return 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t=[],{extractDenseBlock3Params:n}=fg(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return Fn(e,t),{params:a,paramMappings:t}}function XA(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Dn(e),{extractDenseBlock3Params:r}=hg(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 kg=class extends dn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return O(()=>{let a=le(t.toBatchTensor(112,!0),"float32"),s=yr(a,[122.782,117.001,104.298]).div(255),i=cg(s,n.dense0,!0);return i=cg(i,n.dense1),i=cg(i,n.dense2),i=fa(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await kt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return KA(t)}extractParams(t){return XA(t)}};var Ig=class extends fp{constructor(t=new kg){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var YA=class extends gp{};function JA(e,t){return Z(B(e,t.weights),t.biases)}function N1(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Dt(e,s,n,r);return o=Z(o,i),o=JA(o,t.scale),a?Xe(o):o}function ZA(e,t){return N1(e,t,[1,1],!0)}function T1(e,t){return N1(e,t,[1,1],!1)}function Sg(e,t){return N1(e,t,[2,2],!0,"valid")}function vpe(e,t){function n(o,l,u){let p=e(o),d=p.length/(l*u*u);if(h1(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${u}`);return O(()=>$e(Ea(p,[l,d,u,u]),[2,3,1,0]))}function a(o,l,u,p){let d=n(o,l,u),c=Ke(e(l));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:c}}function r(o,l){let u=Ke(e(o)),p=Ke(e(o));return 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a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),u=a(i);return{conv:{filters:o,bias:l},scale:u}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function e$(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=wpe(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),u=a("conv64_1"),p=a("conv64_2"),d=a("conv64_3"),c=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!d1(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:u,conv64_2:p,conv64_3:d,conv128_down:c,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:x};return Fn(e,t),{params:v,paramMappings:t}}function tr(e,t){let n=ZA(e,t.conv1);return n=T1(n,t.conv2),n=Z(n,e),n=Xe(n),n}function Td(e,t){let n=Sg(e,t.conv1);n=T1(n,t.conv2);let a=fa(e,2,2,"valid"),r=It(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=It(o);n=Qe([n,l],1);let u=[...n.shape];u[2]=1;let p=It(u);n=Qe([n,p],2)}return a=s?Qe([a,r],3):a,n=Z(a,n),n=Xe(n),n}var yp=class extends dn{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return O(()=>{let a=le(t.toBatchTensor(150,!0),"float32"),s=yr(a,[122.782,117.001,104.298]).div(255),i=Sg(s,n.conv32_down);i=Mt(i,3,2,"valid"),i=tr(i,n.conv32_1),i=tr(i,n.conv32_2),i=tr(i,n.conv32_3),i=Td(i,n.conv64_down),i=tr(i,n.conv64_1),i=tr(i,n.conv64_2),i=tr(i,n.conv64_3),i=Td(i,n.conv128_down),i=tr(i,n.conv128_1),i=tr(i,n.conv128_2),i=Td(i,n.conv256_down),i=tr(i,n.conv256_1),i=tr(i,n.conv256_2),i=Td(i,n.conv256_down_out);let o=i.mean([1,2]);return Re(o,n.fc)})}async forward(t){return this.forwardInput(await kt(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)!=null&&s.some(i=>i<=0))return new Float32Array(128);let n=await kt(t),a=O(()=>mt(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return e$(t)}extractParams(t){return QA(t)}};function $ke(e){let t=new yp;return t.extractWeights(e),t}function C1(e,t){return{...e,...{descriptor:t}}}function Mke(e){return typeof e.age=="number"}function _1(e,t){return{...e,...{age:t}}}function zke(e){return(e.gender==="male"||e.gender==="female")&&ep(e.genderProbability)}function E1(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function kpe(e,t){function n(l,u){let p=Ea(e(9*l),[3,3,l,1]),d=Ke(e(l)),c=Ke(e(l)),h=Ke(e(l)),m=Ke(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/batch_norm_scale`},{paramPath:`${u}/batch_norm_offset`},{paramPath:`${u}/batch_norm_mean`},{paramPath:`${u}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:c,batch_norm_mean:h,batch_norm_variance:m}}function a(l,u,p,d,c){let h=Ea(e(l*u*p*p),[p,p,l,u]),m=Ke(e(u));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${c?