face-api/dist/face-api.js

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

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r=++this.pendingBackendInitId,s=n.then(a=>r<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,so(`Initialization of backend ${e} failed`),so(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return so(`Initialization of backend ${e} failed`),so(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new 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t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Cf.className="Adamax";lo(Cf);var Fd=class extends Os{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=Array.isArray(e)?e[r].tensor:e[n];if(s==null)return;let a=z.registeredVariables[n];M(()=>{let o=Z(V(this.c,s),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=nn(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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Ye{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=zf(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],bo(this.inputs).length!==this.inputs.length)throw new H(`The list of inputs passed to the model is redundant. 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Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;ds(v===0,"input layer has >1 nodes"),ds(x===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Dc))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},r={},s={},a={},o=[],i=(b,y,v,x,k,T)=>{(x==null||k==null||T==null)&&(x=b.sourceLayer,k=b.nodeIndex,T=b.tensorIndex);let C=x.inboundNodes[k];if(v.indexOf(C)!==-1)throw new Xr(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(C)!==-1)return;this.containerNodes.add(fs.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(C)===-1&&v.push(C);let E=C.inboundLayers.length;for(let F=0;F<E;F++){let O=C.inputTensors[F],D=C.inboundLayers[F],R=C.nodeIndices[F],_=C.tensorIndices[F];i(O,y,v,D,R,_)}for(y.push(C);v.indexOf(C)>=0;)v.splice(v.indexOf(C),1);o.push(C)},u=[],l=[];for(let b of this.outputs)i(b,u,l);let c=o.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=r[b.outboundLayer.id]==null?0:r[b.outboundLayer.id];y=Math.max(y,v),r[b.outboundLayer.id]=y,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],T=b.nodeIndices[x],C=k.inboundNodes[T],E=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(y+1,E),n[C.id]=C}}let d={};for(let b in t){let y=t[b];y in d||(d[y]=[]),d[y].push(n[b])}let p={};for(let b in r){let y=r[b];y in p||(p[y]=[]),p[y].push(s[b])}let h=Object.keys(p).map(b=>parseInt(b,10)).sort(_f);this.layers=[];for(let b of h){let y=p[b];y.sort((v,x)=>{let k=a[v.id],T=a[x.id];return k<T?-1:k>T?1:0});for(let v of y)v instanceof fs&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=p,h=Object.keys(d).map(b=>parseInt(b,10)).sort(_f);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of d[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new Xr(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new H(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,r++}let s=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)s.push([n[o],e[a]]);else if(t)throw new H(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new H(`${a.length} of ${r} weights are not set: ${a}`)}jv(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${tx}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=ex(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return M(()=>{e=It(e);let n=new Yi;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Wd(this.outputs,n,t)})}computeMask(e,t){return M(()=>{e=It(e);let n;return t==null?n=Gi(null,e.length):n=It(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Wf(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],u=t[o],l=i.name+"_0_0";n[l]=u}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(_f);if(r.length>1)for(let o of r){let i=this.nodesByDepth[o];for(let u of i){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let d=l.computeOutputShape(Gn(c)),p=Wf(d),h=l.inboundNodes.indexOf(u);for(let f=0;f<p.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=p[f]}}}let s=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],u=this.outputLayersNodeIndices[o],l=this.outputLayersTensorIndices[o],c=`${i.name}_${u}_${l}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];ds(i in n),s.push(n[i])}return Gn(s)}runInternalGraph(e,t){t==null&&(t=Gi(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let u=this.inputs[i],l=e[i],c=t[i];n[u.id]=[l,c]}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(_f);for(let i of r){let u=this.nodesByDepth[i];for(let l of u){let c=l.outboundLayer,d=l.inputTensors,p=l.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=It(c.call(v,f)),y=It(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=It(c.call(m,f)),y=It(c.computeMask(m,g));if(c.activityRegularizer)throw new De("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<p.length;++v){let x=p[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let s=[],a=[],o=[];for(let i of this.outputs){ds(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[u,l]=n[i.id];o.push(u.shape),s.push(u),a.push(l)}return[s,a,o]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof fs?1:0;for(let s=0;s<r.inboundNodes.length;s++){let a=fs.nodeKey(r,s);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new H(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new H("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new H(`No such layer: ${e}`)}calculateLosses(){return M(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=fs.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let d=a.inboundNodes[c],p=fs.nodeKey(a,c),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],b=d.nodeIndices[m],y=d.tensorIndices[m],v=fs.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=o,l.config=i,l.inboundNodes=u,n.push(l)}e.layers=n;let r=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],u=fs.nodeKey(o,i);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];r.push([o.name,l,c])}e.inputLayers=r;let s=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],u=fs.nodeKey(o,i);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];s.push([o.name,l,c])}return e.outputLayers=s,e}static fromConfig(e,t,n={},r=!1){let s={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in s)){o(m,g);return}let C=s[x];if(C.inboundNodes.length<=k){o(m,g);return}let E=C.inboundNodes[k];b.push(E.outputTensors[T])}b.length>0&&m.apply(Gn(b),y)}function u(m){let g=m.name,b=Jr(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(r),s[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${v}`);o(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!$V(a);)for(let m of c){let g=s[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)i(g,y)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];ds(g in s);let x=s[g].inboundNodes[b].outputTensors;d.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];ds(g in s);let x=s[g].inboundNodes[b].outputTensors;p.push(x[y])}return new e({inputs:d,outputs:p,name:l})}get stateful(){if(this._stateful)throw new H("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){M(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function tG(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let s=[];return t.forEach(a=>{a in e?s.push(e[a]):s.push(null)}),s}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function a2(e,t){return tG(e,t,"classWeight")}async function o2(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let s=M(()=>{if(e.shape.length===1)return Fs(e);if(e.shape.length===2){if(e.shape[1]>1)return xc(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await s.data());Fe(s);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);for(let u=0;u<o.length;u++)w.assert(o[u].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${o[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function u2(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 r=[];for(let s of t){if(n[s]==null)throw new H(`The feature data generated by the dataset lacks the required ${e} key '${s}'.`);r.push(n[s])}return r}}function sG(e){if(e.length===3)throw new De("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function aG(e,t,n){let r=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(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],o=()=>{let c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let d=new Yi(c),p=Wd(this.outputs,d,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](r[f],p[f]);s[f]!=null&&(g=nG(g,s[f]));let b=Ot(g);t.push(b),f===0?h=g:h=Z(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],b=this.metricsTensors[f][1];m=Ot(g(r[b],p[b]))}nn(m),a.push(m)}return h=Ot(h),this.calculateLosses().forEach(f=>{h=Z(h,f)}),h},i=this.collectedTrainableWeights.map(c=>c.read()),u=!0;return[this.optimizer_.minimize(o,u,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>M(()=>{let t=[],n,r=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:r[u]});let o=new Yi(a),i=Wd(this.outputs,o);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=Ot(l(s[u],i[u]));u===0?n=c:n=Z(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],d=Ot(l(s[c],i[c]));t.push(d)}return t})}async fit(e,t,n={}){return lG(this,e,t,n)}async fitDataset(e,t){return aG(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],s=n[1],o=this.makeTrainFunction()(r.concat(s)),i=[];for(let u of o){let l=await u.data();i.push(l[0])}return Fe(o),Gn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,s=this.getWeights(n);for(let a=0;a<r.length;++a)n&&!r[a].trainable||t.push({name:r[a].originalName,tensor:s[a]});return t}set 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e.metrics)s[a]=Hi(e.metrics[a])}this.compile({loss:r,metrics:s,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=tn.getSaveHandlers(e);if(u.length===0)throw new H(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new H(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[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 tn.encodeWeights(this.getNamedWeights(t)),r=!1,s=null,o={modelTopology:this.toJSON(s,r),format:mG,generatedBy:`TensorFlow.js tfjs-layers v${tx}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:l,specs:c}=await tn.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=tn.concatenateArrayBuffers([n.data,l])}if(this.userDefinedMetadata!=null){let 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};mx.className="ThresholdedReLU";oe.registerClass(mx);var gx=class extends Ye{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ux().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Me(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};gx.className="Softmax";oe.registerClass(gx);function Oc(e,t,n){if(typeof e=="number")return Gi(e,t);if(e.length!==t)throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let s=e[r];if(!HV(s))throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${s}`)}return e}function es(e,t,n,r,s=1){if(e==null)return e;let a=t+(t-1)*(s-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+r-1)/r)}function ms(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+vo([n-t,0]);else if(r==="same")e=e*t;else throw new H(`Unsupport padding mode: ${r}.`);return e}function bx(e,t){return M(()=>(Bt(t),t==="channelsFirst"?Oe(e,[0,2,3,1]):e))}function F2(e,t){return M(()=>(Bt(t),t==="channelsFirst"?Oe(e,[0,2,3,4,1]):e))}function TG(e,t,n,r=1,s="valid",a,o=1){return M(()=>{if(a==null&&(a=Kr()),Bt(a),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Oe(e,[0,2,1])),s==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Gy(e,t,r,s==="same"?"same":"valid","NWC",o);return n!=null&&(i=Qr(i,n)),i})}function D2(e,t,n,r=[1,1],s="valid",a,o,i=null){return M(()=>{if(a==null&&(a=Kr()),Bt(a),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=bx(e,a);if(s==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=mo.conv2d({x:u,filter:t,strides:r,pad:s==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(u=Oe(u,[0,3,1,2])),u})}function NG(e,t,n,r=[1,1,1],s="valid",a,o){return M(()=>{if(a==null&&(a=Kr()),Bt(a),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=F2(e,a);if(s==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=qy(i,t,r,s==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Qr(i,n)),a==="channelsFirst"&&(i=Oe(i,[0,4,1,2,3])),i})}var yx=class extends Ye{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",yx.verifyArgs(t),this.rank=e,rn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Oc(t.kernelSize,e,"kernelSize"),this.strides=Oc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,vr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Bt(this.dataFormat),this.activation=ko(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Nt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Zt(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=Oc(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(ds("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Nv(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:wo(this.activation),useBias:this.useBias,biasInitializer:Dt(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Gd=class extends yx{constructor(e,t){super(e,t);this.kernel=null,Gd.verifyArgs(t),this.filters=t.filters,rn(this.filters,"filters"),this.kernelInitializer=Nt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Zt(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return M(()=>{e=Me(e);let n,r=this.bias==null?null:this.bias.read(),s=k0(this.activation.getClassName());if(s!=null&&this.rank===2)n=D2(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=TG(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=D2(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=NG(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ut(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=es(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Dt(this.kernelInitializer),kernelRegularizer:mt(this.kernelRegularizer),kernelConstraint:Qt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new H(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},R2=class extends Gd{constructor(e){super(2,e);R2.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Nv(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)}.`)}},Jf=R2;Jf.className="Conv2D";oe.registerClass(Jf);var P2=class extends Gd{constructor(e){super(3,e);P2.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)}.`)}},em=P2;em.className="Conv3D";oe.registerClass(em);var vx=class extends Jf{constructor(e){super(e);if(this.inputSpec=[new Gt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ut(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Gt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{let n=Me(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=r[a],u=r[o],l=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=ms(i,d,l,this.padding),f=ms(u,p,c,this.padding),m=[s,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Oe(n,[0,2,3,1]));let g=jy(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Oe(g,[0,3,1,2])),this.bias!=null&&(g=Qr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ut(e);let t=e.slice(),n,r,s;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3):(n=3,r=1,s=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[r]=ms(t[r],i,a,this.padding),t[s]=ms(t[s],u,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};vx.className="Conv2DTranspose";oe.registerClass(vx);var xx=class extends em{constructor(e){super(e);if(this.inputSpec=[new Gt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ut(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Gt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{let n=Me(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let u=r[i],l=r[a],c=r[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=ms(u,f,d,this.padding),y=ms(l,m,p,this.padding),v=ms(c,g,h,this.padding),x=[s,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Oe(n,[0,2,3,4,1]));let k=hS(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Oe(k,[0,4,1,2,3])),this.bias!==null&&(k=Qr(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=ut(e);let t=e.slice(),n,r,s,a;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3,a=4):(n=4,r=1,s=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[r]=ms(t[r],l,o,this.padding),t[s]=ms(t[s],c,i,this.padding),t[a]=ms(t[a],d,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};xx.className="Conv3DTranspose";oe.registerClass(xx);var O2=class extends Gd{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new H(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Nt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=Zt(t.depthwiseConstraint),this.pointwiseInitializer=Nt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=Zt(t.pointwiseConstraint)}build(e){if(e=ut(e),e.length<this.rank+2)throw new H(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let o=0;o<this.rank;++o)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Gt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{e=Me(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Oe(e,[0,2,3,1])),n=_c(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Qr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Oe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Dt(this.depthwiseInitializer),e.pointwiseInitializer=Dt(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Qt(this.depthwiseConstraint),e.pointwiseConstraint=Qt(this.pointwiseConstraint),e}};O2.className="SeparableConv";var wx=class extends O2{constructor(e){super(2,e)}};wx.className="SeparableConv2D";oe.registerClass(wx);var M2=class extends Gd{constructor(e){super(1,e);M2.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"&&!Nv(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)}.`)}},kx=M2;kx.className="Conv1D";oe.registerClass(kx);var Ix=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 M(()=>{if(e=Me(e),this.dataFormat==="channelsLast"){let n=Af(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Af(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Af(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Af(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ix.className="Cropping2D";oe.registerClass(Ix);var Sx=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,Bt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,VV(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return M(()=>{let n=Me(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Oe(n,[0,2,3,1]);let s=this.size[0]*r[2],a=this.size[1]*r[3],o=this.interpolation==="nearest"?ar.resizeNearestNeighbor(n,[s,a]):ar.resizeBilinear(n,[s,a]);return Oe(o,[0,3,1,2])}else{let s=this.size[0]*r[1],a=this.size[1]*r[2];return this.interpolation==="nearest"?ar.resizeNearestNeighbor(n,[s,a]):ar.resizeBilinear(n,[s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Sx.className="UpSampling2D";oe.registerClass(Sx);function _G(e,t,n=[1,1],r="valid",s,a){return M(()=>{s==null&&(s=Kr()),Bt(s);let o=bx(e,s);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Bi(o,t,n,r==="same"?"same":"valid","NHWC",a),s==="channelsFirst"&&(o=Oe(o,[0,3,1,2])),o})}var Cx=class extends yx{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Nt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Zt(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=ut(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return M(()=>{e=Me(e);let n=_G(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Qr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=es(t,this.kernelSize[0],this.padding,this.strides[0]),a=es(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,s,a]:[e[0],s,a,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Dt(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Qt(this.depthwiseRegularizer),e}};Cx.className="DepthwiseConv2D";oe.registerClass(Cx);function L2(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new H("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function s(a){return a==null||Array.isArray(a)?a:[a]}return t=s(t),n=s(n),{inputs:e,initialState:t,constants:n}}function B2(e,t,n,r=!1,s,a,o=!1,i=!1){return M(()=>{let u=t.shape.length;if(u<3)throw new H(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(Yr(2,u));if(t=Oe(t,l),a!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=ue(ue(s,"bool"),"float32"),s.rank===u-1&&(s=Nn(s,-1)),s=Oe(s,l)),r&&(t=yr(t,0),s!=null&&(s=yr(s,0)));let c=[],d,p=n,h=t.shape[0],f=vt(t),m;s!=null&&(m=vt(s));for(let b=0;b<h;++b){let y=f[b],v=M(()=>e(y,p));if(s==null)d=v[0],p=v[1];else{let x=M(()=>{let k=m[b],T=he(br(k),k),C=Z(V(v[0],k),V(p[0],T)),E=p.map((F,O)=>Z(V(v[1][O],k),V(F,T)));return{output:C,newStates:E}});d=x.output,p=x.newStates}i&&c.push(d)}let g;return i&&(g=Ut(c,1)),[d,g,p]})}var z2=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 rm({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 Gt({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 Yr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Gv(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[r].concat(s)}else return r}computeMask(e,t){return M(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(s=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new De("Constants support is not implemented in RNN yet.");Gv(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Gt({shape:[n,null,...r]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new De("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new H(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Gt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){M(()=>{if(!this.stateful)throw new Ms("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>kt([n,r])):this.states_=[kt([n,this.cell.stateSize])];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>kt([n,r])):this.states_[0]=kt([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()):Fe(this.states_);for(let r=0;r<this.states_.length;++r){let s=e[r],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,o=[n,a];if(!w.arraysEqual(s.shape,o))throw new H(`State ${r} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${s.shape}`);this.states_[r]=s}}this.states_=this.states_.map(r=>nn(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=L2(e,n,r,this.numConstants);e=s.inputs,n=s.initialState,r=s.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Gt({shape:u.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(t.constants=r,a=a.concat(r),this.numConstants=r.length),a[0]instanceof Zr){let u=[e].concat(a),l=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=l;let d=super.apply(u,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return M(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;e=Me(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new H(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},u=B2((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],d=u[2];this.stateful&&this.resetStates(d,r);let p=this.returnSequences?c:l;return this.returnState?[p].concat(d):p})}getInitialState(e){return M(()=>{let t=kt(e.shape);return t=ve(t,[1,2]),t=Od(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Pv(t,[1,n]):t):this.cell.stateSize>1?[Pv(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()===z2.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let r=t.cell,s=Jr(r,n);return new e(Object.assign(t,{cell:s}))}},zs=z2;zs.className="RNN";oe.registerClass(zs);var Hd=class extends Ye{},tm=class extends Hd{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,rn(this.units,"units"),this.activation=ko(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Nt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=Zt(e.kernelConstraint),this.recurrentConstraint=Zt(e.recurrentConstraint),this.biasConstraint=Zt(e.biasConstraint),this.dropout=Fc([1,vo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fc([1,vo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ut(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return M(()=>{if(e=e,e.length!==2)throw new H(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Io({ones:()=>br(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Io({ones:()=>br(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));let s,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?s=ps(V(e,a),this.kernel.read()):s=ps(e,this.kernel.read()),this.bias!=null&&(s=Qr(s,this.bias.read())),o!=null&&(n=V(n,o));let i=Z(s,ps(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:wo(this.activation),useBias:this.useBias,kernelInitializer:Dt(this.kernelInitializer),recurrentInitializer:Dt(this.recurrentInitializer),biasInitializer:Dt(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Qt(this.kernelConstraint),recurrentConstraint:Qt(this.recurrentConstraint),biasConstraint:Qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};tm.className="SimpleRNNCell";oe.registerClass(tm);var Tx=class extends zs{constructor(e){e.cell=new tm(e);super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return new e(t)}};Tx.className="SimpleRNN";oe.registerClass(Tx);var nm=class extends Hd{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,rn(this.units,"units"),this.activation=ko(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ko(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Nt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=Zt(e.kernelConstraint),this.recurrentConstraint=Zt(e.recurrentConstraint),this.biasConstraint=Zt(e.biasConstraint),this.dropout=Fc([1,vo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fc([1,vo([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=ut(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return M(()=>{if(e=e,e.length!==2)throw new H(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Io({ones:()=>br(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Io({ones:()=>br(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,a=this.recurrentDropoutMask,o,i,u;0<this.dropout&&this.dropout<1&&(e=V(e,s[0]));let l=ps(e,this.kernel.read());this.useBias&&(l=Qr(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=V(r,a[0]));let c=this.recurrentKernel.read(),[d,p]=sr(c,[2*this.units,this.units],c.rank-1),h=ps(r,d),[f,m,g]=sr(l,3,l.rank-1),[b,y]=sr(h,2,h.rank-1);o=this.recurrentActivation.apply(Z(f,b)),i=this.recurrentActivation.apply(Z(m,y));let v=ps(V(i,r),p);u=this.activation.apply(Z(g,v));let x=Z(V(o,r),V(Z(1,Ft(o)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:wo(this.activation),recurrentActivation:wo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Dt(this.kernelInitializer),recurrentInitializer:Dt(this.recurrentInitializer),biasInitializer:Dt(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Qt(this.kernelConstraint),recurrentConstraint:Qt(this.recurrentConstraint),biasConstraint:Qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};nm.className="GRUCell";oe.registerClass(nm);var Nx=class extends zs{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 nm(e);super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Nx.className="GRU";oe.registerClass(Nx);var jd=class extends Hd{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,rn(this.units,"units"),this.activation=ko(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ko(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Nt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=Zt(e.kernelConstraint),this.recurrentConstraint=Zt(e.recurrentConstraint),this.biasConstraint=Zt(e.biasConstraint),this.dropout=Fc([1,vo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Fc([1,vo([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=ut(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;r=new(t=class extends Pr{apply(o,i){let u=s.apply([a]),l=new Ff().apply([a]),c=s.apply([a*2]);return $0($0(u,l),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return M(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Io({ones:()=>br(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Io({ones:()=>br(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let d=ps(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=V(r,o[0])),d=Z(d,ps(r,this.recurrentKernel.read())),this.useBias&&(d=Qr(d,this.bias.read()));let[p,h,f,m]=sr(d,4,d.rank-1);i=this.recurrentActivation.apply(p),u=this.recurrentActivation.apply(h),l=Z(V(u,s),V(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:wo(this.activation),recurrentActivation:wo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Dt(this.kernelInitializer),recurrentInitializer:Dt(this.recurrentInitializer),biasInitializer:Dt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Qt(this.kernelConstraint),recurrentConstraint:Qt(this.recurrentConstraint),biasConstraint:Qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};jd.className="LSTMCell";oe.registerClass(jd);var _x=class extends zs{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 jd(e);super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};_x.className="LSTM";oe.registerClass(_x);var rm=class extends Hd{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return M(()=>{e=e;let n=e.slice(1),r=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?r.push(n.splice(0,o.stateSize.length)):r.push(n.splice(0,1));r.reverse();let s=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=r[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),s.push(a.slice(1))}n=[];for(let o of s.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){Gv(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{qi(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,n={}){let r=[];for(let s of t.cells)r.push(Jr(s,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Hv(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,s=e.splice(r);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}jv(t)}};rm.className="StackedRNNCells";oe.registerClass(rm);function Io(e){let{ones:t,rate:n,training:r=!1,count:s=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):D0(t(),n),i=()=>Ld(o,t,r);return!s||s<=1?nn(i().clone()):Array(s).fill(void 0).map(i).map(l=>nn(l.clone()))}var W2=class extends zs{constructor(e){if(e.unroll)throw new De("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new De("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Gt({ndim:5})]}call(e,t){return M(()=>{if(this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new H("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return M(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)],a=kt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){M(()=>{if(!this.stateful)throw new Ms("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)];if(n[0]==null)throw new H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>kt(s)):this.states_=[kt(s)];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>kt(s)):this.states_[0]=kt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Fe(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],u=s;if(!w.arraysEqual(i.shape,u))throw new H(`State ${o} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>nn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",u=e[i?3:2],l=e[i?4:3],c=es(u,r[0],s,a[0],o[0]),d=es(l,r[1],s,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};W2.className="ConvRNN2D";var sm=class extends jd{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:a,dilationRate:o}=e;super({...e,units:t});this.filters=t,rn(this.filters,"filters"),this.kernelSize=Oc(n,2,"kernelSize"),this.kernelSize.forEach(i=>rn(i,"kernelSize")),this.strides=Oc(r||1,2,"strides"),this.strides.forEach(i=>rn(i,"strides")),this.padding=s||"valid",vr(this.padding),this.dataFormat=a||"channelsLast",Bt(this.dataFormat),this.dilationRate=Oc(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>rn(i,"dilationRate"))}build(e){var t;e=ut(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],s=4,a=this.kernelSize.concat([r,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;i=new(t=class extends Pr{apply(c,d){let p=u.apply([l]),h=rr([l]),f=u.apply([l*2]);return Rv([p,h,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return M(()=>{if(e.length!==3)throw new H(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],s=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Io({ones:()=>br(r),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,u=(Q,ee,re)=>!ee||!ee[re]?Q:V(ee[re],Q),l=u(r,i,0),c=u(r,i,1),d=u(r,i,2),p=u(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Io({ones:()=>br(s),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(s,h,0),m=u(s,h,1),g=u(s,h,2),b=u(s,h,3),y=3,[v,x,k,T]=sr(this.kernel.read(),o,y),[C,E,F,O]=this.useBias?sr(this.bias.read(),o):[null,null,null,null];l=this.inputConv(l,v,C,this.padding),c=this.inputConv(c,x,E,this.padding),d=this.inputConv(d,k,F,this.padding),p=this.inputConv(p,T,O,this.padding);let[D,R,_,L]=sr(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,D),m=this.recurrentConv(m,R),g=this.recurrentConv(g,_),b=this.recurrentConv(b,L);let U=this.recurrentActivation.apply(Z(l,f)),j=this.recurrentActivation.apply(Z(c,m)),K=Z(V(j,a),V(U,this.activation.apply(Z(d,g)))),q=V(this.recurrentActivation.apply(Z(p,b)),this.activation.apply(K));return[q,q,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,r){let s=Wt(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Qr(s,n,this.dataFormat):s}recurrentConv(e,t){return Wt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};sm.className="ConvLSTM2DCell";oe.registerClass(sm);var Ex=class extends W2{constructor(e){let t=new sm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};Ex.className="ConvLSTM2D";oe.registerClass(Ex);var am=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 r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,s=this.getNoiseShape(n);return Ld(()=>D0(n,this.rate,s,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};am.className="Dropout";oe.registerClass(am);var Ax=class extends am{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Ax.className="SpatialDropout1D";oe.registerClass(Ax);var $x=class extends Ye{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,rn(this.units,"units"),this.activation=ko(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Zt(e.kernelConstraint),this.biasConstraint=Zt(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ut(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=ut(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e),r=k0(this.activation.getClassName()),s;return r!=null?s=ps(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=ps(n,this.kernel.read()),this.bias!=null&&(s=Qr(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:wo(this.activation),useBias:this.useBias,kernelInitializer:Dt(this.kernelInitializer),biasInitializer:Dt(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Qt(this.kernelConstraint),biasConstraint:Qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};$x.className="Dense";oe.registerClass($x);var Fx=class extends Ye{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ut(e);for(let t of e.slice(1))if(t==null)throw new H(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],yo(e,1)]}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let s=2;s<n.rank;++s)r.push(s);r.push(1),n=Oe(n,r)}return KV(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Fx.className="Flatten";oe.registerClass(Fx);var Dx=class extends Ye{constructor(e){super(e);this.supportsMasking=!0,this.activation=ko(e.activation)}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.activation.apply(n)})}getConfig(){let e={activation:wo(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="Activation";oe.registerClass(Dx);var Rx=class extends Ye{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return M(()=>(e=Me(e),jV(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Rx.className="RepeatVector";oe.registerClass(Rx);var Px=class extends Ye{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),s=1,a=null;for(let i=0;i<r.length;++i){let u=r[i];if(this.isUnknown(u))if(a===null)a=i;else throw new H("Can only specifiy one unknown dimension.");else s*=u}let o=yo(e);if(a!==null){if(s===0||o%s!=0)throw new H(n);r[a]=o/s}else if(o!==s)throw new H(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e),r=n.shape,s=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return G(n,s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Px.className="Reshape";oe.registerClass(Px);var Ox=class extends Ye{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Yr(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Gt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ut(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return 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Zi=class extends Ye{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let s=e[e.length-t.length+r],a=t[r];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new H("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ut(e)]),e=e,e.length<2)throw new H(`A merge layer should be called on an Array of at least 2 inputs. 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i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ve(V(e,t),a[0]):i=ve(V(Oe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;i=Re(e,t,u,l)}if(o>0){let u;r>s?u=r+s-3:u=r-1;let l=[];for(let c=u;c<u+o;++c)l.push(c);i=Rs(i,l)}return i.shape.length===1&&(i=Nn(i,1)),i})}var Hx=class extends Zi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new H(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new H(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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Ye{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);return Ld(()=>Z($f(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};jx.className="GaussianNoise";oe.registerClass(jx);var qx=class extends Ye{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.rate>0&&this.rate<1?Ld(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return V(n,$f(n.shape,1,s))},()=>n,t.training||!1):n})}};qx.className="GaussianDropout";oe.registerClass(qx);var Kx=class extends Ye{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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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 Gt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return M(()=>{let n=t.training==null?!1:t.training,r=Me(e),s=r.shape,a=s.length,o=Yr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let u=Gi(1,a);u[i]=s[i];let l=o.slice();l.sort();let c=!w.arraysEqual(l,Yr(0,a).slice(0,a-1)),d=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Kd(r,b,y,v,x,this.epsilon)}else return Kd(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[p,h,f]=FG(r,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(b,y,v)=>{M(()=>{let x=1-v,k=b.read(),T=V(he(k,y),x);b.write(he(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Dt(this.betaInitializer),gammaInitializer:Dt(this.gammaInitializer),movingMeanInitializer:Dt(this.movingMeanInitializer),movingVarianceInitializer:Dt(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:Qt(this.betaConstraint),gammaConstraint:Qt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Xx.className="BatchNormalization";oe.registerClass(Xx);var Yx=class extends Ye{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Nt(e.betaInitializer||"zeros"),this.gammaInitializer=Nt(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ut(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==bo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Me(e),r=n.shape,s=r.length;return M(()=>{let a=!0,{mean:o,variance:i}=lf(n,this.axis,a),u=Gi(1,s);for(let f of this.axis)u[f]=r[f];let l=f=>f!=null&&f.shape.length!==s?G(f,u):f,c=l(this.gamma.read()),d=l(this.beta.read()),p=[],h=[];for(let f=0;f<s;++f)this.axis.indexOf(f)!==-1?(p.push(r[f]),h.push(1)):(p.push(1),h.push(r[f]));return o=tr(o,p),i=tr(i,p),c=tr(c,h),d=tr(d,h),Kd(n,o,i,d,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Dt(this.betaInitializer),gammaInitializer:Dt(this.gammaInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Yx.className="LayerNormalization";oe.registerClass(Yx);function DG(e,t,n){return M(()=>{if(e.rank!==4)throw new H(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Kr()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. 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a==="max"?o=Vt(e,t,n,i):o=Ar(e,t,n,i),s==="channelsFirst"&&(o=Oe(o,[0,3,1,2])),o})}function V2(e,t,n,r,s,a){return M(()=>{Bt(s),T0(a),vr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),s==null&&(s=Kr()),a==null&&(a="max"),e=F2(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=nv(e,t,n,i):o=Vy(e,t,n,i),s==="channelsFirst"&&(o=Oe(o,[0,4,1,2,3])),o})}var U2=class extends Ye{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new H(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(rn(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)}`);rn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,vr(this.padding),this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){e=ut(e);let t=es(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return M(()=>{this.invokeCallHook(e,t),e=Od(Me(e),2);let n=this.poolingFunction(Me(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Rs(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Zx=class extends U2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Bt(s),vr(r),om(e,t,n,r,s,"max")}};Zx.className="MaxPooling1D";oe.registerClass(Zx);var Jx=class extends U2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Bt(s),vr(r),om(e,t,n,r,s,"avg")}};Jx.className="AveragePooling1D";oe.registerClass(Jx);var G2=class extends Ye{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new H(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];rn(this.poolSize,"poolSize"),rn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),vr(this.padding),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=es(t,this.poolSize[0],this.padding,this.strides[0]),n=es(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return M(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},ew=class extends G2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Bt(s),vr(r),om(e,t,n,r,s,"max")}};ew.className="MaxPooling2D";oe.registerClass(ew);var tw=class extends G2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Bt(s),vr(r),om(e,t,n,r,s,"avg")}};tw.className="AveragePooling2D";oe.registerClass(tw);var H2=class extends Ye{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new H(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];rn(this.poolSize,"poolSize"),rn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),vr(this.padding),this.inputSpec=[new Gt({ndim:5})]}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=es(t,this.poolSize[0],this.padding,this.strides[0]),n=es(n,this.poolSize[1],this.padding,this.strides[1]),r=es(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return M(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},nw=class extends H2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Bt(s),vr(r),V2(e,t,n,r,s,"max")}};nw.className="MaxPooling3D";oe.registerClass(nw);var rw=class extends H2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Bt(s),vr(r),V2(e,t,n,r,s,"avg")}};rw.className="AveragePooling3D";oe.registerClass(rw);var j2=class extends Ye{constructor(e){super(e);this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},sw=class extends j2{constructor(e){super(e||{})}call(e,t){return M(()=>{let n=Me(e);return Ot(n,1)})}};sw.className="GlobalAveragePooling1D";oe.registerClass(sw);var aw=class extends j2{constructor(e){super(e||{})}call(e,t){return M(()=>{let n=Me(e);return Gr(n,1)})}};aw.className="GlobalMaxPooling1D";oe.registerClass(aw);var q2=class extends Ye{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new De}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},ow=class extends q2{call(e,t){return M(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?Ot(n,[1,2]):Ot(n,[2,3])})}};ow.className="GlobalAveragePooling2D";oe.registerClass(ow);var iw=class extends q2{call(e,t){return M(()=>{let n=Me(e);return 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e(a)}},uw=class extends K2{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ut(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=ut(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return M(()=>(e=Me(e),B2((a,o)=>[Me(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};uw.className="TimeDistributed";oe.registerClass(uw);function RG(e){ji(WV,"BidirectionalMergeMode",e)}var PG="concat",cw=class extends K2{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Jr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Jr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?PG:e.mergeMode,RG(this.mergeMode),e.weights)throw new De("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Gn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=L2(e,n,r,this.numConstants);if(e=s.inputs,n=s.initialState,r=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let u=n.length;if(u%2>0)throw new H("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new 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o;return this.mergeMode==="concat"?o=Rv([r,s]):this.mergeMode==="sum"?o=Z(r,s):this.mergeMode==="ave"?o=V(.5,Z(r,s)):this.mergeMode==="mul"?o=V(r,s):this.mergeMode==null&&(o=[r,s]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){qi(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),qi(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=I(a,e,t,n),i=W6(r,s,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),s=I("indices",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(s,o,a)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),s=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(s,a,o)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),s=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=z6(r,s,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),s=n.getTensorList(r.id),a=I("dtype",e,t,n),o=I("elementShape",e,t,n);return[s.concat(a,o)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),s=I("tensor",e,t,n),a=n.getTensorList(r.id);return a.pushBack(s),[a.idTensor]}case"TensorListPopBack":{let 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l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(Rs(u).shape,o))throw new Error("the input tensors shape does not match");return l?u:G(u,a)});return[Ut(i,r)]});case"Unpack":{let r=I("axis",e,t,n),s=I("tensor",e,t,n);return vt(s,r)}case"Tile":{let r=I("reps",e,t,n);return[tr(I("x",e,t,n),r)]}case"Split":case"SplitV":{let r=I("axis",e,t,n),s=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return sr(a,s,r)}case"ScatterNd":{let r=I("indices",e,t,n),s=I("values",e,t,n),a=I("shape",e,t,n);return[GS(r,s,a)]}case"GatherNd":{let r=I("x",e,t,n),s=I("indices",e,t,n);return[HS(r,s)]}case"SparseToDense":{let r=I("sparseIndices",e,t,n),s=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),o=I("defaultValue",e,t,n);return[yv(r,a,s,a.dtype===o.dtype?o:ue(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},s5=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:a,reverseIndexMap:o}=$d.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[r,s,a,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=$d.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[$d.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[$d.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},a5=(e,t,n)=>{switch(e.op){case"FFT":return[ff(I("x",e,t,n))];case"IFFT":return[Ed(I("x",e,t,n))];case"RFFT":return[mf(I("x",e,t,n))];case"IRFFT":return[hv(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},o5=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=wf.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:a}=wf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[r,s,a]}case"StringToHashBucketFast":return[wf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},i5=(e,t,n)=>{switch(e.op){case"Cast":return[ue(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let r=I("axis",e,t,n);return[Nn(I("x",e,t,n),r)]}case"Squeeze":{let r=I("axis",e,t,n);return[Rs(I("x",e,t,n),r)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[AS(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[Fr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let r=I("blockShape",e,t,n),s=I("paddings",e,t,n);return[df(I("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=I("blockShape",e,t,n),s=I("crops",e,t,n);return[tf(I("x",e,t,n),r,s)]}case"DepthToSpace":{let r=I("blockSize",e,t,n),s=I("dataFormat",e,t,n).toUpperCase();return[mS(I("x",e,t,n),r,s)]}case"BroadcastTo":return[Id(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[oS(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function FC(e,t,n,r){let s=((a,o,i)=>{switch(a.category){case"arithmetic":return M(()=>M6(a,o,i));case"basic_math":return M(()=>L6(a,o,i));case"control":return G6(a,o,i);case"convolution":return M(()=>H6(a,o,i));case"creation":return M(()=>j6(a,o,i));case"dynamic":return q6(a,o,i);case"evaluation":return M(()=>K6(a,o,i));case"image":return M(()=>Z6(a,o,i));case"graph":return M(()=>X6(a,o,i));case"logical":return M(()=>J6(a,o,i));case"matrices":return M(()=>e5(a,o,i));case"normalization":return M(()=>t5(a,o,i));case"reduction":return M(()=>n5(a,o,i));case"slice_join":return M(()=>r5(a,o,i));case"sparse":return M(()=>s5(a,o,i));case"spectral":return M(()=>a5(a,o,i));case"string":return M(()=>o5(a,o,i));case"transformation":return M(()=>i5(a,o,i));case"hash_table":return Q6(a,o,i,r);case"custom":let u=oC(a.op);if(u&&u.customExecutor)return u.customExecutor(new O6(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var DC=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function RC(e,t,n,r){let s=new Set,a=[],o=null,i=null,u=new Set,l=Object.keys(e).map(p=>or(p)[0]),c=[];r!=null&&(c=r.map(p=>or(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((PC(p)||p5(p)||h5(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>s.has(h))),s.add(p.name),n[p.name]==null&&l.indexOf(p.name)===-1&&c.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}function u5(e,t,n){let{usedNodes:r,inputs:s}=n,a=[],o=Object.keys(s).map(c=>or(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{r.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{r.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(d=>{!u.has(d.name)&&r.has(d.name)&&d.inputs.every(p=>u.has(p.name))&&a.push(d)})}return l}var c5=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],l5=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],d5=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function PC(e){return c5.indexOf(e.op)>=0}function p5(e){return l5.indexOf(e.op)>=0}function h5(e){return d5.indexOf(e.op)>=0}var Nw=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 Nw(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(s=>s.name).sort(),r=t.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=RC(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:a}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(r.length>0){let o=t.map(u=>u.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${r}]`)}return u5(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[or(c)[0]]),s=t.map(c=>or(c)[0]),a=s.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(r,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let u={},l={};return M(()=>{let c=new DC(this.weightMap,u,l,this.functionExecutorMap),d={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=or(f),b=[];b[g]=e[f],d[m]=b});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=FC(m,d,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,c,p,s,h)}}return this.parent==null&&c.dispose(p),t.map(f=>An(f,d,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,s,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let u=m6(i.name,n,r);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!s.has(l.id)){let c=o[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[d,p]=gs(t.name,r);this.intermediateTensors[d]?this.intermediateTensors[d][p]=l:(this.intermediateTensors[d]=[],this.intermediateTensors[d][p]=l)}delete o[l.id]}else c!=null&&o[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,r={},s={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=X().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new DC(this.weightMap,r,s,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(l=>An(l,this.tensorsMap,a)),i=o.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...i,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let r=e.reduce((s,a,o)=>(s[this.inputs[o].name]=a,s),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let s=Object.keys(e),a=s.map(y=>this.graph.nodes[or(y)[0]]),o=n.map(y=>or(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:d}=RC(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=or(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let y=this.processStack(a,p,t,h,g,m,o,f,u);await Promise.all(y)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=i.filter(y=>!PC(y)&&!An(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${y}`)}return h}processStack(e,t,n,r,s,a,o,i,u){let l=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&I("isConstant",c.node,r,n)&&([d]=gs(c.node.name,n)),r[c.node.name]==null){let p=FC(c.node,r,n,this._resourceManager);d||([d]=gs(c.node.name,n));let h=n.currentContext;w.isPromise(p)?l.push(p.then(f=>(r[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,c.node,r,n,a,o,i),this.processChildNodes(c.node,t,n,r,s,u),f))):(r[d]=p,this.checkTensorForDisposal(d,c.node,r,n,a,o,i),this.processChildNodes(c.node,t,n,r,s,u))}else this.processChildNodes(c.node,t,n,r,s,u)}return l}processChildNodes(e,t,n,r,s,a){e.children.forEach(o=>{let[i]=gs(o.name,n);s[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!An(u,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(u=>!!An(u,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=or(t),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,u)=>a[u]===-1||a[u]===i);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=or(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=or(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},f5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},m5="?tfjs-format=file",g5="model.json",OC=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new f5}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=tn.browserHTTPRequest(e,this.loadOptions);else{let t=tn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(tn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=tn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Nw(TC.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=TC.Instance.transformGraph(e.modelInitializer);this.initializer=new Nw(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=tn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ae)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function b5(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${g5}${m5}`);let n=new OC(e,t);return await n.load(),n}var y5="0.0.0",MC={};Ee(MC,{CSVDataset:()=>QC,Dataset:()=>Lc,FileDataSource:()=>sT,TextLineDataset:()=>KC,URLDataSource:()=>aT,array:()=>W5,csv:()=>Z5,func:()=>J5,generator:()=>ej,microphone:()=>nj,version_data:()=>rj,webcam:()=>tj,zip:()=>V5});var v5=Bo(ch()),x5=Bo(ch());function w5(e,t){return lm(e,t)}function lm(e,t,n=new Map,r=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let s=t(e);if(s.recurse&&s.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(s.recurse)if(Mc(e)){let a=Array.isArray(e)?[]:{};r.add(e);for(let o in e){let i=e[o],u=lm(i,t,n,r);a[o]=u}return r.delete(e),e.__proto__&&(a.__proto__=e.__proto__),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,s.value),s.value}function k5(e,t=BC){return LC(e,t)}function LC(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let s=t(e);if(s.recurse&&s.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(s.recurse)if(Mc(r)){let a=Array.isArray(r)?[]:{};n.add(r);for(let o in r){let i=e.map(l=>l[o]),u=LC(i,t,n);a[o]=u}return n.delete(r),a}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return s.value}function BC(e){return e===null?null:Mc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function zC(e,t){let n=new Map;lm(e,t,n);for(let s of Array.from(n.keys())){let a=n.get(s);if(w.isPromise(a)){let o=await a;n.set(s,o)}}return lm(e,t,n)}function Mc(e){let t=!1;if(X().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=$I();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ae)&&!(e instanceof Promise)&&!t)}function I5(e){return e==null||S5(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ae||w.isTypedArray(e)}function S5(e){return e===null||typeof e!="object"&&typeof e!="function"}function C5(e){return w5(e,T5)}function T5(e){return e instanceof Ae?{value:e.clone(),recurse:!1}:Mc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var WC=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},VC=class extends WC{constructor(){super(VC.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}},UC=VC;UC.INITIAL_CAPACITY=32;function GC(e){return new E5(e)}function _w(e){return new A5(e)}function N5(e,t){return new jC(e,t)}function _5(e,t=dm.FAIL){return new B5(e,t)}var sn=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new M5(this,e)}filter(e){return new P5(this,e)}map(e){return new O5(this,e)}mapAsync(e){return new HC(this,e)}serialMapAsync(e){return new HC(this,e).serial()}flatmap(e){return new L5(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new R5(this,e,t)}columnMajorBatch(e,t=!0,n=BC){return this.rowMajorBatch(e,t).map(s=>k5(s,n))}concatenate(e,t){return new jC(GC([this,e]),t)}take(e){return e<0||e==null?this:new D5(this,e)}skip(e){return e<0||e==null?this:new F5(this,e)}prefetch(e){return new qC(this,e)}shuffle(e,t){return new z5(this,e,t)}serial(){return new $5(this)}},E5=class extends sn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:C5(e),done:!1}}},A5=class extends sn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},$5=class extends sn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},F5=class extends sn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Fe(e.value)}return this.upstream.next()}},D5=class extends sn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},R5=class extends sn{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},P5=class extends sn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Fe(e.value)}}},O5=class extends sn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=Vr.getTensorsInContainer(n);for(let s of t)Vr.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},M5=class extends sn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},HC=class extends sn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Vr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=Vr.getTensorsInContainer(n);for(let s of t)Vr.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},Ew=class extends sn{constructor(){super();this.outputQueue=new UC,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},L5=class extends Ew{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=Vr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of t)Vr.isTensorInList(s,r)||s.dispose();return!0}},jC=class extends sn{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}},dm;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(dm||(dm={}));var B5=class extends sn{constructor(e,t=0){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(a){return a instanceof sn?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let s=await zC(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},qC=class extends sn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new WC(t)}summary(){return`${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()}},z5=class extends qC{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=x5.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}}},Lc=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let r;return this.size===1/0||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),ir(async()=>(await n.iterator()).columnMajorBatch(e,t,U5),r)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,ir(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,ir(async()=>(await t.iterator()).filter(r=>M(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return ir(async()=>(await t.iterator()).map(n=>M(()=>e(n))),this.size)}mapAsync(e){let t=this;return ir(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return ir(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,ir(async()=>{let r=_w(async()=>({value:await t.iterator(),done:!1}));return N5(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,ir(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,s=v5.alea(t||w.now().toString());return ir(async()=>{let a=s.int32();return n&&(a+=s.int32()),(await r.iterator()).shuffle(e,a.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,ir(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()}};Lc.MAX_BUFFER_SIZE=1e4;function ir(e,t=null){return new class extends Lc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function W5(e){return ir(async()=>GC(e),e.length)}function V5(e){if(!Mc(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 ir(async()=>{let n=await zC(e,r=>{if(r instanceof Lc)return{value:r.iterator(),recurse:!1};if(Mc(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return _5(n,dm.SHORTEST)},t)}function U5(e){if(e===null)return null;let t=e[0];return I5(t)?{value:G5(e),recurse:!1}:{value:null,recurse:!0}}function G5(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ae?Ut(e):er(e)}var KC=class extends Lc{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(r=>(r.endsWith("\r")&&(r=r.slice(0,-1)),r))}},pm='"',Qd=Symbol("out"),XC=Symbol("field"),hm=Symbol("quote"),Aw=Symbol("quoteafterquote"),YC=Symbol("quoteinquote"),QC=class extends Lc{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 KC(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((r,s)=>(r[s]=r[s]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[s],u=null;if(i==="")if(o&&o.default!==void 0)u=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(i);if(isNaN(l))o&&o.dtype==="bool"?u=this.getBoolean(i):u=i;else if(!o||!o.dtype)u=l;else switch(o.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(i);break;default:u=l}}o&&o.isLabel?r[a]=u:n[a]=u}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,s=e.length,a=Qd;for(let o=0;o<s;o++)switch(a){case Qd:switch(e.charAt(o)){case pm:r=o+1,a=hm;break;case this.delimiter:if(r=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Qd;break;default:a=XC,r=o;break}break;case XC:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(r,o)),a=Qd,r=o+1;break;default:}break;case hm:switch(e.charAt(o)){case pm:a=Aw;break;default:}break;case Aw:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(r,o-1)),a=Qd,r=o+1;break;case pm:a=hm;break;default:a=YC;break}break;case YC:switch(e.charAt(o)){case pm:a=hm;break;default:}break;default:}if(a===Aw?n.push(e.substring(r,s-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},ZC=class extends sn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(X().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new ZC(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,s)=>n.set(r,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),er(n,t)}},JC=class extends sn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-r)/2,o=s+n,i=r+a;this.cropBox=jr([a,s,i,o],[1,4])}else this.cropBox=jr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(X().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new JC(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=Pi.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return M(()=>{let t=Nn(ue(e,"float32"),0),n;n=ar.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return G(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},eT=class{},tT=class extends sn{split(e){return new H5(this,e)}},H5=class extends tT{constructor(e,t){super();this.upstream=e,this.impl=new j5(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},j5=class extends Ew{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},q5=class extends sn{decodeUTF8(){return new K5(this)}},K5=class extends tT{constructor(e){super();this.upstream=e,this.impl=new X5(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},X5=class extends Ew{constructor(e){super();if(this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=$I();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}},nT=class extends q5{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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,r)));else{let s=new FileReader;s.onload=o=>{let i=s.result;if(i instanceof 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eT{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return rT(this.url)?new sT(this.url,this.fileOptions).iterator():Y5(this.url,this.fileOptions)}};function Z5(e,t={}){return new QC(new aT(e),t)}function J5(e){let t=_w(e);return ir(async()=>t)}function ej(e){return ir(async()=>{let t=await e();return _w(()=>t.next())})}async function tj(e,t){return JC.create(e,t)}async function nj(e){return ZC.create(e)}var rj="0.0.0";function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var sj=Dr.whereImpl,oT=class extends Mu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ul(this,is())}nextDataId(){return oT.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().get("IS_NODE")&&N.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),u=s,l=[];i!=null&&(u=xr({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(u),o=N.getInnerMostAxes(o.length,u.shape.length)),o=[o[0]],N.assertAxesAreInnerMostDims("argMin",o,u.shape.length);let[c,d]=N.computeOutAndReduceShapes(u.shape,o),p=w.sizeFromShape(c),h=w.makeZerosTypedArray(p,"int32"),f=w.sizeFromShape(d),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k<y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var _q={kernelName:Hu,backendName:"cpu",kernelFunc:Nq},Eq=ct(ju,e=>Math.asin(e)),Aq={kernelName:ju,backendName:"cpu",kernelFunc:Eq},$q=ct(qu,e=>Math.asinh(e)),Fq={kernelName:qu,backendName:"cpu",kernelFunc:$q},Dq=ct(Ku,e=>Math.atan(e)),Rq={kernelName:Ku,backendName:"cpu",kernelFunc:Dq},Pq=Ht((e,t)=>Math.atan2(e,t)),Oq=an(Yu,Pq),Mq={kernelName:Yu,backendName:"cpu",kernelFunc:Oq},Lq=ct(Xu,e=>Math.atanh(e)),Bq={kernelName:Xu,backendName:"cpu",kernelFunc:Lq};function Ww(e,t,n,r,s,a){let o=s.strideHeight,i=s.strideWidth,u=s.dilationHeight,l=s.dilationWidth,c=s.effectiveFilterHeight,d=s.effectiveFilterWidth,p=s.padInfo.top,h=s.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=$e(s.outShape,n),g=m.values,b=s.outShape[1]*s.outShape[2]*s.outShape[3],y=s.outShape[2]*s.outShape[3],v=s.outShape[3];for(let x=0;x<s.batchSize;++x){let k=x*b,T=x*r[0];for(let C=0;C<s.inChannels;++C)for(let E=0;E<s.outHeight;++E){let 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U=L-C,j=m.get(g,R,L,b);j>O&&(O=j,s?D=a?((g*r.inHeight+R)*r.inWidth+L)*r.inChannels+b:(R*r.inWidth+L)*r.inChannels+b:D=_*p+U)}}o.set(D,g,y,T,b)}}return o}function ZT(e,t,n,r,s,a){let o=s.strideDepth,i=s.strideHeight,u=s.strideWidth,l=s.dilationDepth,c=s.dilationHeight,d=s.dilationWidth,p=s.effectiveFilterDepth,h=s.effectiveFilterHeight,f=s.effectiveFilterWidth,m=s.padInfo.front,g=s.padInfo.top,b=s.padInfo.left,y=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=$e(s.outShape,n),x=v.values,k=s.outShape[1]*s.outShape[2]*s.outShape[3]*s.outShape[4],T=s.outShape[2]*s.outShape[3]*s.outShape[4],C=s.outShape[3]*s.outShape[4],E=s.outShape[4];for(let F=0;F<s.batchSize;++F){let O=F*k,D=F*r[0];for(let R=0;R<s.inChannels;++R)for(let _=0;_<s.outDepth;++_){let L=_*o-m,U=L;for(;U<0;)U+=l;let j=Math.min(s.inDepth,p+L),K=O+_*T;for(let q=0;q<s.outHeight;++q){let Q=q*i-g,ee=Q;for(;ee<0;)ee+=c;let re=Math.min(s.inHeight,h+Q),se=K+q*C;for(let ne=0;ne<s.outWidth;++ne){let ie=ne*u-b,te=ie;for(;te<0;)te+=d;let pe=Math.min(s.inWidth,f+ie),be=se+ne*E,Ce=y,Ie=0,Ne=0;for(let Je=U;Je<j;Je+=l){let qe=D+Je*r[1];for(let Ge=ee;Ge<re;Ge+=c){let lt=qe+Ge*r[2];for(let et=te;et<pe;et+=d){let pt=lt+et*r[3],Ct=e[pt+R];if(a==="max"&&Ct>Ce?Ce=Ct:a==="avg"&&(Ie+=Ct,Ne++),isNaN(Ce))break}if(isNaN(Ce))break}if(isNaN(Ce))break}let Le=be+R;x[Le]=a==="avg"?Ie/Ne:Ce}}}}return v}function zq(e,t){let n=$e(t.outShape,"int32"),r=t.strideDepth,s=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,d=t.effectiveFilterWidth,p=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*r-p,v=y;for(;v<0;)v+=o;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let T=k*s-h,C=T;for(;C<0;)C+=i;let E=Math.min(t.inHeight,c+T);for(let F=0;F<t.outWidth;++F){let O=F*a-f,D=O;for(;D<0;)D+=u;let R=Math.min(t.inWidth,d+O),_=Number.NEGATIVE_INFINITY,L=-1;for(let U=v;U<x;U+=o){let j=U-y;for(let K=C;K<E;K+=i){let q=K-T;for(let Q=D;Q<R;Q+=u){let ee=Q-O,re=e.get(m,U,K,Q,g);re>=_&&(_=re,L=j*c*d+q*c+ee)}}}n.set(L,m,b,k,F,g)}}}return n}function Wq(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;we(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=r,l=1;w.assert(N.eitherStridesOrDilationsAreOne(o,l),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=N.computePool3DInfo(a.shape,o,i,1,u,l),d=c.strideDepth,p=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,T=c.effectiveFilterWidth,C=x-1-c.padInfo.front,E=T-1-c.padInfo.left,F=k-1-c.padInfo.top,O=$e(a.shape,"float32"),D=1/(f*m*g),R=n.bufferSync(s);for(let _=0;_<c.batchSize;++_)for(let L=0;L<c.inChannels;++L)for(let U=0;U<c.inDepth;++U)for(let j=0;j<c.inHeight;++j)for(let K=0;K<c.inWidth;++K){let q=U-C,Q=j-F,ee=K-E,re=0;for(let se=0;se<x;se+=b){let ne=(q+se)/d;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let ie=0;ie<k;ie+=y){let te=(Q+ie)/p;if(!(te<0||te>=c.outHeight||Math.floor(te)!==te))for(let pe=0;pe<T;pe+=v){let be=(ee+pe)/h;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;re+=R.get(_,ne,te,be,L)}}}O.set(re*D,_,U,j,K,L)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var jq={kernelName:gh,backendName:"cpu",kernelFunc:Hq};function qq(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;we([s,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:l}=r,c=N.computePool2DInfo(o.shape,i,u,1,l),d=c.strideHeight,p=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=$e(o.shape,"float32"),T=1/(h*f),C=n.data.get(s.dataId).values,E=$e(s.shape,"float32",C);for(let F=0;F<c.batchSize;++F)for(let O=0;O<c.inChannels;++O)for(let D=0;D<c.inHeight;++D)for(let R=0;R<c.inWidth;++R){let _=D-x,L=R-v,U=0;for(let j=0;j<b;j+=m){let K=(_+j)/d;if(!(K<0||K>=c.outHeight||Math.floor(K)!==K))for(let q=0;q<y;q+=g){let Q=(L+q)/p;if(Q<0||Q>=c.outWidth||Math.floor(Q)!==Q)continue;U+=E.get(F,K,Q,O)}}k.set(U*T,F,D,R,O)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var Kq={kernelName:mh,backendName:"cpu",kernelFunc:qq};function Xq(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,scale:a,offset:o,mean:i,variance:u}=t;w.assert(i.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),we([s,i,u,a,o],"batchNorm");let{varianceEpsilon:l}=r;l==null&&(l=.001);let c=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,p=n.data.get(u.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,b=h.length,y=p.length,v=d.length,x=0,k=0,T=0,C=0;for(let E=0;E<c.length;++E)m[E]=f[x++]+(c[E]-d[k++])*h[T++]/Math.sqrt(p[C++]+l),x>=g&&(x=0),k>=v&&(k=0),T>=b&&(T=0),C>=y&&(C=0);return n.makeTensorInfo(s.shape,s.dtype,m)}var Yq={kernelName:Ta,backendName:"cpu",kernelFunc:Xq};function Qq(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;we([s],"batchToSpaceND");let i=a.reduce((b,y)=>b*y),u=N.getReshaped(s.shape,a,i),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(s.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(c,o,a.length),h=Et({inputs:{x:s},backend:n,attrs:{shape:u}}),f=xr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=Et({inputs:{x:f},backend:n,attrs:{shape:c}}),g=eu({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var Zq={kernelName:Uo,backendName:"cpu",kernelFunc:Qq};function Jq(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.data.get(s.dataId).values,u=n.data.get(a.dataId).values,l=Dw(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var eK={kernelName:bh,backendName:"cpu",kernelFunc:Jq};function tK(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,i=N.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var nK={kernelName:yh,backendName:"cpu",kernelFunc:tK},rK=ct(Es,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),sK={kernelName:Es,backendName:"cpu",kernelFunc:rK},aK=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(w.sizeFromShape(t.shape)),s=n.data.get(t.dataId),a=s.complexTensorInfos.real,o=s.complexTensorInfos.imag,i=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values;for(let l=0;l<i.length;l++){let c=i[l],d=u[l];r[l]=Math.hypot(c,d)}return n.makeOutput(r,t.shape,"float32")},oK={kernelName:Kl,backendName:"cpu",kernelFunc:aK};function zc(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.imag,a=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,a)}var iK={kernelName:Zl,backendName:"cpu",kernelFunc:zc};function Wc(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=N.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>w.sizeFromShape(m.shape)>0);if(i.length===1)return bs({inputs:{x:i[0]},backend:n});let u=i.map(m=>m.shape);if(N.assertParamsConsistent(u,a),i[0].dtype==="complex64"){let m=i.map(x=>Ji({inputs:{input:x},backend:n})),g=i.map(x=>zc({inputs:{input:x},backend:n})),b=Wc({inputs:m,backend:n,attrs:{axis:a}}),y=Wc({inputs:g,backend:n,attrs:{axis:a}}),v=ur({inputs:{real:b,imag:y},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),v}let l=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return Et({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=l.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=N.computeOutShape(l.map(m=>m.shape),1);let d=l[0].shape[0]===1,p=Rw(c,o,t[0].dtype,d),h=N.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var uK={kernelName:Go,backendName:"cpu",kernelFunc:Wc};function JT(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=r;we([s,a],"conv2d");let d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(s.shape,a.shape,o,l,i,c,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,b=p.padInfo.left,y=p.padInfo.top,v=p.dataFormat==="channelsLast",x=new Kt(p.outShape,s.dtype),k=w.computeStrides(s.shape),T=w.computeStrides(a.shape),C=k[0],E=v?k[1]:k[2],F=v?k[2]:1,O=v?1:k[1],D=x.strides[0],R=v?x.strides[1]:x.strides[2],_=v?x.strides[2]:1,L=v?1:x.strides[1],U=n.data.get(s.dataId).values,j=n.data.get(a.dataId).values,K=x.values;for(let q=0;q<p.batchSize;++q){let Q=q*C,ee=q*D;for(let re=0;re<p.outHeight;++re){let se=ee+re*R,ne=re*p.strideHeight-y;for(let ie=0;ie<h;++ie){let te=ne+ie*m;if(te<0||te>=p.inHeight)continue;let pe=ie*T[0],be=Q+te*E;for(let Ce=0;Ce<p.outWidth;++Ce){let Ie=se+Ce*_,Ne=Ce*p.strideWidth-b;for(let Le=0;Le<f;++Le){let Je=Ne+Le*g;if(Je<0||Je>=p.inWidth)continue;let qe=pe+Le*T[1],Ge=be+Je*F,lt=qe;for(let et=0;et<p.inChannels;++et){let pt=U[Ge+et*O];for(let Ct=0;Ct<p.outChannels;++Ct)K[Ie+Ct*L]+=pt*j[lt+Ct];lt+=p.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,K)}var cK={kernelName:ga,backendName:"cpu",kernelFunc:JT};function lK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,filterShape:c}=r;we([s,a],"conv2dBackpropFilter");let d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(s.shape,c,o,1,i,l,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,b=p.dataFormat==="channelsLast",y=new Kt(p.filterShape,"float32"),v=p.padInfo.left,x=p.padInfo.top,k=n.data.get(s.dataId).values,T=n.data.get(a.dataId).values,C=new Kt(s.shape,s.dtype,k),E=new Kt(a.shape,a.dtype,T);for(let F=0;F<m;++F){let O=Math.max(0,Math.ceil((x-F)/h)),D=Math.min(p.outHeight,(p.inHeight+x-F)/h);for(let R=0;R<g;++R){let _=Math.max(0,Math.ceil((v-R)/f)),L=Math.min(p.outWidth,(p.inWidth+v-R)/f);for(let U=0;U<p.inChannels;++U)for(let j=0;j<p.outChannels;++j){let K=0;for(let q=0;q<p.batchSize;++q)for(let Q=O;Q<D;++Q){let ee=F+Q*h-x;for(let re=_;re<L;++re){let se=R+re*f-v;b?K+=C.get(q,ee,se,U)*E.get(q,Q,re,j):K+=C.get(q,U,ee,se)*E.get(q,j,Q,re)}}y.set(K,F,R,U,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var dK={kernelName:vh,backendName:"cpu",kernelFunc:lK};function pK(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=r;we([s,a],"conv2dBackpropInput");let d=w.computeStrides(a.shape),p=w.computeStrides(s.shape),h=N.convertConv2DDataFormat(l),f=N.computeConv2DInfo(o,a.shape,i,1,u,c,!1,h),m=new Kt(f.inShape,"float32"),g=m.values,b=n.data.get(s.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=d,{batchSize:T,filterHeight:C,filterWidth:E,inChannels:F,inHeight:O,inWidth:D,outChannels:R,outHeight:_,outWidth:L,strideHeight:U,strideWidth:j}=f;h=f.dataFormat;let K=C-1-f.padInfo.top,q=E-1-f.padInfo.left,Q=h==="channelsLast",ee=m.strides[0],re=Q?m.strides[1]:m.strides[2],se=Q?m.strides[2]:1,ne=Q?1:m.strides[1],ie=p[0],te=Q?p[1]:p[2],pe=Q?p[2]:1,be=Q?1:p[1];for(let Ce=0;Ce<T;++Ce)for(let Ie=0;Ie<F;++Ie)for(let Ne=0;Ne<O;++Ne){let Le=Ne-K,Je=Math.max(0,Math.ceil(Le/U)),qe=Math.min(_,(C+Le)/U);for(let Ge=0;Ge<D;++Ge){let lt=Ge-q,et=Math.max(0,Math.ceil(lt/j)),pt=Math.min(L,(E+lt)/j),Ct=0;for(let tt=Je;tt<qe;++tt){let Qn=tt*U-Le;for(let cn=et;cn<pt;++cn){let Cr=cn*j-lt,zn=ie*Ce+te*tt+pe*cn,Zn=v*(C-1-Qn)+x*(E-1-Cr)+k*Ie;for(let dr=0;dr<R;++dr){let Tr=b[zn+be*dr],pr=y[Zn+dr];Ct+=Tr*pr}}}let Bn=ee*Ce+re*Ne+se*Ge+ne*Ie;g[Bn]=Ct}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var hK={kernelName:ba,backendName:"cpu",kernelFunc:pK};function fK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u}=r;we([s,a],"conv3d");let l=N.computeConv3DInfo(s.shape,a.shape,o,u,i),{filterDepth:c,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Kt(l.outShape,s.dtype),k=n.data.get(s.dataId).values,T=n.data.get(a.dataId).values,C=x.values,E=w.computeStrides(s.shape),F=w.computeStrides(a.shape);for(let O=0;O<l.batchSize;++O){let D=O*E[0],R=O*x.strides[0];for(let _=0;_<l.outDepth;++_){let L=R+_*x.strides[1],U=_*l.strideDepth-b;for(let j=0;j<c;++j){let K=U+j*h;if(K<0||K>=l.inDepth)continue;let q=j*F[0],Q=D+K*E[1];for(let ee=0;ee<l.outHeight;++ee){let re=L+ee*x.strides[2],se=ee*l.strideHeight-v;for(let ne=0;ne<d;++ne){let ie=se+ne*f;if(ie<0||ie>=l.inHeight)continue;let te=q+ne*F[1],pe=Q+ie*E[2];for(let be=0;be<l.outWidth;++be){let Ce=re+be*l.outChannels,Ie=be*l.strideWidth-y;for(let Ne=0;Ne<p;++Ne){let Le=Ie+Ne*m;if(Le<0||Le>=l.inWidth)continue;let Je=te+Ne*F[2],qe=pe+Le*l.inChannels,Ge=Je;for(let lt=0;lt<l.inChannels;++lt){let et=k[qe+lt];for(let pt=0;pt<l.outChannels;++pt)C[Ce+pt]+=et*T[Ge+pt];Ge+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var mK={kernelName:Xl,backendName:"cpu",kernelFunc:fK};function gK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:u}=r;we([s,a],"conv3dBackpropFilterV2");let l=w.computeStrides(s.shape),c=w.computeStrides(a.shape),d=N.computeConv3DInfo(s.shape,u,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,b=d.filterWidth,y=new Kt(d.filterShape,"float32"),v=y.values,[x,k,T,C]=y.strides,E=n.data.get(a.dataId).values,[F,O,D,R]=c,_=n.data.get(s.dataId).values,[L,U,j,K]=l,q=d.padInfo.front,Q=d.padInfo.left,ee=d.padInfo.top;for(let re=0;re<m;++re){let se=Math.max(0,Math.ceil((q-re)/p)),ne=Math.min(d.outDepth,(d.inDepth+q-re)/p),ie=re*x;for(let te=0;te<g;++te){let pe=Math.max(0,Math.ceil((ee-te)/h)),be=Math.min(d.outHeight,(d.inHeight+ee-te)/h),Ce=te*k+ie;for(let Ie=0;Ie<b;++Ie){let Ne=Math.max(0,Math.ceil((Q-Ie)/f)),Le=Math.min(d.outWidth,(d.inWidth+Q-Ie)/f),Je=Ie*T+Ce;for(let qe=0;qe<d.inChannels;++qe){let Ge=qe*C+Je;for(let lt=0;lt<d.outChannels;++lt){let et=0;for(let pt=0;pt<d.batchSize;++pt){let Ct=pt*L,Bn=pt*F;for(let tt=se;tt<ne;++tt){let cn=(re+tt*p-q)*U+Ct,Cr=tt*O+Bn;for(let zn=pe;zn<be;++zn){let dr=(te+zn*h-ee)*j+cn,Tr=zn*D+Cr;for(let pr=Ne;pr<Le;++pr){let Js=(Ie+pr*f-Q)*K+dr,bn=pr*R+Tr;et+=_[Js+qe]*E[bn+lt]}}}}v[Ge+lt]=et}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var bK={kernelName:xh,backendName:"cpu",kernelFunc:gK};function yK(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:u}=r;we([s],"conv3dBackpropInputV2");let l=w.computeStrides(s.shape),c=w.computeStrides(a.shape),d=N.computeConv3DInfo(u,a.shape,i,1,o),p=new Kt(d.inShape,"float32"),h=p.values,[f,m,g,b]=p.strides,y=n.data.get(s.dataId).values,[v,x,k,T]=l,C=n.data.get(a.dataId).values,[E,F,O,D]=c,{batchSize:R,filterDepth:_,filterHeight:L,filterWidth:U,inChannels:j,inDepth:K,inHeight:q,inWidth:Q,outChannels:ee,outDepth:re,outHeight:se,outWidth:ne,strideDepth:ie,strideHeight:te,strideWidth:pe}=d,be=_-1-d.padInfo.front,Ce=L-1-d.padInfo.top,Ie=U-1-d.padInfo.left;for(let Ne=0;Ne<R;++Ne)for(let Le=0;Le<j;++Le)for(let Je=0;Je<K;++Je){let qe=Je-be,Ge=Math.max(0,Math.ceil(qe/ie)),lt=Math.min(re,(_+qe)/ie);for(let et=0;et<q;++et){let pt=et-Ce,Ct=Math.max(0,Math.ceil(pt/te)),Bn=Math.min(se,(L+pt)/te);for(let tt=0;tt<Q;++tt){let Qn=tt-Ie,cn=Math.max(0,Math.ceil(Qn/pe)),Cr=Math.min(ne,(U+Qn)/pe),zn=0;for(let Zn=Ge;Zn<lt;++Zn){let dr=Zn*ie-qe;for(let Tr=Ct;Tr<Bn;++Tr){let pr=Tr*te-pt;for(let Wn=cn;Wn<Cr;++Wn){let Js=Wn*pe-Qn,bn=v*Ne+x*Zn+k*Tr+T*Wn,ea=E*(_-1-dr)+F*(L-1-pr)+O*(U-1-Js)+D*Le;for(let hr=0;hr<ee;++hr){let El=y[bn+hr],Al=C[ea+hr];zn+=El*Al}}}}h[f*Ne+m*Je+g*et+b*tt+Le]=zn}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var vK={kernelName:wh,backendName:"cpu",kernelFunc:yK},xK=ct(ya,e=>Math.cos(e)),wK={kernelName:ya,backendName:"cpu",kernelFunc:xK},kK=ct(va,e=>Math.cosh(e)),IK={kernelName:va,backendName:"cpu",kernelFunc:kK};function SK(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=r,[c,d,p,h]=s.shape,f=a.shape[0],[m,g]=i,b=$e([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(o.dataId).values,x=n.data.get(s.dataId).values,k=w.computeStrides(s.shape),T=w.computeStrides(b.shape);for(let C=0;C<f;C++){let E=C*4,F=y[E],O=y[E+1],D=y[E+2],R=y[E+3],_=v[C];if(_>=c)continue;let L=m>1?(D-F)*(d-1)/(m-1):0,U=g>1?(R-O)*(p-1)/(g-1):0;for(let j=0;j<m;j++){let K=m>1?F*(d-1)+j*L:.5*(F+D)*(d-1);if(K<0||K>d-1){for(let q=0;q<g;q++)for(let Q=0;Q<h;Q++){let ee=Q+q*T[2]+j*T[1]+C*T[0];b.values[ee]=l}continue}if(u==="bilinear"){let q=Math.floor(K),Q=Math.ceil(K),ee=K-q;for(let re=0;re<g;re++){let se=g>1?O*(p-1)+re*U:.5*(O+R)*(p-1);if(se<0||se>p-1){for(let pe=0;pe<h;pe++){let be=pe+re*T[2]+j*T[1]+C*T[0];b.values[be]=l}continue}let ne=Math.floor(se),ie=Math.ceil(se),te=se-ne;for(let pe=0;pe<h;pe++){let be=pe+ne*k[2]+q*k[1]+_*k[0],Ce=x[be];be=pe+ie*k[2]+q*k[1]+_*k[0];let Ie=x[be];be=pe+ne*k[2]+Q*k[1]+_*k[0];let Ne=x[be];be=pe+ie*k[2]+Q*k[1]+_*k[0];let Le=x[be],Je=Ce+(Ie-Ce)*te,qe=Ne+(Le-Ne)*te;be=pe+re*T[2]+j*T[1]+C*T[0],b.values[be]=Je+(qe-Je)*ee}}}else for(let q=0;q<g;++q){let Q=g>1?O*(p-1)+q*U:.5*(O+R)*(p-1);if(Q<0||Q>p-1){for(let se=0;se<h;se++){let ne=se+q*T[2]+j*T[1]+C*T[0];b.values[ne]=l}continue}let ee=Math.round(Q),re=Math.round(K);for(let se=0;se<h;se++){let ne=se+ee*k[2]+re*k[1]+_*k[0],ie=se+q*T[2]+j*T[1]+C*T[0];b.values[ie]=x[ne]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var CK={kernelName:jo,backendName:"cpu",kernelFunc:SK};function TK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;we(s,"cumsum");let u=N.getAxesPermutation([a],s.shape.length),l=s;u!=null&&(l=xr({inputs:{x:s},backend:n,attrs:{perm:u}}));let c=N.getInnerMostAxes(1,s.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let d=In(l.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(l.shape),d),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=i?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)p[v]=o?0:h[v];else{let x=m(b,y-1);p[v]=o?h[x]+p[x]:h[v]+p[x]}}let g=n.makeTensorInfo(l.shape,d,p);if(u!=null){let b=N.getUndoAxesPermutation(u),y=xr({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var NK={kernelName:Ho,backendName:"cpu",kernelFunc:TK};function _K(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let u=n.data.get(s.dataId).values,l=n.data.get(a.dataId).values,c=Dw(u,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(s.shape.length===2){let u=n.bufferSync(s),l=n.bufferSync(a),c=cT(u,l,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var EK={kernelName:kh,backendName:"cpu",kernelFunc:_K};function AK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r;w.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);let i=s.shape[0],u=s.shape[1],l=s.shape[2],c=s.shape[3],d=u*a,p=l*a,h=c/(a*a),f=n.data.get(s.dataId).values,m=new Float32Array(i*d*p*h),g=0;for(let b=0;b<i;++b)for(let y=0;y<d;++y){let v=Math.floor(y/a),x=y%a;for(let k=0;k<p;++k){let T=Math.floor(k/a),C=k%a,E=(x*a+C)*h;for(let F=0;F<h;++F){let D=F+E+c*(T+l*(v+u*b));m[g++]=f[D]}}}return n.makeTensorInfo([i,d,p,h],s.dtype,m)}var $K={kernelName:qo,backendName:"cpu",kernelFunc:AK};function eN(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=r;we([s,a],"depthwiseConv2DNative");let c=w.computeStrides(s.shape),d=w.computeStrides(a.shape),p=u;p==null&&(p=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(o,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);let h=N.computeConv2DInfo(s.shape,a.shape,o,p,i,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,T=new Kt(h.outShape,s.dtype),C=n.data.get(s.dataId).values,E=n.data.get(a.dataId).values,F=T.values;for(let O=0;O<h.batchSize;++O){let D=O*c[0],R=O*T.strides[0];for(let _=0;_<h.outHeight;++_){let L=R+_*T.strides[1],U=_*h.strideHeight-x;for(let j=0;j<f;++j){let K=U+j*g;if(K<0||K>=h.inHeight)continue;let q=j*d[0],Q=D+K*c[1];for(let ee=0;ee<h.outWidth;++ee){let re=L+ee*T.strides[2],se=ee*h.strideWidth-v;for(let ne=0;ne<m;++ne){let ie=se+ne*b;if(ie<0||ie>=h.inWidth)continue;let te=q+ne*d[1],pe=Q+ie*h.inChannels,be=re,Ce=te;for(let Ie=0;Ie<h.inChannels;++Ie){let Ne=C[pe+Ie];for(let Le=0;Le<k;++Le)F[be+Le]+=Ne*E[Ce+Le];be+=k,Ce+=k}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var FK={kernelName:xa,backendName:"cpu",kernelFunc:eN};function DK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=r;we([s,a],"depthwiseConv2dNativeBackpropFilter");let d=N.computeConv2DInfo(s.shape,c,o,i,u,l,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new Kt(d.filterShape,"float32"),b=d.padInfo.left,y=d.padInfo.top,v=d.outChannels/d.inChannels,x=n.data.get(s.dataId).values,k=new Kt(s.shape,s.dtype,x),T=n.data.get(a.dataId).values,C=new Kt(a.shape,a.dtype,T);for(let E=0;E<f;++E){let F=Math.max(0,Math.ceil((y-E)/p)),O=Math.min(d.outHeight,(d.inHeight+y-E)/p);for(let D=0;D<m;++D){let R=Math.max(0,Math.ceil((b-D)/h)),_=Math.min(d.outWidth,(d.inWidth+b-D)/h);for(let L=0;L<d.outChannels;++L){let U=Math.trunc(L/v),j=L%v,K=0;for(let q=0;q<d.batchSize;++q)for(let Q=F;Q<O;++Q){let ee=E+Q*p-y;for(let re=R;re<_;++re){let se=D+re*h-b;K+=k.get(q,ee,se,U)*C.get(q,Q,re,L)}}g.set(K,E,D,U,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var RK={kernelName:Ih,backendName:"cpu",kernelFunc:DK};function PK(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=r;we([s,a],"depthwiseConv2DNativeBackpropInput");let d=w.computeStrides(s.shape),p=w.computeStrides(a.shape),h=N.computeConv2DInfo(c,a.shape,o,i,u,l,!0),f=new Kt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(s.dataId).values,[x,k,T]=d,C=n.data.get(a.dataId).values,[E,F,O]=p,{batchSize:D,filterHeight:R,filterWidth:_,inChannels:L,inHeight:U,inWidth:j,outChannels:K,outHeight:q,outWidth:Q,strideHeight:ee,strideWidth:re}=h,se=R-1-h.padInfo.top,ne=_-1-h.padInfo.left,ie=K/L;for(let te=0;te<D;++te)for(let pe=0;pe<L;++pe)for(let be=0;be<U;++be){let Ce=be-se,Ie=Math.max(0,Math.ceil(Ce/ee)),Ne=Math.min(q,(R+Ce)/ee);for(let Le=0;Le<j;++Le){let Je=Le-ne,qe=Math.max(0,Math.ceil(Je/re)),Ge=Math.min(Q,(_+Je)/re),lt=0;for(let et=Ie;et<Ne;++et){let pt=et*ee-Ce;for(let Ct=qe;Ct<Ge;++Ct){let 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r=w.sizeFromShape(e.shape),s=n.data.get(e.dataId),a=n.data.get(s.complexTensorInfos.real.dataId).values,o=n.data.get(s.complexTensorInfos.imag.dataId).values;if(s8(r)){let i=Gw(a,o,r,t,n),u=[e.shape[0],e.shape[1]];if(t){let l=n.makeTensorInfo(u,"float32",i.real),c=n.makeTensorInfo(u,"float32",i.imag),d=n.makeTensorInfo([],"float32",w.createScalarValue(r,"float32")),p=bs({inputs:{x:d},backend:n}),h=Uw.kernelFunc({inputs:{a:l,b:d},backend:n}),f=Uw.kernelFunc({inputs:{a:c,b:p},backend:n}),m=n.data.get(h.dataId).values,g=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),{real:m,imag:g}}return i}else{let i=N.mergeRealAndImagArrays(a,o),u=a8(i,r,t);return N.splitRealAndImagArrays(u)}}function s8(e){return(e&e-1)==0}function Gw(e,t,n,r,s){if(n===1)return{real:e,imag:t};let 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      bool isnan_custom(float val) {
        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
      }