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,u,p,d){let{filters:c,bias:h}=a(l,u,p,d,!0);return{filters:c,batch_norm_offset:h}}function s(l,u,p){let d=n(l,`${p}/depthwise_conv`),c=r(l,u,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:c}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),p=s(64,128,"mobilenetv1/conv_2"),d=s(128,128,"mobilenetv1/conv_3"),c=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),I=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:p,conv_3:d,conv_4:c,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:I}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),u=r(256,512,3,"prediction_layer/conv_1"),p=r(512,128,1,"prediction_layer/conv_2"),d=r(128,256,3,"prediction_layer/conv_3"),c=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),I=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),T=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),_=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),A=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),F=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),P=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),$=a(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:l,conv_1:u,conv_2:p,conv_3:d,conv_4:c,conv_5:h,conv_6:m,conv_7:f,box_predictor_0:{box_encoding_predictor:g,class_predictor:y},box_predictor_1:{box_encoding_predictor:b,class_predictor:x},box_predictor_2:{box_encoding_predictor:v,class_predictor:I},box_predictor_3:{box_encoding_predictor:T,class_predictor:_},box_predictor_4:{box_encoding_predictor:A,class_predictor:F},box_predictor_5:{box_encoding_predictor:P,class_predictor:$}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function 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l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=cp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function g$(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Fpe(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return Fn(e,n),{params:i,paramMappings:n}}var br=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 A1=class extends dn{constructor(n){super("TinyYolov2");m$(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,a){let r=zr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=zr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=zr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=zr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=zr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=zr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=zr(r,a.conv6),r=zr(r,a.conv7),$o(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?bp($o(n,a.conv0,"valid",!1)):Br(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=Br(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=Br(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=Br(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=Br(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=Br(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=a.conv6?Br(r,a.conv6):r,r=a.conv7?Br(r,a.conv7):r,$o(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let s=le(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?yr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await kt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new br(a),i=await kt(n),o=await this.forwardInput(i,r),l=O(()=>mt(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(y=>y.box),c=p.map(y=>y.score),h=p.map(y=>y.classScore),m=p.map(y=>this.config.classes[y.label]);return SA(d.map(y=>y.rescale(r)),c,this.config.iouThreshold,!0).map(y=>new Cs(c[y],h[y],m[y],d[y],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return g$(n,this.config)}extractParams(n){let a=this.config.filterSizes||A1.DEFAULT_FILTER_SIZES,r=a?a.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return f$(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=O(()=>{let b=n.reshape([p,p,d,this.boxEncodingSize]),x=b.slice([0,0,0,0],[p,p,d,4]),v=b.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Za(b.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):ke(0);return[x,v,I]}),f=[],g=await h.array(),y=await c.array();for(let b=0;b<p;b++)for(let x=0;x<p;x++)for(let v=0;v<d;v++){let I=sg(g[b][x][v][0]);if(!r||I>r){let T=(x+sg(y[b][x][v][0]))/p*l,_=(b+sg(y[b][x][v][1]))/p*u,A=Math.exp(y[b][x][v][2])*this.config.anchors[v].x/p*l,F=Math.exp(y[b][x][v][3])*this.config.anchors[v].y/p*u,P=T-A/2,$=_-F/2,S={row:b,col:x,anchor:v},{classScore:M,label:U}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new tp(P,$,P+A,$+F),score:I,classScore:I*M,label:U,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},Ro=A1;Ro.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var xp=class extends Ro{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:l$,classes:["face"],...t?