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

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

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

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:s,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:u,defineRound:l}}function su(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / ${s}`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${s}`:`index -= ${e[a]} * ${s}`;return`${o}; ${i};`}).join("")}function Nm(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function IY(e,t){let n=e.length,r=e.map(a=>`${t}[${a}]`),s=new Array(n-1);s[n-2]=r[n-1];for(let a=n-3;a>=0;--a)s[a]=`(${s[a+1]} * ${r[a+1]})`;return s}function SY(e,t,n="index"){let r=e.map((a,o)=>o),s=IY(r,t);return s.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${s[o]}`,u=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${s[o]}`:`index -= ${e[o]} * ${s[o]}`;return`${i}; ${u};`}).join("")}function Zw(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 Jw(){return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`}var ON=`
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;

  lowp vec4 encode_float(highp float v) {
    if (isnan(v)) {
      return vec4(255, 255, 255, 255);
    }

    highp float av = abs(v);

    if(av < FLOAT_MIN) {
      return vec4(0.0, 0.0, 0.0, 0.0);
    } else if(v > FLOAT_MAX) {
      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
    } else if(v < -FLOAT_MAX) {
      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
    }

    highp vec4 c = vec4(0,0,0,0);

    highp float e = floor(log2(av));
    highp float m = exp2(fract(log2(av))) - 1.0;

    c[2] = floor(128.0 * m);
    m -= c[2] / 128.0;
    c[1] = floor(32768.0 * m);
    m -= c[1] / 32768.0;
    c[0] = floor(8388608.0 * m);

    highp float ebias = e + 127.0;
    c[3] = floor(ebias / 2.0);
    ebias -= c[3] * 2.0;
    c[2] += floor(ebias) * 128.0;

    c[3] += 128.0 * step(0.0, -v);

    return c / 255.0;
  }
`,{getBroadcastDims:MN}=N;function CY(e,t,n){let r=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=ek(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:r.push(`uniform int ${h.name}Shape;`);break;case 2:r.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:r.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:r.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}r.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:r.push("uniform int outShape;");break;case 2:r.push("uniform ivec2 outShape;"),r.push("uniform int outShapeStrides;");break;case 3:r.push("uniform ivec3 outShape;"),r.push("uniform ivec2 outShapeStrides;");break;case 4:r.push("uniform ivec4 outShape;"),r.push("uniform ivec3 outShapeStrides;");break;default:break}r.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{r.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let s=r.join(`
`),a=e.map(h=>TY(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=$n(),u=EY(i),l,c,d=FY(i);return t.isPacked?(l=NY(t.logicalShape,o,n.enableShapeUniforms),c=$Y(i)):(l=_Y(t.logicalShape,o,n.enableShapeUniforms),c=AY(i)),n.packedInputs&&(d+=OY),[d,u,c,s,l,a,n.userCode].join(`
`)}function Gc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return KY(e,t);case 1:return YY(e,t);case 2:return ZY(e,t);case 3:return e9(e,t);case 4:return n9(e,t);case 5:return r9(e);case 6:return s9(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function LN(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return qY(e);case 1:return XY(e,t);case 2:return QY(e,t);case 3:return JY(e,t);default:return t9(e,t)}}function TY(e,t,n=!1,r){let s="";n?s+=LN(e,r):s+=Gc(e,r);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?s+=a9(e,t):s+=o9(e,t)),s}function NY(e,t,n){switch(e.length){case 0:return BN();case 1:return MY(e,t,n);case 2:return HY(e,t,n);case 3:return BY(e,t,n);default:return WY(e,t,n)}}function _Y(e,t,n){switch(e.length){case 0:return BN();case 1:return LY(e,t,n);case 2:return jY(e,t,n);case 3:return zY(e,t,n);case 4:return VY(e,t,n);case 5:return UY(e,t);case 6:return GY(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function EY(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function AY(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function $Y(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function FY(e){return`${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);
    }

    ${DY}
    ${RY}
    ${PY}
  `}var DY=`
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);
}
`,RY=`
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);
}
`,PY=`
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);
}
`,OY=`
  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 BN(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function MY(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return r[0]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${r[1]}.0);
      }
    `:r[1]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${r[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${r[0]}, ${r[1]}));
      return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);
    }
  `}function LY(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 BY(e,t,n){if(n)return`
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

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

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

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

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

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

      return ivec3(b, r, c);
    }
  `}function zY(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;
    ${Nm(["r","c","d"],e)}
    return ivec3(r, c, d);
  }
`;let r=su(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${r}
      return ivec3(r, c, d);
    }
  `}function WY(e,t,n){if(n)return`
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

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

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

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

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

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

      ${i}

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

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

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

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

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function GY(e,t){let n=su(["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 HY(e,t,n){let r=[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(${r[0]}, ${r[1]}));
      }
    `;let s=Math.ceil(e[1]/2);return n?`
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));

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

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

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

      return ivec2(r, c);
    }
  `}function jY(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 * ${e[1]};
      return ivec2(r, c);
    }
  `}function au(e){return`offset${e}`}function qY(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=$n();return`
    vec4 ${n}() {
      return ${r.texture2D}(${t}, halfCR);
    }
  `}function KY(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${r}() {return ${n};}`;let[s,a]=e.shapeInfo.texShape;if(s===1&&a===1)return`
      float ${r}() {
        return sampleTexture(${n}, halfCR);
      }
    `;let o=au(n);if(t)return`
    float ${r}() {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
      return sampleTexture(${n}, uv);
    }
  `;let[i,u]=e.shapeInfo.texShape;return`
    float ${r}() {
      vec2 uv = uvFromFlat(${i}, ${u}, ${o});
      return sampleTexture(${n}, uv);
    }
  `}function XY(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,a=$n();if(t)return`
    vec4 ${r}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${a.texture2D}(${n}, uv);
    }
  `;let o=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];return`
    vec4 ${r}(int index) {
      vec2 uv = packedUVfrom1D(
        ${o[0]}, ${o[1]}, index);
      return ${a.texture2D}(${n}, uv);
    }
  `}function YY(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
      float ${r}(int index) {
        ${Hc(e)}
      }
    `;let s=e.shapeInfo.texShape,a=s[0],o=s[1];if(o===1&&a===1)return`
      float ${r}(int index) {
        return sampleTexture(${n}, halfCR);
      }
    `;let i=au(n);return o===1?t?`
      float ${r}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${r}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
        return sampleTexture(${n}, uv);
      }
    `:a===1?t?`
      float ${r}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${r}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
        return sampleTexture(${n}, uv);
      }
    `:t?`
    float ${r}(int index) {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
      return sampleTexture(${n}, uv);
    }
  `:`
    float ${r}(int index) {
      vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
      return sampleTexture(${n}, uv);
    }
  `}function QY(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],u=$n();if(a!=null&&w.arraysEqual(n,a))return t?`
      vec4 ${s}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);