{anchors:p$,meanRgb:c$}:{anchors:u$,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new wt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?h$:d$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function kSe(e,t=!0){let n=new xp(t);return n.extractWeights(e),n}var Tg=class extends br{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Ra=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Mo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>mp(l)?r(l):l.detection),i=a||(t instanceof Ae?await wd(t,s):await vd(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ae&&l.dispose()),o}async function vp(e,t,n,a,r){return Mo([e],t,async s=>n(s[0]),a,r)}var y$=.4,b$=[new Oe(1.603231,2.094468),new Oe(6.041143,7.080126),new Oe(2.882459,3.518061),new Oe(4.266906,5.178857),new Oe(9.041765,10.66308)],x$=[117.001,114.697,97.404];var wp=class extends Ro{constructor(){let t={withSeparableConvs:!0,iouThreshold:y$,classes:["face"],anchors:b$,meanRgb:x$,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new wt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new Do,tinyFaceDetector:new wp,tinyYolov2:new xp,faceLandmark68Net:new gp,faceLandmark68TinyNet:new Ig,faceRecognitionNet:new yp,faceExpressionNet:new yg,ageGenderNet:new wg},Dpe=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),ZSe=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),QSe=(e,t)=>rt.tinyYolov2.locateFaces(e,t),Rpe=e=>rt.faceLandmark68Net.detectLandmarks(e),eNe=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),tNe=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),nNe=e=>rt.faceExpressionNet.predictExpressions(e),aNe=e=>rt.ageGenderNet.predictAgeAndGender(e),Mpe=e=>rt.ssdMobilenetv1.load(e),rNe=e=>rt.tinyFaceDetector.load(e),sNe=e=>rt.tinyYolov2.load(e),iNe=e=>rt.faceLandmark68Net.load(e),oNe=e=>rt.faceLandmark68TinyNet.load(e),lNe=e=>rt.faceRecognitionNet.load(e),uNe=e=>rt.faceExpressionNet.load(e),pNe=e=>rt.ageGenderNet.load(e),cNe=Mpe,dNe=Dpe,hNe=Rpe;var Cg=class extends Ra{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Po=class extends Cg{async run(){let t=await this.parentTask,n=await Mo(t,this.input,async a=>Promise.all(a.map(r=>rt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>k1(a,n[r]))}withAgeAndGender(){return new Lo(this,this.input)}},Oo=class extends Cg{async run(){let t=await this.parentTask;if(!t)return;let n=await vp(t,this.input,a=>rt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return k1(t,n)}withAgeAndGender(){return new zo(this,this.input)}},As=class extends Po{withAgeAndGender(){return new Fs(this,this.input)}withFaceDescriptors(){return new Rs(this,this.input)}},$s=class extends Oo{withAgeAndGender(){return new Ds(this,this.input)}withFaceDescriptor(){return new Ms(this,this.input)}};var _g=class extends Ra{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Lo=class extends _g{async run(){let t=await this.parentTask,n=await Mo(t,this.input,async a=>Promise.all(a.map(r=>rt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return _1(E1(a,i,o),s)})}withFaceExpressions(){return new Po(this,this.input)}},zo=class extends _g{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await vp(t,this.input,s=>rt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return _1(E1(t,a,r),n)}withFaceExpressions(){return new Oo(this,this.input)}},Fs=class extends Lo{withFaceExpressions(){return new As(this,this.input)}withFaceDescriptors(){return new Rs(this,this.input)}},Ds=class extends zo{withFaceExpressions(){return new $s(this,this.input)}withFaceDescriptor(){return new Ms(this,this.input)}};var Eg=class extends Ra{constructor(n,a){super();this.parentTask=n;this.input=a}},Rs=class extends Eg{async run(){let t=await this.parentTask;return(await Mo(t,this.input,a=>Promise.all(a.map(r=>rt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>C1(t[r],a))}withFaceExpressions(){return new As(this,this.input)}withAgeAndGender(){return new Fs(this,this.input)}},Ms=class extends Eg{async run(){let t=await this.parentTask;if(!t)return;let n=await vp(t,this.input,a=>rt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return C1(t,n)}withFaceExpressions(){return new $s(this,this.input)}withAgeAndGender(){return new Ds(this,this.input)}};var Ag=class extends Ra{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},$g=class extends Ag{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ae?await wd(this.input,n):await vd(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ae&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Nd(i,r[o]))}withFaceExpressions(){return new As(this,this.input)}withAgeAndGender(){return new Fs(this,this.input)}withFaceDescriptors(){return new Rs(this,this.input)}},Fg=class extends Ag{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ae?await wd(this.input,[n]):await vd(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ae&&s.dispose()),Nd(t,r)}withFaceExpressions(){return new $s(this,this.input)}withAgeAndGender(){return new Ds(this,this.input)}withFaceDescriptor(){return new Ms(this,this.input)}};var Dg=class extends Ra{constructor(n,a=new Da){super();this.input=n;this.options=a}},Cd=class extends Dg{async run(){let{input:t,options:n}=this,a;if(n instanceof Tg)a=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Da)a=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof br)a=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>rp({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new $g(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Po(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Lo(this.runAndExtendWithFaceDetections(),this.input)}},Rg=class extends Dg{async run(){let t=await new Cd(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?rp({},n):void 0)})}withFaceLandmarks(t=!1){return new Fg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Oo(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new zo(this.runAndExtendWithFaceDetection(),this.input)}};function p2e(e,t=new Da){return new Rg(e,t)}function $1(e,t=new Da){return new Cd(e,t)}async function Ppe(e,t){return $1(e,new Da(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function g2e(e,t={}){return $1(e,new br(t)).withFaceLandmarks().withFaceDescriptors()}var y2e=Ppe;function v$(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 Mg=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 ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Mr)return i;if(i instanceof Float32Array)return new Mr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Mr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>v$(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new gd(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 gd("unknown",n.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Mr.fromJSON(a));return new Mg(n,t.distanceThreshold)}};function M2e(e){let t=new wp;return t.extractWeights(e),t}function Ope(e,t){let{width:n,height:a}=new $n(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>Ope(r,{width:n,height:a}));if(mp(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Nd(rp(e,r),s)}return Pr(e)?rp(e,e.detection.forSize(n,a)):e instanceof xa||e instanceof wt?e.forSize(n,a):e}var j2e=VA;export{wg as AgeGenderNet,tp as BoundingBox,ct as Box,Ra as ComposableTask,Rs as ComputeAllFaceDescriptorsTask,Eg as ComputeFaceDescriptorsTaskBase,Ms as ComputeSingleFaceDescriptorTask,$g as DetectAllFaceLandmarksTask,Cd as DetectAllFacesTask,Ag as DetectFaceLandmarksTaskBase,Dg as DetectFacesTaskBase,Fg as DetectSingleFaceLandmarksTask,Rg as DetectSingleFaceTask,$n as Dimensions,zA as FACE_EXPRESSION_LABELS,wt as FaceDetection,o$ as FaceDetectionNet,yg as FaceExpressionNet,Es as FaceExpressions,gp as FaceLandmark68Net,Ig as FaceLandmark68TinyNet,YA as FaceLandmarkNet,xa as FaceLandmarks,TA as FaceLandmarks5,ap as FaceLandmarks68,gd as FaceMatch,Mg as FaceMatcher,yp as FaceRecognitionNet,S1 as Gender,yd as LabeledBox,Mr as LabeledFaceDescriptors,Lr as NetInput,dn as NeuralNetwork,Cs as ObjectDetection,Oe as Point,CA as PredictedBox,np as Rect,Do as SsdMobilenetv1,Da as SsdMobilenetv1Options,wp as TinyFaceDetector,Tg as TinyFaceDetectorOptions,xp as TinyYolov2,br as TinyYolov2Options,y2e as allFaces,Ppe as allFacesSsdMobilenetv1,g2e as allFacesTinyYolov2,_A as awaitMediaLoaded,EA as bufferToImage,tNe as computeFaceDescriptor,lp as createCanvas,ug as createCanvasFromMedia,$Ie as createFaceDetectionNet,$ke as createFaceRecognitionNet,Ape as createSsdMobilenetv1,M2e as createTinyFaceDetector,kSe as createTinyYolov2,$1 as detectAllFaces,Rpe as detectFaceLandmarks,eNe as detectFaceLandmarksTiny,hNe as detectLandmarks,p2e as detectSingleFace,WA as draw,at as env,v$ as euclideanDistance,_1 as extendWithAge,C1 as extendWithFaceDescriptor,rp as extendWithFaceDetection,k1 as extendWithFaceExpressions,Nd as extendWithFaceLandmarks,E1 as extendWithGender,wd as extractFaceTensors,vd as extractFaces,Wxe as fetchImage,FA as fetchJson,jxe as fetchNetWeights,_s as fetchOrThrow,Zxe as fetchVideo,aa as getContext2dOrThrow,op as getMediaDimensions,AA as imageTensorToCanvas,$A as imageToSquare,nye as inverseSigmoid,kA as iou,w1 as isMediaElement,lg as isMediaLoaded,Mke as isWithAge,Pr as isWithFaceDetection,BA as isWithFaceExpressions,mp as isWithFaceLandmarks,zke as isWithGender,pNe as loadAgeGenderModel,cNe as loadFaceDetectionModel,uNe as loadFaceExpressionModel,iNe as loadFaceLandmarkModel,oNe as loadFaceLandmarkTinyModel,lNe as loadFaceRecognitionModel,Mpe as loadSsdMobilenetv1Model,rNe as loadTinyFaceDetectorModel,sNe as loadTinyYolov2Model,RA as loadWeightMap,dNe as locateFaces,sve as matchDimensions,IA as minBbox,rt as nets,SA as nonMaxSuppression,yr as normalize,NA as padToSquare,aNe as predictAgeAndGender,nNe as recognizeFaceExpressions,Ope as resizeResults,sp as resolveInput,eye as shuffleArray,sg as sigmoid,Dpe as ssdMobilenetv1,Be as tf,ZSe as tinyFaceDetector,QSe as tinyYolov2,kt as toNetInput,wA as utils,m$ as validateConfig,j2e as version};
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