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

        return ${u.texture2D}(${r}, uv);
      }
    `;if(t)return`
    vec4 ${s}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${r}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${u.texture2D}(${r}, uv);
    }
  `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`
    vec4 ${s}(int row, int col) {
      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
      return ${u.texture2D}(${r}, uv);
    }
  `}function ZY(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
      float ${s}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `;let p=a[0],h=a[1];return`
    float ${s}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
      return sampleTexture(${r}, uv);
    }
  `}let{newShape:o,keptDims:i}=w.squeezeShape(n),u=o;if(u.length<n.length){let p=jc(e,u),h=["row","col"];return`
      ${Gc(p,t)}
      float ${s}(int row, int col) {
        return ${s}(${qc(h,i)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${Hc(e)}
      }
    `;let l=a[0],c=a[1],d=au(r);return c===1?t?`
      float ${s}(int row, int col) {
        float index = dot(vec3(row, col, ${d}), vec3(${r}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${r}TexShape[0]));
        return sampleTexture(${r}, uv);
      }
    `:`
    float ${s}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
      return sampleTexture(${r}, uv);
    }
  `:l===1?t?`
      float ${s}(int row, int col) {
        float index = dot(vec3(row, col, ${d}), vec3(${r}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${r}TexShape[1]), 0.5);
        return sampleTexture(${r}, uv);
      }
    `:`
    float ${s}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
      return sampleTexture(${r}, uv);
    }
  `:t?`
      float ${s}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${r}Shape[1] + col + ${d};
        vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
        return sampleTexture(${r}, uv);
      }
    `:`
  float ${s}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${n[1]} + col + ${d};
    vec2 uv = uvFromFlat(${l}, ${c}, index);
    return sampleTexture(${r}, uv);
  }
`}function JY(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=jc(e,p),m=["b","row","col"];return`
        ${LN(f,t)}
        vec4 ${s}(int b, int row, int col) {
          return ${s}(${qc(m,h)});
        }
      `}let i=$n();if(t)return`
    vec4 ${s}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${r}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${i.texture2D}(${r}, uv);
    }
  `;let u=o[0],l=o[1],c=Math.ceil(n[2]/2),d=c*Math.ceil(n[1]/2);return`
    vec4 ${s}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${u}, ${l}, ${d}, ${c}, b, row, col);
      return ${i.texture2D}(${r}, uv);
    }
  `}function e9(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:u}=w.squeezeShape(n),l=i;if(l.length<n.length){let m=jc(e,l),g=["row","col","depth"];return`
        ${Gc(m,t)}
        float ${s}(int row, int col, int depth) {
          return ${s}(${qc(g,u)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${a}, ${o}, 1)));
        ${Hc(e)}
      }
    `;let c=e.shapeInfo.texShape,d=c[0],p=c[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
      float ${s}(int row, int col, int depth) {
        int stride1 = ${r}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `:`
        float ${s}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${o}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${p}.0, ${d}.0);
          return sampleTexture(${r}, uv);
        }
      `;if(p===o&&h==null)return t?`
      float ${s}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${r}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `:`
    float ${s}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
      return sampleTexture(${r}, uv);
    }
  `;let f=au(r);return t?`
    float ${s}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${r}Shape[1] * ${r}Shape[2];
      int stride1 = ${r}Shape[2];
      int index = row * ${a} + col * ${o} + depth + ${f};
      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
      return sampleTexture(${r}, uv);
    }
    `:`
      float ${s}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${a} + col * ${o} + depth + ${f};
        vec2 uv = uvFromFlat(${d}, ${p}, index);
        return sampleTexture(${r}, uv);
      }
  `}function t9(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=$n();if(t)return`
    vec4 ${r}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${n}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${s.texture2D}(${n}, uv);
    }
  `;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],l=u[0],c=u[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
    vec4 ${r}(${h}) {
      int index = ${f};
      int texR = index / ${c};
      int texC = index - texR * ${c};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
      return ${s.texture2D}(${n}, uv);
    }
  `}function n9(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=jc(e,u),v=["row","col","depth","depth2"];return`
      ${Gc(y,t)}
      float ${s}(int row, int col, int depth, int depth2) {
        return ${s}(${qc(v,l)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${i}, ${o}, ${a}, 1)));
        ${Hc(e)}
      }
    `;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${r}Shape[3];`,m=`int stride1 = ${r}Shape[2] * stride2;`,g=`int stride0 = ${r}Shape[1] * stride1;`;if(h===i&&c==null)return t?`
      float ${s}(int row, int col, int depth, int depth2) {
        ${f}
        ${m}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `:`
      float ${s}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${o}, ${a}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${p}.0);
        return sampleTexture(${r}, uv);
      }
    `;if(h===a&&c==null)return t?`
      float ${s}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${r}Shape[1] * ${r}Shape[2], ${r}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `:`
      float ${s}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${n[1]*n[2]}, ${n[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${h}.0, ${p}.0);
        return sampleTexture(${r}, uv);
      }
    `;let b=au(r);return t?`
    float ${s}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${f}
      ${m}
      ${g}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${b});
      return sampleTexture(${r}, uv);
    }
  `:`
    float ${s}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${o} +
          depth * ${a} + depth2;
      vec2 uv = uvFromFlat(${p}, ${h}, index + ${b});
      return sampleTexture(${r}, uv);
    }
  `}function r9(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[4],a=t[3]*s,o=t[2]*a,i=t[1]*o,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=jc(e,u),g=["row","col","depth","depth2","depth3"];return`
      ${Gc(m)}
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        return ${r}(${qc(g,l)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${i}, ${o}, ${a}, ${s})) +
          depth3;
        ${Hc(e)}
      }
    `;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&c==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${o}, ${a}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(h===s&&c==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${h}.0, ${p}.0);
        return sampleTexture(${n}, uv);
      }
    `;let f=au(n);return`
    float ${r}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${o} + depth * ${a} +
          depth2 * ${s} + depth3 + ${f};
      vec2 uv = uvFromFlat(${p}, ${h}, index);
      return sampleTexture(${n}, uv);
    }
  `}function s9(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:s,keptDims:a}=w.squeezeShape(t);if(s.length<t.length){let g=jc(e,s),b=["row","col","depth","depth2","depth3","depth4"];return`
      ${Gc(g)}
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${r}(${qc(b,a)});
      }
    `}let o=t[5],i=t[4]*o,u=t[3]*i,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${l}, ${u}, ${i})) +
          dot(
            vec2(depth3, depth4),
            vec2(${o}, 1)));
        ${Hc(e)}
      }
    `;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===c&&d==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${l}, ${u}, ${i}, ${o})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(f===o&&d==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;let m=au(n);return`
    float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${c} + col * ${l} + depth * ${u} +
          depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
      vec2 uv = uvFromFlat(${h}, ${f}, index);
      return sampleTexture(${n}, uv);
    }
  `}function Hc(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
    for (int i = 0; i < ${n}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function a9(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=MN(e.shapeInfo.logicalShape,t.logicalShape),u=gt(o),l=o-a,c,d=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${d[y+l]} = 0;`).join(`
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((y,v)=>`coords.${d[v+l]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(m&&!b)o===1?h=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:h=`
        return vec4(outputValue.x);
      `;else if(i.length){let y=a-2,v=a-1;i.indexOf(y)>-1&&i.indexOf(v)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${s}() {
      ${u} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${r}(${p});
      ${h}
    }
  `}function o9(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`
      float ${s}() {
        return sampleTexture(${n}, resultUV);
      }
    `;let l=gt(u),c=MN(e.shapeInfo.logicalShape,t.logicalShape),d=u-i,p,h=["x","y","z","w","u","v"];i===0?p="":u<2&&c.length>=1?p="coords = 0;":p=c.map(m=>`coords.${h[m+d]} = 0;`).join(`
`);let f="";return u<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
    float ${s}() {
      ${l} coords = getOutputCoords();
      ${p}
      return get${r}(${f});
    }
  `}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function ek(e,t,n){let{newShape:r,keptDims:s}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):r,u=!e&&a>1&&!w.arraysEqual(t,n)&&r.length<a||o;return{useSqueezeShape:u,uniformShape:u?i:t,keptDims:s}}function jc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function qc(e,t){return t.map(n=>e[n]).join(", ")}function i9(e,t,n,r){let s=n.map((x,k)=>{let T={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(T.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[k],shapeInfo:T}}),a=s.map(x=>x.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},i=CY(s,o,t),u=mN(e.gl,i),l=e.createProgram(u),c=null,d=e.getUniformLocation(l,"NAN",!1);X().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(l,"INFINITY",!1));let p=!1,h={},f={},m={};for(let x=0;x<t.variableNames.length;x++){let k=t.variableNames[x];h[k]=e.getUniformLocation(l,k,p),h[`offset${k}`]=e.getUniformLocation(l,`offset${k}`,p),t.enableShapeUniforms&&(f[`${k}Shape`]=e.getUniformLocation(l,`${k}Shape`,p),m[`${k}TexShape`]=e.getUniformLocation(l,`${k}TexShape`,p))}let g,b,y;t.enableShapeUniforms&&(g=e.getUniformLocation(l,"outShape",p),y=e.getUniformLocation(l,"outShapeStrides",p),b=e.getUniformLocation(l,"outTexShape",p));let v=[];return t.customUniforms&&t.customUniforms.forEach((x,k)=>{v[k]=e.getUniformLocation(l,x.name,p)}),{program:t,fragmentShader:u,source:i,webGLProgram:l,uniformLocations:h,customUniformLocations:v,inShapeInfos:a,outShapeInfo:o,infLoc:c,nanLoc:d,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:y,outTexShapeLocation:b}}function zN(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let s=n.logicalShape,a=t[r],o=a.shape;if(!w.arraysEqual(s,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${s} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${u} must match`)})}function u9(e,t,n,r,s){t.program.enableShapeUniforms||(zN(t.inShapeInfos,n),zN([t.outShapeInfo],[r]));let a=r.texData.texture,o=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],d=t.uniformLocations[c],p=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=ek(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),d!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(d,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}u.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture,d,l)}});let i=t.outShapeLocation;if(i)switch(r.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(r.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(r.shape);switch(r.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&s&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],d=s[l];if(u.type==="float")e.gl.uniform1fv(c,d);else if(u.type==="vec2")e.gl.uniform2fv(c,d);else if(u.type==="vec3")e.gl.uniform3fv(c,d);else if(u.type==="vec4")e.gl.uniform4fv(c,d);else if(u.type==="int")e.gl.uniform1iv(c,d);else if(u.type==="ivec2")e.gl.uniform2iv(c,d);else if(u.type==="ivec3")e.gl.uniform3iv(c,d);else if(u.type==="ivec4")e.gl.uniform4iv(c,d);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function c9(e,t,n){let r="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let u=o.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:d}=ek(e.packedInputs,o.shape,u),p="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];p=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,u),b=w.sizeFromShape(o.shape)===1,y=N.getBroadcastDims(o.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;r+=`${m}_${v}_${l?d:""}_${c.length}_${b}_${y}_${g}_${p}_${h}_${f}_${x}_${i}`}else{let u=o.isUniform?"uniform":o.texData.texShape;r+=`${o.shape}_${u}_${i}`}});let s=e.userCode,a=e.constructor.name;return a+="_"+r+"_"+s+`${X().getNumber("WEBGL_VERSION")}`,a}function qn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var l9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=np.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=$n();this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Nm(["r","c","d"],e):su(["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);
        }

        ${t.output} = result;
      }
    `}},d9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=np.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=$n();this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Nm(["r","c","d"],e):su(["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));
        }

        ${t.output} = result;
      }
    `}},p9=class{constructor(e){this.variableNames=["A"],this.outTexUsage=wr.DOWNLOAD;let t=$n();this.outputShape=e,this.userCode=`
      ${ON}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}},h9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=wr.DOWNLOAD;let t=$n();this.outputShape=e,this.userCode=`
      ${ON}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},f9=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=$n();this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)"),this.userCode=`
      ${this.enableShapeUniforms?Jw():Zw(e)}

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

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

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

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

        float result;

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

        ${n.output} = vec4(${r}, 0., 0., 0.);
      }
    `}},m9=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=$n();this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let r="",s="result";t&&(s="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;r+=`
          localCoords = coords;
          if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
          localCoords[2] += ${o};
          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
            localCoords[1] += ${a};

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

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

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

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

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

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

          ${r}

          ${n.output} = ${s};
        }
    `}},WN={};Ee(WN,{bindVertexProgramAttributeStreams:()=>YN,createBufferFromOutputTexture:()=>JN,createFloat16MatrixTexture:()=>jN,createFloat16PackedMatrixTexture:()=>XN,createFloat32MatrixTexture:()=>HN,createIndexBuffer:()=>GN,createPackedMatrixTexture:()=>KN,createUnsignedBytesMatrixTexture:()=>qN,createVertexBuffer:()=>UN,createVertexShader:()=>VN,downloadByteEncodedFloatMatrixFromOutputTexture:()=>t_,downloadFloat32MatrixFromBuffer:()=>e_,downloadMatrixFromPackedOutputTexture:()=>r_,downloadPackedMatrixFromBuffer:()=>n_,getInternalFormatForFloat16MatrixTexture:()=>nk,getInternalFormatForFloat16PackedMatrixTexture:()=>ak,getInternalFormatForFloat32MatrixTexture:()=>tk,getInternalFormatForPackedMatrixTexture:()=>sk,getInternalFormatForUnsignedBytesMatrixTexture:()=>rk,uploadDenseMatrixToTexture:()=>QN,uploadPixelDataToTexture:()=>ZN});function VN(e){let t=$n(),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 fN(e,n)}function UN(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 yN(e,t)}function GN(e){let t=new Uint16Array([0,1,2,2,1,3]);return vN(e,t)}function ip(e,t,n,r,s,a){wN(t,n);let o=xN(e),i=e.TEXTURE_2D;return ge(e,()=>e.bindTexture(i,o)),ge(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),ge(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),X().getNumber("WEBGL_VERSION")===1?ge(e,()=>e.texImage2D(i,0,r,t,n,0,s,a,null)):ge(e,()=>e.texStorage2D(i,1,r,t,n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function tk(e){return e.internalFormatFloat}function HN(e,t,n,r){let[s,a]=rp(t,n);return ip(e,s,a,tk(r),r.textureFormatFloat,e.FLOAT)}function nk(e){return e.internalFormatHalfFloat}function jN(e,t,n,r){let[s,a]=rp(t,n);return ip(e,s,a,nk(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function rk(e){return e.downloadTextureFormat}function qN(e,t,n,r){let[s,a]=rp(t,n);return ip(e,s,a,rk(r),e.RGBA,e.UNSIGNED_BYTE)}function sk(e){return e.internalFormatPackedFloat}function KN(e,t,n,r){let[s,a]=Vc(t,n);return ip(e,s,a,sk(r),e.RGBA,e.FLOAT)}function ak(e){return e.internalFormatPackedHalfFloat}function XN(e,t,n,r){let[s,a]=Vc(t,n);return ip(e,s,a,ak(r),e.RGBA,r.textureTypeHalfFloat)}function YN(e,t,n){let r=0,s=3*4,a=3*4+2*4;return ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Kw(e,t,"clipSpacePos",n,3,a,r)&&Kw(e,t,"uv",n,2,a,s)}function QN(e,t,n,r,s,a){ge(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,u;s instanceof Uint8Array?(o=new Uint8Array(n*r*4),i=e.UNSIGNED_BYTE,u=e.RGBA):(o=new Float32Array(n*r*4),i=e.FLOAT,u=a.internalFormatPackedFloat),o.set(s),X().getNumber("WEBGL_VERSION")===2?ge(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,e.RGBA,i,o)):ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,r,0,e.RGBA,i,o)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function ZN(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)),e.flush()):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)),e.flush()):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 JN(e,t,n,r){let s=e.createBuffer();ge(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,s));let i=4*4*t*n;return ge(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,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)),s}function e_(e,t,n){let r=e,s=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,s),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),s}function t_(e,t,n,r){let[s,a]=rp(t,n),o=4,i=new Uint8Array(lY(t*n,o));return ge(e,()=>e.readPixels(0,0,s,a,r.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function n_(e,t,n,r,s,a,o,i){let u=e,l=new Float32Array(dY(a,o));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function r_(e,t,n){let r=new Float32Array(t*n*4);return ge(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var s_=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,dN(t,e)):this.gl=ys(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(X().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=sp(this.gl,s),kr(this.gl,a))this.textureHalfFloatExtension=sp(this.gl,a);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),kr(this.gl,r))this.colorBufferHalfFloatExtension=sp(this.gl,r);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",kr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(kr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=UN(this.gl),this.indexBuffer=GN(this.gl),this.framebuffer=kN(this.gl),this.textureConfig=qw(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 is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ge(e,()=>e.finish()),ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ge(e,()=>e.deleteFramebuffer(this.framebuffer)),ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ge(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ge(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),HN(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),jN(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),qN(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),ZN(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),QN(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),XN(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),KN(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Xw(this.gl,this.framebuffer),this.outputTexture=null),ge(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>t_(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,s,a){return n_(this.gl,e,t,n,r,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return e_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=JN(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(X().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,s=r.fenceSync(r.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)),bN(t,n),this.debug&&wm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=YN(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&&wm(this.gl,this.program),ge(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?SN(this.gl,e,t):CN(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(),TN(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,s]=Vc(t,n);this.setOutputMatrixTextureDriver(e,r,s)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&wm(this.gl,this.program),ap(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=sp(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,r=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(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,r=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=g9(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(),km(this.gl,e,this.framebuffer),this.debug&&ap(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(km(this.gl,this.outputTexture,this.framebuffer),this.debug&&ap(this.gl)):Xw(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;km(r,e,this.framebuffer),this.debug&&ap(r),this.outputTexture=e,ge(r,()=>r.viewport(0,0,t,n)),ge(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),ge(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function g9(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:b9,bincountImpl:a_,bincountReduceImpl:y9,ceilImpl:v9,concatImpl:x9,equalImpl:w9,expImpl:k9,expm1Impl:I9,floorImpl:S9,gatherNdImpl:C9,gatherV2Impl:T9,greaterImpl:N9,greaterEqualImpl:_9,lessImpl:E9,lessEqualImpl:A9,linSpaceImpl:$9,logImpl:F9,maxImpl:D9,maximumImpl:R9,minimumImpl:P9,multiplyImpl:O9,negImpl:M9,notEqualImpl:L9,prodImpl:B9,rangeImpl:z9,rsqrtImpl:W9,sigmoidImpl:V9,simpleAbsImpl:o_,sliceImpl:U9,sparseFillEmptyRowsImpl:G9,sparseReshapeImpl:H9,sparseSegmentReductionImpl:i_,sqrtImpl:j9,stridedSliceImpl:q9,stringNGramsImpl:K9,stringSplitImpl:X9,stringToHashBucketFastImpl:Y9,subImpl:Q9,tileImpl:Z9,topKImpl:J9,transposeImpl:ok,uniqueImpl:eQ}=fm;function u_(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Fn(e,t){return t===1?[e]:u_(e,t)}function tQ(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var nQ=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=Fn("rc",this.rank),n=gt(this.rank),r=this.getOutOfBoundsCondition(t),s=this.getSetup(t),a=this.getOutput(t);this.userCode=`
        void main() {
          ${n} rc = getOutputCoords();

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

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

      bool cEdge = cp1 >= ${n};
      bool rEdge = rp1 >= ${r};
    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc),
              rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1),
              0, 0`:`getA(${t[0]}),
            cEdge ? 0. : getA(${t[1]}),
            rEdge ? 0. : getA(${t[2]}),
            rEdge || cEdge ? 0. : getA(${t[3]})`}},c_=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 r=0;r<4;r++){let s="thisRC = rc;";r%2==1&&(s+="thisRC.z += 1;"),r>1&&(s+="thisRC.y += 1;"),n+=`
        ${s}
        ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
          int flatIndex = getFlatIndex(thisRC);

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

          result[${r}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${r>0?"}":""}
      `}this.userCode=`
      ${rQ(t,this.enableShapeUniforms)}
      ${this.enableShapeUniforms?Jw():Zw(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 rQ(e,t){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t?SY(["r","c","d"],"inputShape"):su(["r","c","d"],e)}
      return ivec3(r, c, d);
    }
  `}var sQ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=d_(t,n),s=p_(e,r,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=l_(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[s].shift();return this.usedTextures[s].push(i),i}let o;return r===pn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===pn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===pn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===pn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===pn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let s=d_(n,r),a=p_(t,s,r);a in this.freeTextures||(this.freeTextures[a]=[]);let o=l_(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,r),i=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function aQ(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function l_(e,t,n,r,s){let a=oQ(t,r),o;if(s){let[u,l]=Vc(e[0],e[1]);o=u*l}else{let[u,l]=rp(e[0],e[1]);o=u*l}let i=aQ(n,a);return o*i}function oQ(e,t){switch(e){case pn.PACKED_2X2_FLOAT32:return sk(t);case pn.PACKED_2X2_FLOAT16:return ak(t);case pn.UNPACKED_FLOAT32:return tk(t);case pn.UNPACKED_FLOAT16:return nk(t);case pn.PACKED_4X1_UNSIGNED_BYTE:return rk(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function iQ(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?pn.PACKED_2X2_FLOAT32:pn.UNPACKED_FLOAT32:e?pn.PACKED_2X2_FLOAT16:pn.UNPACKED_FLOAT16}function d_(e,t){if(e===wr.UPLOAD)return pn.PACKED_2X2_FLOAT32;if(e===wr.RENDER||e==null)return iQ(t);if(e===wr.DOWNLOAD||e===wr.PIXELS)return pn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function p_(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var To=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length),this.userCode=`
      float unaryOperation(float x) {
        ${t}
      }

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

        setOutput(y);
      }
    `}},ts="if (isnan(x)) return x;",uQ="return x;",h_="return abs(x);",cQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",lQ=ts+`
  return (x < 0.0) ? 0.0 : x;
`,dQ=ts+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,_m="return x;",pQ="return 1.0 / (1.0 + exp(-1.0 * x));",hQ="return x;",fQ=`
  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;
`,mQ=`
  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;
`,gQ=`
  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;
`,bQ="return 1.0 / (1.0 + exp(-1.0 * x));",Kc=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);
      }
    `}},yQ=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=Fn("rc",t),r=gt(t),s=tQ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
      void main() {
        ${r} rc = getOutputCoords();
        vec4 packedInput = getA(${s});

        setOutput(getChannel(packedInput, ${o}));
      }
    `}},vQ=Dr.whereImpl,xQ=1e-7,wQ=1e-4,Em={};function kQ(e){return e in Em||(Em[e]={}),Em[e]}var IQ=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),SQ=600;function CQ(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*SQ/1024/1024}var f_=class extends Mu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=ys(X().getNumber("WEBGL_VERSION"));this.binaryCache=kQ(X().getNumber("WEBGL_VERSION")),this.gpgpu=new s_(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new sQ(this.gpgpu),this.numMBBeforeWarning=CQ(),this.texData=new Ul(this,is())}nextDataId(){return f_.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:wr.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,s){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:wr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:s,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new Kc(o,_m):d=new To(o,_m);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:r}],r),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(r==="complex64"){let d=this.readSync(s.real.dataId),p=this.readSync(s.imag.dataId);c=N.mergeRealAndImagArrays(d,p)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:s,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(s!=null){let h;i?h=new Kc(r,_m):h=new To(r,_m);let f=this.runWebGLProgram(h,[{dataId:e,shape:r,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture,...xm(r))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=N.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;ge(h,()=>h.deleteBuffer(u))}let d=this.convertAndCacheOnCPU(e,c),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&is().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>w.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!pN(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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),s=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...xm(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(d),h}let a=X().getBool("WEBGL_PACK")&&r===!0,o=a?Im(t):t,i=a?new h9(o):new p9(o),u=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture,l.texShape[0],l.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(s);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return 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:r,usage:s,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,u=this.dataRefCount.get(i);u>1?this.dataRefCount.set(i,u-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,s,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=IQ){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return vQ(e.shape,t)}packedUnaryOp(e,t,n){let r=new Kc(e.shape,t),s=this.compileAndRun(r,[e],n);return is().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=o_(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,h_,e.dtype);let t=new To(e.shape,h_),n=this.compileAndRun(t,[e]);return is().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let s=n.map(a=>w.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return is().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new yQ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new nQ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[nu(e.shape),...ru(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[nu(t),...ru(t)],a=new c_(s,n),o=!0,i=[n],u=this.runWebGLProgram(a,[r],e.dtype,i,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=t,a=Im(r),o,i=xm(a);n?o=new d9(a):o=new l9(a);let u=!0,l=[i],c=this.runWebGLProgram(o,[{shape:a,dtype:s,dataId:e}],s,l,u);return{dtype:s,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,r,s=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===np.DENSE){let m=xm(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(a.shape)===0)return o.values=w.getTypedArrayFromDType(a.dtype,0),a;let i=[],u=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&w.sizeFromShape(m.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}if(this.uploadToGPU(m.dataId),!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!op(g.shape,m.shape)){let b=m,y=m.shape;m.shape=g.shape,m=this.packedReshape(m,y),i.push(m),g=this.texData.get(m.dataId),b.shape=y}return{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let l={shape:a.shape,texData:o,isUniform:!1},c=c9(e,u,l),d=this.getAndSaveBinary(c,()=>i9(this.gpgpu,e,u,l)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),u9(this.gpgpu,d,u,l,r),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=X().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=w.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&s===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,r,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=M(()=>{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?xQ:wQ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:s,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=EN(n,i),t.texShape=c),s!=null){let d=Im(n),p,h=c[1],f=c[0],m=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(i||!m)&&([h,f]=Vc(c[0],c[1])),i?p=new m9(d,m):p=new f9(d,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,r),y=this.texData.get(b.dataId);m?y.usage=wr.PIXELS:y.usage=wr.UPLOAD,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,s);let v=[[f,h]],x=!0,k=this.runWebGLProgram(p,[b],r,v,x),T=this.texData.get(k.dataId);t.texture=T.texture,t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,this.disposeIntermediateTensorInfo(b),this.texData.delete(k.dataId),t.values=null,u&&(this.uploadWaitMs+=w.now()-l)}else{let d=this.acquireTexture(c,o,r,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=TQ(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},ik=f_;ik.nextDataId=0;function TQ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var NQ="0.0.0";function m_(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}bc.isBrowser()&&wd("webgl",()=>new ik,2);var _Q={forceHalfFloat:m_},g_=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,Xc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.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));
      }
    `}},Am=`
  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;
`,up=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length;this.enableShapeUniforms=qn(s);let a="";if(r)if(s===0||w.sizeFromShape(this.outputShape)===1)a=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(a=`
          ${gt(s)} coords = getOutputCoords();
        `,s===1)this.enableShapeUniforms?a+=`
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `:a+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=Fn("coords",s);this.enableShapeUniforms?a+=`
            bool nextRowOutOfBounds =
              (${i[s-2]} + 1) >= outShape[${s} - 2];
            bool nextColOutOfBounds =
              (${i[s-1]} + 1) >= outShape[${s} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `:a+=`
            bool nextRowOutOfBounds =
              (${i[s-2]} + 1) >= ${this.outputShape[s-2]};
            bool nextColOutOfBounds =
              (${i[s-1]} + 1) >= ${this.outputShape[s-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

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

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

        setOutput(result);
      }
    `}};function cr(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var EQ={kernelName:_a,backendName:"webgl",kernelFunc:cr};function No(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(a.dataId),i=cr({inputs:{x:r},backend:n}),u=cr({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var AQ={kernelName:ql,backendName:"webgl",kernelFunc:No},b_="return (a < 0.) ? b * a : a;",y_=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function $Q(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new up(y_,s.shape,o.shape):new Xc(b_,s.shape,o.shape),u=n.runWebGLProgram(i,[s,o],"float32");return n.disposeIntermediateTensorInfo(o),u}var FQ={kernelName:ti,backendName:"webgl",kernelFunc:$Q},v_="return (a < 0.) ? b * a : a;",x_=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function DQ(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new up(x_,r.shape,s.shape):new Xc(v_,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],"float32")}var RQ={kernelName:za,backendName:"webgl",kernelFunc:DQ},w_="if (isnan(x)) return x;",PQ=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,OQ=`
  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 Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:s,backend:a})=>{let{x:o}=s,i=a,u=r||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,u);return i.makeTensorInfo(o.shape,u,p)}let l=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Kc(o.shape,t):c=new To(o.shape,e),i.runWebGLProgram(c,[o],u)}}function hn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:a}){return({inputs:o,backend:i})=>{let{a:u,b:l}=o,c=i;if(r&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},C={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new Xc(e,u.shape,l.shape);return c.runWebGLProgram(E,[T,C],In(x.dtype,k.dtype))}),y=No({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let d=a||In(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&s!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?N.fromUint8ToStringArray(f):f,b=u.dtype==="string"?N.fromUint8ToStringArray(m):m,[y,v]=s(u.shape,l.shape,g,b,d),x=c.makeTensorInfo(v,d),k=c.texData.get(x.dataId);return k.values=y,x}let p=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new up(t,u.shape,l.shape,n):h=new Xc(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],d)}}function $m(e,t=!1){if(e==="linear")return t?hQ:uQ;if(e==="relu")return t?mQ:lQ;if(e==="elu")return t?fQ:cQ;if(e==="relu6")return t?gQ:dQ;if(e==="prelu")return t?x_:v_;if(e==="leakyrelu")return t?y_:b_;if(e==="sigmoid")return t?bQ:pQ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var k_=class{constructor(e,t,n,r=!1,s=!1,a=!1,o=null,i=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=qn(this.outputShape.length);let l=r?e[1]:e[2],c=Math.ceil(l/2),d=r?"i * 2, rc.y":"rc.y, i * 2",p=s?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }`:u?m=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }`:m=`vec4 activation(vec4 x) {
          ${o}
        }`,g="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let y="rc.x",v="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${m}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${c}.0;

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

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

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

        ${b}

        ${g}

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

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}},C_="return a * b;";function uk(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=N.upcastType(r.dtype,s.dtype);if(r.dtype==="complex64"){let i=n.texData.get(r.dataId),u=n.texData.get(s.dataId),l=new S_(I_.REAL,r.shape,s.shape),c=new S_(I_.IMAG,r.shape,s.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:r.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:s.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:s.shape}],p=n.runWebGLProgram(l,d,"float32"),h=n.runWebGLProgram(c,d,"float32"),f=No({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([r,s])){let i=n.texData.get(r.dataId),u=n.texData.get(s.dataId),[l,c]=O9(r.shape,s.shape,i.values,u.values,a),d=n.makeTensorInfo(c,a),p=n.texData.get(d.dataId);return p.values=l,d}let o;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new up(C_,r.shape,s.shape):o=new Xc(C_,r.shape,s.shape),n.runWebGLProgram(o,[r,s],a)}var MQ={kernelName:Ma,backendName:"webgl",kernelFunc:uk};function LQ(e,t,n){let r=[nu(e.shape),...ru(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[nu(t),...ru(t)],o=new c_(a,r),i=!0,u=[r],l=n.runWebGLProgram(o,[s],e.dtype,u,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function fe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=n,i=w.sizeFromShape(s.shape),u=w.inferFromImplicitShape(a,i),l=w.sizeFromShape(u);w.assert(i===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${s.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(s.dataId);return c.isPacked&&!op(s.shape,u)&&!(c.texture!==null&&op(c.shape,u))?LQ(s,u,o):(o.incRef(s.dataId),{dataId:s.dataId,shape:u,dtype:s.dtype})}var BQ={kernelName:hi,backendName:"webgl",kernelFunc:fe},T_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o=Math.floor(n/4)*4,i=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";s%n>0&&(l=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

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

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

        float sumValue = 0.0;

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

          ${u}
        }

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

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

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

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

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

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

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

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

          ${d}
        }

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

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

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

          ${d}
        }
        setOutput(${u});
      }
    `}};function WQ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function ou(e,t,n,r){let s=WQ(e.shape),a=e;for(let o=0;o<s.length;o++){let{inSize:i,windowSize:u,outSize:l}=s[o],c,d;n==="mean"?c=o===0?new T_({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},i):new T_({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l}):c=new zQ({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},n),d=a,a=r.runWebGLProgram(c,[a],t),d.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(d)}return a}var VQ=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let r=gt(this.rank),s=UQ(t);this.userCode=`
    void main() {
      ${r} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `}};function UQ(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let s=0;s<e.length;s++)r[e[s]]=n[s];return r.join()}var GQ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=gt(this.rank),s=u_("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=s[l];let o=`vec2(${a.slice(-2).join()})`,i=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
    void main() {
      ${r} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${u};
      if(${i}) {
        result[1] = ${u};
      }
      --${s[this.rank-1]};
      if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${u};
        if(${i}) {
          result[3] = ${u};
        }
      }
      setOutput(result);
    }
    `}};function Fm(e,t,n){let r=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GQ(e.shape,t):new VQ(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function HQ(e,t,n,r){let s=t,a=e.shape.length,o=w.parseAxisParam(s,e.shape),i=o,u=N.getAxesPermutation(i,a),l=u!=null,c=e;l&&(c=Fm(e,u,r),i=N.getInnerMostAxes(i.length,a)),N.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=N.computeOutAndReduceShapes(c.shape,i),h=d;n&&(h=N.expandShapeToKeepDim(d,o));let f=w.sizeFromShape(p),g=w.sizeFromShape(e.shape)/f,b=fe({inputs:{x:c},attrs:{shape:[g,f]},backend:r}),y=bd(e.dtype),v=ou(b,y,"sum",r),x=fe({inputs:{x:v},attrs:{shape:h},backend:r});return r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(v),l&&r.disposeIntermediateTensorInfo(c),x}function Dm(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return HQ(s,a,o,n)}var jQ={kernelName:Ka,backendName:"webgl",kernelFunc:Dm};function Dn(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=s.shape[a[c]];let l;if(o.shouldExecuteOnCPU([s])){let d=o.texData.get(s.dataId).values,p=ok(d,s.shape,s.dtype,a,u);l=o.makeTensorInfo(u,s.dtype);let h=o.texData.get(l.dataId);h.values=p}else l=Fm(s,a,o);return l}var qQ={kernelName:Ja,backendName:"webgl",kernelFunc:Dn},N_=1e3;function Rm({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,d=n?e.shape[l-2]:e.shape[l-1],p=r?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Ri.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let k=n?[b,d,h]:[b,h,d],T=r?[y,f,p]:[y,p,f],C=fe({inputs:{x:e},backend:s,attrs:{shape:k}}),E=fe({inputs:{x:t},backend:s,attrs:{shape:T}}),F=[C,E],O=Math.max(b,y),D=n?C.shape[1]:C.shape[2],R=a!=null,_=o!=null,L=u==="leakyrelu",U=u!=null?$m(u,!0):null,j=R||_||L||U!=null,K;if((h===1||f===1)&&D>N_&&j===!1){let Q=C,ee=E;n&&(Q=Dn({inputs:{x:C},backend:s,attrs:{perm:[0,2,1]}}),F.push(Q)),r&&(ee=Dn({inputs:{x:E},backend:s,attrs:{perm:[0,2,1]}}),F.push(ee));let re=f!==1,se=f===1,ne=Q;re&&(ne=fe({inputs:{x:Q},backend:s,attrs:{shape:[O,D,1]}}),F.push(ne));let ie=f===1?2:1,te=ee;se&&(te=fe({inputs:{x:ee},backend:s,attrs:{shape:[O,1,D]}}),F.push(te));let pe=uk({inputs:{a:ne,b:te},backend:s});K=Dm({inputs:{x:pe},backend:s,attrs:{axis:ie,keepDims:!0}}),F.push(pe)}else{let Q=In(e.dtype,t.dtype),ee=new k_(k,T,[O,h,f],n,r,R,U,_,L),re=[C,E];if(a!=null&&re.push(a),_&&re.push(o),L){let se=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));re.push(se),F.push(se)}K=s.runWebGLProgram(ee,re,Q)}let q=fe({inputs:{x:K},backend:s,attrs:{shape:x}});F.push(K);for(let Q of F)s.disposeIntermediateTensorInfo(Q);return q}function KQ(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:d}=r;return Rm({a:s,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var XQ={kernelName:to,backendName:"webgl",kernelFunc:KQ},__="return abs(x);";function YQ(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let a=n.texData.get(r.dataId),o=o_(a.values);return n.makeTensorInfo(r.shape,r.dtype,o)}let s;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Kc(r.shape,__):s=new To(r.shape,__),n.runWebGLProgram(s,[r],r.dtype)}var QQ={kernelName:Vo,backendName:"webgl",kernelFunc:YQ},ZQ=ts+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,JQ=Ze({opSnippet:ZQ}),eZ={kernelName:Wu,backendName:"webgl",kernelFunc:JQ},tZ=ts+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,nZ=Ze({opSnippet:tZ}),rZ={kernelName:Vu,backendName:"webgl",kernelFunc:nZ},E_="return a + b;",sZ=hn({opSnippet:E_,packedOpSnippet:E_,supportsComplex:!0,cpuKernelImpl:b9}),aZ={kernelName:_s,backendName:"webgl",kernelFunc:sZ},oZ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let r=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

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

        vec4 result = ${r};
        setOutput(result);
      }
    `}};function Pm(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return cr({inputs:{x:r[0]},backend:n});if(r.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),l=Pm({inputs:r.slice(0,u),backend:n}),c=Pm({inputs:r.slice(u),backend:n});return Pm({inputs:[l,c],backend:n})}let s=r.map(u=>u.dtype).reduce((u,l)=>In(u,l)),a=r.map(u=>u.shape),i=X().getBool("WEBGL_PACK")?new iZ(r[0].shape,a):new oZ(r[0].shape,a);return n.runWebGLProgram(i,r,s)}var uZ={kernelName:la,backendName:"webgl",kernelFunc:Pm};function cZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=N.getAxesPermutation(l,i),d=s;c!=null&&(d=Dn({inputs:{x:s},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,i)),N.assertAxesAreInnerMostDims("all",l,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,l),f=w.sizeFromShape(h),m=fe({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=ou(m,m.dtype,"all",n),b;if(o){let y=N.expandShapeToKeepDim(p,u);b=fe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=fe({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),b}var lZ={kernelName:Uu,backendName:"webgl",kernelFunc:cZ};function dZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=N.getAxesPermutation(l,i),d=s;c!=null&&(d=Dn({inputs:{x:s},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,i)),N.assertAxesAreInnerMostDims("any",l,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,l),f=w.sizeFromShape(h),m=fe({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=ou(m,m.dtype,"any",n),b;if(o){let y=N.expandShapeToKeepDim(p,u);b=fe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=fe({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),b}var pZ={kernelName:Gu,backendName:"webgl",kernelFunc:dZ},hZ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${r};

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

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

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

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function A_(e,t,n,r=null){let s=t.shape[0],a=t.shape[1];r!=null&&(s=r.shape[0],a=r.shape[1]);let o=N.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:s,outSize:Math.ceil(a/o)},u=new hZ(i,n,r==null),l=[t];r!=null&&l.push(r);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let d=A_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function $_(e,t,n,r=null){let s=r!=null?r.shape:t.shape,a=s[s.length-1],o=N.computeOptimalWindowSize(a),i=new fZ(s,o,n,r==null),u=r==null?[t]:[t,r],l=e.runWebGLProgram(i,u,"int32");if(l.shape.length===t.shape.length){let c=$_(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function F_(e,t,n,r){let s=[n];if(N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),s,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,u=t;i&&(u=e.unpackTensor(t),a.push(u));let[l,c]=N.computeOutAndReduceShapes(u.shape,s),d=w.sizeFromShape(c),p=fe({inputs:{x:u},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=A_(e,p,r);a.push(h);let f=fe({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return $_(e,t,r)}function mZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),u=s,l=[];i!=null&&(u=Dn({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(u),o=N.getInnerMostAxes(o.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],u.shape.length);let c=F_(n,u,o[0],"max");return l.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var gZ={kernelName:da,backendName:"webgl",kernelFunc:mZ};function bZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),u=s,l=[];i!=null&&(u=Dn({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(u),o=N.getInnerMostAxes(o.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],u.shape.length);let c=F_(n,u,o[0],"min");return l.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var yZ={kernelName:Hu,backendName:"webgl",kernelFunc:bZ},vZ=ts+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,xZ=Ze({opSnippet:vZ}),wZ={kernelName:ju,backendName:"webgl",kernelFunc:xZ},kZ=ts+"return log(x + sqrt(x * x + 1.0));",IZ=Ze({opSnippet:kZ}),SZ={kernelName:qu,backendName:"webgl",kernelFunc:IZ},CZ=ts+`
  return atan(x);
`,TZ=Ze({opSnippet:CZ}),NZ={kernelName:Ku,backendName:"webgl",kernelFunc:TZ},_Z=PQ+`
  return atan(a, b);
`,EZ=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+OQ+`
  return result;
`,AZ=hn({opSnippet:_Z,packedOpSnippet:EZ}),$Z={kernelName:Yu,backendName:"webgl",kernelFunc:AZ},FZ=ts+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,DZ=Ze({opSnippet:FZ}),RZ={kernelName:Xu,backendName:"webgl",kernelFunc:DZ},cp=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
        const ivec2 strides = ivec2(${o}, ${i});
        const ivec2 pads = ivec2(${p}, ${h});

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

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

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

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

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

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

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

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

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

      float count = 0.0;

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

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

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

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

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

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

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

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

            ${T}
          }

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

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

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

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

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

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

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

          for (int wD = 0; wD < ${p};
              wD += ${l}) {
            int xD = xDCorner + wD;

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

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

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

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

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

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

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${F} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${r?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
                      wR * ${f} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let x="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / count");let T=Math.floor(a/4)*4,C=a%4,E=`
      if (${y}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${x}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${o}, ${i}, ${u});
      const ivec3 pads = ivec3(${m}, ${g}, ${b});
      const float initializationValue = ${v};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

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

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

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

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

        for (int wD = 0; wD < ${p};
            wD += ${l}) {
          int xD = xDCorner + wD;

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

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

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

            for (int wC = 0; wC < ${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)
              );

              ${E}
            }

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function WZ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=r,d=[1,1,1],p=N.computePool3DInfo(o.shape,i,u,d,l,c),h=new zZ(p);return n.runWebGLProgram(h,[s],o.dtype)}var VZ={kernelName:gh,backendName:"webgl",kernelFunc:WZ};function UZ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;Uc([s,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:l}=r,c=N.computePool2DInfo(o.shape,i,u,1,l),d=new BZ(c);return n.runWebGLProgram(d,[s],o.dtype)}var GZ={kernelName:mh,backendName:"webgl",kernelFunc:UZ};function HZ(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return Rm({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var jZ={kernelName:ha,backendName:"webgl",kernelFunc:HZ},qZ=class{constructor(e,t,n,r,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="0.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${o};
        float scale = ${i};
        float inv = scale * inversesqrt(variance + float(${a}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}},KZ=class{constructor(e,t,n,r,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${o};
        vec4 scale = ${i};

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

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

        setOutput((x - mean) * inv + offset);
      }
    `}},XZ=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:s,variance:a,offset:o,scale:i}=e;w.assert(s.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||s.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||s.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[r,s,a],c=null;o!=null&&(c=o.shape,l.push(o));let d=null;i!=null&&(d=i.shape,l.push(i));let p=X().getBool("WEBGL_PACK_NORMALIZATION")?new KZ(r.shape,s.shape,a.shape,c,d,u):new qZ(r.shape,s.shape,a.shape,c,d,u);return t.runWebGLProgram(p,l,l[0].dtype)},YZ={kernelName:Ta,backendName:"webgl",kernelFunc:XZ},QZ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=ZZ(this.rank),r,s=e.map((a,o)=>`sourceLoc.${lk[o]} = start[${o}] + coords.${lk[o]};`);r=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${s.join(`
`)}
      `,this.userCode=`
      void main() {
        ${r}
        setOutput(getSource(${n}));
      }
    `}},lk=["x","y","z","w","u","v"];function ZZ(e){if(e===1)return"sourceLoc";if(e<=6)return lk.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var JZ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),n=Fn("coords",this.rank),r=Fn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,a=`getChannel(getSource(${r.join()}), ${s})`,o=`
      result.x = ${a};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${r[this.rank-1]};
        result.y = ${a};
        --${r[this.rank-1]};
      }
    `,i=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${r[this.rank-2]};
        result.z = ${a};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${r[this.rank-1]};
          result.w = ${a};
        }
      }
    `,u=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((l,c)=>`start[${c}]`).join()});`:e.map((l,c)=>`${r[c]} = ${n[c]} + start[${c}];`).join(`
`);this.userCode=`
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${u}
        vec4 result = vec4(0.);
        ${o}
        ${i}
        setOutput(result);
      }
    `}};function eJ(e,t,n,r){let s=r.texData.get(e.dataId),a=r.makeTensorInfo(n,e.dtype),o=r.texData.get(a.dataId);Object.assign(o,s),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=$t.computeFlatOffset(t,w.computeStrides(e.shape));s.slice&&(i+=s.slice.flatOffset),o.slice={flatOffset:i,origDataId:s.slice&&s.slice.origDataId||e.dataId};let u=r.dataRefCount.get(o.slice.origDataId)||1;return r.dataRefCount.set(o.slice.origDataId,u+1),a}function Yc(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,u]=$t.parseSliceParams(s,a,o);if($t.assertParamsValid(s,i,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,s.dtype,[]);if(n.shouldExecuteOnCPU([s])||s.dtype==="string"){let d=n.texData.get(s.dataId),p=U9(d.values,i,u,s.shape,s.dtype);return n.makeTensorInfo(u,s.dtype,p)}let{isPacked:l}=n.texData.get(s.dataId),c=$t.isSliceContinous(s.shape,i,u);if(l||!c){let d=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new JZ(u):new QZ(u),p=[i];return n.runWebGLProgram(d,[s],s.dtype,p)}return n.uploadToGPU(s.dataId),eJ(s,i,u,n)}var tJ={kernelName:yi,backendName:"webgl",kernelFunc:Yc},nJ=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;w.assert(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,v)=>y*v),u=N.getReshaped(s.shape,a,i),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(s.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(c,o,a.length),h=[],f=fe({inputs:{x:s},backend:n,attrs:{shape:u}}),m=Dn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=fe({inputs:{x:m},backend:n,attrs:{shape:c}}),b=Yc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},rJ={kernelName:Uo,backendName:"webgl",kernelFunc:nJ};function sJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.readSync(s.dataId),u=n.readSync(a.dataId),l=a_(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var aJ={kernelName:bh,backendName:"webgl",kernelFunc:sJ};function oJ(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.readSync(r.dataId),o=n.readSync(s.dataId),i=N.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var iJ={kernelName:yh,backendName:"webgl",kernelFunc:oJ},uJ="return float(a != b);",D_=hn({opSnippet:uJ,cpuKernelImpl:L9,dtype:"bool"}),cJ={kernelName:oi,backendName:"webgl",kernelFunc:D_};function lp(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return cr({inputs:{x:s.complexTensorInfos.real},backend:n})}var lJ={kernelName:nd,backendName:"webgl",kernelFunc:lp},dJ="return float(int(x));";function pJ(e,t){let n=new To(e.shape,dJ),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function dk(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return cr({inputs:{x:s},backend:n});let o=kt(s.shape),i=dk({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),u=No({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),u}if(s.dtype==="complex64"){let o=lp({inputs:{input:s},backend:n}),i=dk({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(s.dtype,a)){let o=cr({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return pJ(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=D_({inputs:{a:s,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),u}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var hJ={kernelName:fa,backendName:"webgl",kernelFunc:dk},R_="return ceil(x);",fJ=Ze({opSnippet:R_,packedOpSnippet:R_,cpuKernelImpl:v9}),mJ={kernelName:ma,backendName:"webgl",kernelFunc:fJ},gJ=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));
      }
    `}},bJ=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 yJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i;X().getBool("WEBGL_PACK_CLIP")?i=new bJ(s.shape):i=new gJ(s.shape);let u=[[a],[o]];return n.runWebGLProgram(i,[s],s.dtype,u)}var vJ={kernelName:Es,backendName:"webgl",kernelFunc:yJ},xJ=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 P_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function wJ(e){let{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),a=new xJ(r.shape),o=[P_(r,s.complexTensorInfos.real),P_(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var kJ={kernelName:Kl,backendName:"webgl",kernelFunc:wJ},IJ=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let r=t.length,s=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${s}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

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

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

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

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

        ${a[r-1]} = ${a[r-1]} - 1;
        if (${a[r-2]} < ${n[r-2]} &&
            ${a[r-1]} < ${n[r-1]}) {
          result.b = getValue(${a});
        }
        setOutput(result);
      }
    `}};function Om(e,t,n){let r=e.indexOf(t);return e.map((a,o)=>o===r?`${a} - ${n}`:a).join()}function Mm(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return cr({inputs:{x:s.complexTensorInfos.imag},backend:n})}var CJ={kernelName:Zl,backendName:"webgl",kernelFunc:Mm};function Qc(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(m=>lp({inputs:{input:m},backend:n})),d=e.map(m=>Mm({inputs:{input:m},backend:n})),p=Qc(c,t,n),h=Qc(d,t,n),f=No({inputs:{real:p,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return fe({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),d=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),p=N.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=x9(d,p,r,h),m=N.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,r,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),d=Qc(e.slice(0,c),t,n),p=Qc(e.slice(c),t,n),h=Qc([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new SJ(e.map(d=>d.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:o}=TJ(e,t,n),i=new IJ(a.map(c=>c.shape)),u=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=fe({inputs:{x:u},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(u),l}function TJ(e,t,n){let r=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>fe({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function O_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=N.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(l=>w.sizeFromShape(l.shape)>0);if(i.length===1)return cr({inputs:{x:i[0]},backend:n});let u=i.map(l=>l.shape);return N.assertParamsConsistent(u,a),Qc(i,a,n)}var NJ={kernelName:Go,backendName:"webgl",kernelFunc:O_},M_=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(r?v=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?v=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:v=`
          float activation(float x) {
            ${n}
          }
        `,x="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${v}

      const ivec2 strides = ivec2(${i}, ${u});
      const ivec2 pads = ivec2(${a}, ${o});

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

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

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

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

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

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

            for (int d1 = 0; d1 < ${h}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

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

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

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

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

              if (${m}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${h}),
                  getX(batch, xR, xC, ${h} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${h}, xR, xC),
                  getX(batch, ${h} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

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

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

            }
          }
        }

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

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d2 = coords.u;

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

        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
        // y(yF, yR, yC, d2). ? = to be determined. : = across all
        // values in that axis.
        float dotProd = 0.0;
        for (int wF = 0; wF < ${c}; wF++) {
          int xF = xFCorner + wF * ${i};

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

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

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

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

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

              for (int d1 = 0; d1 < ${h}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${f===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${h}) *
                  getW(wF, wR, wC, ${h}, d2);
              } else if (${f===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${h}),
                  getX(batch, xF, xR, xC, ${h} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${h}, d2),
                  getW(wF, wR, wC, ${h} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${f===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${h}),
                  getX(batch, xF, xR, xC, ${h} + 1),
                  getX(batch, xF, xR, xC, ${h} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${h}, d2),
                  getW(wF, wR, wC, ${h} + 1, d2),
                  getW(wF, wR, wC, ${h} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},EJ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let{dataFormat:n}=t,r=$n(),s=n==="channelsLast",a=s?0:1,o=s?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u="";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`
          blockIndex = rc.y + ${c};
          pos = rc.x + ${l};

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

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

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

                ch = imod(pos, inChannels);

                if (${s}) {
                  innerDims = vec2(d1, ch);
                  result[${l*2+c}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${l*2+c}] = getChannel(
                    getA(ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;this.userCode=`
      void main() {
        ivec2 rc = getOutputCoords();

        vec4 result = vec4(0);

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

        ${u}

        ${r.output} = result;
      }
    `}};function L_({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=e.shape,l=r.texData.get(e.dataId),c=n.inChannels,d=u[0]*u[1]*u[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((d===1||p===1)&&c>N_)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!=0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(op(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let C=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(C);let E=Rm({a:k,b:C,backend:r,transposeA:f,transposeB:m,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),F=r.texData.get(E.dataId);w.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=T,F.shape=n.outShape,g=cr({inputs:{x:E},backend:r}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=fe({inputs:{x:e},backend:r,attrs:{shape:[1,x,n.inChannels]}}),T=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=Rm({a:k,b:T,transposeA:f,transposeB:m,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=fe({inputs:{x:C},backend:r,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(C)}for(let x of b)r.disposeIntermediateTensorInfo(x);return g}function B_({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=p*d,b=[m,g],y=!0,v=!1,x=[],k=fe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),T=fe({inputs:{x:t},backend:r,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let C=new EJ(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],F=r.runWebGLProgram(C,[k],"float32",E),O=fe({inputs:{x:F},backend:r,attrs:{shape:[1,b[0],b[1]]}});x.push(F),x.push(O);let D=s!=null,R=a!=null,_=i==="leakyrelu",L=i?$m(i,!0):null,U=new k_(O.shape,T.shape,[1,g,n.outChannels],y,v,D,L,R,_),j=[O,T];if(s&&j.push(s),R&&j.push(a),_){let ee=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));j.push(ee),x.push(ee)}let K=r.runWebGLProgram(U,j,"float32"),q=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],Q=fe({inputs:{x:K},backend:r,attrs:{shape:q}});x.push(K);for(let ee of x)r.disposeIntermediateTensorInfo(ee);return Q}function AJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=r,d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(s.shape,a.shape,o,l,i,c,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=L_({x:s,filter:a,convInfo:p,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)h=B_({x:s,filter:a,convInfo:p,backend:n});else{let m=new M_(p);h=n.runWebGLProgram(m,[s,a],"float32")}let f=fe({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var $J={kernelName:ga,backendName:"webgl",kernelFunc:AJ},FJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        float dotProd = 0.0;

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function OJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,filterShape:c}=r,d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(s.shape,c,o,1,i,l,!1,d),h=new FJ(p);return n.runWebGLProgram(h,[s,a],"float32")}var MJ={kernelName:vh,backendName:"webgl",kernelFunc:OJ};function LJ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=r,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(o,a.shape,i,1,u,c,!1,d),h=new DJ(p);return n.runWebGLProgram(h,[s,a],"float32")}var BJ={kernelName:ba,backendName:"webgl",kernelFunc:LJ};function zJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u}=r,l=N.computeConv3DInfo(s.shape,a.shape,o,u,i),c=new _J(l);return n.runWebGLProgram(c,[s,a],"float32")}var WJ={kernelName:Xl,backendName:"webgl",kernelFunc:zJ};function VJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:u}=r,l=N.computeConv3DInfo(s.shape,u,o,1,i),c=new RJ(l);return n.runWebGLProgram(c,[s,a],"float32")}var UJ={kernelName:xh,backendName:"webgl",kernelFunc:VJ};function GJ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:u}=r,l=N.computeConv3DInfo(u,a.shape,i,1,o),c=new PJ(l);return n.runWebGLProgram(c,[s,a],"float32")}var HJ={kernelName:wh,backendName:"webgl",kernelFunc:GJ},jJ=w_+`
  return cos(x);
`,qJ=Ze({opSnippet:jJ}),KJ={kernelName:ya,backendName:"webgl",kernelFunc:qJ},XJ=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,YJ=Ze({opSnippet:XJ}),QJ={kernelName:va,backendName:"webgl",kernelFunc:YJ},ZJ=class{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,u]=e,[l]=t,[c,d]=n;this.outputShape=[l,c,d,u];let p=r==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,b]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
      const float height_ratio = float(${m});
      const float width_ratio = float(${y});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

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

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

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

        float in_y = ${b};
        if( in_y < 0.0 || in_y > ${h} ) {
          setOutput(float(${s}));
          return;
        }
        float in_x = ${x};
        if( in_x < 0.0 || in_x > ${f} ) {
          setOutput(float(${s}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${p} == 1) {
          // Compute the four integer indices.
          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));

          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}},JJ=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=r,c=new ZJ(s.shape,a.shape,i,u,l);return n.runWebGLProgram(c,[s,a,o],"float32")},eee={kernelName:jo,backendName:"webgl",kernelFunc:JJ},z_=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let r=e.length,s=t?"0.0":`getX(${W_(r,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${gt(r)} coords = getOutputCoords();
        int end = ${V_(r,"coords")};
        float val = ${s};
        int pow2 = int(pow(2.0, index));
        if (${o}) {
          int idx = ${i};
          ${V_(r,"coords")} = idx;
          val += getX(${W_(r,"coords")});
        }
        setOutput(val);
      }
    `}};function W_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function V_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function tee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,u=s.shape.length,l=N.getAxesPermutation([a],u),c=s;l!=null&&(c=Dn({inputs:{x:s},backend:n,attrs:{perm:l}}));let d=N.getInnerMostAxes(1,u)[0];if(d!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${s.shape.length-1} but got axis=${a}`);let p=c.shape[d],h=cr({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new z_(c.shape,!1,i),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(o){let f=new z_(c.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=N.getUndoAxesPermutation(l),m=Dn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var nee={kernelName:Ho,backendName:"webgl",kernelFunc:tee};function ree(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let u=n.readSync(s.dataId),l=n.readSync(a.dataId),c=a_(u,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(s.shape.length===2){let u=n.bufferSync(s),l=n.bufferSync(a),c=y9(u,l,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var see={kernelName:kh,backendName:"webgl",kernelFunc:ree},aee=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 oee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],u=o==="NHWC"?s.shape[1]:s.shape[2],l=o==="NHWC"?s.shape[2]:s.shape[3],c=o==="NHWC"?s.shape[3]:s.shape[1],d=u*a,p=l*a,h=c/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new aee(f,a,o);return n.runWebGLProgram(m,[s],s.dtype)}var iee={kernelName:qo,backendName:"webgl",kernelFunc:oee},U_=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=qn(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,u="",l="";n&&(r?u=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?u=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:u=`
          float activation(float x) {
            ${n}
          }
        `,l="result = activation(result);");let c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${u}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        ${p}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${m}
        ${f}
        setOutput(result);
      }
    `}};function uee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=r,c=u;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let d=N.computeConv2DInfo(s.shape,a.shape,o,c,i,l,!0),p;X().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new G_(d):p=new U_(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[s,a],"float32",h)}var cee={kernelName:xa,backendName:"webgl",kernelFunc:uee},lee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${a} + dm;

        float dotProd = 0.0;

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

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

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

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

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

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

        float dotProd = 0.0;

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

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

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

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

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

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

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${i}; dm++) {
              int d2 = d1 * ${i} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function pee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=r,d=N.computeConv2DInfo(s.shape,c,o,i,u,l,!0),p=new lee(d);return n.runWebGLProgram(p,[s,a],"float32")}var hee={kernelName:Ih,backendName:"webgl",kernelFunc:pee};function fee(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=r,d=N.computeConv2DInfo(c,a.shape,o,i,u,l,!0),p=new dee(d);return n.runWebGLProgram(p,[s,a],"float32")}var mee={kernelName:Sh,backendName:"webgl",kernelFunc:fee},gee=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function bee(e){let{inputs:t,backend:n}=e,{x:r}=t,s=[...r.shape,...r.shape],a=w.sizeFromShape(r.shape),o=fe({inputs:{x:r},backend:n,attrs:{shape:[a]}}),i=new gee(a),u=n.runWebGLProgram(i,[o],o.dtype),l=fe({inputs:{x:u},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),l}var yee={kernelName:Ch,backendName:"webgl",kernelFunc:bee},vee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:u,dilationWidth:l}=e,{top:c,left:d}=r;this.userCode=`
      const ivec2 strides = ivec2(${s}, ${a});
      const ivec2 pads = ivec2(${c}, ${d});
      const float neg_infinity = -3.4e38;

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

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

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

              if (wIn >= 0 && wIn < ${n}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);

                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }

        float result = curVal;
        setOutput(result);
      }
    `}};function xee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u}=r,l=N.computeDilation2DInfo(s.shape,a.shape,o,i,"NHWC",u),c,d=new vee(l);c=n.runWebGLProgram(d,[s,a],"float32");let p=fe({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),p}var wee={kernelName:Yl,backendName:"webgl",kernelFunc:xee};function kee(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:u}=N.decodeEinsumEquation(s,a.length);N.checkEinsumDimSizes(o.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(i,u),d=c.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=Dn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=fe({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),p===null?p=v:(p=uk({inputs:{a:v,b:p},backend:n}),f.push(p))}m<d-1&&(l[m]>=0&&(p=Dm({inputs:{x:p},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var Iee={kernelName:Ql,backendName:"webgl",kernelFunc:kee},See="return (x >= 0.0) ? x : (exp(x) - 1.0);",Cee=`
  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;
`,Tee=Ze({opSnippet:See,packedOpSnippet:Cee}),Nee={kernelName:ka,backendName:"webgl",kernelFunc:Tee},_ee="return (b >= 1.0) ? a : a * (b + 1.0);",Eee=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Aee=e=>{let{inputs:t,backend:n}=e,{dy:r,y:s}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new up(Eee,r.shape,s.shape):new Xc(_ee,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)},$ee={kernelName:_h,backendName:"webgl",kernelFunc:Aee},Fee=`
  return vec4(equal(a, b));
`,Dee="return float(a == b);",Ree=hn({opSnippet:Dee,packedOpSnippet:Fee,dtype:"bool",cpuKernelImpl:w9}),Pee={kernelName:Ko,backendName:"webgl",kernelFunc:Ree},Oee=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${N.ERF_P};
  float a1 = ${N.ERF_A1};
  float a2 = ${N.ERF_A2};
  float a3 = ${N.ERF_A3};
  float a4 = ${N.ERF_A4};
  float a5 = ${N.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Mee=Ze({opSnippet:Oee}),Lee={kernelName:Qu,backendName:"webgl",kernelFunc:Mee},H_="return exp(x);",j_=Ze({opSnippet:H_,packedOpSnippet:H_,cpuKernelImpl:k9,dtype:"float32"}),Bee={kernelName:Ia,backendName:"webgl",kernelFunc:j_};function pk(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=s;return s<0&&(w.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+s+1),i.splice(u,0,1),fe({inputs:{x:a},backend:r,attrs:{shape:i}})}var zee={kernelName:Xo,backendName:"webgl",kernelFunc:pk},q_="return exp(x) - 1.0;",Wee=Ze({opSnippet:q_,packedOpSnippet:q_,cpuKernelImpl:I9}),Vee={kernelName:Yo,backendName:"webgl",kernelFunc:Wee},K_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${r}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
      const float exponentMultiplier = ${s};

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

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

        float result = 0.0;

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

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

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function X_(e,t,n){let r=n.texData.get(e.dataId),s=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=s/a,i=fe({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),u=i.shape,l=new K_("real",u,t),c=new K_("imag",u,t),d=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:u},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:u}],p=n.runWebGLProgram(l,d,"float32"),h=n.runWebGLProgram(c,d,"float32"),f=No({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=fe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function Uee(e){let{inputs:t,backend:n}=e,{input:r}=t;return X_(r,!1,n)}var Gee={kernelName:Eh,backendName:"webgl",kernelFunc:Uee},Hee=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 dp(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||w.inferDtype(s),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new Hee(r,s),i=[[s]];return t.runWebGLProgram(o,[],a,i)}}var jee={kernelName:Zu,backendName:"webgl",kernelFunc:dp},qee=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);
        }
    `}},Kee={kernelName:Qo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new qee(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},Y_="return floor(x);",Xee=Ze({opSnippet:Y_,packedOpSnippet:Y_,cpuKernelImpl:S9}),Yee={kernelName:Sa,backendName:"webgl",kernelFunc:Xee},Qee=`
  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;
  }
`,Zee=`
  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);
`,Jee=hn({opSnippet:Qee,packedOpSnippet:Zee,dtype:"int32"}),ete={kernelName:Ca,backendName:"webgl",kernelFunc:Jee},tte=class{constructor(e){this.variableNames=["A"];let t=$n(),[n,r]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);

        vec4 values = ${t.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }

        setOutput(floor(value * 255.0 + 0.5));
      }
    `}},nte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=$n(),[n,r]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${r}.0, ${n}.0);
            vec4 values = ${t.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${t.output} = result;
      }
    `}},rte={kernelName:cd,backendName:"webgl",kernelFunc:ste},Zc;function ste(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r,o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,[u,l]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],c=[l,u],d=[l,u,a];(i||o)&&(Zc==null&&(Zc=document.createElement("canvas").getContext("2d")),Zc.canvas.width=u,Zc.canvas.height=l,Zc.drawImage(s,0,0,u,l),s=Zc.canvas);let p=n.makeTensorInfo(c,"int32");n.texData.get(p.dataId).usage=wr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),s);let h=X().getBool("WEBGL_PACK")?new nte(d):new tte(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function ate(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=r,m=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(s.shape,a.shape,u,d,l,p,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=L_({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(X().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)b=B_({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let x=o!=null,k=i!=null,T=h==="leakyrelu",C=h?$m(h,!1):null,E=new M_(g,x,C,k,T),F=[s,a];if(o&&F.push(o),i&&F.push(i),T){let O=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));F.push(O),y.push(O)}b=n.runWebGLProgram(E,F,"float32")}let v=fe({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var ote={kernelName:no,backendName:"webgl",kernelFunc:ate};function ite(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=r,f=[],m=c;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=N.computeConv2DInfo(s.shape,a.shape,u,m,l,d,!0),b=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=p?$m(p,b):null,v=[s,a],x=o!=null,k=i!=null,T=p==="leakyrelu";if(x&&v.push(o),k&&v.push(i),T){let O=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(O),f.push(O)}let C;b?C=new G_(g,x,y,k,T):C=new U_(g,x,y,k,T);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=n.runWebGLProgram(C,v,"float32",E);return f.forEach(O=>n.disposeIntermediateTensorInfo(O)),F}var ute={kernelName:ro,backendName:"webgl",kernelFunc:ite},cte=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=gt(t.length),s=gt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${r} strides = ${r}(${this.strides});
         void main() {
          ${s} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${a};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}};function lte(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=w.sizeFromShape(r.shape),[u,l,c,d]=N.prepareAndValidate(r,s),p=fe({inputs:{x:s},backend:n,attrs:{shape:[l,o]}}),h=fe({inputs:{x:r},backend:n,attrs:{shape:[w.sizeFromShape(r.shape)/c,c]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.readSync(s.dataId),y=n.bufferSync(r),v=C9(b,y,r.dtype,l,o,c,d,r.shape,i);return n.makeTensorInfo(u,r.dtype,v.values)}let f=new cte(o,d,[l,c]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=fe({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var dte={kernelName:Jo,backendName:"webgl",kernelFunc:lte},pte=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=gt(this.rank),r=hte(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function hte(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e.length;s++)s===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[s]}`);return r.join()}function Q_(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r,u=w.parseAxisParam(o,s.shape)[0],l=n.readSync(a.dataId),c=s.shape[u];for(let x=0;x<l.length;++x){let k=l[x];w.assert(k<=c-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${c-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(s,a,u,i),p=w.sizeFromShape(a.shape),h=[],f=fe({inputs:{x:s},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=fe({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([s,a])||s.dtype==="string"){let x=n.bufferSync(m),k=n.bufferSync(f),T=T9(k,x,g);return h.forEach(C=>n.disposeIntermediateTensorInfo(C)),n.makeTensorInfo(d.outputShape,T.dtype,T.values)}let b=new pte(f.shape,g),y=n.runWebGLProgram(b,[f,m],f.dtype);h.push(y);let v=fe({inputs:{x:y},backend:n,attrs:{shape:d.outputShape}});return h.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var fte={kernelName:Zo,backendName:"webgl",kernelFunc:Q_},mte="return float(a > b);",gte=`
  return vec4(greaterThan(a, b));
`,bte=hn({opSnippet:mte,packedOpSnippet:gte,cpuKernelImpl:N9,dtype:"bool"}),yte={kernelName:ei,backendName:"webgl",kernelFunc:bte},vte="return float(a >= b);",xte=`
  return vec4(greaterThanEqual(a, b));
`,wte=hn({opSnippet:vte,packedOpSnippet:xte,dtype:"bool",cpuKernelImpl:_9}),kte={kernelName:Na,backendName:"webgl",kernelFunc:wte};function Ite(e){let{inputs:t,backend:n}=e,{input:r}=t;return X_(r,!0,n)}var Ste={kernelName:Ah,backendName:"webgl",kernelFunc:Ite},Cte="return float(!isnan(x) && !isinf(x));",Tte=Ze({opSnippet:Cte,dtype:"bool"}),Nte={kernelName:Ju,backendName:"webgl",kernelFunc:Tte},_te="return float(isinf(x));",Ete=Ze({opSnippet:_te,dtype:"bool"}),Ate={kernelName:ec,backendName:"webgl",kernelFunc:Ete},$te="return float(isnan(x));",Fte=Ze({opSnippet:$te,dtype:"bool"}),Dte={kernelName:tc,backendName:"webgl",kernelFunc:Fte},Rte="return float(a < b);",Pte=`
  return vec4(lessThan(a, b));
`,Ote=hn({opSnippet:Rte,packedOpSnippet:Pte,cpuKernelImpl:E9,dtype:"bool"}),Mte={kernelName:ni,backendName:"webgl",kernelFunc:Ote},Lte="return float(a <= b);",Bte=`
  return vec4(lessThanEqual(a, b));
`,zte=hn({opSnippet:Lte,packedOpSnippet:Bte,cpuKernelImpl:A9,dtype:"bool"}),Wte={kernelName:ri,backendName:"webgl",kernelFunc:zte};function Vte(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=$9(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var Ute={kernelName:$h,backendName:"webgl",kernelFunc:Vte},Gte=`if (x < 0.0) return NAN;
  return log(x);`,Hte=`
  vec4 result = log(x);
  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
  result.r = isNaN.r == 1.0 ? NAN : result.r;
  result.g = isNaN.g == 1.0 ? NAN : result.g;
  result.b = isNaN.b == 1.0 ? NAN : result.b;
  result.a = isNaN.a == 1.0 ? NAN : result.a;

  return result;
`,jte=Ze({opSnippet:Gte,packedOpSnippet:Hte,cpuKernelImpl:F9}),qte={kernelName:Ea,backendName:"webgl",kernelFunc:jte},Kte="return log(1.0 + x);",Xte=Ze({opSnippet:Kte}),Yte={kernelName:nc,backendName:"webgl",kernelFunc:Xte},Qte="return float(a >= 1.0 && b >= 1.0);",Zte=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,Jte=hn({opSnippet:Qte,packedOpSnippet:Zte,dtype:"bool"}),ene={kernelName:si,backendName:"webgl",kernelFunc:Jte},tne="return float(!(x >= 1.0));",nne=Ze({opSnippet:tne}),rne={kernelName:rc,backendName:"webgl",kernelFunc:nne},sne="return float(a >= 1.0 || b >= 1.0);",ane=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,one=hn({opSnippet:sne,packedOpSnippet:ane,dtype:"bool"}),ine={kernelName:Jl,backendName:"webgl",kernelFunc:one},une=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${u})`:s===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${a}; j <= ${a}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${o}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${i};
        setOutput(val);
      }
    `}},cne=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${u})`:s===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

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

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

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

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

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

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

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

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

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

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

          norm = float(${r}) * norm + float(${n});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${r})
                * float(${s})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${s});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},hne=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:u,alpha:l,beta:c}=r,d=new pne(s.shape,i,u,l,c);return n.runWebGLProgram(d,[s,a,o],s.dtype)},fne={kernelName:Fh,backendName:"webgl",kernelFunc:hne};function mne(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=fe({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),u=ou(i,e.dtype,"max",r),l=fe({inputs:{x:u},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(u),l}function Z_(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=N.getAxesPermutation(l,i),d=c!=null,p=n.shouldExecuteOnCPU([s]),h=s;if(d){if(p){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let C=0;C<x.length;C++)x[C]=s.shape[c[C]];let k=ok(v,s.shape,s.dtype,c,x);h=n.makeTensorInfo(x,s.dtype);let T=n.texData.get(h.dataId);T.values=k}else h=Fm(s,c,n);l=N.getInnerMostAxes(l.length,i)}N.assertAxesAreInnerMostDims("max",l,i);let[f,m]=N.computeOutAndReduceShapes(h.shape,l),g=f;o&&(g=N.expandShapeToKeepDim(f,u));let b;if(p){let v=n.texData.get(h.dataId).values,x=D9(v,w.sizeFromShape(m),g,s.dtype);b=n.makeTensorInfo(g,s.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=mne(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),b}var gne={kernelName:Aa,backendName:"webgl",kernelFunc:Z_},bne=g_+`
  return max(a, b);
`,yne=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Am+`
  return result;
`,vne=hn({opSnippet:bne,packedOpSnippet:yne,cpuKernelImpl:R9}),xne={kernelName:$a,backendName:"webgl",kernelFunc:vne};function wne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Uc(s,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=r,l=1;w.assert(N.eitherStridesOrDilationsAreOne(o,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let c=N.computePool2DInfo(s.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return cr({inputs:{x:s},backend:n});let d=new cp(c,"max",!1);return n.runWebGLProgram(d,[s],s.dtype)}var kne={kernelName:Fa,backendName:"webgl",kernelFunc:wne};function Ine(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dataFormat:u,dimRoundingMode:l}=r,c=[1,1,1],d=N.computePool3DInfo(s.shape,a,o,c,i,l,u),p=new ck(d,"max",!1);return n.runWebGLProgram(p,[s],s.dtype)}var Sne={kernelName:td,backendName:"webgl",kernelFunc:Ine},Cne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=s-1-e.padInfo.top,i=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${i});

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function Nne(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=r,d=[1,1,1],p=N.computePool3DInfo(o.shape,i,u,d,l,c),h=new ck(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Tne(p),g=n.runWebGLProgram(m,[s,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var _ne={kernelName:Rh,backendName:"webgl",kernelFunc:Nne};function Ene(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;Uc([a,o],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:d}=r,p=N.computePool2DInfo(i.shape,u,l,1,c,d),h=!0,f=new cp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Cne(p),b=n.runWebGLProgram(g,[s,m],i.dtype);return n.disposeIntermediateTensorInfo(m),b}var Ane={kernelName:Dh,backendName:"webgl",kernelFunc:Ene};function $ne(e,t,n,r){let s=new cp(n,"max",!1),a=r.runWebGLProgram(s,[e],"float32");s=new cp(n,"max",!0,!0,t);let o=r.runWebGLProgram(s,[e],"float32");return[a,o]}var Fne={kernelName:Ph,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:s,strides:a,pad:o,includeBatchInIndex:i}=t,u=n;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let l=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,s,a,l,o),[d,p]=$ne(r,i,c,u);return[d,p]}};function Dne(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=fe({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),u=ou(i,"float32","mean",r),l=fe({inputs:{x:u},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(u),l}var Rne={kernelName:Da,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:s,axis:a}=t,o=n,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,i),d=c!=null,p=o.shouldExecuteOnCPU([r]),h=[],f=r;if(d){if(p){let x=o.texData.get(f.dataId).values,k=new Array(i);for(let E=0;E<k.length;E++)k[E]=r.shape[c[E]];let T=ok(x,r.shape,r.dtype,c,k);f=o.makeTensorInfo(k,r.dtype);let C=o.texData.get(f.dataId);C.values=T}else f=Fm(r,c,o);h.push(f),l=N.getInnerMostAxes(l.length,i)}N.assertAxesAreInnerMostDims("sum",l,i);let[m,g]=N.computeOutAndReduceShapes(f.shape,l),b=m;s&&(b=N.expandShapeToKeepDim(m,u));let y=Dne(f,g,b,o);for(let v of h)o.disposeIntermediateTensorInfo(v);return y}};function Pne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=N.getAxesPermutation(l,i),d=s;c!=null&&(d=Dn({inputs:{x:s},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,s.shape.length)),N.assertAxesAreInnerMostDims("min",l,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,l),f=w.sizeFromShape(h),m=fe({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=ou(m,m.dtype,"min",n),b;if(o){let y=N.expandShapeToKeepDim(p,u);b=fe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=fe({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),b}var One={kernelName:Ra,backendName:"webgl",kernelFunc:Pne},Mne=g_+`
  return min(a, b);
`,Lne=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Am+`
  return result;
`,Bne=hn({opSnippet:Mne,packedOpSnippet:Lne,cpuKernelImpl:P9}),zne={kernelName:Pa,backendName:"webgl",kernelFunc:Bne},Wne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,s=gt(r),a=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),u=n==="reflect"?0:1;if(r===1){this.userCode=`
        int start = ${a};
        int end = ${o};

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

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

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${p}
        setOutput(result);
      }
    `}},Une=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:s,mode:a}=n,o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Vne(r.shape,s,a):new Wne(r.shape,s,a);return t.runWebGLProgram(o,[r],r.dtype)},Gne={kernelName:Oa,backendName:"webgl",kernelFunc:Une},Hne=`if (b == 0.0) return NAN;
  return mod(a, b);`,jne=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+Am+`
  return result;
`,qne=hn({opSnippet:Hne,packedOpSnippet:jne}),Kne={kernelName:sc,backendName:"webgl",kernelFunc:qne},Xne=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}));
      }
    `}},Yne=`
if (a == b) {
  return 1.0;
};
return a / b;`,Qne=`
  // 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;
`,J_=hn({opSnippet:Yne,packedOpSnippet:Qne,checkOutOfBounds:!0}),Zne={kernelName:wa,backendName:"webgl",kernelFunc:J_},eE="return a - b;",tE=hn({opSnippet:eE,packedOpSnippet:eE,supportsComplex:!0,cpuKernelImpl:Q9}),Jne={kernelName:Qa,backendName:"webgl",kernelFunc:tE};function nE(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=Z_({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=N.expandShapeToKeepDim(i.shape,o),l=fe({inputs:{x:i},backend:n,attrs:{shape:u}}),c=tE({inputs:{a:s,b:l},backend:n}),d=j_({inputs:{x:c},backend:n}),p=Dm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=fe({inputs:{x:p},backend:n,attrs:{shape:u}}),f=J_({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var ere={kernelName:Xa,backendName:"webgl",kernelFunc:nE};function tre(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,u=i?s:nE({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),l=u.shape[0],c=u.shape[1],d=new Xne(l,c,a),p=[[o]],h=n.runWebGLProgram(d,[u],"int32",p);return i||n.disposeIntermediateTensorInfo(u),h}var nre={kernelName:Oh,backendName:"webgl",kernelFunc:tre},rE="return -x;";function rre(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.texData.get(r.dataId),[o,i]=M9(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Kc(r.shape,rE):s=new To(r.shape,rE),n.runWebGLProgram(s,[r],r.dtype)}var sre={kernelName:ai,backendName:"webgl",kernelFunc:rre},are=Dr.nonMaxSuppressionV3Impl;function ore(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=r,l=n.readSync(s.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=are(l,c,o,i,u);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var ire={kernelName:ii,backendName:"webgl",kernelFunc:ore},ure=Dr.nonMaxSuppressionV4Impl;function cre(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,padToMaxOutputSize:l}=r,c=n.readSync(s.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=ure(c,d,o,i,u,l);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var lre={kernelName:ac,backendName:"webgl",kernelFunc:cre},dre=Dr.nonMaxSuppressionV5Impl;function pre(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=r,c=n.readSync(s.dataId),d=n.readSync(a.dataId),p=o,h=i,f=u,m=l,{selectedIndices:g,selectedScores:b}=dre(c,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var hre={kernelName:ui,backendName:"webgl",kernelFunc:pre},fre=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${r}), float(${n}),
                      float(index == coords.y)));
      }
    `}},mre=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{depth:a,onValue:o,offValue:i}=r,u=w.sizeFromShape(s.shape),l=new fre(u,a,o,i),c=fe({inputs:{x:s},backend:n,attrs:{shape:[u]}}),d=n.runWebGLProgram(l,[c],s.dtype);n.disposeIntermediateTensorInfo(c);let p=[...s.shape,a],h=fe({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},gre={kernelName:li,backendName:"webgl",kernelFunc:mre};function Lm(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=lp({inputs:{input:r},backend:n}),a=Lm({inputs:{x:s},backend:n}),o=Mm({inputs:{input:r},backend:n}),i=Lm({inputs:{x:o},backend:n}),u=No({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return dp({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var bre={kernelName:Ni,backendName:"webgl",kernelFunc:Lm};function sE(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=lp({inputs:{input:r},backend:n}),a=sE({inputs:{x:s},backend:n}),o=Mm({inputs:{input:r},backend:n}),i=Lm({inputs:{x:o},backend:n}),u=No({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return dp({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var yre={kernelName:ci,backendName:"webgl",kernelFunc:sE};function vre(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return pk({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(c=>{let d=pk({inputs:{input:c},backend:n,attrs:{dim:s}});return i.push(d),d}),l=O_({inputs:u,backend:n,attrs:{axis:s}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var xre={kernelName:di,backendName:"webgl",kernelFunc:vre},wre=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let r=e.length,s=gt(r),a=t.map(u=>u[0]).join(","),o=t.map((u,l)=>u[0]+e[l]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
        int start = ${a};
        int end = ${o};

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

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

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `}},aE=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;if(w.sizeFromShape(s.shape)===0){let l=a.map((c,d)=>c[0]+s.shape[d]+c[1]);return dp({backend:n,attrs:{shape:l,value:o,dtype:s.dtype}})}let i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new kre(s.shape,a,o):new wre(s.shape,a,o),u=[[o]];return n.runWebGLProgram(i,[s],s.dtype,u)},Ire={kernelName:La,backendName:"webgl",kernelFunc:aE},Sre=`
  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);
`,Cre=`
  // 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));
  `+Am+`
  return result;
`,Tre=hn({opSnippet:Sre,packedOpSnippet:Cre}),Nre={kernelName:Ba,backendName:"webgl",kernelFunc:Tre};function _re(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=[],l=w.parseAxisParam(a,s.shape),c=l,d=N.getAxesPermutation(c,i),p=s;d!=null&&(p=Dn({inputs:{x:s},backend:n,attrs:{perm:d}}),c=N.getInnerMostAxes(c.length,i),u.push(p)),N.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:b}=B9(p.shape,p.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,c),g=w.sizeFromShape(m),b=fe({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),y=bd(s.dtype),v=ou(b,y,"prod",n);h=fe({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(o){u.push(h);let f=N.expandShapeToKeepDim(h.shape,l);h=fe({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Ere={kernelName:pi,backendName:"webgl",kernelFunc:_re},oE=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=z9(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},Are={kernelName:oc,backendName:"webgl",kernelFunc:oE},$re="return 1.0 / x;",Fre=Ze({opSnippet:$re}),Dre={kernelName:ic,backendName:"webgl",kernelFunc:Fre},Rre=ts+`
  return (x < 0.0) ? 0.0 : x;
`,Pre=`
  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;
`,Ore=Ze({opSnippet:Rre,packedOpSnippet:Pre}),Mre={kernelName:Wa,backendName:"webgl",kernelFunc:Ore},Lre=ts+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Bre=`
  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;
`,zre=Ze({opSnippet:Lre,packedOpSnippet:Bre}),Wre={kernelName:Ua,backendName:"webgl",kernelFunc:zre},Vre=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],d;s?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${l[0]/c[0]},
          ${l[1]/c[1]});
      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);

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

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

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

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

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

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

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

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

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

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

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

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

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

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

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

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

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

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

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

        float accumulator = 0.0;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        setOutput(newValue);
      }
    `}},Yre=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0",p;s?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${l[0]/c[0]},
          ${l[1]/c[1]},
          ${l[1]/c[1]});
      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
                                     ${i}.0);

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

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

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

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

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

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

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

        float accumulator = 0.0;

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

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

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

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

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

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

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

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

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

            float sourceFracRow =
              float(${i[0]}) *
                (float(dyR) / float(${u[0]}));

            float sourceFracCol =
                float(${i[1]}) *
                  (float(dyC) / float(${u[1]}));

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

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

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

        setOutput(accumulator);
      }
    `}};function ese(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new Jre(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var tse={kernelName:Mh,backendName:"webgl",kernelFunc:ese},nse=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let r=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,s=e.map((o,i)=>r(i)).join(","),a=gt(n);this.userCode=`
      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `}},rse=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=Fn("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=gt(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${s}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${o} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${i(r.slice())};
          if(${s}){
            result.g = ${u(r.slice())};
          }
          if(${a}) {
            result.b = ${l(r.slice())};
            if(${s}) {
              result.a = ${c(r.slice())};
            }
          }
          setOutput(result);
        }
    `;function i(h){return d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((b,y)=>p(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function sse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r,o=s.shape.length,i=w.parseAxisParam(a,s.shape);if(o===0)return cr({inputs:{x:s},backend:n});let u=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rse(s.shape,i):new nse(s.shape,i);return n.runWebGLProgram(u,[s],s.dtype)}var ase={kernelName:fi,backendName:"webgl",kernelFunc:sse},ose=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],r=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
        vec3 fill = vec3(${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]));
          ${s}
          if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}},ise={kernelName:_i,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,u=new ose(r.shape,a),[l,c]=N.getImageCenter(o,r.shape[1],r.shape[2]),d=[[l,c,Math.sin(s),Math.cos(s)]];return i.runWebGLProgram(u,[r],r.dtype,d)}},use=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,cse=Ze({opSnippet:use}),lse={kernelName:mi,backendName:"webgl",kernelFunc:cse},dse="return inversesqrt(x);",pse=Ze({opSnippet:dse,cpuKernelImpl:W9}),hse={kernelName:Ga,backendName:"webgl",kernelFunc:pse},iE=class{constructor(e,t,n,r,s,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=gt(s.length),u=gt(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,d="";r===1?d="i":r===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
        ${i} strides = ${i}(${s});

        void main() {
          ${u} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${e}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${t}; j++) {
              int index = round(${c});
              flattenedIndex += index * ${h};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${p};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function fse(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:d}=N.calculateShapes(a,s,o),p=[d/l,l];if(d===0)return n.makeTensorInfo(o,s.dtype);let h=fe({inputs:{x:s},backend:n,attrs:{shape:[u,i]}}),f=fe({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new iE(u,i,h.shape.length,f.shape.length,c,p),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=fe({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var mse={kernelName:gi,backendName:"webgl",kernelFunc:fse},gse=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",r="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],u=[];for(let l=0;l<t.length;l++)u.push(`${o[l]}`),l<e&&i.push(`${o[l]}`);r=i.join(),s=u.join()}let a=gt(n);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        float cVal = getC(${r});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `}};function bse(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new gse(r.shape.length,s.shape,s.shape.length);return n.runWebGLProgram(o,[r,s,a],In(s.dtype,a.dtype))}var yse={kernelName:bi,backendName:"webgl",kernelFunc:bse},vse=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${N.SELU_SCALEALPHA};
  float scale = ${N.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,xse=Ze({opSnippet:vse}),wse={kernelName:cc,backendName:"webgl",kernelFunc:xse},uE="return 1.0 / (1.0 + exp(-1.0 * x));",kse=Ze({opSnippet:uE,packedOpSnippet:uE,cpuKernelImpl:V9}),Ise={kernelName:ja,backendName:"webgl",kernelFunc:kse},Sse=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,Cse=Ze({opSnippet:Sse}),Tse={kernelName:lc,backendName:"webgl",kernelFunc:Cse},Nse=w_+`
  return sin(x);
`,_se=Ze({opSnippet:Nse}),Ese={kernelName:Ha,backendName:"webgl",kernelFunc:_se},Ase=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,$se=Ze({opSnippet:Ase}),Fse={kernelName:vi,backendName:"webgl",kernelFunc:$se},Dse=`
  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;
`,Rse=Ze({opSnippet:Dse}),Pse={kernelName:dc,backendName:"webgl",kernelFunc:Rse},Ose=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r;w.assert(s.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...o);for(let b=1+a.length;b<s.shape.length;++b)u.push([0,0]);let l=[],c=aE({inputs:{x:s},backend:n,attrs:{paddings:u,constantValue:0}}),d=N.getReshaped(c.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,i,!1),f=fe({inputs:{x:c},backend:n,attrs:{shape:d}}),m=Dn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=fe({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},Mse={kernelName:xi,backendName:"webgl",kernelFunc:Ose};function Lse(e){let{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${a.shape}`);if(r.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${r.shape}`);if(s.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${s.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${o.shape}`);let i=n.readSync(r.dataId),u=n.readSync(s.dataId),l=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[d,p,h,f,m]=G9(i,r.shape,r.dtype,u,s.dtype,l,c);return[n.makeTensorInfo(p,r.dtype,d),n.makeTensorInfo([p[0]],s.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}var Bse={kernelName:rd,backendName:"webgl",kernelFunc:Lse};function zse(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:s,newShape:a}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${s.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(s.dataId)),i=n.readSync(r.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,d]=H9(i,r.shape,r.dtype,o,u);return[n.makeTensorInfo(c,r.dtype,l),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Wse={kernelName:pc,backendName:"webgl",kernelFunc:zse};function Vse(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),u=n.readSync(a.dataId),[l,c]=i_(o,r.shape,r.dtype,i,u,!0);return n.makeTensorInfo(c,r.dtype,l)}var Use={kernelName:sd,backendName:"webgl",kernelFunc:Vse};function Gse(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),u=n.readSync(a.dataId),[l,c]=i_(o,r.shape,r.dtype,i,u);return n.makeTensorInfo(c,r.dtype,l)}var Hse={kernelName:ad,backendName:"webgl",kernelFunc:Gse};function jse(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:u,numUpdates:l,strides:c,outputSize:d}=N.calculateShapes(a,s,i),p=!1,h=new iE(l,u,s.shape.length,a.shape.length,c,[d,1],p),f=n.runWebGLProgram(h,[a,s,o],a.dtype),m=fe({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var qse={kernelName:od,backendName:"webgl",kernelFunc:jse};function Kse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=w.parseAxisParam(o,s.shape)[0],u=N.prepareSplitSize(s,a,i),l=s.shape.length,c=new Array(l).fill(0),d=s.shape.slice();return u.map(p=>{let h=[...d];h[i]=p;let f=Yc({inputs:{x:s},backend:n,attrs:{begin:c,size:h}});return c[i]+=p,f})}var Xse={kernelName:wi,backendName:"webgl",kernelFunc:Kse},cE="return sqrt(x);",Yse=Ze({opSnippet:cE,packedOpSnippet:cE,cpuKernelImpl:j9}),Qse={kernelName:qa,backendName:"webgl",kernelFunc:Yse},Zse="return x * x;",Jse=Ze({opSnippet:Zse}),eae={kernelName:hc,backendName:"webgl",kernelFunc:Jse},lE="return (a - b) * (a - b);",tae=hn({opSnippet:lE,packedOpSnippet:lE}),nae={kernelName:Ya,backendName:"webgl",kernelFunc:tae};function rae({inputs:e,attrs:t,backend:n}){let{x:r}=e,s=ts+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,a=new To(r.shape,s);return n.runWebGLProgram(a,[r],r.dtype)}var sae={kernelName:eo,backendName:"webgl",kernelFunc:rae},aae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,s=gt(n.length),a=gt(n.length),o="";if(r===1)o="coords * strides + begin";else{let i=0;o=n.map((u,l)=>(i++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${i-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
      ${s} begin = ${s}(${e});
      ${s} strides = ${s}(${t});

      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${o}));
      }
    `}};function oae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=r,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=$t.sliceInfo(s.shape,a,o,i,u,l,c,d,p),k;if(m)k=fe({inputs:{x:s},backend:n,attrs:{shape:f}});else if(g||b){w.assert(s.shape.length>=1,()=>`Input must have rank at least 1, got: ${s.shape.length}`);let C=$t.computeOutShape(y,v,x),E=Yc({inputs:{x:s},backend:n,attrs:{begin:y,size:C}});k=fe({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([s])){let E=n.readSync(s.dataId),F=$e(s.shape,s.dtype,E),O=q9(h,F,x,y);k=n.makeTensorInfo(f,s.dtype,O.values)}else{let E=new aae(y,x,h);k=n.runWebGLProgram(E,[s],s.dtype)}let T=fe({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),T}var iae={kernelName:ki,backendName:"webgl",kernelFunc:oae};function uae(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=r,{data:c,dataSplits:d}=t,p=n.readSync(c.dataId),h=n.readSync(d.dataId),[f,m]=K9(p,h,s,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var cae={kernelName:id,backendName:"webgl",kernelFunc:uae};function lae(e){let{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[l,c,d]=X9(i,u,s),p=c.length;return[n.makeTensorInfo([p,2],"int32",l),n.makeTensorInfo([p],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var dae={kernelName:Bh,backendName:"webgl",kernelFunc:lae};function pae(e){let{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=Y9(o,s);return n.makeTensorInfo(a.shape,"int32",i)}var hae={kernelName:zh,backendName:"webgl",kernelFunc:pae},fae="return tan(x);",mae=Ze({opSnippet:fae}),gae={kernelName:Ii,backendName:"webgl",kernelFunc:mae},bae=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,yae=Ze({opSnippet:bae}),vae={kernelName:Za,backendName:"webgl",kernelFunc:yae},xae=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let r=gt(this.rank),s=wae(e);this.userCode=`
      void main() {
        ${r} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}};function wae(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<e.length;s++)r.push(`imod(${n[s]}, ${e[s]})`);return r.join()}function dE(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(s.dtype==="string"||s.shape.length>5){let u=n.readSync(s.dataId),l=s.dtype==="string"?u.map(p=>w.decodeString(p)):u,c=$e(s.shape,s.dtype,l),d=Z9(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new xae(s.shape,a);return n.runWebGLProgram(o,[s],s.dtype)}var kae={kernelName:As,backendName:"webgl",kernelFunc:dE},Iae=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));
         }
       }
     `}},Sae=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 iu(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function pE(e){let t=1;for(;t<e;)t*=2;return t}function Cae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r,i=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=s.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([s])||c<i||a>u){let O=n.readSync(s.dataId),[D,R]=J9(O,l,s.dtype,a,o);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,s.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[s,dp({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let d=n.texData.get(s.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(s):s,m=w.sizeFromShape(l)/c,g=fe({inputs:{x:h},attrs:{shape:[m,c]},backend:n});p&&iu(n,h);let b=pE(a),y=pE(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(O,D,R)=>{let _=x(),L=new Iae(R),j=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[O],[D]],K=v;v=n.runWebGLProgram(L,_,"int32",j),iu(n,K)};for(let O=1;O<b;O*=2){let D=O*2;for(let R=O;R>=1;R/=2)k(D,R,[m,y])}for(let O=y;O>b;O/=2){let D=x(),R=new Sae([m,O/2]),L=[[c],[v===null?1:0],[b]],U=v;v=n.runWebGLProgram(R,D,"int32",L),iu(n,U);let j=b/2,K=j*2;for(let q=j;q>=1;q/=2)k(K,q,v.shape)}let T=v;v=Yc({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),iu(n,T);let C=Q_({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});iu(n,g);let E=l.slice(0,-1);E.push(a),T=v,v=fe({inputs:{x:v},attrs:{shape:E},backend:n}),iu(n,T);let F=C;return C=fe({inputs:{x:C},attrs:{shape:E},backend:n}),iu(n,F),[C,v]}var Tae={kernelName:Si,backendName:"webgl",kernelFunc:Cae},Nae=class{constructor(e,t,n,r,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(r){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${i} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

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

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

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

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

      float getSegmentIdAtIndex(int inIdx) {
        ${h}
        return getSegmentIds(inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${a})) * float(${n}));
        int currentSeg = int(mod(float(outIdx), float(${a})));

        float sumValue = 0.0;

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

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

          ${d}
        }

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

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

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

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

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

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

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

          ${d}
        }
        setOutput(${u});
      }
    `}};function Pae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:a}=t,{numSegments:o}=r,i=s.shape.length,u=[],l=0,c=N.getAxesPermutation([l],i),d=s;c!=null&&(d=Dn({inputs:{x:s},backend:n,attrs:{perm:c}}),u.push(d),l=N.getInnerMostAxes(1,i)[0]);let p=N.segment_util.computeOutShape(d.shape,l,o),h=w.sizeFromShape([d.shape[l]]),f=fe({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=bd(s.dtype),g=(x,k,T,C,E)=>{let F=x.shape[0],O=x.shape[1],D=N.segment_util.segOpComputeOptimalWindowSize(O,E),R={windowSize:D,inSize:O,batchSize:F,numSegments:E},_=new Rae(R,k),L=n.compileAndRun(_,[x,T],C);if(u.push(L),L.shape[1]===E)return L;let U=oE({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),j=dE({inputs:{x:U},backend:n,attrs:{reps:[O/D]}});return u.push(U),u.push(j),g(L,k,j,C,E)},b=g(f,"unsortedSegmentSum",a,m,o),y=fe({inputs:{x:b},backend:n,attrs:{shape:p}}),v=y;if(c!=null){u.push(y);let x=N.getUndoAxesPermutation(c);v=Dn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var Oae={kernelName:ud,backendName:"webgl",kernelFunc:Pae},Mae=[dne,fne,XQ,QQ,eZ,rZ,aZ,uZ,lZ,pZ,gZ,yZ,wZ,SZ,$Z,NZ,RZ,LZ,OZ,VZ,GZ,jZ,YZ,rJ,aJ,iJ,hJ,mJ,vJ,kJ,AQ,NJ,MJ,BJ,$J,UJ,HJ,WJ,KJ,QJ,eee,nee,see,iee,hee,mee,cee,yee,wee,Iee,Nee,$ee,Pee,Lee,Bee,zee,Vee,Gee,jee,Kee,Yee,ete,rte,ote,ute,dte,fte,yte,kte,EQ,Ste,CJ,Nte,Ate,Dte,FQ,Mte,Wte,Ute,Yte,qte,ene,rne,ine,gne,Sne,kne,_ne,Ane,Fne,xne,Rne,One,zne,Gne,Kne,nre,MQ,sre,ire,lre,hre,cJ,gre,yre,xre,Ire,Nre,RQ,Ere,Are,lJ,Zne,Dre,Wre,Mre,BQ,Hre,Kre,Zre,tse,ase,ise,lse,hse,mse,yse,wse,Ise,Tse,Ese,Fse,tJ,ere,Pse,Mse,Bse,Wse,Use,Hse,qse,Xse,Qse,eae,nae,sae,iae,cae,dae,hae,Jne,jQ,gae,vae,kae,Tae,Eae,qQ,$ae,Dae,Oae,bre];for(let e of Mae)mc(e);var vs=X();vs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);vs.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);vs.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);vs.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);vs.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);vs.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);vs.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);vs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);vs.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);vs.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Lae(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,r=e.map(a=>`${t}[${a}]`),s=new Array(n-1);s[n-2]=r[n-1];for(let a=n-3;a>=0;--a)s[a]=`(${s[a+1]} * ${r[a+1]})`;return s}function on(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function Bm(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function zm(){return`
  [[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
`}function hk(){return`
  ${zm()}
  fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
          [[builtin(global_invocation_id)]] globalId : vec3<u32>,
          [[builtin(num_workgroups)]] numWorkgroups: vec3<u32>)
`}function Jc(){return`
  ${zm()}
  fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
          [[builtin(global_invocation_id)]] globalId : vec3<u32>)
`}function Ue(){return`
    ${hk()} {
      let index = getGlobalIndex(globalId, localId, numWorkgroups);
`}function Bae(e,t,n,r=!1){let s=`
    let workGroupSizeX = ${n.workGroupSize[0]}u;
    let workGroupSizeY = ${n.workGroupSize[1]}u;
    let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(r===!0){let h=mE(t.shape),f=`
      [[block]] struct Matrix0 {
        numbers: array<${Bm(t.dtype,n.isVec4)}>;
      };
      [[block]] struct Uniform {
        size            : i32;
        numChannels     : i32;
        outShapeStrides : vec2<i32>;
        dispatchSize    : vec3<u32>;
      };

      [[group(0), binding(0)]] var<storage, write> result : Matrix0;
      [[group(0), binding(2)]] var<uniform> uniforms: Uniform;
    `;return[hE,f,s,fE,h,n.getUserCode()].join(`
`)}let a=[],o="[[block]] struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${on(e[f].shape.length)}; `}),o+=`outShape : ${on(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
       outShapeStrides: ${on(i)}; `,n.size&&(o+="size : i32; "),n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(`
    [[block]] struct Matrix0 {
        numbers: array<atomic<i32>>;
    };

    [[group(0), binding(0)]] var<storage, read_write> result : Matrix0;
  `):a.push(`
    [[block]] struct Matrix0 {
        numbers: array<${Bm(t.dtype,n.isVec4)}>;
    };

    [[group(0), binding(0)]] var<storage, write> result : Matrix0;
  `),n.variableNames.forEach((h,f)=>{a.push(`
    [[block]] struct Matrix${1+f} {
      numbers: array<${Bm(e[f].dtype,n.isVec4)}>;
    };
    [[group(0), binding(${1+f})]] var<storage, read> ${h} : Matrix${1+f};
    `)}),o!==""&&a.push(`
    [[group(0), binding(${1+n.variableNames.length})]] var<uniform> uniforms : Uniforms;
    `),a.push(s);let[u,l]=Hae(t.shape,n.dispatchLayout),c=mE(t.shape),d=[hE,a.join(`
`),fE,c,u,zae(t.shape.length)];if(n.atomic||d.push(Wae(t.shape,t.dtype,n.isVec4)),l===t.shape.length){let h=e.map(f=>Vae(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
`)}var hE=`
  fn idiv(a: i32, b: i32, sign: f32) -> i32 {
    var res: i32 = a / b;
    let mod: i32 = a % b;
    if (sign < 0. && mod != 0) {
      res = res - 1;
    }
    return res;
  }

  fn isNanCustom(val : f32) -> bool {
    if (val > 0.0) {
      return false;
    }
    if (val < 0.0) {
      return false;
    }
    if (val == 0.0) {
      return false;
    }
    return true;
  }

  fn isNanCustomVec4F32(val : vec4<f32>) -> vec4<f32> {
    var res = vec4<f32> (0.0);
    for (var i = 0u; i < 4u; i = i + 1u) {
      if (isNanCustom(val[i])) {
        res[i] = 1.0;
      } else {
        res[i] = 0.0;
      }
    }
    return res;
  }

  // Checks whether coordinates lie within the bounds of the shape.
  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
    return all(coord >= vec4<i32>(0)) &&
        all(coord < shape);
  }

  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
    return all(coord >= vec3<i32>(0)) &&
        all(coord < shape);
  }

  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
    return all(coord >= vec2<i32>(0)) &&
        all(coord < shape);
  }
  `,fE=`
  fn getFlatIndex1D(coord : i32, shape : i32) -> i32 {
    return coord;
  }

  fn getFlatIndex2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
    return dot(coords, vec2<i32>(shape.y, 1));
  }

  fn getFlatIndex3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
  }

  fn getFlatIndex4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
    return dot(coords, vec4<i32>(
        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
  }

  // Only used when the y/z dimension of workgroup size is 1.
  fn getGlobalIndex(globalId : vec3<u32>, localId : vec3<u32>, numWorkgroups: vec3<u32>) -> i32 {
    if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
      return i32(globalId.x);
    }

    let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
        localId.y * workGroupSizeX + localId.x;
    let workGroupID = (globalId - localId)/vec3<u32>(
        workGroupSizeX, workGroupSizeY, workGroupSizeZ);

    return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
      workGroupID.y * numWorkgroups.x + workGroupID.x) *
      (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
      localInvocationIndex);
  }
`;function zae(e){let t="";switch(e){case 0:case 1:t+=`
        fn getOutputFlatIndex(coords : i32) -> i32 {
          return coords;
        }
        `;break;case 2:t+=`
        fn getOutputFlatIndex(coords : vec2<i32>) -> i32 {
          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
        }
        `;break;case 3:t+=`
        fn getOutputFlatIndex(coords : vec3<i32>) -> i32 {
          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
        }
        `;break;case 4:t+=`
        fn getOutputFlatIndex(coords : vec4<i32>) -> i32 {
          return dot(coords, vec4<i32>(
            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
        }
        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Wae(e,t,n){let r=e.length,s=Bm(t,n),a;if(n?a=`fn setOutputFlat(flatIndex : i32, value : vec4<f32>) {
      result.numbers[flatIndex] = ${s}(value);
    }
    fn setOutputFlatI32(flatIndex : i32, value : vec4<i32>) {
      result.numbers[flatIndex] = ${s}(value);
    }`:a=`fn setOutputFlat(flatIndex : i32, value : f32) {
      result.numbers[flatIndex] = ${s}(value);
    }
    fn setOutputFlatI32(flatIndex : i32, value : i32) {
      result.numbers[flatIndex] = ${s}(value);
    }`,r>=2){let o=["d0","d1","d2","d3"].slice(0,r),i=on(r);n?a+=`
      fn setOutput(${o.map(u=>`${u} : i32`).join(", ")}, value : vec4<f32>) {
        let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
        setOutputFlat(flatIndex / 4, value);
      }
      fn setOutputI32(${o.map(u=>`${u} : i32`).join(", ")}, value : vec4<i32>) {
        let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
        setOutputFlatI32(flatIndex / 4, value);
      }
    `:a+=`
      fn setOutput(${o.map(u=>`${u} : i32`).join(", ")}, value : f32) {
        let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
        setOutputFlat(flatIndex, value);
      }
      fn setOutputI32(${o.map(u=>`${u} : i32`).join(", ")}, value : i32) {
        let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
        setOutputFlatI32(flatIndex, value);
      }
    `}return a}function Vae(e,t,n,r){let s=Uae(e,n);return e.shape.length<=t.length&&(s+=Gae(e,t,n,r)),s}function Uae(e,t){let n=e.name,r=e.shape.length,s=on(r),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,r),i=o.map(c=>`${c} : i32`).join(", ");if(r<1)return t?`
        fn ${a}() -> vec4<f32> {
          return vec4<f32>(${n}.numbers[0]);
        }
      `:`
      fn ${a}() ->f32 {
        return f32(${n}.numbers[0]);
      }
    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${r}D`;return r===0&&(l="1D"),t?`
      fn ${a}(${i}) -> vec4<f32> {
        return vec4<f32>(${n}.numbers[getFlatIndex${l}(${s}(${o.join(",")}),
          ${u}) / 4]);
      }
      `:`
    fn ${a}(${i}) -> f32 {
      return f32(${n}.numbers[getFlatIndex${l}(${s}(${o.join(",")}),
        ${u})]);
    }
   `}function Gae(e,t,n,r){let s=e.name,a=s.charAt(0).toUpperCase()+s.slice(1),o="get"+a+"AtOutCoords",i=e.shape.length,u=t.length,l=on(u);if(w.arraysEqual(e.shape,t)&&r)return n?`
        fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4<f32> {
          return vec4<f32>(${s}.numbers[globalIndex]);
        }

        fn ${o}ByCoords(coords : ${l}) -> vec4<f32> {
          return vec4<f32>(${s}.numbers[${u>1?"getOutputFlatIndex(coords)":"coords"} / 4]);
        }
        `:`
      fn ${o}ByGlobalIndex(globalIndex : i32) -> f32 {
        return f32(${s}.numbers[globalIndex]);
      }

      fn ${o}ByCoords(coords : ${l}) -> f32 {
        return f32(${s}.numbers[${u>1?"getOutputFlatIndex(coords)":"coords"}]);
      }
      `;let c=N.getBroadcastDims(e.shape,t),d=u-i,p="";if(i===0)return n?`
      fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4<f32> {
        return get${a}();
      }

      fn ${o}ByCoords(coords : ${l}) -> vec4<f32> {
        return get${a}();
      }
    `:`
      fn ${o}ByGlobalIndex(globalIndex : i32) -> f32{
        return get${a}();
      }

      fn ${o}ByCoords(coords : ${l}) -> f32{
        return get${a}();
      }
    `;u<2&&c.length>=1?p="coords = 0;":p=c.map(g=>`coords[${g+d}] = 0;`).join(`
`);let h="";if(u<2&&i>0)h="coords";else if(u>1){let g=on(i),b=e.shape.map((y,v)=>`coords[${v+d}]`).join(", ");h=`${g}(${b})`}else h="coords";let f=`uniforms.${s.charAt(0).toLowerCase()+s.slice(1)}Shape`,m=`${i}D`;return n?`
      fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4<f32> {
        var coords = getCoordsFromFlatIndex(globalIndex);
        ${p}
        return ${s}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
      }

      fn ${o}ByCoords(coordsIn : ${l}) -> vec4<f32> {
        var coords = coordsIn;
        ${p}
        return ${s}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
      }
    `:`
    fn ${o}ByGlobalIndex(globalIndex : i32) -> f32 {
      var coords = getCoordsFromFlatIndex(globalIndex);
      ${p}
      return f32(${s}.numbers[getFlatIndex${m}(${h}, ${f})]);
    }

    fn ${o}ByCoords(coordsIn : ${l}) -> f32 {
      var coords = coordsIn;
      ${p}
      return f32(${s}.numbers[getFlatIndex${m}(${h}, ${f})]);
    }
  `}function Hae(e,t){let{x:n,y:r=[],z:s=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoordsWithFlatDispatchLayout(globalId : vec3<u32>, localId : vec3<u32>, numWorkgroups: vec3<u32>) -> ${on(a)}{
      let globalIndex = getGlobalIndex(globalId, localId, numWorkgroups);
      return getCoordsFromFlatIndex(globalIndex);
    }
    `,a];let o="",i=[n,r,s],u=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(u+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=Lae(h,"uniforms.outShape");o+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:o+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let l=[];for(let p=0;p<u;p++)l.push(`d${p}`);let c=on(u),d=`fn getOutputCoordsWithNonFlatDispatchLayout(globalId : vec3<u32>) -> ${c} {
    ${o}
  `;return l.length===0?d+=`return ${c}(0); }`:d+=`return ${c}(${l.join(",")}); }`,[d,u]}function mE(e){let t=e.length;if(t<=1)return"fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),r=on(t),s=[];for(let o=0;o<t;o++)s.push(`d${o}`);if(n.length===1)return`    fn getCoordsFromFlatIndex(index : i32) -> vec2<i32> {
      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
      return vec2<i32>(d0, d1);
    }`;let a="var index2 = index;"+n.map((o,i)=>{let u=`let ${s[i]} = index2 / uniforms.outShapeStrides[${i}]`,l=i===n.length-1?`let ${s[i+1]} = index2 - ${s[i]} * uniforms.outShapeStrides[${i}]`:`index2 = index2 - ${s[i]} * uniforms.outShapeStrides[${i}]`;return`${u}; ${l};`}).join("");return`
    fn getCoordsFromFlatIndex(index : i32) -> ${r} {
      ${a}
      return ${r}(${s.join(",")});
    }
  `}var gE={};Ee(gE,{ArrayBufferToTypedArray:()=>bE,GPUBytesPerElement:()=>bk,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>fk,computeWorkGroupSizeForMatMul:()=>mk,computeWorkPerThreadForConv2d:()=>gk,flatDispatchLayout:()=>ze,isWebGPUSupported:()=>yk,tilesFitEvenlyIntoShape:()=>Us});var el=65535,uu=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Us(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,r)=>n%e[r]==0)}function _e(e,t,n=[1,1,1],r=[1,1,1]){let[s,a,o]=[Math.ceil(uu(e.x.map(u=>t[u]))/(n[0]*r[0])),e.y?Math.ceil(uu(e.y.map(u=>t[u]))/(n[1]*r[1])):1,e.z?Math.ceil(uu(e.z.map(u=>t[u]))/(n[2]*r[2])):1];if(s<=el&&a<=el&&o<=el)return[s,a,o];w.assert(s>el&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let i=Math.ceil(Math.sqrt(s));return i>el?(i=Math.ceil(Math.cbrt(s)),w.assert(i<=el,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function fk(e,t){let n=uu(e.x.map(s=>t[s])),r=uu(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function mk(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function gk(e,t){let n=uu(e.x.map(s=>t[s])),r=uu(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function ze(e){return{x:e.map((t,n)=>n)}}function bk(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function bE(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string"){let n=new Int32Array(e),r=new ArrayBuffer(n.length),s=new Uint8Array(r);for(let a=0;a<n.length;a++)s[a]=n[a];return s}else throw new Error(`Unknown dtype ${t}`)}function yk(){return!!navigator.gpu}var Mt;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.SUB=2]="SUB",e[e.DIV=3]="DIV",e[e.EQUAL=4]="EQUAL",e[e.GREATER=5]="GREATER",e[e.GREATER_EQUAL=6]="GREATER_EQUAL",e[e.LESS=7]="LESS",e[e.LESS_EQUAL=8]="LESS_EQUAL",e[e.LOGICAL_AND=9]="LOGICAL_AND",e[e.NOT_EQUAL=10]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=11]="SQUARED_DIFFERENCE",e[e.INT_DIV=12]="INT_DIV",e[e.POW=13]="POW",e[e.PRELU=14]="PRELU",e[e.MAX=15]="MAX",e[e.MIN=16]="MIN",e[e.COMPLEX_MULTIPLY_REAL=17]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=18]="COMPLEX_MULTIPLY_IMAG"})(Mt||(Mt={}));var jae="return a + b;",qae="return areal * breal - aimag * bimag;",Kae="return areal * bimag + aimag * breal;",Xae="return a / b;",Yae="return a * b;",Qae="return (a - b) * (a - b);",Zae="return a - b;",Jae="return f32(a == b);",eoe="return vec4<f32>(a == b);",toe="return f32(a > b);",noe="return vec4<f32>(a > b);",roe="return f32(a >= b);",soe="return vec4<f32>(a >= b);",aoe="return f32(a < b);",ooe="return vec4<f32>(a < b);",ioe="return f32(a <= b);",uoe="return vec4<f32>(a <= b);",coe="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",loe=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
  vec4<f32>(b >= vec4<f32>(1.0)));`,doe=`
  if (isNanCustom(a)) { return a; }
  if (isNanCustom(b)) { return b; }
  `,yE=`
  if (isNaN.r > 0.) {
    resultTemp.r = uniforms.NAN;
  }
  if (isNaN.g > 0.) {
    resultTemp.g = uniforms.NAN;
  }
  if (isNaN.b > 0.) {
    resultTemp.b = uniforms.NAN;
  }
  if (isNaN.a > 0.) {
    resultTemp.a = uniforms.NAN;
  }
  `,poe=`
  let s = sign(a) * sign(b);
  let ia = i32(round(a));
  let ib = i32(round(b));
  return f32(idiv(ia, ib, s));
  `,hoe=`
  let ia = vec4<i32>(round(a));
  let ib = vec4<i32>(round(b));
  let cond = ib != vec4<i32>(0);
  var resultTemp = vec4<i32>(0);
  let s = sign(a) * sign(b);

  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
  if (cond[0]) {
    resultTemp[0] = idiv(ia[0], ib[0], s[0]);
  }
  if (cond[1]) {
    resultTemp[1] = idiv(ia[1], ib[1], s[1]);
  }
  if (cond[2]) {
    resultTemp[2] = idiv(ia[2], ib[2], s[2]);
  }
  if (cond[3]) {
    resultTemp[3] = idiv(ia[3], ib[3], s[3]);
  }
  return vec4<f32>(resultTemp);
  `,foe="return f32(a != b);",moe="return vec4<f32>(a != b);",goe=`
  if(a < 0.0 && floor(b) < b) {
    return uniforms.NAN;
  }
  if (b == 0.0) {
    return 1.0;
  }
  if (round(abs(b) % 2.0) != 1.0) {
    return pow(abs(a), b);
  }
  return sign(a) * pow(abs(a), b);
  `,boe=`
  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
  let isModRound1 = vec4<f32>(isModRound1Bool);
  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
  var resultTemp = multiplier * pow(abs(a), b);

  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
  let isExpZero = b == vec4<f32>(0.0);
  if (isExpZero.r) {
    resultTemp.r = 1.0;
  }
  if (isExpZero.g) {
    resultTemp.g = 1.0;
  }
  if (isExpZero.b) {
    resultTemp.b = 1.0;
  }
  if (isExpZero.a) {
    resultTemp.a = 1.0;
  }
  let isNaN = vec4<f32>(a < vec4<f32>(0.0)) * vec4<f32>(floor(b) < b);
  ${yE}
  return resultTemp;
  `,yoe="if (a < 0.0) { return b * a; }  return a;",voe=`
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
  `;function vE(e,t){let n=t?yE:doe;return t?`
    var resultTemp = vec4<f32>(${e}(a, b));
    let isNaN = min(vec4<f32>(isNanCustomVec4F32(a)) + vec4<f32>(isNanCustomVec4F32(b)), vec4<f32>(1.0));
    `+n+`
    return resultTemp;
  `:n+`
    return ${e}(a, b);
  `}function pp(e,t){switch(e){case 0:return Yae;case 1:return jae;case 2:return Zae;case 3:return Xae;case 4:return t?eoe:Jae;case 5:return t?noe:toe;case 6:return t?soe:roe;case 7:return t?ooe:aoe;case 8:return t?uoe:ioe;case 9:return t?loe:coe;case 10:return t?moe:foe;case 11:return Qae;case 12:return t?hoe:poe;case 14:return t?voe:yoe;case 15:return vE("max",t);case 16:return vE("min",t);case 13:return t?boe:goe;case 17:return qae;case 18:return Kae;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var bt;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(bt||(bt={}));var xoe="return abs(a);",woe="return ceil(a);",koe="return cos(a);",Ioe=`
  let e2x = exp(-a);
  return (e2x + 1.0 / e2x) / 2.0;
`,Soe="return exp(a) - 1.0;",Coe="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",Toe=`
  var resFloat = exp(a) - vec4<f32>(1.0);
  if (a.r >= 0.0) {
    resFloat.r = a.r;
  }
  if (a.g >= 0.0) {
    resFloat.g = a.g;
  }
  if (a.b >= 0.0) {
    resFloat.b = a.b;
  }
  if (a.a >= 0.0) {
    resFloat.a = a.a;
  }
  return resFloat;
`,Noe="return exp(a);",_oe="return floor(a);",Eoe="return a;",Aoe=`if (a < 0.0) { return 1.0/0.0; }
  return log(a);`,$oe="return f32(!(a >= 1.0));",Foe="return -a;",Doe="return (a < 0.0) ? b * a : a;",Roe="return max(a, 0.0);",Poe="return clamp(a, 0.0, 6.0);",Ooe="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Moe=`
  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
  let isNaN = isNan(a);

  if (isNaN.r) {
    resFloat.r = a.r;
  }
  if (isNaN.g) {
    resFloat.g = a.g;
  }
  if (isNaN.b) {
    resFloat.b = a.b;
  }
  if (isNaN.a) {
    resFloat.a = a.a;
  }
  return resFloat;
`,Loe="return 1.0/sqrt(a);",Boe="return 1.0 / (1.0 + exp(-1.0 * a));",zoe="return sin(a);",Woe=`
  let e2x = exp(a);
  return (e2x - 1.0 / e2x) / 2.0;
`,Voe="return sqrt(a);",Uoe="return a * a;",Goe=`
  let e2x = exp(-2.0 * abs(a));
  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,Hoe="return f32(i32((a)));";function tl(e,t){switch(e){case 0:return xoe;case 2:return koe;case 3:return Ioe;case 1:return woe;case 4:return t?Toe:Coe;case 5:return Noe;case 6:return Soe;case 7:return _oe;case 8:return Eoe;case 9:return Aoe;case 10:return $oe;case 11:return Foe;case 12:return Doe;case 13:return t?Moe:Roe;case 14:return t?Ooe:Poe;case 15:return Loe;case 18:return Boe;case 16:return zoe;case 17:return Woe;case 19:return Voe;case 20:return Uoe;case 21:return Goe;case 22:return Hoe;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Gs(e,t=!1){if(e===null)return null;if(e==="linear")return tl(bt.LINEAR);if(e==="relu")return tl(bt.RELU,t);if(e==="elu")return tl(bt.ELU,t);if(e==="relu6")return tl(bt.RELU6,t);if(e==="prelu")return pp(Mt.PRELU,t);if(e==="sigmoid")return tl(bt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function xE(e,t){let n={RowPerThread:e[1],ColPerThread:e[0],TileAOuter:t[1]*e[1],TileBOuter:t[0]*e[0],TileInner:t[0]*e[0]};return`
  var<workgroup> mm_Asub : array<array<vec4<f32>, ${n.TileInner/n.ColPerThread}>, ${n.TileAOuter}>;
  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n.TileBOuter/n.ColPerThread}>, ${n.TileInner}>;

  let RowPerThread = ${n.RowPerThread};
  let ColPerThread = ${n.ColPerThread}; // only support ColPerThread = 4
  let TileAOuter = ${n.TileAOuter};
  let TileBOuter = ${n.TileBOuter};
  let TileInner = ${n.TileInner};

  ${Jc()} {

    let tileRow = i32(localId.y) * RowPerThread;
    let tileCol = i32(localId.x);

    let globalRow = i32(globalId.y) * RowPerThread;
    let globalCol = i32(globalId.x);
    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;

    var acc: array<vec4<f32>, ${n.RowPerThread}>;
    var ACached : vec4<f32>;
    var BCached : array<vec4<f32>, 4>;

    // Loop over shared dimension.
    var globalColA = tileCol;
    let RowPerThreadB = TileInner / ${t[1]};
    let tileRowB = i32(localId.y) * RowPerThreadB;
    for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
            let inputRow = tileRow + innerRow;
            let inputCol = tileCol;
            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
        }
        globalColA = globalColA + TileInner / ColPerThread;

        // Load one tile of B into local memory.
        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
            let inputRow = tileRowB + innerRow;
            let inputCol = tileCol;
            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
        }

        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
            BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
            BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
            BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
            BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];

            for (var i = 0; i < RowPerThread; i = i + 1) {
                ACached = mm_Asub[tileRow + i][k];
                acc[i] = BCached[0] * ACached.x + acc[i];
                acc[i] = BCached[1] * ACached.y + acc[i];
                acc[i] = BCached[2] * ACached.z + acc[i];
                acc[i] = BCached[3] * ACached.w + acc[i];
            }
        }

        workgroupBarrier();
    }

    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
        mm_write(globalRow + innerRow,
                 globalCol,
                 acc[innerRow], globalId);
    }
}`}function joe(e){return`
  var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
  let tileSize = ${e[0]*4};
  ${Jc()} {
    let tileCol = i32(localId.x);
    let globalCol = i32(globalId.x);
    let globalRow = i32(globalId.y);

    let numTiles = (uniforms.dimInner - 1) / tileSize + 1;

    // Without this initialization strange values show up in acc.
    var acc = vec4<f32>(0.0);

    // Loop over shared dimension.
    for (var t = 0; t < numTiles; t = t + 1) {
      // Load one tile of A into local memory.
      let colA = t * tileSize / 4 + tileCol;
      mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);

      workgroupBarrier();

      // Compute acc values for a single thread.
      for (var k = 0; k < tileSize / 4; k = k + 1) {
        let rowB = t * tileSize + k * 4;
        let BCached0 = mm_readB(rowB, globalCol, globalId);
        let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
        let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
        let BCached3 = mm_readB(rowB + 3, globalCol, globalId);

        let ACached = mm_Asub[k];
        acc = acc + BCached0 * ACached.x;
        acc = acc + BCached1 * ACached.y;
        acc = acc + BCached2 * ACached.z;
        acc = acc + BCached3 * ACached.w;
      }

      workgroupBarrier();
    }

    if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
      mm_write(globalRow, globalCol, acc, globalId);
    }
  }
`}var qoe=class{constructor(e,t,n,r=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=mk(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=r!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=s,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${n}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],r=this.workGroupSize[1]*this.workPerThread,s=this.workGroupSize[0]*this.vecSize,a=s,o=[r,a],i=[a,s];return[Us(o,this.aShape.slice(1)),Us(i,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
            return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
        }
        return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
        }
        return vec4<f32>(0.0)`,n="",r="";if(this.activation){let o=Gs(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
                  let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
                  ${o}
                }`:n=`
            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
              ${o}
            }`,r="value = activation(value, outCoord);"}let s=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
      ${n}
      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / ${this.vecSize};
        let batch = i32(globalId.z);
        ${e};
      }

      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / ${this.vecSize};
        let batch = i32(globalId.z);
        ${t};
      }

      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
        {
          var value = valueIn;
          let batch = i32(globalId.z);
          let outCoord = vec3<i32>(batch, row, col * 4);
          ${s}
          ${r}
          setOutput(outCoord[0], outCoord[1], outCoord[2], value);
        }
      }
      ${this.outputShape[1]>1?xE([this.vecSize,this.workPerThread,1],this.workGroupSize):joe(this.workGroupSize)}

    `}};function vk(e,t){let n=t[1]*e[1],r=t[0]*e[0],s=n>r?n:r;return`
    var<workgroup> mm_Asub : array<array<f32, ${s}>, ${n}>;
    var<workgroup> mm_Bsub : array<array<f32, ${r}>, ${s}>;
    ${Jc()} {
      let tileRow = i32(localId.y) * ${e[1]};
      let tileCol = i32(localId.x) * ${e[0]};

      let globalRow = i32(globalId.y) * ${e[1]};
      let globalCol = i32(globalId.x) * ${e[0]};

      let numTiles = (uniforms.dimInner - 1) / ${s} + 1;

      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
      var ACached : f32;
      var BCached : array<f32, ${e[0]}>;

      // Without this initialization strange values show up in acc.
      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
          acc[innerRow][innerCol] = 0.0;
        }
      }

      let ColPerThreadA = ${s} / ${t[0]};
      let tileColA = i32(localId.x) * ColPerThreadA;
      let RowPerThreadB = ${s} / ${t[1]};
      let tileRowB = i32(localId.y) * RowPerThreadB;

      // Loop over shared dimension.
      for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
            let inputRow = tileRow + innerRow;
            let inputCol = tileColA + innerCol;

            mm_Asub[inputRow][inputCol] = mm_readA(
                globalRow + innerRow,
                t * ${s} + inputCol, globalId);
          }
        }
        // Load one tile of B into local memory.
        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
            let inputRow = tileRowB + innerRow;
            let inputCol = tileCol + innerCol;

            mm_Bsub[inputRow][inputCol] = mm_readB(
              t * ${s} + inputRow,
              globalCol + innerCol, globalId);
          }
        }

        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < ${s}; k = k + 1) {
          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
            BCached[inner] = mm_Bsub[k][tileCol + inner];
          }

          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
            ACached = mm_Asub[tileRow + innerRow][k];
            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
            }
          }
        }

        workgroupBarrier();
      }

      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {

          if ((globalCol + innerCol) < uniforms.dimBOuter &&
              (globalRow + innerRow) < uniforms.dimAOuter) {
            mm_write(globalRow + innerRow,
                     globalCol + innerCol,
                     acc[innerRow][innerCol], globalId);
          }
        }
      }
    }
  `}function Koe(e){return`
    let TileSize = ${e[0]*4};
    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;

    ${Jc()} {
      let tileCol = i32(localId.x);
      let globalCol = i32(globalId.x);
      let globalRow = i32(globalId.y);

      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;

      // Without this initialization strange values show up in acc.
      var acc = 0.0;

      // Loop over shared dimension.
      for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        let colA = t * TileSize + tileCol * 4;
        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
                                mm_readA(globalRow, colA + 1, globalId),
                                mm_readA(globalRow, colA + 2, globalId),
                                mm_readA(globalRow, colA + 3, globalId));
        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < TileSize / 4; k = k + 1) {
          let rowB = t * TileSize + k * 4;
          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
                              mm_readB(rowB + 1, globalCol, globalId),
                              mm_readB(rowB + 2, globalCol, globalId),
                              mm_readB(rowB + 3, globalCol, globalId));

          let ACached = mm_Asub[k];
          acc = acc + dot(ACached, BCached);
        }

        workgroupBarrier();
      }

      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
        mm_write(globalRow, globalCol, acc, globalId);
      }
    }
  `}var wE=class{constructor(e,t,n,r=!1,s=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=r?e[1]:e[2];this.workGroupSize=mk(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=i!=null;l&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=r,this.transposeB=s,this.addBias=l,this.activation=o,this.hasPreluActivationWeights=c;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,u]:[this.outputShape[0],u,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${r}_${s}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,r=t>n?t:n;this.outputShape[1]===1&&(r*=4),w.assert(r%this.workGroupSize[0]==0&&r%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[t,r],a=[r,n];return[Us(s,this.aShape.slice(1)),Us(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
           }
           return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
           }
           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
           }
           return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
           }
           return 0.0;`;let n="",r="";if(this.activation){let o=Gs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
               let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
               ${o}
            }`:n=`
              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                ${o}
              }
            `,r="value = activation(value, outCoord);"}let s=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
      ${n}

      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        let batch = i32(globalId.z);
        ${e}
      }

      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        let batch = i32(globalId.z);
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
        ${t}
      }

      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
        var value = valueIn;
        let batch = i32(globalId.z);
        let outCoord = vec3<i32>(batch, row, col);
        ${s}
        ${r}
        setOutput(batch, row, col, value);
      }
      ${this.outputShape[1]>1?vk([this.workPerThread,this.workPerThread,1],this.workGroupSize):Koe(this.workGroupSize)}
    `}};function Xoe(){return`
    var<workgroup> sumValues : array<f32, workGroupSizeX>;
    ${Jc()} {
      let coords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
      let batch = coords[0];
      let row = coords[1];
      let col = coords[2];
      var sum = 0.0;
      let Length = uniforms.dimInner;
      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
        let dataA = mm_readA(batch, row, k);
        let dataB = mm_readB(batch, k, col);
        sum = sum + dataA * dataB;
      }
      sumValues[localId.x] = sum;
      workgroupBarrier();

      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
          currentSize = currentSize / 2u) {
        if (localId.x < currentSize)
        {
          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
        }
        workgroupBarrier();
      }

      if (localId.x == 0u) {
        sum = sumValues[0] + sumValues[1];
        mm_write(batch, row, col, sum);
      }
    }
  `}var Yoe=class{constructor(e,t=!1,n=!1,r=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=r!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=s,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":e="return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";let n="",r="";if(this.activation){let o=Gs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
               let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
               ${o}
            }`:n=`
              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                ${o}
              }
            `,r="value = activation(value, outCoord);"}let s=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
      ${n}

      fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        ${e}
      }

      fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
        ${t}
      }

      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
        var value = valueIn;
        let outCoord = vec3<i32>(batch, row, col);
        ${s}
        ${r}
        setOutput(batch, row, col, value);
      }
      ${Xoe()}
    `}};function Qoe(e){let t=e[1]/2,n=e[0],r=t>n?t:n;return`
  var<workgroup> mm_Asub1 : array<array<f32, ${r}>, ${t}>;
  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${r}>;
  var<workgroup> mm_Asub2 : array<array<f32, ${r}>, ${t}>;
  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${r}>;

  // If the output size is small for matrix multiplication, avoid to use vec4
  // and handle some elements per thread to optimally utilize the ALU.
  // Introduces two shared memory buffers, some logical threads could handle
  // arithmetic operations and others handle IO operations between barrier api,
  // makes ALUs and load/store units work simultaneously, could improves
  // the performance.
  ${Jc()} {
    let tileRow = i32(localId.y);
    let tileCol = i32(localId.x);
    let globalRow = i32(globalId.y);
    let globalCol = i32(globalId.x);

    // uniforms.dimInner should be greater than 0.
    let numTiles = (uniforms.dimInner - 1) / ${r} + 1;
    var acc = 0.0;

    var globalColA = tileCol;
    var globalRowB = tileRow;
    for (var t = 0; t < numTiles; t = t + 1) {
      if (t == 0) {
        if (tileRow < ${t}) {
          // Load one tile of A and B into local memory.
          // globalRow is always greater than or equal tileRow.
          mm_Asub1[tileRow][tileCol] =
              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
          globalColA = globalColA + ${r};
          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${r};
        }
      } else {
        if (tileRow < ${t}) {
          // Load one tile of A and B into local memory.
          // globalRow is always greater than or equal tileRow.
          mm_Asub1[tileRow][tileCol] =
              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
          globalColA = globalColA + ${r};
          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${r};
        } else {
          // Compute acc values for a single thread.
          for (var k = 0; k < ${r}; k = k + 1) {
            let subRow = tileRow - ${t};
            if (subRow < 0) {
              continue;
            }
            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
          }
        }
      }
      workgroupBarrier();
      if (t != 0) {
        t = t + 1;
      }

      if (t < numTiles) {
        if (tileRow < ${t}) {
          // Load one tile of A and B into local memory.
          // globalRow is always greater than or equal tileRow.
          mm_Asub2[tileRow][tileCol] =
              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
          globalColA = globalColA + ${r};
          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${r};
        } else {
          // Compute acc values for a single thread.
          for (var k = 0; k < ${r}; k = k + 1) {
            let subRow = tileRow - ${t};
            if (subRow < 0) {
              continue;
            }
            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
          }
        }
      }
      workgroupBarrier();
    }
    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
    if (tileRow >= ${t} && writeCol >= 0) {
      mm_write(writeCol, globalCol, acc, globalId);
    }
  }
  `}var Zoe=class{constructor(e,t,n,r=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=r!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=s,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
          return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
        }
        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
         }
         return 0.0;`,n="",r="";if(this.activation){let o=Gs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
            let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
            ${o}
            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
            ${o}
        }`,r="value = activation(value, outCoord);"}let s=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
      ${n}

      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        let batch = i32(globalId.z);
        ${e}
      }
      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        let batch = i32(globalId.z);
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
        ${t}
      }
      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
          let batch = i32(globalId.z);
          let outCoord = vec3<i32>(batch, row, col);
          var value = valueIn;
          ${s}
          ${r}
          setOutput(batch, row, col, value);
        }
      }
      ${Qoe(this.workGroupSize)}
    `}};function We(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:s}=n,a=w.sizeFromShape(r.shape),o=w.inferFromImplicitShape(s,a),i=w.sizeFromShape(o);return w.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${r.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:o,dtype:r.dtype}}var Joe={kernelName:hi,backendName:"webgpu",kernelFunc:We};function xk({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,d=n?e.shape[l-2]:e.shape[l-1],p=r?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Ri.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let k=n?[b,d,h]:[b,h,d],T=r?[y,f,p]:[y,p,f],C=We({inputs:{x:e},backend:s,attrs:{shape:k}}),E=We({inputs:{x:t},backend:s,attrs:{shape:T}}),F=[C,E],O=Math.max(b,y),D=d%4==0&&f%4==0&&!n&&!r&&f>=32,R;h*f<=32?R=new Yoe([O,h,f],n,r,a,u,o):!n&&!r&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?R=new Zoe(k,T,[O,h,f],a,u,o):D?R=new qoe(k,[O,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,o):R=new wE(k,[O,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,r,a,u,o);let _=[C,E];a&&_.push(a),o&&_.push(o);let L=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],U=s.runWebGPUProgram(R,_,e.dtype,L),j=We({inputs:{x:U},backend:s,attrs:{shape:x}});F.push(U);for(let K of F)s.disposeData(K.dataId);return j}function eie(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:d}=r;return xk({a:s,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var tie={kernelName:to,backendName:"webgpu",kernelFunc:eie},kE=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
      fn binaryOpComplex(
          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
        ${pp(this.op,!1)}
      }

      ${Ue()}
        if(index < uniforms.size) {
          let areal = getARealAtOutCoordsByGlobalIndex(index);
          let aimag = getAImagAtOutCoordsByGlobalIndex(index);
          let breal = getBRealAtOutCoordsByGlobalIndex(index);
          let bimag = getBImagAtOutCoordsByGlobalIndex(index);
          setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
        }
      }
    `}},nie=class{constructor(e,t,n,r){this.variableNames=["A","B"],this.size=!0;let s=256;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.lastDimensionSize=r?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=r,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAAtOutCoordsByCoords(coords);
         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
         let b = getBAtOutCoordsByCoords(coords);`;return`
        fn binaryOperation(a : f32, b : f32) -> f32 {
          ${pp(this.op,!1)}
        }
        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
        ${Ue()}

          // Fill in the shared memory buffer. Here we need a loop to make sure
          // that all data in A|B are uploaded when |sharedMemorySize| is larger
          // than work group size.
          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
          }
          workgroupBarrier();

          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
            let flatIndex = index * ${this.workPerThread} + i;
            if(flatIndex < uniforms.size) {
              let coords = getCoordsFromFlatIndex(flatIndex);

              ${t}
              setOutputFlat(flatIndex, binaryOperation(a, b));
            }
          }
        }
        `}},rie=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
        ${pp(this.op,this.isVec4)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAAtOutCoordsByGlobalIndex(index);
          let b = getBAtOutCoordsByGlobalIndex(index);
          setOutputFlat(index, binaryOperation(a, b));
        }
      }
    `}},IE=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
      fn binaryOperation(a : f32, b : f32) -> f32 {
        ${pp(this.op,!1)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAAtOutCoordsByGlobalIndex(index);
          let b = getBAtOutCoordsByGlobalIndex(index);
          setOutputFlat(index, binaryOperation(a, b));
        }
      }
      `}};function SE(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4==0)return new rie(e,t,n);let s=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return s||a?new nie(e,t,n,a):new IE(e,t,n)}function Lr(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var sie={kernelName:_a,backendName:"webgpu",kernelFunc:Lr};function nl(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=Lr({inputs:{x:r},backend:n}),u=Lr({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var aie={kernelName:ql,backendName:"webgpu",kernelFunc:nl},Wm=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
      fn unaryOperation(a : f32) -> f32 {
        ${tl(this.op,!1)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAAtOutCoordsByGlobalIndex(index);
          setOutputFlat(index, unaryOperation(a));
        }
      }
      `}};function fn({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:r,backend:s})=>{let{x:a}=r,o=s,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let l=o.tensorMap.get(a.dataId),c=t(l.values,i);return o.makeTensorInfo(a.shape,i,c)}let u=new Wm(a.shape,e);return o.runWebGPUProgram(u,[a],i)}}function Rn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:r}){return({inputs:s,backend:a})=>{let{a:o,b:i}=s,u=a;if(n&&o.dtype==="complex64"){let d=u.tensorMap.get(o.dataId),p=u.tensorMap.get(i.dataId),h,f;if(e!==Mt.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:o.shape},x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},k=SE(e,o.shape,i.shape);return u.runWebGPUProgram(k,[v,x],In(b.dtype,y.dtype))});else{let g=new kE(Mt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),b=new kE(Mt.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),y=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=nl({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=r||In(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||u.shouldExecuteOnCPU([o,i]))&&t!=null){let d=u.tensorMap.get(o.dataId).values,p=u.tensorMap.get(i.dataId).values,h=o.dtype==="string"?N.fromUint8ToStringArray(d):d,f=o.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=SE(e,o.shape,i.shape);return u.runWebGPUProgram(c,[o,i],l)}}var{addImpl:oie,ceilImpl:iie,concatImpl:uie,equalImpl:cie,expImpl:lie,expm1Impl:die,floorImpl:pie,gatherNdImpl:hie,gatherV2Impl:fie,greaterEqualImpl:mie,greaterImpl:gie,lessEqualImpl:bie,lessImpl:yie,logImpl:vie,maxImpl:xie,maximumImpl:wie,minimumImpl:kie,multiplyImpl:Iie,negImpl:Sie,notEqualImpl:Cie,prodImpl:Tie,rangeImpl:Nie,rsqrtImpl:_ie,simpleAbsImpl:Eie,sliceImpl:Aie,stridedSliceImpl:$ie,stringNGramsImpl:Fie,subImpl:Die,tileImpl:Rie,topKImpl:Pie,transposeImpl:Oie,uniqueImpl:abe}=fm,Mie=fn({opType:bt.ABS,cpuKernelImpl:Eie}),Lie={kernelName:Vo,backendName:"webgpu",kernelFunc:Mie},Bie=Rn({opSnippet:Mt.ADD,cpuKernelImpl:oie,supportsComplex:!0}),zie={kernelName:_s,backendName:"webgpu",kernelFunc:Bie},Wie=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}AtOutCoordsByCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
      ${Ue()}
        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if (flatIndex < uniforms.size) {
            let coords = getCoordsFromFlatIndex(flatIndex);
            ${e.join(`
        `)}
            setOutputFlat(flatIndex, ${t});
          }
        }
      }
    `}};function Vie(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Lr({inputs:{x:r[0]},backend:n});let s=r.map(i=>i.dtype).reduce((i,u)=>In(i,u)),a=r.map(i=>i.shape),o=new Wie(a);return n.runWebGPUProgram(o,r,s)}var Uie={kernelName:la,backendName:"webgpu",kernelFunc:Vie},CE=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let r=[t];N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,e.length),this.op=n==="min"?"<":">";let[s]=N.computeOutAndReduceShapes(e,r);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
    `,t=(s,a)=>this.outputShape.length===1?s:`${s}[${a}]`,n=s=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${s}]`;return`
      fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
      }

      ${e}

      // In order to get a flattened index into the input tensor, we need to
      // add back the index along the reduced dimension to |outputCoords|.
      // This function outputs the offset to the first value along
      // |axis| and the stride to get the next value of the input along |axis|.
      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{
        let outputCoords = getCoordsFromFlatIndex(outputIndex);
        var i = ${this.outputShape.length-1};

        var stride = 1;
        var inputStride = 1;
        var offset = 0;

        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
          let length = ${n(`${this.inputShape.length} - r`)};
          if (${this.inputShape.length} - r == uniforms.axis) {
            inputStride = stride;
          } else {
            offset = offset + ${t("outputCoords","i")} * stride;
            i = i - 1;
          }
          stride = stride * length;
        }

        return vec2<i32>(offset, inputStride);
      }

      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
        return coordInfo[0] + coordInfo[1] * index;
      }

      ${Ue()}
        let outputIndex = index / i32(workGroupSizeX);
        let coordInfo = getInputCoordInfo(outputIndex);
        let Length = ${n("uniforms.axis")};

        var bestIndex = i32(localId.x);
        var bestValue = uniforms.infinityValue;

        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
            k = k + i32(workGroupSizeX)) {
          let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);
          if (!isNanCustom(candidate) && candidate ${this.op} bestValue) {
            bestValue = candidate;
            bestIndex = k;
          }
        }
        xBestValues[localId.x] = bestValue;
        xBestIndices[localId.x] = bestIndex;
        workgroupBarrier();

        var reduceSize = min(u32(Length), workGroupSizeX);
        for (var currentSize = reduceSize / 2u; reduceSize > 1u;
            currentSize = reduceSize / 2u) {
          let interval = DIV_CEIL(reduceSize, 2u);
          if (localId.x < currentSize) {
            let candidate = xBestValues[localId.x + interval];
            if (candidate ${this.op} bestValue) {
              bestValue = candidate;
              xBestValues[localId.x] = bestValue;
              xBestIndices[localId.x] = xBestIndices[localId.x + interval];
            }
          }
          reduceSize = interval;
          workgroupBarrier();
        }

        if (localId.x == 0u && outputIndex < uniforms.size) {
          setOutputFlatI32(outputIndex, xBestIndices[localId.x]);
        }
      }
    `}},Gie=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
      let TILE_DIM = ${this.workGroupSize[0]};
      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
      ${zm()}
      fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
              [[builtin(workgroup_id)]] workgroupId : vec3<u32>) {
        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
        let width = uniforms.outShape[0];
        let height = uniforms.outShape[1];
        if (x < width && y < height) {
          tile[localId.y][localId.x] =
              A.numbers[y * width + x];
        }
        workgroupBarrier();

        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
        if (x < height && y < width) {
          setOutputFlat((y * height + x), tile[localId.x]
            [localId.y]);
        }
      }
    `}},Hie=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=on(this.outputShape.length),t=jie(this.newDim);return`
      ${Ue()}

        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if(flatIndex < uniforms.size) {
            let resRC = getCoordsFromFlatIndex(flatIndex);
            setOutputFlat(flatIndex, A.numbers[getFlatIndex${this.outputShape.length}D(
              ${e}(${t}), uniforms.aShape)]);
          }
        }
      }
    `}};function jie(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let r=0;r<e.length;r++)n[e[r]]=`resRC[${r}]`;return n.join()}function cu(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=s.shape[a[c]];if(n.shouldExecuteOnCPU([s])){let d=o.tensorMap.get(s.dataId).values,p=Oie(d,s.shape,s.dtype,a,u);return n.makeTensorInfo(u,s.dtype,p)}if(s.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new Gie(s.shape,a);return o.runWebGPUProgram(c,[s],s.dtype)}let l=new Hie(s.shape,a);return o.runWebGPUProgram(l,[s],s.dtype)}var qie={kernelName:Ja,backendName:"webgpu",kernelFunc:cu};function Kie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),u=s,l=[];i!=null&&(u=cu({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(u),o=N.getInnerMostAxes(o.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],u.shape.length);let c=new CE(u.shape,o[0],"max"),d=[{type:"int32",data:[o[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=n.runWebGPUProgram(c,[u],"int32",d);return l.forEach(h=>n.disposeData(h.dataId)),p}var Xie={kernelName:da,backendName:"webgpu",kernelFunc:Kie};function Yie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),u=s,l=[];i!=null&&(u=cu({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(u),o=N.getInnerMostAxes(o.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],u.shape.length);let c=new CE(u.shape,o[0],"min"),d=[{type:"int32",data:[o[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=n.runWebGPUProgram(c,[u],"int32",d);return l.forEach(h=>n.disposeData(h.dataId)),p}var Qie={kernelName:Hu,backendName:"webgpu",kernelFunc:Yie},TE=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
      ${Ue()}
      if (index < uniforms.size) {
        let coords = getCoordsFromFlatIndex(index);
          let batch = coords[0];
          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
          let xRCorner = xRCCorner.x;
          let xCCorner = xRCCorner.y;

          var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
          var count = 0.0;

          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
            let xR = xRCorner + wR;

            if (xR < 0 || xR >= uniforms.convDims.x) {
              continue;
            }

            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
              let xC = xCCorner + wC;
              if (xC < 0 || xC >= uniforms.convDims.y) {
                continue;
              }

              let value = getX(batch, xR, xC, coords[3]);
              ${e}
            }
          }

          setOutputFlat(index, ${t});
        }
      }
    `}},NE=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
      ${Ue()}
        if (index < uniforms.size) {
          let coords = getCoordsFromFlatIndex(index);
          let batch = coords[0];
          let d = coords[3];

          let xRCCorner = coords.yz * uniforms.stride;
          let xRCorner = xRCCorner.x;
          let xCCorner = xRCCorner.y;

          let value = getX(batch, xRCorner, xCCorner, d);
          setOutputFlat(index, value);
        }
      }
    `}};function Zie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=r,l=1,c=N.computePool2DInfo(s.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Lr({inputs:{x:s},backend:n});let d,p=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?d=new NE(c):(d=new TE(c,"avg"),p.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(d,[s],s.dtype,p)}var Jie={kernelName:pa,backendName:"webgpu",kernelFunc:Zie};function eue(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return xk({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var tue={kernelName:ha,backendName:"webgpu",kernelFunc:eue},nue=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${on(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=on(this.rank),t=rue(this.rank),n;return this.start.length===1?n=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((s,a)=>`sourceLoc.${wk[a]} = uniforms.start[${a}] + coords.${wk[a]};`),`
      ${Ue()}
        if (index < uniforms.size) {
          var sourceLoc : ${e};
          let coords = getCoordsFromFlatIndex(index);
          ${n.join(`
`)}
          setOutputFlat(index, getSource(${t}));
        }
      }
    `}},wk=["x","y","z","w","u","v"];function rue(e){if(e===1)return"sourceLoc";if(e<=6)return wk.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function rl(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,u]=$t.parseSliceParams(s,a,o);if($t.assertParamsValid(s,i,u),n.shouldExecuteOnCPU([s])||s.dtype==="string"){let d=n.tensorMap.get(s.dataId),p=Aie(d.values,i,u,s.shape,s.dtype);return n.makeTensorInfo(u,s.dtype,p)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,s.dtype,[]);let l=new nue(i,u),c=[{type:"int32",data:i}];return n.runWebGPUProgram(l,[s],s.dtype,c)}var sue={kernelName:yi,backendName:"webgpu",kernelFunc:rl},aue=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;w.assert(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,v)=>y*v),u=N.getReshaped(s.shape,a,i),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(s.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(c,o,a.length),h=[],f=We({inputs:{x:s},backend:n,attrs:{shape:u}}),m=cu({inputs:{x:f},backend:n,attrs:{perm:l}}),g=We({inputs:{x:m},backend:n,attrs:{shape:c}}),b=rl({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},oue={kernelName:Uo,backendName:"webgpu",kernelFunc:aue},_E=Rn({opSnippet:Mt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Cie}),iue={kernelName:oi,backendName:"webgpu",kernelFunc:_E};function hp(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.tensorMap.get(r.dataId);return Lr({inputs:{x:s.complexTensorInfos.real},backend:n})}var uue={kernelName:nd,backendName:"webgpu",kernelFunc:hp};function cue(e,t){let n=new Wm(e.shape,bt.TO_INT),r=t.runWebGPUProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function kk(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return Lr({inputs:{x:s},backend:n});let o=kt(s.shape),i=kk({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),u=nl({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),u}if(s.dtype==="complex64"){let o=hp({inputs:{input:s},backend:n}),i=kk({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!w.hasEncodingLoss(s.dtype,a)){let o=Lr({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return cue(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=_E({inputs:{a:s,b:o},backend:n});return n.disposeData(o.dataId),u}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var lue={kernelName:fa,backendName:"webgpu",kernelFunc:kk},due=fn({opType:bt.CEIL,cpuKernelImpl:iie}),pue={kernelName:ma,backendName:"webgpu",kernelFunc:due},hue=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
      ${Ue()}
        if(index < uniforms.size) {
          let value = getAAtOutCoordsByGlobalIndex(index);
          var clampedValue : vec4<f32>;
          for (var i = 0; i < 4; i = i + 1) {
            if (isNanCustom(value[i])) {
              clampedValue[i] = value[i];
            } else {
              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
            }
          }

          setOutputFlat(index, clampedValue);
        }
      }
    `}},fue=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
      ${Ue()}
        if(index < uniforms.size) {
          let value = getAAtOutCoordsByGlobalIndex(index);
          if (isNanCustom(value)) {
            setOutputFlat(index, value);
            return;
          }
          setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
        }
      }
    `}};function mue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i,u=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return w.sizeFromShape(s.shape)%4==0?i=new hue(s.shape):i=new fue(s.shape),n.runWebGPUProgram(i,[s],s.dtype,u)}var gue={kernelName:Es,backendName:"webgpu",kernelFunc:mue},bue=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32;`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutput(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;s<this.offsetLength;s++)e.push(`elseif (yC < uniforms.offset${[s]}){ setOutput(coords.x, coords.y, getT${s}(yR, yC - uniforms.offset${s-1})); }`);let n=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutput(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutput(coords.x, coords.y, getT0(yR, yC));");return`
      ${Ue()}
        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if(flatIndex < uniforms.size) {
            let coords = getCoordsFromFlatIndex(flatIndex);
            let yR = coords.x;
            let yC = coords.y;

            ${e.join(`
        `)}
          }
        }
      }
    `}};function Vm(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.tensorMap.get(r.dataId);return Lr({inputs:{x:s.complexTensorInfos.imag},backend:n})}var yue={kernelName:Zl,backendName:"webgpu",kernelFunc:Vm};function Ik(e,t,n){let r=e[0].dtype;if(r==="complex64"){let h=e.map(y=>hp({inputs:{input:y},backend:n})),f=e.map(y=>Vm({inputs:{input:y},backend:n})),m=Ik(h,t,n),g=Ik(f,t,n),b=nl({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return We({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=N.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=uie(f,m,r,g),y=N.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,r,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:o}=vue(e,t,n),i=a.map(h=>h.shape),u=new bue(i),l=[],c=new Array(i.length-1);if(c.length>0){c[0]=i[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+i[h][1],l.push({type:"int32",data:[c[h]]})}let d=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let p=We({inputs:{x:d},backend:n,attrs:{shape:o}});return n.disposeData(d.dataId),p}function vue(e,t,n){let r=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>We({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:r}}function EE(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=N.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(l=>w.sizeFromShape(l.shape)>0);if(i.length===1)return Lr({inputs:{x:i[0]},backend:n});let u=i.map(l=>l.shape);return N.assertParamsConsistent(u,a),Ik(i,a,n)}var xue={kernelName:Go,backendName:"webgpu",kernelFunc:EE},wue=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
      inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
    ${Ue()}

      for(var i = 0; i<${this.workPerThread}; i = i + 1) {
        let flatIndex = index * ${this.workPerThread} + i;

        let rc = getCoordsFromFlatIndex(flatIndex);

        if(flatIndex < uniforms.size) {
          let blockIndex = rc[0];
          let pos = rc[1];

          let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
          let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
          var value = 0.0;
          if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
            let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
              uniforms.pad[0];
            let d1 = offsetX + uniforms.dilation[0] * ((pos %
              uniforms.itemsPerBlockRow) / uniforms.inChannels);
            let ch = pos % uniforms.inChannels;
            if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
              value = getA(d0, d1, ch);
            }
          }
          setOutputFlat(flatIndex, value);
        }
      }
    }
  `}};function AE({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=e.shape,l=n.dataFormat==="channelsLast",c=!1,d=!1,p=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],h=We({inputs:{x:e},backend:r,attrs:{shape:[1,p,n.inChannels]}}),f=We({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=xk({a:h,b:f,transposeA:c,transposeB:d,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=We({inputs:{x:m},backend:r,attrs:{shape:n.outShape}});return r.disposeData(h.dataId),r.disposeData(f.dataId),r.disposeData(m.dataId),g}function kue({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y==="channelsLast",x=u*l*c,k=m*f,T=[k,x],C=!1,E=!1,F=[],O=We({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),D=We({inputs:{x:t},backend:r,attrs:{shape:[1,x,-1]}});F.push(O),F.push(D);let R=new wue(T,v),_=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,b]},{type:"int32",data:[f]},{type:"int32",data:[c*u]},{type:"int32",data:[c]}],L=r.runWebGPUProgram(R,[O],O.dtype,_),U=We({inputs:{x:L},backend:r,attrs:{shape:[1,T[0],T[1]]}});F.push(L),F.push(U);let j=[1,T[0],T[1]],K=new wE(j,[1,k,n.outChannels],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),C,E),q=j[1],Q=j[2],ee=n.outChannels,re=[{type:"int32",data:[q]},{type:"int32",data:[ee]},{type:"int32",data:[Q]}],se=r.runWebGPUProgram(K,[U,D],U.dtype,re),ne=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],ie=We({inputs:{x:se},backend:r,attrs:{shape:ne}});F.push(se);for(let te of F)r.disposeData(te.dataId);return ie}var $E=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
      dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=[8,8,1];let a=[4,4,1];this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,a),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=r,this.hasLeakyreluAlpha=s,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),[this.fitA,this.fitB]=this.getShapeFit(a),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(e){let t=this.workGroupSize[1]*e[1],n=this.workGroupSize[0]*e[0],r=n,s=[t,r],a=[r,n],o=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],u=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Us(s,[o,u]),Us(a,[u,i])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getFlatIndex4D(coord, uniforms.xShape);
    let divBy4Remainder${e} = flatIndex${e} % 4;
    let divBy4Index${e} = flatIndex${e} / 4;
    let curData${e} = x.numbers[divBy4Index${e}];
    if (divBy4Remainder${e} == 0) {
      temp = curData${e};
    } else {
      // TODO: This could end up being a redundant load with another one in
      // the same shader invocation. Perhaps there's an opportunity for
      // optimization
      let nextData${e} = x.numbers[divBy4Index${e} + 1];
      if (divBy4Remainder${e} == 1) {
        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
      } elseif (divBy4Remainder${e} == 2) {
        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
      } elseif (divBy4Remainder${e} == 3) {
        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
      }
    }
    `}getUserCode(){let t=xE([4,4,1],this.workGroupSize),s=`let outRow = r / uniforms.outShape[2];
        let outCol = r % uniforms.outShape[2];
        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
        let inChCoord = c % uniforms.xShape[3];
        var coord = vec4<i32>(
            batch,
            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
            inChCoord);
        var resData = vec4<f32>(0.0);
        ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
          // the 'same' padding type.
          if (coordsInBounds4D(coord, uniforms.xShape)) {
            resData = x.numbers[getFlatIndex4D(coord, uniforms.xShape) / 4];
          } else {
            resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
          ${this.getSampleAWithRemainder(1)}
          resData = temp;
          if (WCol == (uniforms.filterDims[1] - 1)) {
            coord = vec4<i32>(
              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
              ${this.getSampleAWithRemainder(2)}
            if (inChCoord == 0) {
              resData = vec4<f32>(resData.xyz, temp.x);
            } elseif (inChCoord == 1) {
              resData = vec4<f32>(resData.xy, temp.xy);
            } else {
              resData = vec4<f32>(resData.x, temp.xyz);
            }
          }
          `}
        return resData;`,a=this.fitA?`${s}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
          ${s}
         }
         return vec4<f32>(0.0);
        `,o=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W.numbers[row * uniforms.dimBOuter / 4 + col];
         }
         return vec4<f32>(0.0);
        `,i="",u="";if(this.activation){let d=Gs(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
          ${d}
        }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(a: vec4<f32>) -> vec4<f32> {
          let b = getLeakyreluAlphaAtOutCoords();
          ${d}
        }`,new Error("Leakyrelu is not supported.");i=`
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          ${d}
        }`}u="value = activation(value, outCoord);"}let l=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
        ${i}
        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
          let r = row;
          let c = col * 4;
          var batch = i32(globalId.z);
          ${a}
        }

        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
          ${o}
        }

        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
          var batch = i32(globalId.z);
          var value = valueInput;
          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
          {
            let outCoord = vec4<i32>(
              batch,
              row / uniforms.outShape[2],
              row % uniforms.outShape[2],
              col * 4);
            ${l}
            ${u}
            setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
              value);
          }
        }
        ${t}
      `}},FE=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=fk(this.dispatchLayout,this.outputShape),this.elementsPerThread=gk(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=r,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[e,n],s=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Us(r,[a,i]),Us(s,[i,o])]}getUserCode(){let e=vk(this.elementsPerThread,this.workGroupSize),t=`
    let outRow = row / uniforms.outShape[2];
    let outCol = row % uniforms.outShape[2];

    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
    let coord = vec4<i32>(
        batch,
        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
        col % uniforms.xShape[3]);
    // The bounds checking is always needed since we use it to pad zero for the
    // 'same' padding type.
    if(coordsInBounds4D(coord, uniforms.xShape)) {
      return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
    }
    return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      ${t}
    }
    return 0.0;
    `,r=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W.numbers[row * uniforms.dimBOuter + col];
	 }
	 return 0.0;
	 `,s="",a="";if(this.activation){let u=Gs(this.activation,!1);this.hasPreluActivationWeights?s=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
                  let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
                  ${u}
                }`:s=`
                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
                    ${u}
                  }
                `,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
    ${s}
    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      var batch = i32(globalId.z);
      ${n}
    }

    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      ${r}
    }

    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
      var batch = i32(globalId.z);
      var value = valueInput;
      let outCoord = vec4<i32>(
          batch,
          row / uniforms.outShape[2],
          row % uniforms.outShape[2],
          col);
      ${o}
      ${a}
      result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
    }
    ${e}
  `}},DE=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=r,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let s=Gs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
               let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
               ${s}
             }`:e=`
                  fn activation(a : f32, outCoord : vec4<i32>) -> f32{
                    ${s}
                  }
                `,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
      ${e}
      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
        let coord = vec4<i32>(batch, row, col, chan);
        if(coordsInBounds4D(coord, uniforms.xShape)) {
          return getX(batch, row, col, chan);
        }
        return 0.0;
      }

      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
        let coord = vec4<i32>(row, col, xChannel, outChannel);
        if(coordsInBounds4D(coord, uniforms.wShape)) {
          return getW(row, col, xChannel, outChannel);
        }
        return 0.0;
      }

      fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
        let coord = vec4<i32>(batch, row, col, chan);
        if (coordsInBounds4D(coord, uniforms.outShape)) {
          ${n}
          ${t}
          setOutput(batch, row, col, chan, value);
        }
      }

      ${hk()} {
        let coords = getOutputCoordsWithFlatDispatchLayout(globalId, localId, numWorkgroups);
        let batch = coords[0];
        let outChannel = coords[3];

        var acc = 0.0;

        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
              let v = readInp(batch, coordRow, coordCol, xChannel);
              let f = readFilt(row, col, xChannel, outChannel);
              acc = acc + v * f;
            }
          }
        }

        writeResult(batch, coords[1], coords[2], outChannel, acc);
      }
    `}};function Iue(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=n,d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(s.shape,a.shape,o,l,i,c,!1,d);if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))return AE({x:s,filter:a,convInfo:p,backend:r});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&s.shape[0]===1)return kue({x:s,filter:a,convInfo:p,backend:r});let h,f=[p.padInfo.top,p.padInfo.left],m=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]}],g=X().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new DE(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new $E(p):h=new FE(p),!g){let b=p.outShape[1]*p.outShape[2],y=p.outShape[3],v=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}return r.runWebGPUProgram(h,[s,a],s.dtype,m)}var Sue={kernelName:ga,backendName:"webgpu",kernelFunc:Iue},Cue=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=fk(this.dispatchLayout,this.outputShape),this.elementsPerThread=gk(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      var batch = i32(globalId.z);
      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      
    let outRow = row / uniforms.outShape[2];
    let outCol = row % uniforms.outShape[2];

    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
      return 0.0;
    }
    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
      return 0.0;
    }
    let coord = vec4<i32>(
        batch,
        i32(xR),
        i32(xC),
        col % uniforms.outBackprop[3]);
    return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
    }
    return 0.0;
    }

    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      let coordX = uniforms.filterDims.x - 1 -
          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
      let coordY = uniforms.filterDims.y - 1 -
          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
          coordX >= 0 && coordY >= 0) {
        let coord = vec4<i32>(coordX, coordY, col,
            row % uniforms.outBackprop[3]);
        return W.numbers[getFlatIndex4D(coord, uniforms.wShape)];
      }
      return 0.0;
    }

    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
      var batch = i32(globalId.z);
      var value = valueInput;
      let outCoord = vec4<i32>(
          batch,
          row / uniforms.outShape[2],
          row % uniforms.outShape[2],
          col);
      result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
    }

    ${vk(this.elementsPerThread,this.workGroupSize)}
  `}},Tue=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
    ${Ue()} {
      if(index < uniforms.size) {
        let coords = getCoordsFromFlatIndex(index);
        let batch = coords[0];
        let d1 = coords[${n}];

        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
        let dyRCorner = dyCorner.x;
        let dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        var dotProd = 0.0;
        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
          let wRPerm = uniforms.filterDims.x - 1 - wR;
          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
              wRPerm < 0) {
            continue;
          }
          let idyR = dyR;

          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
            let wCPerm = uniforms.filterDims.y - 1 - wC;
            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
                fract(dyC) > 0.0 || wCPerm < 0) {
              continue;
            }
            let idyC = dyC;

            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
              if (${this.isChannelsLast}) {
                let xValue = getDy(batch, idyR, idyC, d2);
                let wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd = dotProd + xValue * wValue;
              } else {
                let xValue = getDy(batch, d2, idyR, idyC);
                let wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd = dotProd + xValue * wValue;
              }

            }
          }
        }
        setOutputFlat(index, dotProd);
      }
    }
  `}};function Nue(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=r,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(o,a.shape,i,1,u,c,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Tue(p);else{f=new Cue(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],b=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[s,a],"float32",h)}var _ue={kernelName:ba,backendName:"webgpu",kernelFunc:Nue},Eue=fn({opType:bt.COS}),Aue={kernelName:ya,backendName:"webgpu",kernelFunc:Eue},$ue=fn({opType:bt.COSH}),Fue={kernelName:va,backendName:"webgpu",kernelFunc:$ue},Due=class{constructor(e,t,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,n[0],n[1],e],this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=r==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,r,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
      ${Ue()}
      if (index < uniforms.size) {
        let coords = getCoordsFromFlatIndex(index);
        let height_ratio = f32(${n});
        let width_ratio = f32(${a});
        let b = coords[0];
        let y = coords[1];
        let x = coords[2];
        let d = coords[3];
        // get box vals
        let y1 = getBoxes(b, 0);
        let x1 = getBoxes(b, 1);
        let y2 = getBoxes(b, 2);
        let x2 = getBoxes(b, 3);
        // get image in batch index
        let bInd = i32(round(getBoxInd(b)));
        if(bInd < 0 || bInd >= uniforms.outShape[0]) {
          return;
        }
        let height_scale = ${r};
        let width_scale = ${o};
        let in_y = ${s};
        if( in_y < 0.0 || in_y > ${e} ) {
          setOutputFlat(index, uniforms.extrapolationValue);
          return;
        }
        let in_x = ${i};
        if( in_x < 0.0 || in_x > ${t} ) {
          setOutputFlat(index, uniforms.extrapolationValue);
          return;
        }
        let sourceFracIndexCR = vec2<f32>(in_x,in_y);
        if(${this.methodId} == 1) {
          // Compute the four integer indices.
          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
          let top = topLeft + (topRight - topLeft) * fracCR.x;
          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          let newValue = top + (bottom - top) * fracCR.y;
          setOutputFlat(index, newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          let sourceNearestCR = vec2<i32>(floor(
            sourceFracIndexCR + vec2<f32>(0.5,0.5)));
          let newValue = getImage(
            bInd, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutputFlat(index, newValue);
        }
      }
    }
    `}},Rue=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=r,c=new Due(s.shape[3],a.shape,i,u),d=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[s,a,o],"float32",d)},Pue={kernelName:jo,backendName:"webgpu",kernelFunc:Rue},Oue=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
      ${Ue()}
        if (index < uniforms.size) {
          let coords = getCoordsFromFlatIndex(index);
          let b = coords[0];
          let h = ${this.getHeightCoordString()};
          let w = ${this.getWidthCoordString()};
          let d = ${this.getDepthCoordString()};

          let in_h = h / uniforms.blockSize;
          let offset_h = h % uniforms.blockSize;
          let in_w = w / uniforms.blockSize;
          let offset_w = w % uniforms.blockSize;
          let offset_d = (offset_h * uniforms.blockSize + offset_w) *
            ${this.getOutputDepthSize()};
          let in_d = d + offset_d;

          let rlt = ${this.getInputSamplingString()};
          setOutputFlat(index, rlt);
        }
      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Mue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],u=o==="NHWC"?s.shape[1]:s.shape[2],l=o==="NHWC"?s.shape[2]:s.shape[3],c=o==="NHWC"?s.shape[3]:s.shape[1],d=u*a,p=l*a,h=c/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new Oue(f,o);return n.runWebGPUProgram(g,[s],s.dtype,m)}var Lue={kernelName:qo,backendName:"webgpu",kernelFunc:Mue},RE=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=r,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let s=Gs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
          ${s}
        }`:e=`
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
            ${s}
          }
        `,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);":"";return`
      ${e}

      ${zm()}
      fn main([[builtin(global_invocation_id)]] globalId: vec3<u32>) {
        let batch = 0;
        let r = i32(globalId.x);
        let c = i32(globalId.y) * 4;
        let d2 = i32(globalId.z) * 4;
        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
        let d1 = d2;
        let q = 0;

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

        var wVals : array<vec4<f32>, 9>;
        wVals[0] = getW(0, 0, d1, q);
        wVals[1] = getW(0, 1, d1, q);
        wVals[2] = getW(0, 2, d1, q);
        wVals[3] = getW(1, 0, d1, q);
        wVals[4] = getW(1, 1, d1, q);
        wVals[5] = getW(1, 2, d1, q);
        wVals[6] = getW(2, 0, d1, q);
        wVals[7] = getW(2, 1, d1, q);
        wVals[8] = getW(2, 2, d1, q);

        var xVals : array<array<vec4<f32>, 6>, 3>;
        for (var wR = 0; wR < 3; wR = wR + 1) {
          let xR = xRCorner + wR * uniforms.dilation[0];
          for (var wC = 0; wC < 6; wC = wC + 1) {
            let xC = xCCorner + wC * uniforms.dilation[1];
            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
              xVals[wR][wC] = vec4<f32>(0.0);
            } else {
              xVals[wR][wC] = getX(batch, xR, xC, d1);
            }
          }
        }

        var dotProd : array<vec4<f32>, 4>;
        dotProd[0] = vec4<f32>(0.0);
        dotProd[1] = vec4<f32>(0.0);
        dotProd[2] = vec4<f32>(0.0);
        dotProd[3] = vec4<f32>(0.0);

        for (var wR = 0; wR < 3; wR = wR + 1) {
          for (var wC = 0; wC < 3; wC = wC + 1) {
            let indexW = wR * 3 + wC;
            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
          }
        }

        for (var i = 0; i < 4; i = i + 1) {
          let coords = vec4<i32>(batch, r, c + i, d2);
          if (coordsInBounds4D(coords, uniforms.outShape)) {
            ${n}
            ${t}
            setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
          }
        }
      }
    `}},PE=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
      inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
      channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=r,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let s=Gs(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
          let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
          ${s}
        }`:e=`
          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
            ${s}
          }
        `,t="dotProd = activation(dotProd, coords);"}let n=this.addBias?"dotProd = dotProd + getBiasAtOutCoordsByCoords(coords);":"";return`
      ${e}

      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
          value : f32) {
        let coord = vec4<i32>(batch, row, col, chan);
        if (coordsInBounds4D(coord, uniforms.outShape)) {
          setOutput(batch, row, col, chan, value);
        }
      }

      ${hk()} {
        let coords = getOutputCoordsWithFlatDispatchLayout(globalId,
            localId, numWorkgroups);
        let batch = coords[0];
        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
        let d2 = coords[3];
        let d1 = d2 / uniforms.channelMul;
        let q = d2 - d1 * uniforms.channelMul;

        let inputRowStart = xRCCorner.x;
        let inputColStart = xRCCorner.y;
        let inputRowEnd = inputRowStart + uniforms.filterHeight *
            uniforms.dilation[0];
        let inputColEnd = inputColStart + uniforms.filterWidth *
            uniforms.dilation[1];

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        var dotProd = 0.0;

        // Extract if checking out of for loop for performance.
        if (inputRowStart >= 0 && inputColStart >= 0 &&
          inputRowEnd < uniforms.inDims[0] &&
              inputColEnd < uniforms.inDims[1]) {
            // Here using a constant value |this.convInfo.filterHeight| instead
            // of uniform value is in order to loop unrolling.
            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
              let xR = inputRowStart + wR * uniforms.dilation[0];

              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
                let xC = inputColStart + wC * uniforms.dilation[1];

                let xVal = getX(batch, xR, xC, d1);
                let wVal = getW(wR, wC, d1, q);
                dotProd = dotProd + xVal * wVal;
              }
            }
          } else {
            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
              let xR = inputRowStart + wR * uniforms.dilation[0];

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

              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
                let xC = inputColStart + wC * uniforms.dilation[1];

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

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

        ${n}
        ${t}
        writeResult(batch, coords[1], coords[2], d2, dotProd);
      }
    `}};function Bue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=r,c=u;c==null&&(c=[1,1]);let d=N.computeConv2DInfo(s.shape,a.shape,o,c,i,l,!0),p=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}],h;return d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?h=new RE(d):(h=new PE(d),p.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.outChannels/d.inChannels]})),n.runWebGPUProgram(h,[s,a],s.dtype,p)}var zue={kernelName:xa,backendName:"webgpu",kernelFunc:Bue},OE=Rn({opSnippet:Mt.MUL,cpuKernelImpl:Iie,supportsComplex:!0}),Wue={kernelName:Ma,backendName:"webgpu",kernelFunc:OE},Vue=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
         if (isNanCustom(candidate)) {
          bestValue = uniforms.NAN;
         } elseif (!isNanCustom(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
           {  bestValue = candidate; }`,t="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let n=this.reduceType==="mean"?"setOutputFlat(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(outputIndex, bestValue);";return`
       fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
       }

       ${`
         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
       `}
       fn getOffset(outputIndex : i32) -> i32 {
         let outputCoords = getCoordsFromFlatIndex(outputIndex);
         let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
          return offset;
       }
       ${Ue()}
         let outputIndex = index / i32(workGroupSizeX);
         let offset = getOffset(outputIndex);
         var bestValue = ${t};
         let Length = uniforms.reduceSize;
         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
             k = k + i32(workGroupSizeX)) {
           let candidate = f32(x.numbers[offset + k]);
           ${e}
         }
         xBestValues[localId.x] = bestValue;
         workgroupBarrier();

         var reduceSize = min(u32(Length), workGroupSizeX);
         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
             currentSize = reduceSize / 2u) {
           let interval = DIV_CEIL(reduceSize, 2u);
           if (localId.x < currentSize) {
            let candidate = xBestValues[localId.x + interval];
            ${e}
            xBestValues[localId.x] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (localId.x == 0u && outputIndex < uniforms.size) {
          ${n}
        }
       }
     `}};function fp(e,t,n,r,s){let a=e.shape.length,o=[],i=w.parseAxisParam(t,e.shape),u=i,l=N.getAxesPermutation(u,a),c=e;l!=null&&(c=cu({inputs:{x:e},attrs:{perm:l},backend:s}),u=N.getInnerMostAxes(u.length,a),o.push(c)),N.assertAxesAreInnerMostDims(r,u,a);let[d,p]=N.computeOutAndReduceShapes(c.shape,u),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let f;if((r==="max"||r==="prod")&&s.shouldExecuteOnCPU([c])){let m=s.tensorMap.get(c.dataId).values;switch(r){case"max":let g=xie(m,w.sizeFromShape(p),h,e.dtype);f=s.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=Tie(c.shape,c.dtype,m,u);f=s.makeTensorInfo(y,v,b);break;default:throw new Error(`${r} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(p),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=r==="mean"?"float32":bd(e.dtype),x=[{type:"int32",data:[m]}],k=new Vue(y,r),T=s.runWebGPUProgram(k,[c],v,x);o.push(T),f=We({inputs:{x:T},attrs:{shape:h},backend:s})}return o.forEach(m=>s.disposeData(m.dataId)),f}function Sk(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return fp(s,a,o,"sum",n)}var Uue={kernelName:Ka,backendName:"webgpu",kernelFunc:Sk};function Gue(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:u}=N.decodeEinsumEquation(s,a.length);N.checkEinsumDimSizes(o.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(i,u),d=c.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=cu({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=We({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),p===null?p=v:(p=OE({inputs:{a:v,b:p},backend:n}),f.push(p))}m<d-1&&(l[m]>=0&&(p=Sk({inputs:{x:p},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var Hue={kernelName:Ql,backendName:"webgpu",kernelFunc:Gue},jue=fn({opType:bt.ELU}),que={kernelName:ka,backendName:"webgpu",kernelFunc:jue},Kue=Rn({opSnippet:Mt.EQUAL,dtype:"bool",cpuKernelImpl:cie}),Xue={kernelName:Ko,backendName:"webgpu",kernelFunc:Kue},ME=fn({opType:bt.EXP,cpuKernelImpl:lie,dtype:"float32"}),Yue={kernelName:Ia,backendName:"webgpu",kernelFunc:ME};function Ck(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=s;return s<0&&(w.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+s+1),i.splice(u,0,1),We({inputs:{x:a},backend:r,attrs:{shape:i}})}var Que={kernelName:Xo,backendName:"webgpu",kernelFunc:Ck},Zue=fn({opType:bt.EXPM1,cpuKernelImpl:die}),Jue={kernelName:Yo,backendName:"webgpu",kernelFunc:Zue},ece=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
    ${Ue()}
      if (index < uniforms.size) {
        setOutputFlat(index, uniforms.value);
      }
    }
  `}};function sl(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||w.inferDtype(s),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new ece(r),i=[{type:"float32",data:[s]}];return t.runWebGPUProgram(o,[],a,i)}}var tce={kernelName:Zu,backendName:"webgpu",kernelFunc:sl},nce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
      ${Ue()}
        if (index < uniforms.size) {
          let coords = getCoordsFromFlatIndex(index);
          let coordX = uniforms.xShape[2] - coords[2] - 1;
          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
          setOutputFlat(index, outputValue);
        }
      }
    `}},rce={kernelName:Qo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new nce(n.shape);return r.runWebGPUProgram(s,[n],n.dtype)}},sce=fn({opType:bt.FLOOR,cpuKernelImpl:pie}),ace={kernelName:Sa,backendName:"webgpu",kernelFunc:sce},oce=Rn({opSnippet:Mt.INT_DIV,dtype:"int32"}),ice={kernelName:Ca,backendName:"webgpu",kernelFunc:oce},uce=(e,t,n,r,s)=>{let a=[r,...n];return s&&a.push(s),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},LE=(e,t,n,r,s,a=!1)=>{let o={dtype:s.dtype,shape:s.shape},i=Bae(r,o,t,a),u=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"}})};function BE(e,t,n,r="",s=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+r+s}function zE(e){let{externalImage:t,backend:n,attrs:r,outShape:s,useImport:a}=e,{numChannels:o}=r,i=w.sizeFromShape(s),u=w.computeStrides(s),l=n.makeTensorInfo(s,"int32"),c=n.getFromPixelsProgram(a?"import":"copyExternal");c.updateOutputShape(s);let d=[l.shape],p=[l.dtype,a?"import":"copyExternal"],h=BE(c,d,p),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>LE(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,s[1],s[0])},[s[1],s[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[i,o,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var cce={kernelName:cd,backendName:"webgpu",kernelFunc:lce},al;function lce(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r;if(s==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&s instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&s instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&s instanceof ImageBitmap,[c,d]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],p=[d,c,a];if(X().getBool("WEBGPU_USE_IMPORT")&&o)return zE({externalImage:s,backend:n,attrs:r,outShape:p,useImport:!0});if((o||i)&&(al==null&&(al=document.createElement("canvas").getContext("2d")),al.canvas.width=c,al.canvas.height=d,al.drawImage(s,0,0,c,d),s=al.canvas),l||u||o||i)return zE({externalImage:s,backend:n,attrs:r,outShape:p,useImport:!1});let h=s.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(s.width*s.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(p,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var dce=class{constructor(e,t,n,r,s){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset")),s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale")),this.offsetShape=r,this.scaleShape=s,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetAtOutCoordsByGlobalIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleAtOutCoordsByGlobalIndex(index)"),`
      ${Ue()}
        if (index < uniforms.size)
        {
          let xValue = getXAtOutCoordsByGlobalIndex(index);
          let meanValue = getMeanAtOutCoordsByGlobalIndex(index);
          let varianValue = getVarianceAtOutCoordsByGlobalIndex(index);
          let offsetValue = ${e};
          let scaleValue = ${t};
          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
          setOutputFlat(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
        }
      }
  `}},pce={kernelName:Ta,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r,scale:s,offset:a,mean:o,variance:i}=e,{varianceEpsilon:u}=t,l=n,c=[r,o,i],d=null;a!=null&&(d=a.shape,c.push(a));let p=null;s!=null&&(p=s.shape,c.push(s));let h=new dce(r.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,r.dtype,f)}};function hce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=r,m=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(s.shape,a.shape,u,d,l,p,!1,m),b=o!=null,y=i!=null,v;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"))return AE({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let x=X().getBool("WEBGPU_USE_NAIVE_CONV2D"),k=g.inChannels%4==0&&g.outChannels%4==0,T=[g.padInfo.top,g.padInfo.left],C=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...T]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(x)v=new DE(g,b,h,y);else{k?v=new $E(g,b,h,y):v=new FE(g,b,h,y);let F=g.outShape[1]*g.outShape[2],O=g.outShape[3],D=g.filterHeight*g.filterWidth*g.inShape[3];C.push({type:"int32",data:[F]},{type:"int32",data:[O]},{type:"int32",data:[D]})}let E=[s,a];return b&&E.push(o),y&&E.push(i),n.runWebGPUProgram(v,E,s.dtype,C)}var fce={kernelName:no,backendName:"webgpu",kernelFunc:hce};function mce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:d,activation:p}=r,h=c;h==null&&(h=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let f=N.computeConv2DInfo(s.shape,a.shape,u,h,l,d,!0),m=[s,a],g=o!=null,b=i!=null;g&&m.push(o),b&&m.push(i);let y=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],v;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4==0?v=new RE(f,g,p,b):(v=new PE(f,g,p,b),y.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),n.runWebGPUProgram(v,m,"float32",y)}var gce={kernelName:ro,backendName:"webgpu",kernelFunc:mce},bce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${on(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
        ${Ue()}
        if (index < uniforms.size) {
          let coords = getCoordsFromFlatIndex(index);
          var flattenIndex = 0;
          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
            let indexTemp = i32(round(getIndices(coords[0], j)));
            let strideNum = ${e};
            flattenIndex = flattenIndex + indexTemp * strideNum;
          }

          setOutputFlat(index, getA(flattenIndex, coords[1]));
        }
      }
      `}};function yce(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=w.sizeFromShape(r.shape),[u,l,c,d]=N.prepareAndValidate(r,s),p=We({inputs:{x:s},backend:n,attrs:{shape:[l,o]}}),h=We({inputs:{x:r},backend:n,attrs:{shape:[w.sizeFromShape(r.shape)/c,c]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let y=n.readSync(s.dataId),v=n.bufferSync(r),x=hie(y,v,r.dtype,l,o,c,d,r.shape,i);return n.makeTensorInfo(u,r.dtype,x.values)}let f=new bce(o,[l,c]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),b=We({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var vce={kernelName:Jo,backendName:"webgpu",kernelFunc:yce},xce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=wce(this.aShape,"i32");return`
      ${Ue()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromFlatIndex(index);
          setOutputFlat(index, getA(${e}));
        }
      }
    `}};function wce(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e.length;s++)s===2?r.push(`${t}(getIndices(resRC.x, resRC.z))`):r.push(`${n[s]}`);return r.join()}function WE(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r,u=w.parseAxisParam(o,s.shape)[0],l=N.segment_util.collectGatherOpShapeInfo(s,a,u,i),c=w.sizeFromShape(a.shape),d=[],p=We({inputs:{x:s},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=We({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});d.push(p),d.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([s,a])){let v=n.tensorMap.get(h.dataId).values,x=$e(h.shape,h.dtype,v),T=n.tensorMap.get(p.dataId).values,C=$e(p.shape,p.dtype,T),E=fie(C,x,f);return d.forEach(F=>n.disposeData(F.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new xce(p.shape,f),g=n.runWebGPUProgram(m,[p,h],p.dtype);d.push(g);let b=We({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return d.forEach(y=>n.disposeData(y.dataId)),b}var kce={kernelName:Zo,backendName:"webgpu",kernelFunc:WE},Ice=Rn({opSnippet:Mt.GREATER,cpuKernelImpl:gie,dtype:"bool"}),Sce={kernelName:ei,backendName:"webgpu",kernelFunc:Ice},Cce=Rn({opSnippet:Mt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:mie}),Tce={kernelName:Na,backendName:"webgpu",kernelFunc:Cce},Nce=Rn({opSnippet:Mt.LESS,dtype:"bool",cpuKernelImpl:yie}),_ce={kernelName:ni,backendName:"webgpu",kernelFunc:Nce},Ece=Rn({opSnippet:Mt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:bie}),Ace={kernelName:ri,backendName:"webgpu",kernelFunc:Ece},$ce=fn({opType:bt.LOG,cpuKernelImpl:vie}),Fce={kernelName:Ea,backendName:"webgpu",kernelFunc:$ce},Dce=Rn({opSnippet:Mt.LOGICAL_AND,dtype:"bool"}),Rce={kernelName:si,backendName:"webgpu",kernelFunc:Dce},Pce=fn({opType:bt.LOGICAL_NOT}),Oce={kernelName:rc,backendName:"webgpu",kernelFunc:Pce};function VE(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r;return fp(s,a,o,"max",n)}var Mce={kernelName:Aa,backendName:"webgpu",kernelFunc:VE},Lce=Rn({opSnippet:Mt.MAX,cpuKernelImpl:wie}),Bce={kernelName:$a,backendName:"webgpu",kernelFunc:Lce};function zce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=r,l=1,c=N.computePool2DInfo(s.shape,a,o,l,i,u),d,p=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Lr({inputs:{x:s},backend:n});d=new NE(c),p.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else d=new TE(c,"max"),p.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(d,[s],s.dtype,p)}var Wce={kernelName:Fa,backendName:"webgpu",kernelFunc:zce};function Vce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{keepDims:a,axis:o}=r;return fp(s,o,a,"mean",n)}var Uce={kernelName:Da,backendName:"webgpu",kernelFunc:Vce};function Gce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return fp(s,a,o,"min",n)}var Hce={kernelName:Ra,backendName:"webgpu",kernelFunc:Gce},jce=Rn({opSnippet:Mt.MIN,cpuKernelImpl:kie}),qce={kernelName:Pa,backendName:"webgpu",kernelFunc:jce},Kce=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,s)=>r[0]+e[s]+r[1]),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((r,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),r=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=on(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
      ${Ue()}
        if (index < uniforms.size) {
          let start = ${o}(${t});
          let end = ${o}(${n});
          var outC = getCoordsFromFlatIndex(index);
          for (var i = 0; i < ${e}; i = i + 1) {
            if (${a} < ${r}) {
              ${a} = ${r} * 2 - ${a} - ${this.offset};
            } elseif(${a} >= ${s}) {
              ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};
            }
          }
          let coords = outC - start;
          setOutputFlat(index, getX(${i}));
        }
      }
    `}},Xce={kernelName:Oa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{paddings:s,mode:a}=t,o=n,i=s.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new Kce(r.shape,s,a);return o.runWebGPUProgram(u,[r],r.dtype,i)}};function Yce(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.tensorMap.get(r.dataId),[o,i]=Sie(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s=new Wm(r.shape,bt.NEG);return n.runWebGPUProgram(s,[r],r.dtype)}var Qce={kernelName:ai,backendName:"webgpu",kernelFunc:Yce};function Zce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=r,l=n.readSync(s.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=Dr.nonMaxSuppressionV3Impl(l,c,o,i,u);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Jce={kernelName:ii,backendName:"webgpu",kernelFunc:Zce};function ele(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=r,c=n.readSync(s.dataId),d=n.readSync(a.dataId),p=o,h=i,f=u,m=l,{selectedIndices:g,selectedScores:b}=Dr.nonMaxSuppressionV5Impl(c,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var tle={kernelName:ui,backendName:"webgpu",kernelFunc:ele};function Um(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=hp({inputs:{input:r},backend:n}),a=Um({inputs:{x:s},backend:n}),o=Vm({inputs:{input:r},backend:n}),i=Um({inputs:{x:o},backend:n}),u=nl({inputs:{real:a,imag:i},backend:n});return n.disposeData(s.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),u}else return sl({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var nle={kernelName:Ni,backendName:"webgpu",kernelFunc:Um};function UE(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=hp({inputs:{input:r},backend:n}),a=UE({inputs:{x:s},backend:n}),o=Vm({inputs:{input:r},backend:n}),i=Um({inputs:{x:o},backend:n}),u=nl({inputs:{real:a,imag:i},backend:n});return n.disposeData(s.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),u}else return sl({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var rle={kernelName:ci,backendName:"webgpu",kernelFunc:UE};function sle(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return Ck({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(c=>{let d=Ck({inputs:{input:c},backend:n,attrs:{dim:s}});return i.push(d),d}),l=EE({inputs:u,backend:n,attrs:{axis:s}});return i.forEach(c=>n.disposeData(c.dataId)),l}var ale={kernelName:di,backendName:"webgpu",kernelFunc:sle},ole=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=on(e),n=this.xShape.map((c,d)=>`uniforms.pad${d}[0]`).join(","),r=this.xShape.map((c,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),s=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${r})`:`${r}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
      ${Ue()}
        if (index < uniforms.size) {
          let start = ${s};
          let end = ${a};
          let outC = getCoordsFromFlatIndex(index);

          if (${o} || ${i}) {
            setOutputFlat(index, uniforms.constantValue);
          } else {
            let coords = outC - start;
            setOutputFlat(index, getX(${u}));
          }
        }
      }
    `}},GE=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;if(a.every(l=>w.arraysEqual(l,[0,0])))return Lr({inputs:{x:s},backend:n});if(w.sizeFromShape(s.shape)===0){let l=a.map((c,d)=>c[0]+s.shape[d]+c[1]);return sl({backend:n,attrs:{shape:l,value:o,dtype:s.dtype}})}let i=[{type:"float32",data:[o]}];a.map(l=>i.push({type:"int32",data:[l[0],l[1]]}));let u=new ole(s.shape,a);return n.runWebGPUProgram(u,[s],s.dtype,i)},ile={kernelName:La,backendName:"webgpu",kernelFunc:GE},ule=Rn({opSnippet:Mt.POW}),cle={kernelName:Ba,backendName:"webgpu",kernelFunc:ule};function lle(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=new IE(Mt.PRELU,r.shape,s.shape);return n.runWebGPUProgram(a,[r,s],"float32")}var dle={kernelName:za,backendName:"webgpu",kernelFunc:lle};function ple(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return fp(s,a,o,"prod",n)}var hle={kernelName:pi,backendName:"webgpu",kernelFunc:ple},fle=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=Nie(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},mle={kernelName:oc,backendName:"webgpu",kernelFunc:fle},HE=Rn({opSnippet:Mt.DIV}),gle={kernelName:wa,backendName:"webgpu",kernelFunc:HE},ble=fn({opType:bt.RELU}),yle={kernelName:Wa,backendName:"webgpu",kernelFunc:ble},vle=fn({opType:bt.RELU6}),xle={kernelName:Ua,backendName:"webgpu",kernelFunc:vle},wle=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
      ${Ue()}
        if (index < uniforms.size) {
        let coords = getCoordsFromFlatIndex(index);
          let b = coords[0];
          let d = coords[3];
          let rc = coords.yz;

          let effectiveInSize = vec2<f32>(
            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);

          let effectiveOutSize = vec2<f32>(
            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);

          let effectiveInputOverOutputRatioRC =
              effectiveInSize / effectiveOutSize;

          // Fractional source index
          let sourceFracIndexRC =
            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);

          // Compute the four integer indices.
          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
          let sourceCeilRC = vec2<i32>(
            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));

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

          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);

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

          setOutputFlat(index, newValue);
        }
      }
    `}};function kle(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,size:o,halfPixelCenters:i}=r,[u,l]=o,c=a&&u>1?1:0,d=a&&l>1?1:0,h=[{type:"float32",data:[c,d]},{type:"float32",data:[i?.5:0]}],f=new wle(s.shape,u,l);return n.runWebGPUProgram(f,[s],"float32",h)}var Ile={kernelName:Va,backendName:"webgpu",kernelFunc:kle},Sle=class{constructor(e,t,n,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; roundBase : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${r}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
      ${Ue()}
        if (index < uniforms.size) {
          let coords = getCoordsFromFlatIndex(index);
          let b = coords[0];
          let d = coords[3];
          let rc = coords.yz;

          let effectiveInSize = vec2<f32>(
            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);

          let effectiveOutSize = vec2<f32>(
            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);

          let effectiveInputOverOutputRatioRC =
              effectiveInSize / effectiveOutSize;

          // Fractional source index
          let sourceFracIndexRC = ${e};

          // Compute the coordinators of nearest neighbor point.
          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
          let sourceNearestRC = vec2<i32>(
            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);

          setOutputFlat(index, newValue);
        }
      }
    `}};function Cle(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[u,l]=i,c=a&&u>1?1:0,d=a&&l>1?1:0,h=[{type:"float32",data:[c,d]},{type:"float32",data:[a?.5:0]}],f=new Sle(s.shape,u,l,o);return n.runWebGPUProgram(f,[s],s.dtype,h)}var Tle={kernelName:uc,backendName:"webgpu",kernelFunc:Cle},Nle=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
          cosRadians : f32;`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
        ${Ue()}

          if (index < uniforms.size) {
            let coords = getCoordsFromFlatIndex(index);
            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
                uniforms.sinRadians;
            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
                uniforms.cosRadians;
            let coordX = i32(round(coordXFloat + uniforms.centerX));
            let coordY = i32(round(coordYFloat + uniforms.centerY));
            ${this.fillSnippet}
            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
                coordY < uniforms.xShape[1]) {
              outputValue = getX(coords[0], coordY, coordX, coords[3]);
            }
            setOutputFlat(index, outputValue);
          }
        }
      `}},_le={kernelName:_i,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,u=new Nle(r.shape,a),[l,c]=N.getImageCenter(o,r.shape[1],r.shape[2]),d=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(s)]},{type:"float32",data:[Math.cos(s)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(u,[r],r.dtype,d)}},Ele=fn({opType:bt.RSQRT,cpuKernelImpl:_ie}),Ale={kernelName:Ga,backendName:"webgpu",kernelFunc:Ele},$le=class{constructor(e,t,n,r,s,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=ze(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${r}_${this.sliceDimGreaterThanOne}_${o}`;let i=on(s.length);this.uniforms=`sliceDim : i32; strides: ${i}; size: i32;`,this.updatesRank=r,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",r="",s="",a="";this.updatesRank===1?(r="coords[0]",s="flattenedIndex",a=`
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
        return index;
      }
      `):this.updatesRank===2&&(r="coords[0], coords[1]",s="vec2<i32>(flattenedIndex, coords[1])",a=`
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
        let d0 = index / uniforms.updatesShape[1];
        let d1 = index - d0 * uniforms.updatesShape[1];
        return vec2<i32>(d0, d1);
      }
      `);let o=`getUpdates(${r})`,i=this.type==="int32"?"atomicAdd(&(result.numbers[flatIndex]), i32(updateValue));":`
     var assumed = atomicLoad(&(result.numbers[flatIndex]));
     var success = 0;
     for (; success == 0;) {
       let new = bitcast<f32>(assumed) + updateValue;
       let newI32 = bitcast<i32>(new);
       let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
       assumed = resValue[0];
       success = resValue[1];
     }
     `;return`
    ${a}

      ${Ue()}

        if (index < uniforms.size) {
          let coords = getUpdatesCoordsFromFlatIndex(index);
          var flattenedIndex = 0;
          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
            let indexInside = i32(round(${t}));
            flattenedIndex = flattenedIndex + indexInside * ${n};
          }
          let updateValue = ${o};
          let flatIndex = getOutputFlatIndex(${s});

         ${i}
        }
      }`}};function Fle(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:d}=N.calculateShapes(a,s,o),p=[d/l,l];if(d===0)return n.makeTensorInfo(o,s.dtype);let h=We({inputs:{x:s},backend:n,attrs:{shape:[u,i]}}),f=We({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=sl({backend:n,attrs:{shape:p,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new $le(f.shape,i,h.shape.length,f.shape.length,c,p,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=We({inputs:{x},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var Dle={kernelName:gi,backendName:"webgpu",kernelFunc:Fle},Rle=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${r[o]}`),o<this.cRank&&s.push(`${r[o]}`);e=s.join(),t=a.join()}return`
      ${Ue()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromFlatIndex(index);
          let cVal = getC(${e});
          if (cVal >= 1.0) {
            setOutputFlat(index, getA(${t}));
          } else {
            setOutputFlat(index, getB(${t}));
          }
        }
      }
    `}};function Ple(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new Rle(r.shape.length,s.shape,s.shape.length);return n.runWebGPUProgram(o,[r,s,a],In(s.dtype,a.dtype))}var Ole={kernelName:bi,backendName:"webgpu",kernelFunc:Ple},Mle=fn({opType:bt.SIGMOID}),Lle={kernelName:ja,backendName:"webgpu",kernelFunc:Mle},Ble=fn({opType:bt.SIN}),zle={kernelName:Ha,backendName:"webgpu",kernelFunc:Ble},Wle=fn({opType:bt.SINH}),Vle={kernelName:vi,backendName:"webgpu",kernelFunc:Wle},jE=Rn({opSnippet:Mt.SUB,cpuKernelImpl:Die,supportsComplex:!0}),Ule={kernelName:Qa,backendName:"webgpu",kernelFunc:jE};function Gle(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=VE({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=N.expandShapeToKeepDim(i.shape,o),l=We({inputs:{x:i},backend:n,attrs:{shape:u}}),c=jE({inputs:{a:s,b:l},backend:n}),d=ME({inputs:{x:c},backend:n}),p=Sk({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=We({inputs:{x:p},backend:n,attrs:{shape:u}}),f=HE({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var Hle={kernelName:Xa,backendName:"webgpu",kernelFunc:Gle},jle=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r;w.assert(s.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...o);for(let b=1+a.length;b<s.shape.length;++b)u.push([0,0]);let l=[],c=GE({inputs:{x:s},backend:n,attrs:{paddings:u,constantValue:0}}),d=N.getReshaped(c.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,i,!1),f=We({inputs:{x:c},backend:n,attrs:{shape:d}}),m=cu({inputs:{x:f},backend:n,attrs:{perm:p}}),g=We({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},qle={kernelName:xi,backendName:"webgpu",kernelFunc:jle},Kle=class{constructor(e,t,n,r,s,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${r}_${i}`;let u=on(s.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${u};`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";r===1?c="i":r===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
      ${Ue()}

        let globalIndex = index * ${this.workPerThread};
        if (globalIndex < uniforms.size) {
          var sum = vec4<f32>(0.0);
          var found = vec4<bool>(false);
          for (var i = 0; i < uniforms.updateSize; i = i + 1) {
            var flattenedIndex = 0;
            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
              let indexInside = i32(round(${this.indicesSnippet}));
              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
            }
            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
              let curIndex = globalIndex + innerIndex;
              let coords = getCoordsFromFlatIndex(curIndex);
              if (flattenedIndex == coords[0]) {
                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
                found[innerIndex] = true;
              }
            }
          }
          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
            let curIndex = globalIndex + innerIndex;
            if (curIndex < uniforms.size)
            {
              setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
            }
          }
        }
      }`}};function Xle(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:u,numUpdates:l,strides:c,outputSize:d}=N.calculateShapes(a,s,i),p=!1,h=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:c}],f=new Kle(l,u,s.shape.length,a.shape.length,c,[d,1],p),m=n.runWebGPUProgram(f,[a,s,o],a.dtype,h),g=We({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var Yle={kernelName:od,backendName:"webgpu",kernelFunc:Xle};function Qle(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=w.parseAxisParam(o,s.shape)[0],u=N.prepareSplitSize(s,a,i),l=s.shape.length,c=new Array(l).fill(0),d=s.shape.slice();return u.map(p=>{let h=[...d];h[i]=p;let f=rl({inputs:{x:s},backend:n,attrs:{begin:c,size:h}});return c[i]+=p,f})}var Zle={kernelName:wi,backendName:"webgpu",kernelFunc:Qle},Jle=fn({opType:bt.SQRT}),ede={kernelName:qa,backendName:"webgpu",kernelFunc:Jle},tde={kernelName:hc,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t,s=new Wm(n.shape,bt.SQUARE);return r.runWebGPUProgram(s,[n],n.dtype)}},nde=Rn({opSnippet:Mt.SQUARED_DIFFERENCE}),rde={kernelName:Ya,backendName:"webgpu",kernelFunc:nde},sde=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=on(this.outputShape.length);this.uniforms=`begin : ${t};  strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((s,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromFlatIndex(index);
           setOutputFlat(index, getX(${t}));
         }
       }
     `}};function ade(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=r,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=$t.sliceInfo(s.shape,a,o,i,u,l,c,d,p),k;if(m)k=We({inputs:{x:s},backend:n,attrs:{shape:f}});else if(g||b){w.assert(s.shape.length>=1,()=>`Input must have rank at least 1, got: ${s.shape.length}`);let T=$t.computeOutShape(y,v,x),C=rl({inputs:{x:s},backend:n,attrs:{begin:y,size:T}});k=We({inputs:{x:C},backend:n,attrs:{shape:f}}),n.disposeData(C.dataId)}else if(n.shouldExecuteOnCPU([s])){let C=n.readSync(s.dataId),E=$e(s.shape,s.dtype,C),F=$ie(h,E,x,y);k=n.makeTensorInfo(f,s.dtype,F.values)}else{let C=new sde(h),E=[{type:"int32",data:y},{type:"int32",data:x}],F=n.runWebGPUProgram(C,[s],s.dtype,E);k=We({inputs:{x:F},backend:n,attrs:{shape:f}}),n.disposeData(F.dataId)}return k}var ode={kernelName:ki,backendName:"webgpu",kernelFunc:ade};function ide(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=r,{data:c,dataSplits:d}=t,p=n.readSync(c.dataId),h=n.readSync(d.dataId),[f,m]=Fie(p,h,s,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var ude={kernelName:id,backendName:"webgpu",kernelFunc:ide},cde=fn({opType:bt.TANH}),lde={kernelName:Za,backendName:"webgpu",kernelFunc:cde},dde=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=pde(this.rank,"uniforms.");return`
      ${Ue()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromFlatIndex(index);
          setOutputFlat(index, getA(${e}));
        }
      }
    `}};function pde(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e;s++)r.push(`(${n[s]} % ${t}aShape[${s}])`);return r.join()}function hde(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(n.shouldExecuteOnCPU([s])||s.dtype==="string"||s.shape.length>=5){let u=n.readSync(s.dataId),l=s.dtype==="string"?u.map(p=>w.decodeString(p)):u,c=$e(s.shape,s.dtype,l),d=Rie(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new dde(s.shape,a);return n.runWebGPUProgram(o,[s],s.dtype)}var fde={kernelName:As,backendName:"webgpu",kernelFunc:hde},mde=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
        dir : i32; inc : i32;`,this.shaderKey="swap"}getUserCode(){return`
        ${Ue()}
          if (index < uniforms.size) {
            let outC = getCoordsFromFlatIndex(index);
            let batch = outC[0];
            let elemIdx = outC[1];
            // We compare elements pair-wise within a group of size 2 * inc.
            // The comparing rule for each group alternates between ascending
            // and descending. Within each group, we compare each pair at
            // positions i and i+inc. To decide whether an element at position i
            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
            // inc, it is in the first half of the group, we denote it as x0,
            // otherwise we denote it as x1.
            // For example, as shown in the Bitonic top K paper referenced
            // above, Figure5(a) shows that element[1] is in the second half of
            // the group when group size is 2, but it is in the first half of
            // the group when group size is 4.
            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
            var i = 0;
            if (isFirstInPair) {
              i = elemIdx;
            } else {
              i = elemIdx - uniforms.inc;
            }

            var i0 = 0;
            if (uniforms.firstPass == 1) {
              i0 = i;
            } else {
              i0 = i32(getIndices(batch, i));
            }

            var i1 = 0;
            if (uniforms.firstPass == 1) {
              i1 = i + uniforms.inc;
            } else {
              i1 = i32(getIndices(batch, i + uniforms.inc));
            }

            var x0 = f32(0.0);
            var x1 = f32(0.0);
            if (i0 < uniforms.inputSize) {
              x0 = getX(batch, i0);
            } else {
              x0 = uniforms.negativeInf;
            }
            if (i1 < uniforms.inputSize) {
              x1 = getX(batch, i1);
            } else {
              x1 = uniforms.negativeInf;
            }

            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
            if (reverse == isGreater) {
              // Elements in opposite order of direction
              let iTemp = i0;
              i0 = i1;
              i1 = iTemp;
            }
            if (isFirstInPair) {
              setOutputFlat(index, f32(i0));
            } else {
              setOutputFlat(index, f32(i1));
            }
          }
        }
      `}},gde=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge"}getUserCode(){return`
        ${Ue()}
          if (index < uniforms.size) {
            let outC = getCoordsFromFlatIndex(index);
            let batch = outC[0];
            let elemIdx = outC[1];
            // The output size is half of the previous size.
            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _
            // (k=4), we only need to output the indices at positions |, the
            // indices at positions _ can be thrown away, see Figure5(b) After
            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced
            // above.
            // For example, the paper shows we only need to output the orange
            // bars. The output sequence should look like this | | | | | | | |.
            // Because the sequence is halved, to map the output index back to
            // the previous sequence to find the corresponding value, we need
            // to double the index. When we double the index, we basically
            // interpolate a position, so 2i looks like
            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
            // position of each 2k positions by - elemIdx % k. E.g. for output
            // at index 4,5,6,7, we want to get the corresponding element at
            // original index 8,9,10,11, for output at index 8,9,10,11,
            // we want to get the corresponding element at original index
            // 16,17,18,19, so on and so forth.

            var i = 0;
            if (elemIdx < uniforms.k) {
              i = elemIdx;
            } else {
              i = elemIdx * 2 - elemIdx % uniforms.k;
            }
            var i0 = 0;
            if (uniforms.firstPass == 1) {
              i0 = i;
            } else {
              i0 = i32(getIndices(batch, i));
            }
            var i1 = 0;
            if (uniforms.firstPass == 1) {
              i1 = i + uniforms.k;
            } else {
              i1 = i32(getIndices(batch, i + uniforms.k));
            }

            let x0 = getX(batch, i0);
            var x1 = f32(0.0);
            if (i1 < uniforms.inputSize) {
              x1 = getX(batch, i1);
            } else {
              x1 = x0;
            }

            if (x0 >= x1) {
              setOutputFlat(index, f32(i0));
            } else {
              setOutputFlat(index, f32(i1));
            }
          }
        }
      `}};function ol(e,t){t!==null&&e.disposeData(t.dataId)}function qE(e){let t=1;for(;t<e;)t*=2;return t}function bde(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r,i=s.shape,u=i[i.length-1];if(n.shouldExecuteOnCPU([s])){let k=n.readSync(s.dataId),[T,C]=Pie(k,i,s.dtype,a,o);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(C.shape,C.dtype,C.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,s.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(u===1)return[s,sl({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(i)/u,d=We({inputs:{x:s},attrs:{shape:[c,u]},backend:n}),p=qE(a),h=qE(u),f=null,m=()=>f===null?[d,d]:[d,f],g=(k,T,C)=>{let E=m(),F=new mde(C),D=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[T]}],R=f;f=n.runWebGPUProgram(F,E,"int32",D),ol(n,R)};for(let k=1;k<p;k*=2){let T=k*2;for(let C=k;C>=1;C/=2)g(T,C,[c,h])}for(let k=h;k>p;k/=2){let T=m(),C=new gde([c,k/2]),F=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[p]}],O=f;f=n.runWebGPUProgram(C,T,"int32",F),ol(n,O);let D=p/2,R=D*2;for(let _=D;_>=1;_/=2)g(R,_,f.shape)}let b=f;f=rl({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),ol(n,b);let y=WE({inputs:{x:d,indices:f},backend:n,attrs:{axis:1,batchDims:1}});ol(n,d);let v=i.slice(0,-1);v.push(a),b=f,f=We({inputs:{x:f},attrs:{shape:v},backend:n}),ol(n,b);let x=y;return y=We({inputs:{x:y},attrs:{shape:v},backend:n}),ol(n,x),[y,f]}var yde={kernelName:Si,backendName:"webgpu",kernelFunc:bde},vde=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
          fn mapCoord(outCoord : f32, len : f32) -> f32{
            var inCoord = outCoord;
            if(uniforms.fillModeId == 2) {
              if (inCoord < 0.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz2 = 2.0 * len;
                  if (inCoord < sz2) {
                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
                    inCoord;
                  }
                  if (inCoord < -len) {
                    inCoord = inCoord + sz2;
                  } else {
                    inCoord = -inCoord - 1.0;
                  }
                }
              } elseif (inCoord > len - 1.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz2 = 2.0 * len;
                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
                  if (inCoord >= len) {
                    inCoord = sz2 - inCoord - 1.0;
                  }
                }
              }
              return clamp(inCoord, 0.0, len - 1.0);
            } elseif (uniforms.fillModeId == 3) {
              if (inCoord < 0.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz = len - 1.0;
                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
                }
              } elseif (inCoord > len - 1.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz = len - 1.0;
                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
                }
              }
              return clamp(inCoord, 0.0, len - 1.0);
            } elseif (uniforms.fillModeId == 4) {
              return clamp(outCoord, 0.0, len - 1.0);
            }
            return outCoord;
          }
          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
            channel : i32) -> f32 {
            var outputValue : f32;
            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
                outputValue = getImage(batch, coordY, coordX, channel);
            } else {
              outputValue = uniforms.fillValue;
            }
            return outputValue;
          }

          ${Ue()}
            if (index < uniforms.size) {
              let coords = getCoordsFromFlatIndex(index);
              var outputValue : f32;
              let batch = coords[0];
              let x = coords[2];
              let y = coords[1];
              let channel = coords[3];
              let xf = f32(x);
              let yf = f32(y);
              let a1 = getTransforms(batch, 0);
              let a2 = getTransforms(batch, 1);
              let a3 = getTransforms(batch, 2);
              let b1 = getTransforms(batch, 3);
              let b2 = getTransforms(batch, 4);
              let b3 = getTransforms(batch, 5);
              let c1 = getTransforms(batch, 6);
              let c2 = getTransforms(batch, 7);
              let projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = uniforms.fillValue;
              } else {
                let inX = (a1 * xf + a2 * yf + a3) / projection;
                let inY = (b1 * xf + b2 * yf + b3) / projection;
                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));

                if (uniforms.interpolationModeId == 1) {
                  let coordY = i32(round(mapY));
                  let coordX = i32(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  let yFloor = floor(mapY);
                  let xFloor = floor(mapX);
                  let yCeil = yFloor + 1.0;
                  let xCeil = xFloor + 1.0;
                  let valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
                  let valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutputFlat(index, outputValue);
            }
          }
        `}};function xde(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=r,[c,d,p,h]=s.shape,[f,m]=l!=null?l:[d,p],g=[c,f,m,h],b=new vde(g),y=o==="nearest"?1:2,v;switch(i){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[s,a],"float32",x)}var wde={kernelName:Ci,backendName:"webgpu",kernelFunc:xde};function kde(e){let{inputs:t,backend:n,attrs:r}=e,{value:s}=t,{axis:a}=r;a<0&&(a+=s.shape.length);let o=s,i=o.shape.length,u=s.shape[a],l=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(l[c++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){p[a]=m;let g=rl({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),b=We({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,d.push(g)}return d.forEach(m=>n.disposeData(m.dataId)),f}var Ide={kernelName:Ti,backendName:"webgpu",kernelFunc:kde},Sde=[tie,Lie,zie,Uie,Xie,Qie,Jie,tue,oue,lue,pue,gue,aie,xue,Sue,_ue,Aue,Fue,Pue,Lue,zue,Hue,que,Xue,Que,Yue,Jue,tce,rce,cce,ace,ice,pce,fce,gce,vce,kce,Sce,Tce,sie,yue,_ce,Ace,Fce,Rce,Oce,Mce,Bce,Wce,Uce,Hce,qce,Xce,Wue,Qce,Jce,tle,iue,rle,ale,ile,dle,hle,cle,mle,uue,gle,yle,xle,Joe,Ile,Tle,_le,Ale,Dle,Ole,Lle,zle,Vle,sue,ode,ude,Hle,qle,Zle,Yle,ede,tde,rde,Ule,Uue,lde,fde,yde,wde,qie,Ide,nle];for(let e of Sde)mc(e);var Cde=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=KE(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let r=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let r=KE(t,n);this.freeBuffers.has(r)||this.freeBuffers.set(r,[]),this.freeBuffers.get(r).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let s=this.usedBuffers.get(r),a=s.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");s.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function KE(e,t){return`${e}_${t}`}var XE=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
      [[binding(1), group(0)]] var src: ${this.useImport?"texture_external":"texture_2d<f32>"};

      ${Ue()}
        let flatIndexBase = index * uniforms.numChannels;
        for (var i = 0; i < uniforms.numChannels; i = i + 1) {
          let flatIndex = flatIndexBase + i;
          if (flatIndex < uniforms.size) {
            let coords = getCoordsFromFlatIndex(flatIndexBase);
            let values = ${e};
            result.numbers[flatIndex] = i32(floor(255.0 * values[i]));
          }
        }
      }
  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,r)=>n===this.lastUniformData[r])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),r=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:r}}},Tde=class extends XE{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),r=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:r}}},Nde=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),YE=class extends Mu{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!yk())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Cde(this.device),this.tensorMap=new Ul(this,is()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return YE.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:r}=this.tensorMap.get(e);r!=null&&(this.disposeData(r.real.dataId,!0),this.disposeData(r.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()},s=w.sizeFromShape(t)*bk(n);return n==="bool"&&e instanceof Uint8Array&&(e=Int32Array.from(e)),this.tensorMap.set(r,{dtype:n,values:e,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:1}),r}move(e,t,n,r,s){if(r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*bk(r);this.tensorMap.set(e,{dtype:r,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new XE),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Tde),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let r;if(t.dtype==="complex64"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],o=s[1];r=N.mergeRealAndImagArrays(a,o)}else{let s=await this.getBufferData(t);r=bE(s,t.dtype)}return this.convertAndCacheOnCPU(e,r),r}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>w.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return $e(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(s);return o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let s=n.map(a=>w.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values&&this.queue.writeBuffer(t.bufferInfo.buffer,0,t.values))}makeUniformsDataView(e){let t=this.acquireBuffer(e.byteLength,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(t,0,e),{offset:0,size:e.byteLength,buffer:t}}arrayToDataView(e,t){let n=4,r=new DataView(new ArrayBuffer(t*n)),s=0;return e.forEach(a=>{let o=a.data;if(a.type!=="int32"&&a.type!=="float32"&&a.type!=="uint32")throw new Error(`${a.type} not supported!`);a.type==="int32"?o.forEach(i=>{r.setInt32(s*n,i,!0),s++}):a.type==="uint32"?o.forEach(i=>{r.setUint32(s*n,i,!0),s++}):o.forEach(i=>{r.setFloat32(s*n,i,!0),s++})}),r}computePadding(e){let t=0,n=0,r=0,s=[];return e.forEach((a,o)=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 0:i=1;break;case 1:i=1;break;case 2:i=2;break;case 3:i=4;break;case 4:i=4;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}n=Math.ceil(t/i)*i-t;for(let u=0;u<n;++u)s.push({type:a.type,data:[0]}),r++;s.push({type:a.type,data:a.data}),r=r+a.data.length,t+=a.data.length+n}),this.arrayToDataView(s,r)}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let s=0;s<e;s++)t.push({binding:s+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),r=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:r}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,r,s){if(!s){if(s=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(s.shape)===0){let E=this.tensorMap.get(s.dataId);return E.values=w.getTypedArrayFromDType(s.dtype,0),s}this.uploadToGPU(s.dataId)}let a=[{type:"float32",data:[NaN]}],o=t.concat(s).map(E=>E.shape),i="int32";o.map(E=>{a.push({type:i,data:E})});let u=w.computeStrides(s.shape);if(a.push({type:i,data:u}),e.size){let E=w.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?E/4:E]})}r&&(a=[...a,...r]);let l=null,c=this.computePadding(a),d=c.byteLength;l=this.makeUniformsDataView(c);let p=t.map((E,F)=>{if(E.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(E.dataId),{dtype:this.tensorMap.get(E.dataId).dtype,shape:E.shape,name:e.variableNames[F]}}),h=p.map(E=>E.dtype).concat(s.dtype),f=p.map(E=>N.getBroadcastDims(E.shape,s.shape)),m=p.map(E=>w.arraysEqual(E.shape,s.shape)).join("_"),g=f.map(E=>E.join("_")).join(";"),b=BE(e,o,h,g,m),{bindGroupLayout:y,pipelineLayout:v}=this.getCachedOrCreateLayout(e.variableNames.length),x=this.getAndSavePipeline(b,()=>LE(this.device,e,v,p,s)),k=this.activeTimers!=null,T=uce(this.device,y,t.map(E=>this.tensorToBinding(E)),this.tensorToBinding(s),l);this.ensureCommandEncoderReady();let C=this.getComputePass();if(k&&this.supportTimeQuery&&C.writeTimestamp(this.querySet,0),C.setPipeline(x),C.setBindGroup(0,T),C.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),k&&this.supportTimeQuery&&C.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(E=>{this.commandQueueOwnedIds.add(E.dataId)}),this.commandQueueOwnedIds.add(s.dataId),l){let E={byteSize:d,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};this.uniformDisposalQueue.push(E)}return X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),k&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}runFromPixelsProgram(e,t,n,r,s){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:r},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let o=this.getComputePass(),i=this.activeTimers!=null;i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,0),o.setPipeline(e.pipeline),o.setBindGroup(0,a),o.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(s),this.submitQueue(),i&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let r=new BigUint64Array(n.getMappedRange()),s=Number(r[1]-r[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=Nde){return X().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Tk=YE;Tk.nextDataId=0;var QE={};Ee(QE,{WebGPUBackend:()=>Tk,webgpu_util:()=>gE});bc.isBrowser()&&yk()&&wd("webgpu",async()=>{X().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:X().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},r=t.features.has("timestamp-query");r?n={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let s=await t.requestDevice(n);return new Tk(s,r)},3);var Lt;(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"})(Lt||(Lt={}));var mp;(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"})(mp||(mp={}));var ZE;function _de(e){ZE=e.wasm.cwrap(to,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Ede(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:d}=r,p=n.dataIdMap.get(s.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let E=n.dataIdMap.get(o.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=mp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?s.shape[2]:s.shape[1],y=l?a.shape[1]:a.shape[2],v=Ri.assertAndGetBroadcastShape(s.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],s.dtype),k=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(s.shape).buffer),C=new Uint8Array(new Int32Array(a.shape).buffer);return ZE(p,T,s.shape.length,h,C,a.shape.length,u,l,g,f,m,d||0,k),x}var Ade={kernelName:to,backendName:"wasm",setupFunc:_de,kernelFunc:Ede};function mn(e,t){let n;function r(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function s(a){let{backend:o,inputs:{x:i}}=a,u=o.dataIdMap.get(i.dataId).id,l=o.makeOutput(i.shape,t||i.dtype),c=o.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,Lt[i.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var $de=mn(Vo);function Pn(e,t,n){let r;function s(o){r=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:u}=o,{a:l,b:c}=u,d=i.dataIdMap.get(l.dataId).id,p=i.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=N.assertAndGetBroadcastShape(l.shape,c.shape),m=i.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(m.dataId).id;return(()=>r(d,g,l.shape.length,p,b,c.shape.length,Lt[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:a}}var Fde=!0,Dde=Pn(_s,Fde),JE;function Rde(e){JE=e.wasm.cwrap(la,null,["array","number","number","number"])}function Pde(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(r.shape)===0)return r;let s=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(s).buffer),o=n.dataIdMap.get(r.dataId).id;return JE(a,s.length,Lt[r.dtype],o),r}var Ode={kernelName:la,backendName:"wasm",setupFunc:Rde,kernelFunc:Pde};function Gm(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var Mde={kernelName:_a,backendName:"wasm",kernelFunc:Gm},eA;function Lde(e){eA=e.wasm.cwrap(Ja,null,["number","array","number","number","number","array","number"])}function il(e){let{inputs:t,backend:n,attrs:r}=e,[s,a]=zde(t.x.shape,r.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=Bde(t.x.shape,r.perm),u={dataId:t.x.dataId,shape:s,dtype:t.x.dtype};if(o){let f=Gm({inputs:t,backend:n});return f.shape=i,f}let l=n.makeOutput(i,u.dtype),c=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(l.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return eA(c,h,u.shape.length,Lt[u.dtype],d,p,a.length),l}function Bde(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function zde(e,t){let n=[],r=[];for(let s=0;s<e.length;++s)e[s]!==1&&n.push(e[s]),e[t[s]]!==1&&r.push(t[s]);for(let s=0;s<r.length;++s){let a=-1;for(let o=0;o<r.length;++o)r[o]>=s&&(a===-1||r[a]>r[o])&&(a=o);r[a]=s}return[n,r]}var Wde={kernelName:Ja,backendName:"wasm",kernelFunc:il,setupFunc:Lde};function _o(e,t,n){let r=e.shape,s=e.shape.length,a=w.parseAxisParam(t,r),o=a,i=N.getAxesPermutation(o,s),u=null,l=!1;if(i!=null){let c=new Array(s);for(let h=0;h<c.length;h++)c[h]=r[i[h]];o=N.getInnerMostAxes(o.length,s),u=il({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==d&&(l=!0)}return{transposed:u,originalAxes:a,axes:o,inputWasTransposed:l}}var tA;function Vde(e){tA=e.wasm.cwrap(Uu,null,["number, number, number"])}function Ude(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=_o(o,s,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims("all",d,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,d),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;tA(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,p);y.shape=v}return y}var Gde={kernelName:Uu,backendName:"wasm",setupFunc:Vde,kernelFunc:Ude},nA;function Hde(e){nA=e.wasm.cwrap(Gu,null,["number, number, number"])}function jde(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=_o(o,s,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims("any",d,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,d),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;nA(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,p);y.shape=v}return y}var qde={kernelName:Gu,backendName:"wasm",setupFunc:Hde,kernelFunc:jde},rA;function Kde(e){rA=e.wasm.cwrap(da,null,["number","number","number","number","number"])}function Xde(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s}=r,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,u=a,{transposed:l,axes:c,inputWasTransposed:d}=_o(a,s,t);if(d){let b=t.dataIdMap.get(l.dataId).id;b!==o&&(u=l,i=b)}let p=u.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return rA(i,Lt[u.dtype],m,g,f),d&&t.disposeData(l.dataId),h}var Yde={kernelName:da,backendName:"wasm",kernelFunc:Xde,setupFunc:Kde},sA;function Qde(e){sA=e.wasm.cwrap(pa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zde(e){let{inputs:t,attrs:n,backend:r}=e,s=t.x,a=r.dataIdMap.get(s.dataId).id,{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=n,c=N.computePool2DInfo(s.shape,o,i,1,u,l),d=c.filterHeight,p=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=r.makeOutput(c.outShape,"float32"),k=r.dataIdMap.get(x.dataId).id;return sA(a,s.shape[0],s.shape[1],s.shape[2],d,p,h,f,m,g,b,y,v,k),x}var Jde={kernelName:pa,backendName:"wasm",setupFunc:Qde,kernelFunc:Zde};function Kn(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:s}=n,a=w.sizeFromShape(r.shape),o=w.inferFromImplicitShape(s,a);return w.assert(a===w.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:o,dtype:r.dtype}}var epe={kernelName:hi,backendName:"wasm",kernelFunc:Kn},aA;function tpe(e){aA=e.wasm.cwrap(ha,null,["number","array","number","number","array","number","number","number","number"])}function npe(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=s.shape.length,l=a.shape.length,c=o?s.shape[u-2]:s.shape[u-1],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[u-1]:s.shape[u-2],h=i?a.shape[l-2]:a.shape[l-1],f=s.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=Ri.assertAndGetBroadcastShape(s.shape.slice(0,-2),a.shape.slice(0,-2)).concat([p,h]);w.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${s.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let x=o?[g,c,p]:[g,p,c],k=i?[b,h,d]:[b,d,h],T=Kn({inputs:{x:s},backend:n,attrs:{shape:x}}),C=Kn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(T.dataId).id,F=n.dataIdMap.get(C.dataId).id,O=o?T.shape[2]:T.shape[1],D=i?C.shape[1]:C.shape[2],R=Math.max(g,b),_=n.makeOutput([R,O,D],T.dtype),L=n.dataIdMap.get(_.dataId).id,U=new Uint8Array(new Int32Array(T.shape).buffer),j=new Uint8Array(new Int32Array(C.shape).buffer);return aA(E,U,T.shape.length,F,j,C.shape.length,o,i,L),n.disposeData(T.dataId),n.disposeData(C.dataId),_.shape=v,_}var rpe={kernelName:ha,backendName:"wasm",setupFunc:tpe,kernelFunc:npe};function lu(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:s}=e,[a,o]=$t.parseSliceParams(t,n,r),i=$t.isSliceContinous(t.shape,a,o),u=s.readSync(t.dataId),l=s.makeOutput(o,t.dtype),c=w.computeStrides(t.shape),d=s.dataIdMap.get(l.dataId);if(i){let f=$t.computeFlatOffset(a,c);return t.dtype==="string"?d.stringBytes=u.slice(f,f+w.sizeFromShape(o)):s.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(o))),l}if(t.dtype==="string"){let f=bm(u,a,o,t.shape,t.dtype);return d.stringBytes=f,l}let p=s.typedArrayFromHeap(l),h=t.shape.length;if(h===2)spe(u,c[0],p,a,o);else if(h===3)ape(u,c[0],c[1],p,a,o);else if(h===4)ope(u,c[0],c[1],c[2],p,a,o);else{let f=bm(u,a,o,t.shape,t.dtype);p.set(f)}return l}function spe(e,t,n,r,s){let a=0,o=r[0],i=r[1],u=o+s[0];for(let l=o;l<u;l++){let c=l*t+i;n.set(e.subarray(c,c+s[1]),a),a+=s[1]}}function ape(e,t,n,r,s,a){let o=0,i=s[0],u=s[1],l=s[2],c=i+a[0],d=u+a[1];for(let p=i;p<c;p++)for(let h=u;h<d;h++){let f=p*t+h*n+l;r.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function ope(e,t,n,r,s,a,o){let i=0,u=a[0],l=a[1],c=a[2],d=u+o[0],p=l+o[1],h=c+o[2],f=a[3];for(let m=u;m<d;m++)for(let g=l;g<p;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*r+f;s.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var ipe={kernelName:yi,backendName:"wasm",kernelFunc:lu};function upe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r,i=a.reduce((b,y)=>b*y),u=N.getReshaped(s.shape,a,i),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(s.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(c,o,a.length),h=Kn({inputs:{x:s},backend:n,attrs:{shape:u}}),f=il({inputs:{x:h},backend:n,attrs:{perm:l}}),m=Kn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=lu({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var cpe={kernelName:Uo,backendName:"wasm",kernelFunc:upe};function gp(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,s=r.makeOutput(t.shape,n),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(s).set(a),s}var lpe={kernelName:fa,backendName:"wasm",kernelFunc:gp},dpe=mn(ma),oA;function ppe(e){oA=e.wasm.cwrap(Es,null,["number","number","number","number"])}function hpe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(s.shape,s.dtype),l=n.dataIdMap.get(u.dataId).id;return oA(i,a,o,l),u}var fpe={kernelName:Es,backendName:"wasm",setupFunc:ppe,kernelFunc:hpe};function iA(e){let{inputs:t,backend:n}=e,r=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],s=N.computeOutShape(t.map(h=>h.shape),r),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return Gm({inputs:{x:a[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(w.sizeFromShape(s)===0)return o;let i=a.map(h=>h.shape);if(N.assertParamsConsistent(i,r),a[0].dtype==="string"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(r));return Kn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));s=N.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=Rw(f,s,t[0].dtype,m),b=N.computeOutShape(a.map(v=>v.shape),r);o.shape=b;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=N.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),o}let u=w.sizeFromShape(a[0].shape.slice(0,r)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(r));return l+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<d.length;m++){let g=c[m],b=h*g,y=d[m].subarray(b,b+g);p.set(y,f),f+=g}}return o}var mpe={kernelName:Go,backendName:"wasm",kernelFunc:iA},uA;function gpe(e){uA=e.wasm.cwrap(ga,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bpe(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:d,dataFormat:p}=n,h=N.convertConv2DDataFormat(p),f=N.computeConv2DInfo(s.shape,a.shape,u,l,c,d,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,T=f.dilationWidth,C=f.strideHeight,E=f.strideWidth,F=f.inChannels,O=f.outChannels,D=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Pe(this.left,this.top)}get topRight(){return new Pe(this.right,this.top)}get bottomLeft(){return new Pe(this.left,this.bottom)}get bottomRight(){return new Pe(this.right,this.bottom)}round(){let[t,n,r,s]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new dt({x:t,y:n,width:r,height:s})}floor(){let[t,n,r,s]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new dt({x:t,y:n,width:r,height:s})}toSquare(){let{x:t,y:n,width:r,height:s}=this,a=Math.abs(r-s);return r<s&&(t-=a/2,r+=a),s<r&&(n-=a/2,s+=a),new dt({x:t,y:n,width:r,height:s})}rescale(t){let n=qm(t)?t.width:t,r=qm(t)?t.height:t;return new dt({x:this.x*n,y:this.y*r,width:this.width*n,height:this.height*r})}pad(t,n){let[r,s,a,o]=[this.x-t/2,this.y-n/2,this.width+t,this.height+n];return new dt({x:r,y:s,width:a,height:o})}clipAtImageBorders(t,n){let{x:r,y:s,right:a,bottom:o}=this,i=Math.max(r,0),u=Math.max(s,0),l=a-i,c=o-u,d=Math.min(l,t-i),p=Math.min(c,n-u);return new dt({x:i,y:u,width:d,height:p}).floor()}shift(t,n){let{width:r,height:s}=this,a=this.x+t,o=this.y+n;return new dt({x:a,y:o,width:r,height:s})}padAtBorders(t,n){let r=this.width+1,s=this.height+1,a=1,o=1,i=r,u=s,l=this.left,c=this.top,d=this.right,p=this.bottom;return d>n&&(i=-d+n+r,d=n),p>t&&(u=-p+t+s,p=t),l<1&&(u=2-l,l=1),c<1&&(u=2-c,c=1),{dy:o,edy:u,dx:a,edx:i,y:c,ey:p,x:l,ex:d,w:r,h:s}}calibrate(t){return new dt({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 cl=class extends dt{constructor(t,n,r,s,a=!1){super({left:t,top:n,right:r,bottom:s},a)}};var Ao=class{constructor(t,n,r,s,a){this._imageDims=new On(a.width,a.height),this._score=t,this._classScore=n,this._className=r,this._box=new dt(s).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new dt(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Ao(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var St=class extends Ao{constructor(t,n,r){super(t,t,"",n,r)}forSize(t,n){let{score:r,relativeBox:s,imageDims:a}=super.forSize(t,n);return new St(r,s,a)}};function Bk(e,t,n=!0){let r=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),s=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),a=r*s;return n?a/(e.area+t.area-a):a/Math.min(e.area,t.area)}function zk(e){let t=e.map(i=>i.x),n=e.map(i=>i.y),r=t.reduce((i,u)=>u<i?u:i,1/0),s=n.reduce((i,u)=>u<i?u:i,1/0),a=t.reduce((i,u)=>i<u?u:i,0),o=n.reduce((i,u)=>i<u?u:i,0);return new cl(r,s,a,o)}function Wk(e,t,n,r=!0){let s=t.map((o,i)=>({score:o,boxIndex:i})).sort((o,i)=>o.score-i.score).map(o=>o.boxIndex),a=[];for(;s.length>0;){let o=s.pop();a.push(o);let i=s,u=[];for(let l=0;l<i.length;l++){let c=i[l],d=e[o],p=e[c];u.push(Bk(d,p,r))}s=s.filter((l,c)=>u[c]<=n)}return a}function rs(e,t){return M(()=>{let[n,r,s]=t,a=_n([...e.shape.slice(0,3),1],n,"float32"),o=_n([...e.shape.slice(0,3),1],r,"float32"),i=_n([...e.shape.slice(0,3),1],s,"float32"),u=ot([a,o,i],3);return he(e,u)})}function Vk(e,t=!1){return M(()=>{let[n,r]=e.shape.slice(1);if(n===r)return e;let s=Math.abs(n-r),a=Math.round(s*(t?.5:1)),o=n>r?2:1,i=p=>{let h=e.shape.slice();return h[o]=p,_n(h,0,"float32")},u=i(a),l=s-u.shape[o],d=[t&&l?i(l):null,e,u].filter(p=>!!p).map(p=>ue(p,"float32"));return ot(d,o)})}function ege(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let r=Math.floor(Math.random()*(n+1)),s=t[n];t[n]=t[r],t[r]=s}return t}function xp(e){return 1/(1+Math.exp(-e))}function tge(e){return Math.log(e/(1-e))}var ll=class extends dt{constructor(t,n,r,s,a=!1){super({x:t,y:n,width:r,height:s},a)}};var nge=.5,rge=.43,sge=.45,Sr=class{constructor(t,n,r=new Pe(0,0)){let{width:s,height:a}=n;this._imgDims=new On(s,a),this._shift=r,this._positions=t.map(o=>o.mul(new Pe(s,a)).add(r))}get shift(){return new Pe(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 Pe(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 Pe(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let a=t instanceof St?t.box.floor():new dt(t);return this.shiftBy(a.x,a.y).align(null,n)}let{useDlibAlignment:r,minBoxPadding:s}={useDlibAlignment:!1,minBoxPadding:.2,...n};return r?this.alignDlib():this.alignMinBbox(s)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,r,s]=t,a=d=>s.sub(d).magnitude(),o=(a(n)+a(r))/2,i=Math.floor(o/sge),u=hu(t),l=Math.floor(Math.max(0,u.x-nge*i)),c=Math.floor(Math.max(0,u.y-rge*i));return new ll(l,c,Math.min(i,this.imageWidth+l),Math.min(i,this.imageHeight+c))}alignMinBbox(t){let n=zk(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var r$=class extends Sr{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],hu([t[3],t[4]])]}};var dl=class extends Sr{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(hu)}};var wp=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?` (${pu(this.distance)})`:""}`}};var kp=class extends dt{static assertIsValidLabeledBox(t,n){if(dt.assertIsValidBox(t,n),!ns(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var qs=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(r=>new Float32Array(r));return new qs(t.label,n)}};var s$=class extends kp{static assertIsValidPredictedBox(t,n){if(kp.assertIsValidLabeledBox(t,n),!ul(t.score)||!ul(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,r,s){super(t,n);this._score=r,this._classScore=s}get score(){return this._score}get classScore(){return this._classScore}};function ws(e){return e.detection instanceof St}function fu(e,t){return{...e,...{detection:t}}}function Uk(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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implementation for nodejs environment")},s=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},a=()=>{if(n)return new n;throw new Error("createVideoElement - missing Video implementation for nodejs environment")},o=global.fetch,i=Km();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:r,createImageElement:s,createVideoElement:a,fetch:o,...i}}function Hk(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var un;function age(){if(!un)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return un}function jk(e){un=e}function qk(){return Hk()?jk(Uk()):Ip()?jk(Gk()):null}function oge(e){if(un||qk(),!un)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=un.Canvas,Image:n=un.Image}=e;un.Canvas=t,un.Image=n,un.createCanvasElement=e.createCanvasElement||(()=>new t),un.createImageElement=e.createImageElement||(()=>new n),un.ImageData=e.ImageData||un.ImageData,un.Video=e.Video||un.Video,un.fetch=e.fetch||un.fetch,un.readFile=e.readFile||un.readFile}var rt={getEnv:age,setEnv:jk,initialize:qk,createBrowserEnv:Uk,createFileSystem:Km,createNodejsEnv:Gk,monkeyPatch:oge,isBrowser:Hk,isNodejs:Ip};qk();function mu(e){return!rt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Xn(e){let{Canvas:t,CanvasRenderingContext2D:n}=rt.getEnv();if(e instanceof n)return e;let r=mu(e);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let s=r.getContext("2d");if(!s)throw new Error("resolveContext2d - canvas 2d context is null");return s}var ks;(function(s){s.TOP_LEFT="TOP_LEFT",s.TOP_RIGHT="TOP_RIGHT",s.BOTTOM_LEFT="BOTTOM_LEFT",s.BOTTOM_RIGHT="BOTTOM_RIGHT"})(ks||(ks={}));var Sp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:r,fontColor:s,fontSize:a,fontStyle:o,padding:i}=t;this.anchorPosition=n||ks.TOP_LEFT,this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=s||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=o||"Georgia",this.padding=i||4}},$o=class{constructor(t,n,r={}){this.text=typeof t=="string"?[t]:t instanceof $o?t.text:t,this.anchor=n,this.options=new Sp(r)}measureWidth(t){let{padding:n}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,s)=>r<s?s:r,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:r}=this.options,s=r===ks.BOTTOM_RIGHT||r===ks.TOP_RIGHT,a=r===ks.BOTTOM_LEFT||r===ks.BOTTOM_RIGHT,o=this.measureWidth(t),i=this.measureHeight(),u=s?this.anchor.x-o:this.anchor.x,l=a?this.anchor.y-i:this.anchor.y;if(n){let{width:c,height:d}=n,p=Math.max(Math.min(u,c-o),0),h=Math.max(Math.min(l,d-i),0);return{x:p,y:h}}return{x:u,y:l}}draw(t){let n=mu(t),r=Xn(n),{backgroundColor:s,fontColor:a,fontSize:o,fontStyle:i,padding:u}=this.options;r.font=`${o}px ${i}`;let l=this.measureWidth(r),c=this.measureHeight();r.fillStyle=s;let d=this.getUpperLeft(r,n);r.fillRect(d.x,d.y,l,c),r.fillStyle=a,this.text.forEach((p,h)=>{let f=u+d.x,m=u+d.y+(h+1)*o;r.fillText(p,f,m)})}};var Kk=class{constructor(t={}){let{boxColor:n,lineWidth:r,label:s,drawLabelOptions:a}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=s;let o={anchorPosition:ks.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Sp({...o,...a})}},Xm=class{constructor(t,n={}){this.box=new dt(t),this.options=new Kk(n)}draw(t){let n=Xn(t),{boxColor:r,lineWidth:s}=this.options,{x:a,y:o,width:i,height:u}=this.box;n.strokeStyle=r,n.lineWidth=s,n.strokeRect(a,o,i,u);let{label:l}=this.options;l&&new $o([l],{x:a-s/2,y:o},this.options.drawLabelOptions).draw(t)}};function ige(e,t){(Array.isArray(t)?t:[t]).forEach(r=>{let s=r instanceof St?r.score:ws(r)?r.detection.score:void 0,a=r instanceof St?r.box:ws(r)?r.detection.box:new dt(r),o=s?`${pu(s)}`:void 0;new Xm(a,{label:o}).draw(e)})}function Cp(e){let{Image:t,Video:n}=rt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function Xk(e){return new Promise((t,n)=>{(e instanceof rt.getEnv().Canvas||Cp(e))&&t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",s),a.currentTarget.removeEventListener("error",r),n(a))}function s(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",s),a.currentTarget.removeEventListener("error",r),t(a))}e.addEventListener("load",s),e.addEventListener("error",r)})}function Yk(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let s=rt.getEnv().createImageElement();s.onload=()=>t(s),s.onerror=n,s.src=r.result},r.onerror=n,r.readAsDataURL(e)})}function gu(e){let{Image:t,Video:n}=rt.getEnv();return e instanceof t?new On(e.naturalWidth,e.naturalHeight):e instanceof n?new On(e.videoWidth,e.videoHeight):new On(e.width,e.height)}function bu({width:e,height:t}){let{createCanvasElement:n}=rt.getEnv(),r=n();return r.width=e,r.height=t,r}function Tp(e,t){let{ImageData:n}=rt.getEnv();if(!(e instanceof n)&&!Cp(e))throw new Error("createCanvasFromMedia - media has not finished 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Ks=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((r,s)=>{if(js(r)){this._imageTensors[s]=r,this._inputDimensions[s]=r.shape;return}if(Ir(r)){let o=r.shape[0];if(o!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${o} passed, but not supported in input array`);this._imageTensors[s]=r,this._inputDimensions[s]=r.shape.slice(1);return}let a=r instanceof rt.getEnv().Canvas?r:Tp(r);this._canvases[s]=a,this._inputDimensions[s]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return 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t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:r}=Cge(e,t),s=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!js(s))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${s}`);let a={mobilenetv1:n(),prediction_layer:r(),output_layer:{extra_dim:s}};return Mn(e,t),{params:a,paramMappings:t}}function Br(e,t,n){return M(()=>{let r=Wt(e,t.filters,n,"same");return r=Z(r,t.batch_norm_offset),dn(r,0,6)})}var Tge=.0010000000474974513;function Nge(e,t,n){return M(()=>{let r=Bi(e,t.filters,n,"same");return r=ho(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Tge),dn(r,0,6)})}function _ge(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function C$(e,t){return M(()=>{let n,r=Br(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,o)=>{let 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Age(e){let t=vt(Oe(e,[1,0])),n=[he(t[2],t[0]),he(t[3],t[1])],r=[Z(t[0],me(n[0],2)),Z(t[1],me(n[1],2))];return{sizes:n,centers:r}}function $ge(e,t){let{sizes:n,centers:r}=Age(e),s=vt(Oe(t,[1,0])),a=me(V(Tn(me(s[2],5)),n[0]),2),o=Z(V(me(s[0],10),n[0]),r[0]),i=me(V(Tn(me(s[3],5)),n[1]),2),u=Z(V(me(s[1],10),n[1]),r[1]);return Oe(Ut([he(o,a),he(u,i),Z(o,a),Z(u,i)]),[1,0])}function N$(e,t,n){return M(()=>{let r=e.shape[0],s=$ge(G(tr(n.extra_dim,[r,1,1]),[-1,4]),G(e,[-1,4]));s=G(s,[r,s.shape[0]/r,4]);let a=$r(Ve(t,[0,0,1],[-1,-1,-1])),o=Ve(a,[0,0,0],[-1,-1,1]);o=G(o,[r,o.shape[1]]);let i=vt(s),u=vt(o);return{boxes:i,scores:u}})}function xu(e,t){return M(()=>{let n=e.shape[0],r=G(yu(e,t.box_encoding_predictor),[n,-1,1,4]),s=G(yu(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:r,classPrediction:s}})}function _$(e,t,n){return M(()=>{let 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c=Array.from(l.dataSync()),p=T$(u,c,r,.5,s),h=a.getReshapedInputDimensions(0),f=a.inputSize,m=f/h.width,g=f/h.height,b=u.arraySync(),y=p.map(v=>{let[x,k]=[Math.max(0,b[v][0]),Math.min(1,b[v][2])].map(E=>E*g),[T,C]=[Math.max(0,b[v][1]),Math.min(1,b[v][3])].map(E=>E*m);return new St(c[v],new ll(T,x,C-T,k-x),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return u.dispose(),l.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return S$(t)}extractParams(t){return I$(t)}};function E$(e){let t=new wu;return t.extractWeights(e),t}function Fge(e){return E$(e)}var A$=class extends wu{};var $$=.4,F$=[new Pe(.738768,.874946),new Pe(2.42204,2.65704),new Pe(4.30971,7.04493),new Pe(10.246,4.59428),new Pe(12.6868,11.8741)],D$=[new Pe(1.603231,2.094468),new Pe(6.041143,7.080126),new Pe(2.882459,3.518061),new Pe(4.266906,5.178857),new Pe(9.041765,10.66308)],R$=[117.001,114.697,97.404],P$="tiny_yolov2_model",O$="tiny_yolov2_separable_conv_model";var 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M$(e,t,n,r){let{extractWeights:s,getRemainingWeights:a}=Ln(e),o=[],{extractConvParams:i,extractConvWithBatchNormParams:u,extractSeparableConvParams:l}=Dge(s,o),c;if(t.withSeparableConvs){let[d,p,h,f,m,g,b,y,v]=r,x=t.isFirstLayerConv2d?i(d,p,3,"conv0"):l(d,p,"conv0"),k=l(p,h,"conv1"),T=l(h,f,"conv2"),C=l(f,m,"conv3"),E=l(m,g,"conv4"),F=l(g,b,"conv5"),O=y?l(b,y,"conv6"):void 0,D=v?l(y,v,"conv7"):void 0,R=i(v||y||b,5*n,1,"conv8");c={conv0:x,conv1:k,conv2:T,conv3:C,conv4:E,conv5:F,conv6:O,conv7:D,conv8:R}}else{let[d,p,h,f,m,g,b,y,v]=r,x=u(d,p,"conv0"),k=u(p,h,"conv1"),T=u(h,f,"conv2"),C=u(f,m,"conv3"),E=u(m,g,"conv4"),F=u(g,b,"conv5"),O=u(b,y,"conv6"),D=u(y,v,"conv7"),R=i(v,5*n,1,"conv8");c={conv0:x,conv1:k,conv2:T,conv3:C,conv4:E,conv5:F,conv6:O,conv7:D,conv8:R}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:c,paramMappings:o}}function Rge(e,t){let n=lr(e,t);function r(i){let u=n(`${i}/sub`,1),l=n(`${i}/truediv`,1);return{sub:u,truediv:l}}function s(i){let u=n(`${i}/filters`,4),l=n(`${i}/bias`,1);return{filters:u,bias:l}}function a(i){let u=s(`${i}/conv`),l=r(`${i}/bn`);return{conv:u,bn:l}}let o=gl(n);return{extractConvParams:s,extractConvWithBatchNormParams:a,extractSeparableConvParams:o}}function L$(e,t){let n=[],{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}=Rge(e,n),o;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else o={conv0:s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:s("conv6"),conv7:s("conv7"),conv8:r("conv8")};return Mn(e,n),{params:o,paramMappings:n}}var Is=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 pI=class extends gn{constructor(t){super("TinyYolov2");dI(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let r=Qs(t,n.conv0);return r=Vt(r,[2,2],[2,2],"same"),r=Qs(r,n.conv1),r=Vt(r,[2,2],[2,2],"same"),r=Qs(r,n.conv2),r=Vt(r,[2,2],[2,2],"same"),r=Qs(r,n.conv3),r=Vt(r,[2,2],[2,2],"same"),r=Qs(r,n.conv4),r=Vt(r,[2,2],[2,2],"same"),r=Qs(r,n.conv5),r=Vt(r,[2,2],[1,1],"same"),r=Qs(r,n.conv6),r=Qs(r,n.conv7),yu(r,n.conv8,"valid",!1)}runMobilenet(t,n){let r=this.config.isFirstLayerConv2d?xl(yu(t,n.conv0,"valid",!1)):Zs(t,n.conv0);return r=Vt(r,[2,2],[2,2],"same"),r=Zs(r,n.conv1),r=Vt(r,[2,2],[2,2],"same"),r=Zs(r,n.conv2),r=Vt(r,[2,2],[2,2],"same"),r=Zs(r,n.conv3),r=Vt(r,[2,2],[2,2],"same"),r=Zs(r,n.conv4),r=Vt(r,[2,2],[2,2],"same"),r=Zs(r,n.conv5),r=Vt(r,[2,2],[1,1],"same"),r=n.conv6?Zs(r,n.conv6):r,r=n.conv7?Zs(r,n.conv7):r,yu(r,n.conv8,"valid",!1)}forwardInput(t,n){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let s=ue(t.toBatchTensor(n,!1),"float32");return s=this.config.meanRgb?rs(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(t,n){return this.forwardInput(await xt(t),n)}async detect(t,n={}){let{inputSize:r,scoreThreshold:s}=new Is(n),a=await xt(t),o=await this.forwardInput(a,r),i=M(()=>vt(o)[0].expandDims()),u={width:a.getInputWidth(0),height:a.getInputHeight(0)},l=await this.extractBoxes(i,a.getReshapedInputDimensions(0),s);o.dispose(),i.dispose();let c=l.map(g=>g.box),d=l.map(g=>g.score),p=l.map(g=>g.classScore),h=l.map(g=>this.config.classes[g.label]);return Wk(c.map(g=>g.rescale(r)),d,this.config.iouThreshold,!0).map(g=>new Ao(d[g],p[g],h[g],c[g],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return L$(t,this.config)}extractParams(t){let n=this.config.filterSizes||pI.DEFAULT_FILTER_SIZES,r=n?n.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return M$(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,r){let{width:s,height:a}=n,o=Math.max(s,a),i=o/s,u=o/a,l=t.shape[1],c=this.config.anchors.length,[d,p,h]=M(()=>{let b=t.reshape([l,l,c,this.boxEncodingSize]),y=b.slice([0,0,0,0],[l,l,c,4]),v=b.slice([0,0,0,4],[l,l,c,1]),x=this.withClassScores?ls(b.slice([0,0,0,5],[l,l,c,this.config.classes.length]),3):ke(0);return[y,v,x]}),f=[],m=await p.array(),g=await d.array();for(let b=0;b<l;b++)for(let y=0;y<l;y++)for(let v=0;v<c;v++){let x=xp(m[b][y][v][0]);if(!r||x>r){let k=(y+xp(g[b][y][v][0]))/l*i,T=(b+xp(g[b][y][v][1]))/l*u,C=Math.exp(g[b][y][v][2])*this.config.anchors[v].x/l*i,E=Math.exp(g[b][y][v][3])*this.config.anchors[v].y/l*u,F=k-C/2,O=T-E/2,D={row:b,col:y,anchor:v},{classScore:R,label:_}=this.withClassScores?await this.extractPredictedClass(h,D):{classScore:1,label:0};f.push({box:new cl(F,O,F+C,O+E),score:x,classScore:x*R,label:_,...D})}}return d.dispose(),p.dispose(),h.dispose(),f}async extractPredictedClass(t,n){let{row:r,col:s,anchor:a}=n,o=await t.array();return Array(this.config.classes.length).fill(0).map((i,u)=>o[r][s][a][u]).map((i,u)=>({classScore:i,label:u})).reduce((i,u)=>i.classScore>u.classScore?i:u)}},wl=pI;wl.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var kl=class extends wl{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:$$,classes:["face"],...t?{anchors:D$,meanRgb:R$}:{anchors:F$,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new St(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?O$:P$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Pge(e,t=!0){let n=new kl(t);return n.extractWeights(e),n}var fg=class extends Is{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Wr=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function ku(e,t,n,r,s=({alignedRect:a})=>a){let a=e.map(u=>vu(u)?s(u):u.detection),o=r||(t instanceof Ae?await hl(t,a):await pl(t,a)),i=await n(o);return o.forEach(u=>u instanceof Ae&&u.dispose()),i}async function Il(e,t,n,r,s){return ku([e],t,async a=>n(a[0]),r,s)}var B$=.4,z$=[new Pe(1.603231,2.094468),new Pe(6.041143,7.080126),new Pe(2.882459,3.518061),new Pe(4.266906,5.178857),new Pe(9.041765,10.66308)],W$=[117.001,114.697,97.404];var Sl=class extends wl{constructor(){let t={withSeparableConvs:!0,iouThreshold:B$,classes:["face"],anchors:z$,meanRgb:W$,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new St(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var st={ssdMobilenetv1:new wu,tinyFaceDetector:new Sl,tinyYolov2:new kl,faceLandmark68Net:new yl,faceLandmark68TinyNet:new ug,faceRecognitionNet:new vl,faceExpressionNet:new ag,ageGenderNet:new ig},V$=(e,t)=>st.ssdMobilenetv1.locateFaces(e,t),Oge=(e,t)=>st.tinyFaceDetector.locateFaces(e,t),Mge=(e,t)=>st.tinyYolov2.locateFaces(e,t),U$=e=>st.faceLandmark68Net.detectLandmarks(e),Lge=e=>st.faceLandmark68TinyNet.detectLandmarks(e),Bge=e=>st.faceRecognitionNet.computeFaceDescriptor(e),zge=e=>st.faceExpressionNet.predictExpressions(e),Wge=e=>st.ageGenderNet.predictAgeAndGender(e),G$=e=>st.ssdMobilenetv1.load(e),Vge=e=>st.tinyFaceDetector.load(e),Uge=e=>st.tinyYolov2.load(e),Gge=e=>st.faceLandmark68Net.load(e),Hge=e=>st.faceLandmark68TinyNet.load(e),jge=e=>st.faceRecognitionNet.load(e),qge=e=>st.faceExpressionNet.load(e),Kge=e=>st.ageGenderNet.load(e),Xge=G$,Yge=V$,Qge=U$;var hI=class extends Wr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},Cl=class extends hI{async run(){let t=await this.parentTask,n=await ku(t,this.input,async r=>Promise.all(r.map(s=>st.faceExpressionNet.predictExpressions(s))),this.extractedFaces);return t.map((r,s)=>og(r,n[s]))}withAgeAndGender(){return new Nl(this,this.input)}},Tl=class extends hI{async run(){let t=await this.parentTask;if(!t)return;let n=await Il(t,this.input,r=>st.faceExpressionNet.predictExpressions(r),this.extractedFaces);return og(t,n)}withAgeAndGender(){return new _l(this,this.input)}},Iu=class extends Cl{withAgeAndGender(){return new Cu(this,this.input)}withFaceDescriptors(){return new Do(this,this.input)}},Su=class extends Tl{withAgeAndGender(){return new Tu(this,this.input)}withFaceDescriptor(){return new Ro(this,this.input)}};var fI=class extends Wr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},Nl=class extends fI{async run(){let t=await this.parentTask,n=await ku(t,this.input,async r=>Promise.all(r.map(s=>st.ageGenderNet.predictAgeAndGender(s))),this.extractedFaces);return t.map((r,s)=>{let{age:a,gender:o,genderProbability:i}=n[s];return dg(pg(r,o,i),a)})}withFaceExpressions(){return new Cl(this,this.input)}},_l=class extends fI{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:r,genderProbability:s}=await Il(t,this.input,a=>st.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return dg(pg(t,r,s),n)}withFaceExpressions(){return new Tl(this,this.input)}},Cu=class extends Nl{withFaceExpressions(){return new Iu(this,this.input)}withFaceDescriptors(){return new Do(this,this.input)}},Tu=class extends _l{withFaceExpressions(){return new Su(this,this.input)}withFaceDescriptor(){return new Ro(this,this.input)}};var mg=class extends Wr{constructor(t,n){super();this.parentTask=t;this.input=n}},Do=class extends mg{async run(){let t=await this.parentTask;return(await ku(t,this.input,r=>Promise.all(r.map(s=>st.faceRecognitionNet.computeFaceDescriptor(s))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,s)=>lg(t[s],r))}withFaceExpressions(){return new Iu(this,this.input)}withAgeAndGender(){return new Cu(this,this.input)}},Ro=class extends mg{async run(){let t=await this.parentTask;if(!t)return;let n=await Il(t,this.input,r=>st.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return lg(t,n)}withFaceExpressions(){return new Su(this,this.input)}withAgeAndGender(){return new Tu(this,this.input)}};var gg=class extends Wr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?st.faceLandmark68TinyNet:st.faceLandmark68Net}},bg=class extends gg{async run(){let t=await this.parentTask,n=t.map(a=>a.detection),r=this.input instanceof Ae?await hl(this.input,n):await pl(this.input,n),s=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Ae&&a.dispose()),t.map((a,o)=>bl(a,s[o]))}withFaceExpressions(){return new Iu(this,this.input)}withAgeAndGender(){return new Cu(this,this.input)}withFaceDescriptors(){return new Do(this,this.input)}},yg=class extends gg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,r=this.input instanceof Ae?await hl(this.input,[n]):await pl(this.input,[n]),s=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ae&&a.dispose()),bl(t,s)}withFaceExpressions(){return new Su(this,this.input)}withAgeAndGender(){return new Tu(this,this.input)}withFaceDescriptor(){return new Ro(this,this.input)}};var vg=class extends Wr{constructor(t,n=new zr){super();this.input=t;this.options=n}},Dp=class extends vg{async run(){let{input:t,options:n}=this,r;if(n instanceof fg)r=st.tinyFaceDetector.locateFaces(t,n);else if(n instanceof zr)r=st.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Is)r=st.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(r=>t(r.map(s=>fu({},s)))).catch(r=>n(r))})}withFaceLandmarks(t=!1){return new bg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Cl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Nl(this.runAndExtendWithFaceDetections(),this.input)}},xg=class extends vg{async run(){let t=await new Dp(this.input,this.options),n=t[0];return t.forEach(r=>{r.score>n.score&&(n=r)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?fu({},n):void 0)})}withFaceLandmarks(t=!1){return new yg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Tl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new _l(this.runAndExtendWithFaceDetection(),this.input)}};function Zge(e,t=new zr){return new xg(e,t)}function wg(e,t=new zr){return new Dp(e,t)}async function H$(e,t){return wg(e,new zr(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Jge(e,t={}){return wg(e,new Is(t)).withFaceLandmarks().withFaceDescriptors()}var ebe=H$;function mI(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),r=Array.from(t);return Math.sqrt(n.map((s,a)=>s-r[a]).reduce((s,a)=>s+a**2,0))}var kg=class{constructor(t,n=.6){this._distanceThreshold=n;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let s=1,a=()=>`person ${s++}`;this._labeledDescriptors=r.map(o=>{if(o instanceof qs)return o;if(o instanceof Float32Array)return new qs(a(),[o]);if(o.descriptor&&o.descriptor instanceof Float32Array)return new qs(a(),[o.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(r=>mI(r,t)).reduce((r,s)=>r+s,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:r})=>new wp(r,this.computeMeanDistance(t,n))).reduce((n,r)=>n.distance<r.distance?n:r)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this._distanceThreshold?n:new wp("unknown",n.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(r=>qs.fromJSON(r));return new kg(n,t.distanceThreshold)}};function tbe(e){let t=new Sl;return t.extractWeights(e),t}function j$(e,t){let{width:n,height:r}=new On(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(s=>j$(s,{width:n,height:r}));if(vu(e)){let s=e.detection.forSize(n,r),a=e.unshiftedLandmarks.forSize(s.box.width,s.box.height);return bl(fu(e,s),a)}return ws(e)?fu(e,e.detection.forSize(n,r)):e instanceof Sr||e instanceof St?e.forSize(n,r):e}var nbe=l$;return rbe;})();
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use backend file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */