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

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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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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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 Dd=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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s=z.registeredVariables[n],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${n}/rms`,variable:M(()=>Xe(s).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${n}/momentum`,variable:M(()=>Xe(s).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${n}/mg`,variable:M(()=>Xe(s).variable(a))});let o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[r].variable,u=this.accumulatedMoments[r].variable;M(()=>{let l=Z(V(i,this.decay),V(ft(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[r].variable,d=Z(V(c,this.decay),V(o,1-this.decay)),p=me(V(o,this.learningRate),vn(he(l,Z(ft(d),this.epsilon)))),h=Z(V(u,this.momentum),p);i.assign(l),c.assign(d),u.assign(h);let f=he(s,h);s.assign(f)}else{let 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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. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);bo(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;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 Rc))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 Vd(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(;!FV(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 nG(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: ${JSON.stringify(e)}`)}function o2(e,t){return nG(e,t,"classWeight")}async function i2(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let s=M(()=>{if(e.shape.length===1)return Fs(e);if(e.shape.length===2){if(e.shape[1]>1)return wc(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 c2(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 aG(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 oG(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()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let s=n.validationData!=null,a,o;if(s)if(l2(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=aG(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),l;s?l=u.slice().concat(u.map(g=>"val_"+g)):l=u.slice();let c=K0(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=X0(c,d,n.epochs,null,null,iG(t,n),null,s,l);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await p.onEpochBegin(f);let b=0,y=0;for(r||(m=await t.iterator());r?b<n.batchesPerEpoch:!0;){let v=await m.next();if(r&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],o=()=>{let 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=Vd(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=rG(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 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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 Mc(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(!jV(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 D2(e,t){return M(()=>(Bt(t),t==="channelsFirst"?Oe(e,[0,2,3,4,1]):e))}function NG(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 R2(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 _G(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 ${e.rank}.`);let i=D2(e,a);if(s==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=qy(i,t,r,s==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Qr(i,n)),a==="channelsFirst"&&(i=Oe(i,[0,4,1,2,3])),i})}var yx=class extends Ye{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",yx.verifyArgs(t),this.rank=e,rn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Mc(t.kernelSize,e,"kernelSize"),this.strides=Mc(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=Mc(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}},Hd=class extends yx{constructor(e,t){super(e,t);this.kernel=null,Hd.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=I0(this.activation.getClassName());if(s!=null&&this.rank===2)n=R2(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=NG(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=R2(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=_G(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)}`)}},P2=class extends Hd{constructor(e){super(2,e);P2.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=P2;Jf.className="Conv2D";oe.registerClass(Jf);var O2=class extends Hd{constructor(e){super(3,e);O2.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=O2;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=fS(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 M2=class extends Hd{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=Ec(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}};M2.className="SeparableConv";var wx=class extends M2{constructor(e){super(2,e)}};wx.className="SeparableConv2D";oe.registerClass(wx);var L2=class extends Hd{constructor(e){super(1,e);L2.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=L2;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,UV(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 EG(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=EG(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 B2(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 z2(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 W2=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=B2(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=z2((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=Md(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()===W2.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=W2;zs.className="RNN";oe.registerClass(zs);var jd=class extends Ye{},tm=class extends jd{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=Dc([1,vo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Dc([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 jd{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=Dc([1,vo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Dc([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 qd=class extends jd{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=Dc([1,vo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Dc([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 F0(F0(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}}};qd.className="LSTMCell";oe.registerClass(qd);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 qd(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 jd{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):R0(t(),n),i=()=>Bd(o,t,r);return!s||s<=1?nn(i().clone()):Array(s).fill(void 0).map(i).map(l=>nn(l.clone()))}var V2=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]]}};V2.className="ConvRNN2D";var sm=class extends qd{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=Mc(n,2,"kernelSize"),this.kernelSize.forEach(i=>rn(i,"kernelSize")),this.strides=Mc(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=Mc(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 V2{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 Bd(()=>R0(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=I0(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)}). 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M(()=>(e=Me(e),qV(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 ${e.length} 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Ye{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);return Bd(()=>Z($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?Bd(()=>{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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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),Xd(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 RG(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 U2(e,t,n,r,s,a){return M(()=>{Bt(s),N0(a),vr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),s==null&&(s=Kr()),a==null&&(a="max"),e=D2(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 G2=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=Md(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 G2{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 G2{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 H2=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 H2{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 H2{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 j2=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 j2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Bt(s),vr(r),U2(e,t,n,r,s,"max")}};nw.className="MaxPooling3D";oe.registerClass(nw);var rw=class extends j2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Bt(s),vr(r),U2(e,t,n,r,s,"avg")}};rw.className="AveragePooling3D";oe.registerClass(rw);var q2=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 q2{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 q2{constructor(e){super(e||{})}call(e,t){return M(()=>{let n=Me(e);return Gr(n,1)})}};aw.className="GlobalMaxPooling1D";oe.registerClass(aw);var K2=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 K2{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 K2{call(e,t){return M(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?Gr(n,[1,2]):Gr(n,[2,3])})}};iw.className="GlobalMaxPooling2D";oe.registerClass(iw);var X2=class extends Ye{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,s=Jr(r,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},uw=class extends X2{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),z2((a,o)=>[Me(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};uw.className="TimeDistributed";oe.registerClass(uw);function PG(e){ji(VV,"BidirectionalMergeMode",e)}var OG="concat",cw=class extends X2{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?OG:e.mergeMode,PG(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=B2(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=V6(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=W6(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[HS(r,s,a)]}case"GatherNd":{let r=I("x",e,t,n),s=I("indices",e,t,n);return[jS(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`)}},a5=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:a,reverseIndexMap:o}=Fd.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[r,s,a,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=Fd.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[Fd.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Fd.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},o5=(e,t,n)=>{switch(e.op){case"FFT":return[ff(I("x",e,t,n))];case"IFFT":return[Ad(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`)}},i5=(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 ${e.op} is not implemented`)}},u5=(e,t,n)=>{switch(e.op){case"Cast":return[ue(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let r=I("axis",e,t,n);return[Nn(I("x",e,t,n),r)]}case"Squeeze":{let r=I("axis",e,t,n);return[Rs(I("x",e,t,n),r)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[$S(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[gS(I("x",e,t,n),r,s)]}case"BroadcastTo":return[Sd(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[iS(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function DC(e,t,n,r){let s=((a,o,i)=>{switch(a.category){case"arithmetic":return M(()=>L6(a,o,i));case"basic_math":return M(()=>B6(a,o,i));case"control":return H6(a,o,i);case"convolution":return M(()=>j6(a,o,i));case"creation":return M(()=>q6(a,o,i));case"dynamic":return K6(a,o,i);case"evaluation":return M(()=>X6(a,o,i));case"image":return M(()=>J6(a,o,i));case"graph":return M(()=>Y6(a,o,i));case"logical":return M(()=>e5(a,o,i));case"matrices":return M(()=>t5(a,o,i));case"normalization":return M(()=>n5(a,o,i));case"reduction":return M(()=>r5(a,o,i));case"slice_join":return M(()=>s5(a,o,i));case"sparse":return M(()=>a5(a,o,i));case"spectral":return M(()=>o5(a,o,i));case"string":return M(()=>i5(a,o,i));case"transformation":return M(()=>u5(a,o,i));case"hash_table":return Z6(a,o,i,r);case"custom":let u=iC(a.op);if(u&&u.customExecutor)return u.customExecutor(new M6(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 RC=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 PC(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((OC(p)||h5(p)||f5(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 c5(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 l5=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],d5=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],p5=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function OC(e){return l5.indexOf(e.op)>=0}function h5(e){return d5.indexOf(e.op)>=0}function f5(e){return p5.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=PC(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 c5(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 RC(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=DC(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=g6(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 RC(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}=PC(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=>!OC(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}]. ${y}`)}return h}processStack(e,t,n,r,s,a,o,i,u){let l=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&I("isConstant",c.node,r,n)&&([d]=gs(c.node.name,n)),r[c.node.name]==null){let p=DC(c.node,r,n,this._resourceManager);d||([d]=gs(c.node.name,n));let h=n.currentContext;w.isPromise(p)?l.push(p.then(f=>(r[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,c.node,r,n,a,o,i),this.processChildNodes(c.node,t,n,r,s,u),f))):(r[d]=p,this.checkTensorForDisposal(d,c.node,r,n,a,o,i),this.processChildNodes(c.node,t,n,r,s,u))}else this.processChildNodes(c.node,t,n,r,s,u)}return l}processChildNodes(e,t,n,r,s,a){e.children.forEach(o=>{let[i]=gs(o.name,n);s[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!An(u,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(u=>!!An(u,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=or(t),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,u)=>a[u]===-1||a[u]===i);w.assert(o,()=>`The shape of dict['${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 '${t}' is not found in the graph`)})}},m5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},g5="?tfjs-format=file",b5="model.json",MC=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new m5}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=tn.browserHTTPRequest(e,this.loadOptions);else{let t=tn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(tn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let r=tn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Nw(NC.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=NC.Instance.transformGraph(e.modelInitializer);this.initializer=new Nw(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=tn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function y5(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${b5}${g5}`);let n=new MC(e,t);return await n.load(),n}var v5="0.0.0",LC={};Ee(LC,{CSVDataset:()=>ZC,Dataset:()=>Bc,FileDataSource:()=>aT,TextLineDataset:()=>XC,URLDataSource:()=>oT,array:()=>V5,csv:()=>J5,func:()=>ej,generator:()=>tj,microphone:()=>rj,version_data:()=>sj,webcam:()=>nj,zip:()=>U5});var x5=Bo(ch()),w5=Bo(ch());function k5(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(Lc(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 I5(e,t=zC){return BC(e,t)}function BC(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(Lc(r)){let a=Array.isArray(r)?[]:{};n.add(r);for(let o in r){let i=e.map(l=>l[o]),u=BC(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 zC(e){return e===null?null:Lc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function WC(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 Lc(e){let t=!1;if(X().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=FI();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ae)&&!(e instanceof Promise)&&!t)}function S5(e){return e==null||C5(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ae||w.isTypedArray(e)}function C5(e){return e===null||typeof e!="object"&&typeof e!="function"}function T5(e){return k5(e,N5)}function N5(e){return e instanceof Ae?{value:e.clone(),recurse:!1}:Lc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var VC=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}},UC=class extends VC{constructor(){super(UC.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}},GC=UC;GC.INITIAL_CAPACITY=32;function HC(e){return new A5(e)}function _w(e){return new $5(e)}function _5(e,t){return new qC(e,t)}function E5(e,t=dm.FAIL){return new z5(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 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e=this.items[this.trav];return this.trav++,{value:T5(e),done:!1}}},$5=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: ${e.message}`,e}}},F5=class extends sn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},D5=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`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Fe(e.value)}return this.upstream.next()}},R5=class extends sn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},P5=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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},O5=class extends sn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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)}}},M5=class extends sn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=Vr.getTensorsInContainer(n);for(let s of t)Vr.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},L5=class extends sn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},jC=class extends sn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Vr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=Vr.getTensorsInContainer(n);for(let s of t)Vr.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},Ew=class extends sn{constructor(){super();this.outputQueue=new GC,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},B5=class extends Ew{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await 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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 WC(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 ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},KC=class extends sn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new VC(t)}summary(){return`${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()}},W5=class extends KC{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=w5.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}}},Bc=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let r;return this.size===1/0||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),ir(async()=>(await n.iterator()).columnMajorBatch(e,t,G5),r)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,ir(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,ir(async()=>(await t.iterator()).filter(r=>M(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return ir(async()=>(await t.iterator()).map(n=>M(()=>e(n))),this.size)}mapAsync(e){let t=this;return ir(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return ir(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,ir(async()=>{let r=_w(async()=>({value:await t.iterator(),done:!1}));return _5(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,ir(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,s=x5.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()}};Bc.MAX_BUFFER_SIZE=1e4;function ir(e,t=null){return new class extends Bc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function V5(e){return ir(async()=>HC(e),e.length)}function U5(e){if(!Lc(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 WC(e,r=>{if(r instanceof Bc)return{value:r.iterator(),recurse:!1};if(Lc(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return E5(n,dm.SHORTEST)},t)}function G5(e){if(e===null)return null;let t=e[0];return S5(t)?{value:H5(e),recurse:!1}:{value:null,recurse:!0}}function H5(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 XC=class extends Bc{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='"',Zd=Symbol("out"),YC=Symbol("field"),hm=Symbol("quote"),Aw=Symbol("quoteafterquote"),QC=Symbol("quoteinquote"),ZC=class extends Bc{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 XC(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=Zd;for(let o=0;o<s;o++)switch(a){case Zd: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=Zd;break;default:a=YC,r=o;break}break;case YC:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(r,o)),a=Zd,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=Zd,r=o+1;break;case pm:a=hm;break;default:a=QC;break}break;case QC: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}},JC=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 JC(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)}},eT=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 eT(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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return M(()=>{let t=Nn(ue(e,"float32"),0),n;n=ar.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return G(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},tT=class{},nT=class extends sn{split(e){return new j5(this,e)}},j5=class extends nT{constructor(e,t){super();this.upstream=e,this.impl=new q5(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},q5=class extends Ew{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},K5=class extends sn{decodeUTF8(){return new X5(this)}},X5=class extends nT{constructor(e){super();this.upstream=e,this.impl=new Y5(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Y5=class extends Ew{constructor(e){super();if(this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=FI();this.decoder=new t("utf8")}}summary(){return`${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}},rT=class extends K5{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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,r)));else{let s=new FileReader;s.onload=o=>{let i=s.result;if(i instanceof 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tT{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return sT(this.url)?new aT(this.url,this.fileOptions).iterator():Q5(this.url,this.fileOptions)}};function J5(e,t={}){return new ZC(new oT(e),t)}function ej(e){let t=_w(e);return ir(async()=>t)}function tj(e){return ir(async()=>{let t=await e();return _w(()=>t.next())})}async function nj(e,t){return eT.create(e,t)}async function rj(e){return JC.create(e)}var sj="0.0.0";function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var aj=Dr.whereImpl,iT=class extends Mu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Gl(this,is())}nextDataId(){return iT.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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c=t.slice();return c[c.length-1]=r,[$e(c,n,u),$e(c,"int32",l)]}function HT(e,t,n,r){let s=w.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<s;f++)a[0]*=n[f];a[1]=n[s];for(let f=s+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[s]),u=new Kt(a,r,e),l=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[s];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let b=0;b<a[0];b++)for(let y=0;y<a[2];y++)g.push(u.get(b,f,y));m=g.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let g=Object.keys(o).length;o[m]=g,i[f]=g,l.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let p=new Kt(d,r);l.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let b=0;b<a[2];b++)p.set(u.get(g,f,b),g,m,b)});let h=n.slice();return h[s]=d[1],{outputValues:p.values,outputShape:h,indices:i}}var aq="0.0.0";kd("cpu",()=>new $w,1);var jT=ct(ka,e=>e>=0?e:Math.exp(e)-1),oq={kernelName:ka,backendName:"cpu",kernelFunc:jT};function qT(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r;we([s],"leakyRelu");let 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hq={kernelName:ha,backendName:"cpu",kernelFunc:QT};function fq(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:d}=r,p,h,f,m=[];p=QT({inputs:{a:s,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),o&&(h=Jd({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),c&&(f=zw(n,p,c,i,d),m.push(p),p=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return p}var mq={kernelName:to,backendName:"cpu",kernelFunc:fq},gq=ct(Vu,e=>Math.acos(e)),bq={kernelName:Vu,backendName:"cpu",kernelFunc:gq},yq=ct(Uu,e=>Math.acosh(e)),vq={kernelName:Uu,backendName:"cpu",kernelFunc:yq};function xq(e){let{inputs:t,backend:n}=e,r=t;we(t,"addN");let s=r.map(i=>n.data.get(i.dataId).values),a=$e(r[0].shape,r[0].dtype),o=a.values;for(let i=0;i<r.length;i++){let u=s[i];for(let l=0;l<o.length;l++)o[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var wq={kernelName:la,backendName:"cpu",kernelFunc:xq};function 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i=w.parseAxisParam(a,s.shape),u=i,l=N.getAxesPermutation(u,s.shape.length),c=s;l!=null&&(c=xr({inputs:{x:s},backend:n,attrs:{perm:l}}),u=N.getInnerMostAxes(u.length,s.shape.length)),N.assertAxesAreInnerMostDims("any",u,c.shape.length);let[d,p]=N.computeOutAndReduceShapes(c.shape,u),h=w.sizeFromShape(p),f=w.makeZerosTypedArray(w.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v||k}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(d,c.dtype,f);if(o){let b=N.expandShapeToKeepDim(d,i),y=Et({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var Cq={kernelName:Hu,backendName:"cpu",kernelFunc:Sq};function Tq(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r;we(s,"argMax");let 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("argMax",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 Nq={kernelName:da,backendName:"cpu",kernelFunc:Tq};function _q(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r;we(s,"argMin");let 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 Eq={kernelName:ju,backendName:"cpu",kernelFunc:_q},Aq=ct(qu,e=>Math.asin(e)),$q={kernelName:qu,backendName:"cpu",kernelFunc:Aq},Fq=ct(Ku,e=>Math.asinh(e)),Dq={kernelName:Ku,backendName:"cpu",kernelFunc:Fq},Rq=ct(Xu,e=>Math.atan(e)),Pq={kernelName:Xu,backendName:"cpu",kernelFunc:Rq},Oq=Ht((e,t)=>Math.atan2(e,t)),Mq=an(Qu,Oq),Lq={kernelName:Qu,backendName:"cpu",kernelFunc:Mq},Bq=ct(Yu,e=>Math.atanh(e)),zq={kernelName:Yu,backendName:"cpu",kernelFunc:Bq};function Ww(e,t,n,r,s,a){let o=s.strideHeight,i=s.strideWidth,u=s.dilationHeight,l=s.dilationWidth,c=s.effectiveFilterHeight,d=s.effectiveFilterWidth,p=s.padInfo.top,h=s.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=$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 JT(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=$e(s.outShape,n),x=v.values,k=s.outShape[1]*s.outShape[2]*s.outShape[3]*s.outShape[4],T=s.outShape[2]*s.outShape[3]*s.outShape[4],C=s.outShape[3]*s.outShape[4],E=s.outShape[4];for(let F=0;F<s.batchSize;++F){let O=F*k,D=F*r[0];for(let R=0;R<s.inChannels;++R)for(let _=0;_<s.outDepth;++_){let L=_*o-m,U=L;for(;U<0;)U+=l;let j=Math.min(s.inDepth,p+L),K=O+_*T;for(let q=0;q<s.outHeight;++q){let Q=q*i-g,ee=Q;for(;ee<0;)ee+=c;let re=Math.min(s.inHeight,h+Q),se=K+q*C;for(let ne=0;ne<s.outWidth;++ne){let ie=ne*u-b,te=ie;for(;te<0;)te+=d;let pe=Math.min(s.inWidth,f+ie),be=se+ne*E,Ce=y,Ie=0,Ne=0;for(let Je=U;Je<j;Je+=l){let qe=D+Je*r[1];for(let Ge=ee;Ge<re;Ge+=c){let lt=qe+Ge*r[2];for(let et=te;et<pe;et+=d){let pt=lt+et*r[3],Ct=e[pt+R];if(a==="max"&&Ct>Ce?Ce=Ct:a==="avg"&&(Ie+=Ct,Ne++),isNaN(Ce))break}if(isNaN(Ce))break}if(isNaN(Ce))break}let Le=be+R;x[Le]=a==="avg"?Ie/Ne:Ce}}}}return v}function Wq(e,t){let n=$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 Vq(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=$e(a.shape,"float32"),D=1/(f*m*g),R=n.bufferSync(s);for(let _=0;_<c.batchSize;++_)for(let L=0;L<c.inChannels;++L)for(let U=0;U<c.inDepth;++U)for(let j=0;j<c.inHeight;++j)for(let K=0;K<c.inWidth;++K){let q=U-C,Q=j-F,ee=K-E,re=0;for(let se=0;se<x;se+=b){let ne=(q+se)/d;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let ie=0;ie<k;ie+=y){let te=(Q+ie)/p;if(!(te<0||te>=c.outHeight||Math.floor(te)!==te))for(let pe=0;pe<T;pe+=v){let be=(ee+pe)/h;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;re+=R.get(_,ne,te,be,L)}}}O.set(re*D,_,U,j,K,L)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var qq={kernelName:gh,backendName:"cpu",kernelFunc:jq};function Kq(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;we([s,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:l}=r,c=N.computePool2DInfo(o.shape,i,u,1,l),d=c.strideHeight,p=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=$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 Xq={kernelName:mh,backendName:"cpu",kernelFunc:Kq};function Yq(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 Qq={kernelName:Ta,backendName:"cpu",kernelFunc:Yq};function Zq(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 Jq={kernelName:Uo,backendName:"cpu",kernelFunc:Zq};function eK(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 tK={kernelName:bh,backendName:"cpu",kernelFunc:eK};function nK(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 rK={kernelName:yh,backendName:"cpu",kernelFunc:nK},sK=ct(Es,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),aK={kernelName:Es,backendName:"cpu",kernelFunc:sK},oK=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")},iK={kernelName:Xl,backendName:"cpu",kernelFunc:oK};function Wc(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 uK={kernelName:Jl,backendName:"cpu",kernelFunc:Wc};function Vc(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=>Wc({inputs:{input:x},backend:n})),b=Vc({inputs:m,backend:n,attrs:{axis:a}}),y=Vc({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 cK={kernelName:Go,backendName:"cpu",kernelFunc:Vc};function eN(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 lK={kernelName:ga,backendName:"cpu",kernelFunc:eN};function dK(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 pK={kernelName:vh,backendName:"cpu",kernelFunc:dK};function hK(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 fK={kernelName:ba,backendName:"cpu",kernelFunc:hK};function mK(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 gK={kernelName:Yl,backendName:"cpu",kernelFunc:mK};function bK(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 yK={kernelName:xh,backendName:"cpu",kernelFunc:bK};function vK(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 Al=y[bn+hr],$l=C[ea+hr];zn+=Al*$l}}}}h[f*Ne+m*Je+g*et+b*tt+Le]=zn}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var xK={kernelName:wh,backendName:"cpu",kernelFunc:vK},wK=ct(ya,e=>Math.cos(e)),kK={kernelName:ya,backendName:"cpu",kernelFunc:wK},IK=ct(va,e=>Math.cosh(e)),SK={kernelName:va,backendName:"cpu",kernelFunc:IK};function CK(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=r,[c,d,p,h]=s.shape,f=a.shape[0],[m,g]=i,b=$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 TK={kernelName:jo,backendName:"cpu",kernelFunc:CK};function NK(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 _K={kernelName:Ho,backendName:"cpu",kernelFunc:NK};function EK(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=lT(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 AK={kernelName:kh,backendName:"cpu",kernelFunc:EK};function $K(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 ${o}`);let i=s.shape[0],u=s.shape[1],l=s.shape[2],c=s.shape[3],d=u*a,p=l*a,h=c/(a*a),f=n.data.get(s.dataId).values,m=new Float32Array(i*d*p*h),g=0;for(let b=0;b<i;++b)for(let y=0;y<d;++y){let v=Math.floor(y/a),x=y%a;for(let k=0;k<p;++k){let T=Math.floor(k/a),C=k%a,E=(x*a+C)*h;for(let F=0;F<h;++F){let D=F+E+c*(T+l*(v+u*b));m[g++]=f[D]}}}return n.makeTensorInfo([i,d,p,h],s.dtype,m)}var FK={kernelName:qo,backendName:"cpu",kernelFunc:$K};function tN(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. 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RK(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 PK={kernelName:Ih,backendName:"cpu",kernelFunc:RK};function OK(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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BK={kernelName:Ch,backendName:"cpu",kernelFunc:LK},zK={kernelName:Ql,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s}=e,{strides:a,pad:o,dilations:i}=n,u=t,l=u.data.get(r.dataId).values,c=r.shape.length,d=u.data.get(s.dataId).values,p=s.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:F,outShape:O}=N.computeDilation2DInfo(r.shape,s.shape,a,o,"NHWC",i),D=w.sizeFromShape(O),R=O.length,_=w.getArrayFromDType(r.dtype,D);for(let U=0;U<h;++U)for(let j=0;j<b;++j){let K=j*x-v.top;for(let q=0;q<y;++q){let Q=q*k-v.left;for(let ee=0;ee<g;++ee){let re=Number.MIN_SAFE_INTEGER;for(let ne=0;ne<T;++ne){let ie=K+ne*E;if(ie>=0&&ie<f)for(let te=0;te<C;++te){let pe=Q+te*F;if(pe>=0&&pe<m){let be=w.locToIndex([U,ie,pe,ee],c,w.computeStrides(r.shape)),Ce=w.locToIndex([ne,te,ee],p,w.computeStrides(s.shape)),Ie=l[be]+d[Ce];Ie>re&&(re=Ie)}}}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(a8(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=o8(i,r,t);return N.splitRealAndImagArrays(u)}}function a8(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),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,u="",l=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",r="varying",s="texture2D",a="gl_FragColor",o="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,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}; ${i};`}).join("")}function SY(e,t){let n=e.length,r=e.map(a=>`${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 CY(e,t,n="index"){let r=e.map((a,o)=>o),s=SY(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 MN=`
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:LN}=N;function TY(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?`[${h.arrayIndex}]`:""};`)});let s=r.join(`
`),a=e.map(h=>NY(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=$n(),u=AY(i),l,c,d=DY(i);return t.isPacked?(l=_Y(t.logicalShape,o,n.enableShapeUniforms),c=FY(i)):(l=EY(t.logicalShape,o,n.enableShapeUniforms),c=$Y(i)),n.packedInputs&&(d+=MY),[d,u,c,s,l,a,n.userCode].join(`
`)}function Hc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return XY(e,t);case 1:return QY(e,t);case 2:return JY(e,t);case 3:return t9(e,t);case 4:return r9(e,t);case 5:return s9(e);case 6:return a9(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function BN(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return KY(e);case 1:return YY(e,t);case 2:return ZY(e,t);case 3:return e9(e,t);default:return n9(e,t)}}function NY(e,t,n=!1,r){let s="";n?s+=BN(e,r):s+=Hc(e,r);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?s+=o9(e,t):s+=i9(e,t)),s}function _Y(e,t,n){switch(e.length){case 0:return zN();case 1:return LY(e,t,n);case 2:return jY(e,t,n);case 3:return zY(e,t,n);default:return VY(e,t,n)}}function EY(e,t,n){switch(e.length){case 0:return zN();case 1:return BY(e,t,n);case 2:return qY(e,t,n);case 3:return WY(e,t,n);case 4:return UY(e,t,n);case 5:return GY(e,t);case 6:return HY(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function AY(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function $Y(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function FY(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function DY(e){return`${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);
}
${RY}
${PY}
${OY}
`}var RY=`
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);
}
`,PY=`
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);
}
`,OY=`
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);
}
`,MY=`
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 zN(){return`
int getOutputCoords() {
return 0;
}
`}function LY(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 BY(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 * ${t[1]} + resTexRC.y;
}
`}function zY(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),a=s*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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 WY(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;
${r}
return ivec3(r, c, d);
}
`}function VY(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),a=s*Math.ceil(e[e.length-2]/2),o=a,i="",u="b, r, c";for(let l=2;l<e.length-1;l++)o*=e[e.length-l-1],i=`
int b${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 UY(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;
${r}
return ivec4(r, c, d, d2);
}
`}function GY(e,t){let n=su(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function HY(e,t){let n=su(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function jY(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${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, ${s}) * 2;
return ivec2(r, c);
}
`}function qY(e,t,n){return w.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${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 KY(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 XY(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 YY(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 QY(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${r}(int index) {
${jc(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 ZY(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 JY(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=qc(e,u),h=["row","col"];return`
${Hc(p,t)}
float ${s}(int row, int col) {
return ${s}(${Kc(h,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${jc(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 e9(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=qc(e,p),m=["b","row","col"];return`
${BN(f,t)}
vec4 ${s}(int b, int row, int col) {
return ${s}(${Kc(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 t9(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=qc(e,l),g=["row","col","depth"];return`
${Hc(m,t)}
float ${s}(int row, int col, int depth) {
return ${s}(${Kc(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)));
${jc(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 n9(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 r9(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=qc(e,u),v=["row","col","depth","depth2"];return`
${Hc(y,t)}
float ${s}(int row, int col, int depth, int depth2) {
return ${s}(${Kc(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)));
${jc(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 s9(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=qc(e,u),g=["row","col","depth","depth2","depth3"];return`
${Hc(m)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${Kc(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;
${jc(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 a9(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=qc(e,s),b=["row","col","depth","depth2","depth3","depth4"];return`
${Hc(g)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${Kc(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)));
${jc(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 jc(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 ${t}[i];
}
}
`}function o9(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=LN(e.shapeInfo.logicalShape,t.logicalShape),u=gt(o),l=o-a,c,d=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${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 i9(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=LN(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 ${e} is not yet supported`)}function ek(e,t,n){let{newShape:r,keptDims:s}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):r,u=!e&&a>1&&!w.arraysEqual(t,n)&&r.length<a||o;return{useSqueezeShape:u,uniformShape:u?i:t,keptDims:s}}function qc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Kc(e,t){return t.map(n=>e[n]).join(", ")}function u9(e,t,n,r){let s=n.map((x,k)=>{let T={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(T.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[k],shapeInfo:T}}),a=s.map(x=>x.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},i=TY(s,o,t),u=gN(e.gl,i),l=e.createProgram(u),c=null,d=e.getUniformLocation(l,"NAN",!1);X().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(l,"INFINITY",!1));let p=!1,h={},f={},m={};for(let x=0;x<t.variableNames.length;x++){let k=t.variableNames[x];h[k]=e.getUniformLocation(l,k,p),h[`offset${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 WN(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 ${u} must match`)})}function c9(e,t,n,r,s){t.program.enableShapeUniforms||(WN(t.inShapeInfos,n),WN([t.outShapeInfo],[r]));let a=r.texData.texture,o=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],d=t.uniformLocations[c],p=t.uniformLocations[`offset${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 ${u.type} is not supported yet.`)}),e.executeProgram()}function l9(e,t,n){let r="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let u=o.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:d}=ek(e.packedInputs,o.shape,u),p="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];p=`${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+`${X().getNumber("WEBGL_VERSION")}`,a}function qn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var d9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=rp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=$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);
}
${t.output} = result;
}
`}},p9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=rp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=$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));
}
${t.output} = result;
}
`}},h9=class{constructor(e){this.variableNames=["A"],this.outTexUsage=wr.DOWNLOAD;let t=$n();this.outputShape=e,this.userCode=`
${MN}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},f9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=wr.DOWNLOAD;let t=$n();this.outputShape=e,this.userCode=`
${MN}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},m9=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=$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.);
}
`}},g9=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} = ${s};
}
`}},VN={};Ee(VN,{bindVertexProgramAttributeStreams:()=>QN,createBufferFromOutputTexture:()=>e_,createFloat16MatrixTexture:()=>qN,createFloat16PackedMatrixTexture:()=>YN,createFloat32MatrixTexture:()=>jN,createIndexBuffer:()=>HN,createPackedMatrixTexture:()=>XN,createUnsignedBytesMatrixTexture:()=>KN,createVertexBuffer:()=>GN,createVertexShader:()=>UN,downloadByteEncodedFloatMatrixFromOutputTexture:()=>n_,downloadFloat32MatrixFromBuffer:()=>t_,downloadMatrixFromPackedOutputTexture:()=>s_,downloadPackedMatrixFromBuffer:()=>r_,getInternalFormatForFloat16MatrixTexture:()=>nk,getInternalFormatForFloat16PackedMatrixTexture:()=>ak,getInternalFormatForFloat32MatrixTexture:()=>tk,getInternalFormatForPackedMatrixTexture:()=>sk,getInternalFormatForUnsignedBytesMatrixTexture:()=>rk,uploadDenseMatrixToTexture:()=>ZN,uploadPixelDataToTexture:()=>JN});function UN(e){let t=$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 mN(e,n)}function GN(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 vN(e,t)}function HN(e){let t=new Uint16Array([0,1,2,2,1,3]);return xN(e,t)}function up(e,t,n,r,s,a){kN(t,n);let o=wN(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 jN(e,t,n,r){let[s,a]=sp(t,n);return up(e,s,a,tk(r),r.textureFormatFloat,e.FLOAT)}function nk(e){return e.internalFormatHalfFloat}function qN(e,t,n,r){let[s,a]=sp(t,n);return up(e,s,a,nk(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function rk(e){return e.downloadTextureFormat}function KN(e,t,n,r){let[s,a]=sp(t,n);return up(e,s,a,rk(r),e.RGBA,e.UNSIGNED_BYTE)}function sk(e){return e.internalFormatPackedFloat}function XN(e,t,n,r){let[s,a]=Uc(t,n);return up(e,s,a,sk(r),e.RGBA,e.FLOAT)}function ak(e){return e.internalFormatPackedHalfFloat}function YN(e,t,n,r){let[s,a]=Uc(t,n);return up(e,s,a,ak(r),e.RGBA,r.textureTypeHalfFloat)}function QN(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 ZN(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 JN(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 e_(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 t_(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 n_(e,t,n,r){let[s,a]=sp(t,n),o=4,i=new Uint8Array(dY(t*n,o));return ge(e,()=>e.readPixels(0,0,s,a,r.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function r_(e,t,n,r,s,a,o,i){let u=e,l=new Float32Array(pY(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 s_(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 a_=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,pN(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=ap(this.gl,s),kr(this.gl,a))this.textureHalfFloatExtension=ap(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=ap(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=GN(this.gl),this.indexBuffer=HN(this.gl),this.framebuffer=IN(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.throwIfDisposed(),YN(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),XN(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,()=>n_(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,s,a){return r_(this.gl,e,t,n,r,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return t_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=e_(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 a=r.clientWaitSync(s,0,0);return a===r.ALREADY_SIGNALED||a===r.CONDITION_SATISFIED},t=s}else X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>s_(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=UN(t));let n=bN(t);return ge(t,()=>t.attachShader(n,this.vertexShader)),ge(t,()=>t.attachShader(n,e)),yN(t,n),this.debug&&wm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=QN(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?CN(this.gl,e,t):TN(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(),NN(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,s]=Uc(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),op(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=ap(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=b9(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&&op(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(km(this.gl,this.outputTexture,this.framebuffer),this.debug&&op(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&&op(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 b9(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:y9,bincountImpl:o_,bincountReduceImpl:v9,ceilImpl:x9,concatImpl:w9,equalImpl:k9,expImpl:I9,expm1Impl:S9,floorImpl:C9,gatherNdImpl:T9,gatherV2Impl:N9,greaterImpl:_9,greaterEqualImpl:E9,lessImpl:A9,lessEqualImpl:$9,linSpaceImpl:F9,logImpl:D9,maxImpl:R9,maximumImpl:P9,minimumImpl:O9,multiplyImpl:M9,negImpl:L9,notEqualImpl:B9,prodImpl:z9,rangeImpl:W9,rsqrtImpl:V9,sigmoidImpl:U9,simpleAbsImpl:i_,sliceImpl:G9,sparseFillEmptyRowsImpl:H9,sparseReshapeImpl:j9,sparseSegmentReductionImpl:u_,sqrtImpl:q9,stridedSliceImpl:K9,stringNGramsImpl:X9,stringSplitImpl:Y9,stringToHashBucketFastImpl:Q9,subImpl:Z9,tileImpl:J9,topKImpl:eQ,transposeImpl:ok,uniqueImpl:tQ}=fm;function c_(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Fn(e,t){return t===1?[e]:c_(e,t)}function nQ(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 rQ=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]})`}},l_=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let n="";for(let 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=`
${sQ(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 sQ(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?CY(["r","c","d"],"inputShape"):su(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var aQ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=p_(t,n),s=h_(e,r,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=d_(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[s].shift();return this.usedTextures[s].push(i),i}let o;return r===pn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===pn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===pn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===pn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===pn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let s=p_(n,r),a=h_(t,s,r);a in this.freeTextures||(this.freeTextures[a]=[]);let o=d_(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,r),i=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 oQ(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 d_(e,t,n,r,s){let a=iQ(t,r),o;if(s){let[u,l]=Uc(e[0],e[1]);o=u*l}else{let[u,l]=sp(e[0],e[1]);o=u*l}let i=oQ(n,a);return o*i}function iQ(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 uQ(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 p_(e,t){if(e===wr.UPLOAD)return pn.PACKED_2X2_FLOAT32;if(e===wr.RENDER||e==null)return uQ(t);if(e===wr.DOWNLOAD||e===wr.PIXELS)return pn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function h_(e,t,n){return`${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;",cQ="return x;",f_="return abs(x);",lQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",dQ=ts+`
return (x < 0.0) ? 0.0 : x;
`,pQ=ts+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,_m="return x;",hQ="return 1.0 / (1.0 + exp(-1.0 * x));",fQ="return x;",mQ=`
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;
`,gQ=`
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;
`,bQ=`
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;
`,yQ="return 1.0 / (1.0 + exp(-1.0 * x));",Xc=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) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},vQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let t=e.length,n=Fn("rc",t),r=gt(t),s=nQ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${o}));
}
`}},xQ=Dr.whereImpl,wQ=1e-7,kQ=1e-4,Em={};function IQ(e){return e in Em||(Em[e]={}),Em[e]}var SQ=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),CQ=600;function TQ(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*CQ/1024/1024}var m_=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=IQ(X().getNumber("WEBGL_VERSION")),this.gpgpu=new a_(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 aQ(this.gpgpu),this.numMBBeforeWarning=TQ(),this.texData=new Gl(this,is())}nextDataId(){return m_.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 Xc(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 Xc(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(!hN(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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),s=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...xm(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(d),h}let a=X().getBool("WEBGL_PACK")&&r===!0,o=a?Im(t):t,i=a?new f9(o):new h9(o),u=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture,l.texShape[0],l.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(s);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((u,l)=>({name:a[l],ms:u})).map(u=>`${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=SQ){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 xQ(e.shape,t)}packedUnaryOp(e,t,n){let r=new Xc(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=i_(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,f_,e.dtype);let t=new To(e.shape,f_),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 vQ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new rQ(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 l_(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 p9(a):o=new d9(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===rp.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&&!ip(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=l9(e,u,l),d=this.getAndSaveBinary(c,()=>u9(this.gpgpu,e,u,l)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),c9(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?wQ:kQ}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=AN(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]=Uc(c[0],c[1])),i?p=new g9(d,m):p=new m9(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=NQ(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=m_;ik.nextDataId=0;function NQ(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 _Q="0.0.0";function g_(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}yc.isBrowser()&&kd("webgl",()=>new ik,2);var EQ={forceHalfFloat:g_},b_=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Yc=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;
`,cp=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 AQ={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 $Q={kernelName:Kl,backendName:"webgl",kernelFunc:No},y_="return (a < 0.) ? b * a : a;",v_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function FQ(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 cp(v_,s.shape,o.shape):new Yc(y_,s.shape,o.shape),u=n.runWebGLProgram(i,[s,o],"float32");return n.disposeIntermediateTensorInfo(o),u}var DQ={kernelName:ti,backendName:"webgl",kernelFunc:FQ},x_="return (a < 0.) ? b * a : a;",w_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function RQ(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cp(w_,r.shape,s.shape):new Yc(x_,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],"float32")}var PQ={kernelName:za,backendName:"webgl",kernelFunc:RQ},k_="if (isnan(x)) return x;",OQ=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,MQ=`
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 Xc(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 Yc(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 cp(t,u.shape,l.shape,n):h=new Yc(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],d)}}function $m(e,t=!1){if(e==="linear")return t?fQ:cQ;if(e==="relu")return t?gQ:dQ;if(e==="elu")return t?mQ:lQ;if(e==="relu6")return t?bQ:pQ;if(e==="prelu")return t?w_:x_;if(e==="leakyrelu")return t?v_:y_;if(e==="sigmoid")return t?yQ:hQ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var I_=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);
}
`}},S_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},C_=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) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},T_="return a * b;";function uk(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=N.upcastType(r.dtype,s.dtype);if(r.dtype==="complex64"){let i=n.texData.get(r.dataId),u=n.texData.get(s.dataId),l=new C_(S_.REAL,r.shape,s.shape),c=new C_(S_.IMAG,r.shape,s.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:r.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:s.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:s.shape}],p=n.runWebGLProgram(l,d,"float32"),h=n.runWebGLProgram(c,d,"float32"),f=No({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([r,s])){let i=n.texData.get(r.dataId),u=n.texData.get(s.dataId),[l,c]=M9(r.shape,s.shape,i.values,u.values,a),d=n.makeTensorInfo(c,a),p=n.texData.get(d.dataId);return p.values=l,d}let o;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new cp(T_,r.shape,s.shape):o=new Yc(T_,r.shape,s.shape),n.runWebGLProgram(o,[r,s],a)}var LQ={kernelName:Ma,backendName:"webgl",kernelFunc:uk};function BQ(e,t,n){let r=[nu(e.shape),...ru(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[nu(t),...ru(t)],o=new l_(a,r),i=!0,u=[r],l=n.runWebGLProgram(o,[s],e.dtype,u,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function fe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=n,i=w.sizeFromShape(s.shape),u=w.inferFromImplicitShape(a,i),l=w.sizeFromShape(u);w.assert(i===l,()=>`The new shape (${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&&!ip(s.shape,u)&&!(c.texture!==null&&ip(c.shape,u))?BQ(s,u,o):(o.incRef(s.dataId),{dataId:s.dataId,shape:u,dtype:s.dtype})}var zQ={kernelName:hi,backendName:"webgl",kernelFunc:fe},N_=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);
${u}
}
setOutput(sumValue);
}
`}},WQ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let u=`${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 VQ(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=VQ(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 N_({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},i):new N_({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l}):c=new WQ({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 UQ=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=GQ(t);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function GQ(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 HQ=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 ${this.rank} is not yet supported.`);let r=gt(this.rank),s=c_("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=s[l];let o=`vec2(${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] = ${u};
}
}
setOutput(result);
}
`}};function Fm(e,t,n){let r=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new HQ(e.shape,t):new UQ(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function jQ(e,t,n,r){let s=t,a=e.shape.length,o=w.parseAxisParam(s,e.shape),i=o,u=N.getAxesPermutation(i,a),l=u!=null,c=e;l&&(c=Fm(e,u,r),i=N.getInnerMostAxes(i.length,a)),N.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=N.computeOutAndReduceShapes(c.shape,i),h=d;n&&(h=N.expandShapeToKeepDim(d,o));let f=w.sizeFromShape(p),g=w.sizeFromShape(e.shape)/f,b=fe({inputs:{x:c},attrs:{shape:[g,f]},backend:r}),y=yd(e.dtype),v=ou(b,y,"sum",r),x=fe({inputs:{x:v},attrs:{shape:h},backend:r});return r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(v),l&&r.disposeIntermediateTensorInfo(c),x}function Dm(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return jQ(s,a,o,n)}var qQ={kernelName:Ka,backendName:"webgl",kernelFunc:Dm};function Dn(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=s.shape[a[c]];let l;if(o.shouldExecuteOnCPU([s])){let d=o.texData.get(s.dataId).values,p=ok(d,s.shape,s.dtype,a,u);l=o.makeTensorInfo(u,s.dtype);let h=o.texData.get(l.dataId);h.values=p}else l=Fm(s,a,o);return l}var KQ={kernelName:Ja,backendName:"webgl",kernelFunc:Dn},__=1e3;function Rm({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,d=n?e.shape[l-2]:e.shape[l-1],p=r?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Ri.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${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>__&&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 I_(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 XQ(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 YQ={kernelName:to,backendName:"webgl",kernelFunc:XQ},E_="return abs(x);";function QQ(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=i_(a.values);return n.makeTensorInfo(r.shape,r.dtype,o)}let s;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Xc(r.shape,E_):s=new To(r.shape,E_),n.runWebGLProgram(s,[r],r.dtype)}var ZQ={kernelName:Vo,backendName:"webgl",kernelFunc:QQ},JQ=ts+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,eZ=Ze({opSnippet:JQ}),tZ={kernelName:Vu,backendName:"webgl",kernelFunc:eZ},nZ=ts+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,rZ=Ze({opSnippet:nZ}),sZ={kernelName:Uu,backendName:"webgl",kernelFunc:rZ},A_="return a + b;",aZ=hn({opSnippet:A_,packedOpSnippet:A_,supportsComplex:!0,cpuKernelImpl:y9}),oZ={kernelName:_s,backendName:"webgl",kernelFunc:aZ},iZ=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 = ${r};
setOutput(result);
}
`}},uZ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${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 uZ(r[0].shape,a):new iZ(r[0].shape,a);return n.runWebGLProgram(i,r,s)}var cZ={kernelName:la,backendName:"webgl",kernelFunc:Pm};function lZ(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 dZ={kernelName:Gu,backendName:"webgl",kernelFunc:lZ};function pZ(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 hZ={kernelName:Hu,backendName:"webgl",kernelFunc:pZ},fZ=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 ${o} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},mZ=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${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 $_(e,t,n,r=null){let s=t.shape[0],a=t.shape[1];r!=null&&(s=r.shape[0],a=r.shape[1]);let o=N.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:s,outSize:Math.ceil(a/o)},u=new fZ(i,n,r==null),l=[t];r!=null&&l.push(r);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let d=$_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function F_(e,t,n,r=null){let s=r!=null?r.shape:t.shape,a=s[s.length-1],o=N.computeOptimalWindowSize(a),i=new mZ(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=F_(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function D_(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=$_(e,p,r);a.push(h);let f=fe({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return F_(e,t,r)}function gZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),u=s,l=[];i!=null&&(u=Dn({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(u),o=N.getInnerMostAxes(o.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],u.shape.length);let c=D_(n,u,o[0],"max");return l.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var bZ={kernelName:da,backendName:"webgl",kernelFunc:gZ};function yZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),u=s,l=[];i!=null&&(u=Dn({inputs:{x:s},backend:n,attrs:{perm:i}}),l.push(u),o=N.getInnerMostAxes(o.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],u.shape.length);let c=D_(n,u,o[0],"min");return l.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var vZ={kernelName:ju,backendName:"webgl",kernelFunc:yZ},xZ=ts+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,wZ=Ze({opSnippet:xZ}),kZ={kernelName:qu,backendName:"webgl",kernelFunc:wZ},IZ=ts+"return log(x + sqrt(x * x + 1.0));",SZ=Ze({opSnippet:IZ}),CZ={kernelName:Ku,backendName:"webgl",kernelFunc:SZ},TZ=ts+`
return atan(x);
`,NZ=Ze({opSnippet:TZ}),_Z={kernelName:Xu,backendName:"webgl",kernelFunc:NZ},EZ=OQ+`
return atan(a, b);
`,AZ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+MQ+`
return result;
`,$Z=hn({opSnippet:EZ,packedOpSnippet:AZ}),FZ={kernelName:Qu,backendName:"webgl",kernelFunc:$Z},DZ=ts+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,RZ=Ze({opSnippet:DZ}),PZ={kernelName:Yu,backendName:"webgl",kernelFunc:RZ},lp=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 OZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Gc(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 lp(c,"avg",!1);return n.runWebGLProgram(d,[s],"float32")}var MZ={kernelName:pa,backendName:"webgl",kernelFunc:OZ};function LZ(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 BZ={kernelName:ql,backendName:"webgl",kernelFunc:LZ},zZ=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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},WZ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`
const ivec3 pads = ivec3(${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 VZ(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 WZ(p);return n.runWebGLProgram(h,[s],o.dtype)}var UZ={kernelName:gh,backendName:"webgl",kernelFunc:VZ};function GZ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;Gc([s,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:l}=r,c=N.computePool2DInfo(o.shape,i,u,1,l),d=new zZ(c);return n.runWebGLProgram(d,[s],o.dtype)}var HZ={kernelName:mh,backendName:"webgl",kernelFunc:GZ};function jZ(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 qZ={kernelName:ha,backendName:"webgl",kernelFunc:jZ},KZ=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)));
}
`}},XZ=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);
}
`}},YZ=({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 XZ(r.shape,s.shape,a.shape,c,d,u):new KZ(r.shape,s.shape,a.shape,c,d,u);return t.runWebGLProgram(p,l,l[0].dtype)},QZ={kernelName:Ta,backendName:"webgl",kernelFunc:YZ},ZZ=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=JZ(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 JZ(e){if(e===1)return"sourceLoc";if(e<=6)return lk.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var eJ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),n=Fn("coords",this.rank),r=Fn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${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}
${i}
setOutput(result);
}
`}};function tJ(e,t,n,r){let s=r.texData.get(e.dataId),a=r.makeTensorInfo(n,e.dtype),o=r.texData.get(a.dataId);Object.assign(o,s),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=$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 Qc(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=G9(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 eJ(u):new ZZ(u),p=[i];return n.runWebGLProgram(d,[s],s.dtype,p)}return n.uploadToGPU(s.dataId),tJ(s,i,u,n)}var nJ={kernelName:yi,backendName:"webgl",kernelFunc:Qc},rJ=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=Qc({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},sJ={kernelName:Uo,backendName:"webgl",kernelFunc:rJ};function aJ(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=o_(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var oJ={kernelName:bh,backendName:"webgl",kernelFunc:aJ};function iJ(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 uJ={kernelName:yh,backendName:"webgl",kernelFunc:iJ},cJ="return float(a != b);",R_=hn({opSnippet:cJ,cpuKernelImpl:B9,dtype:"bool"}),lJ={kernelName:oi,backendName:"webgl",kernelFunc:R_};function dp(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 dJ={kernelName:rd,backendName:"webgl",kernelFunc:dp},pJ="return float(int(x));";function hJ(e,t){let n=new To(e.shape,pJ),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=dp({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 hJ(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=R_({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 fJ={kernelName:fa,backendName:"webgl",kernelFunc:dk},P_="return ceil(x);",mJ=Ze({opSnippet:P_,packedOpSnippet:P_,cpuKernelImpl:x9}),gJ={kernelName:ma,backendName:"webgl",kernelFunc:mJ},bJ=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));
}
`}},yJ=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 vJ(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 yJ(s.shape):i=new bJ(s.shape);let u=[[a],[o]];return n.runWebGLProgram(i,[s],s.dtype,u)}var xJ={kernelName:Es,backendName:"webgl",kernelFunc:vJ},wJ=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 O_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function kJ(e){let{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),a=new wJ(r.shape),o=[O_(r,s.complexTensorInfos.real),O_(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var IJ={kernelName:Xl,backendName:"webgl",kernelFunc:kJ},SJ=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;
${n.join(`
`)}
}
`}},CJ=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,r=n.length,s=gt(r),a=Fn("coords",r),o=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${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 TJ={kernelName:Jl,backendName:"webgl",kernelFunc:Mm};function Zc(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(m=>dp({inputs:{input:m},backend:n})),d=e.map(m=>Mm({inputs:{input:m},backend:n})),p=Zc(c,t,n),h=Zc(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=w9(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=Zc(e.slice(0,c),t,n),p=Zc(e.slice(c),t,n),h=Zc([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 CJ(e.map(d=>d.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:o}=NJ(e,t,n),i=new SJ(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 NJ(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 M_(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),Zc(i,a,n)}var _J={kernelName:Go,backendName:"webgl",kernelFunc:M_},L_=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}
${x}
setOutput(result);
}
`}},EJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${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, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},AJ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=qn(this.outputShape.length);let{dataFormat:n}=t,r=$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 B_({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>__)&&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(ip(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let C=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(C);let E=Rm({a:k,b:C,backend:r,transposeA:f,transposeB:m,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),F=r.texData.get(E.dataId);w.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=T,F.shape=n.outShape,g=cr({inputs:{x:E},backend:r}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=fe({inputs:{x:e},backend:r,attrs:{shape:[1,x,n.inChannels]}}),T=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=Rm({a:k,b:T,transposeA:f,transposeB:m,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=fe({inputs:{x:C},backend:r,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(C)}for(let x of b)r.disposeIntermediateTensorInfo(x);return g}function z_({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=p*d,b=[m,g],y=!0,v=!1,x=[],k=fe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),T=fe({inputs:{x:t},backend:r,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let C=new AJ(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],F=r.runWebGLProgram(C,[k],"float32",E),O=fe({inputs:{x:F},backend:r,attrs:{shape:[1,b[0],b[1]]}});x.push(F),x.push(O);let D=s!=null,R=a!=null,_=i==="leakyrelu",L=i?$m(i,!0):null,U=new I_(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 $J(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=r,d=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(s.shape,a.shape,o,l,i,c,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=B_({x:s,filter:a,convInfo:p,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)h=z_({x:s,filter:a,convInfo:p,backend:n});else{let m=new L_(p);h=n.runWebGLProgram(m,[s,a],"float32")}let f=fe({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var FJ={kernelName:ga,backendName:"webgl",kernelFunc:$J},DJ=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);
}
`}},RJ=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);
}
`}},PJ=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},OJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${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 MJ(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 DJ(p);return n.runWebGLProgram(h,[s,a],"float32")}var LJ={kernelName:vh,backendName:"webgl",kernelFunc:MJ};function BJ(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 RJ(p);return n.runWebGLProgram(h,[s,a],"float32")}var zJ={kernelName:ba,backendName:"webgl",kernelFunc:BJ};function WJ(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 EJ(l);return n.runWebGLProgram(c,[s,a],"float32")}var VJ={kernelName:Yl,backendName:"webgl",kernelFunc:WJ};function UJ(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 PJ(l);return n.runWebGLProgram(c,[s,a],"float32")}var GJ={kernelName:xh,backendName:"webgl",kernelFunc:UJ};function HJ(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 OJ(l);return n.runWebGLProgram(c,[s,a],"float32")}var jJ={kernelName:wh,backendName:"webgl",kernelFunc:HJ},qJ=k_+`
return cos(x);
`,KJ=Ze({opSnippet:qJ}),XJ={kernelName:ya,backendName:"webgl",kernelFunc:KJ},YJ=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,QJ=Ze({opSnippet:YJ}),ZJ={kernelName:va,backendName:"webgl",kernelFunc:QJ},JJ=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(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},eee=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=r,c=new JJ(s.shape,a.shape,i,u,l);return n.runWebGLProgram(c,[s,a,o],"float32")},tee={kernelName:jo,backendName:"webgl",kernelFunc:eee},W_=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let r=e.length,s=t?"0.0":`getX(${V_(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 = ${U_(r,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${o}) {
int idx = ${i};
${U_(r,"coords")} = idx;
val += getX(${V_(r,"coords")});
}
setOutput(val);
}
`}};function V_(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 ${e} is not yet supported`)}function U_(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function nee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,u=s.shape.length,l=N.getAxesPermutation([a],u),c=s;l!=null&&(c=Dn({inputs:{x:s},backend:n,attrs:{perm:l}}));let d=N.getInnerMostAxes(1,u)[0];if(d!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${s.shape.length-1} but got axis=${a}`);let p=c.shape[d],h=cr({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new W_(c.shape,!1,i),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(o){let f=new W_(c.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=N.getUndoAxesPermutation(l),m=Dn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var ree={kernelName:Ho,backendName:"webgl",kernelFunc:nee};function see(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let u=n.readSync(s.dataId),l=n.readSync(a.dataId),c=o_(u,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(s.shape.length===2){let u=n.bufferSync(s),l=n.bufferSync(a),c=v9(u,l,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var aee={kernelName:kh,backendName:"webgl",kernelFunc:see},oee=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 iee(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 oee(f,a,o);return n.runWebGLProgram(m,[s],s.dtype)}var uee={kernelName:qo,backendName:"webgl",kernelFunc:iee},G_=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}
${l}
setOutput(result);
}
`}},H_=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=qn(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,d=c,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)p+=`
vec4 xTexelC${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 cee(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 H_(d):p=new G_(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 lee={kernelName:xa,backendName:"webgl",kernelFunc:cee},dee=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},pee=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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 * ${i} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function hee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=r,d=N.computeConv2DInfo(s.shape,c,o,i,u,l,!0),p=new dee(d);return n.runWebGLProgram(p,[s,a],"float32")}var fee={kernelName:Ih,backendName:"webgl",kernelFunc:hee};function mee(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=r,d=N.computeConv2DInfo(c,a.shape,o,i,u,l,!0),p=new pee(d);return n.runWebGLProgram(p,[s,a],"float32")}var gee={kernelName:Sh,backendName:"webgl",kernelFunc:mee},bee=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function yee(e){let{inputs:t,backend:n}=e,{x:r}=t,s=[...r.shape,...r.shape],a=w.sizeFromShape(r.shape),o=fe({inputs:{x:r},backend:n,attrs:{shape:[a]}}),i=new bee(a),u=n.runWebGLProgram(i,[o],o.dtype),l=fe({inputs:{x:u},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),l}var vee={kernelName:Ch,backendName:"webgl",kernelFunc:yee},xee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:u,dilationWidth:l}=e,{top:c,left:d}=r;this.userCode=`
const ivec2 strides = ivec2(${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 wee(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 xee(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 kee={kernelName:Ql,backendName:"webgl",kernelFunc:wee};function Iee(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 See={kernelName:Zl,backendName:"webgl",kernelFunc:Iee},Cee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Tee=`
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;
`,Nee=Ze({opSnippet:Cee,packedOpSnippet:Tee}),_ee={kernelName:ka,backendName:"webgl",kernelFunc:Nee},Eee="return (b >= 1.0) ? a : a * (b + 1.0);",Aee=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,$ee=e=>{let{inputs:t,backend:n}=e,{dy:r,y:s}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cp(Aee,r.shape,s.shape):new Yc(Eee,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)},Fee={kernelName:_h,backendName:"webgl",kernelFunc:$ee},Dee=`
return vec4(equal(a, b));
`,Ree="return float(a == b);",Pee=hn({opSnippet:Ree,packedOpSnippet:Dee,dtype:"bool",cpuKernelImpl:k9}),Oee={kernelName:Ko,backendName:"webgl",kernelFunc:Pee},Mee=`
// 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 = ${N.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Lee=Ze({opSnippet:Mee}),Bee={kernelName:Zu,backendName:"webgl",kernelFunc:Lee},j_="return exp(x);",q_=Ze({opSnippet:j_,packedOpSnippet:j_,cpuKernelImpl:I9,dtype:"float32"}),zee={kernelName:Ia,backendName:"webgl",kernelFunc:q_};function pk(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=s;return s<0&&(w.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+s+1),i.splice(u,0,1),fe({inputs:{x:a},backend:r,attrs:{shape:i}})}var Wee={kernelName:Xo,backendName:"webgl",kernelFunc:pk},K_="return exp(x) - 1.0;",Vee=Ze({opSnippet:K_,packedOpSnippet:K_,cpuKernelImpl:S9}),Uee={kernelName:Yo,backendName:"webgl",kernelFunc:Vee},X_=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 Y_(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 X_("real",u,t),c=new X_("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 Gee(e){let{inputs:t,backend:n}=e,{input:r}=t;return Y_(r,!1,n)}var Hee={kernelName:Eh,backendName:"webgl",kernelFunc:Gee},jee=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 pp(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 jee(r,s),i=[[s]];return t.runWebGLProgram(o,[],a,i)}}var qee={kernelName:Ju,backendName:"webgl",kernelFunc:pp},Kee=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},Xee={kernelName:Qo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new Kee(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},Q_="return floor(x);",Yee=Ze({opSnippet:Q_,packedOpSnippet:Q_,cpuKernelImpl:C9}),Qee={kernelName:Sa,backendName:"webgl",kernelFunc:Yee},Zee=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,Jee=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,ete=hn({opSnippet:Zee,packedOpSnippet:Jee,dtype:"int32"}),tte={kernelName:Ca,backendName:"webgl",kernelFunc:ete},nte=class{constructor(e){this.variableNames=["A"];let t=$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 = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},rte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=$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);
}
}
${t.output} = result;
}
`}},ste={kernelName:ld,backendName:"webgl",kernelFunc:ate},Jc;function ate(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r,o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,[u,l]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],c=[l,u],d=[l,u,a];(i||o)&&(Jc==null&&(Jc=document.createElement("canvas").getContext("2d")),Jc.canvas.width=u,Jc.canvas.height=l,Jc.drawImage(s,0,0,u,l),s=Jc.canvas);let p=n.makeTensorInfo(c,"int32");n.texData.get(p.dataId).usage=wr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),s);let h=X().getBool("WEBGL_PACK")?new rte(d):new nte(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function ote(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=r,m=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(s.shape,a.shape,u,d,l,p,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=B_({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(X().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)b=z_({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let x=o!=null,k=i!=null,T=h==="leakyrelu",C=h?$m(h,!1):null,E=new L_(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 ite={kernelName:no,backendName:"webgl",kernelFunc:ote};function ute(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 H_(g,x,y,k,T):C=new G_(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 cte={kernelName:ro,backendName:"webgl",kernelFunc:ute},lte=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 dte(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=T9(b,y,r.dtype,l,o,c,d,r.shape,i);return n.makeTensorInfo(u,r.dtype,v.values)}let f=new lte(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 pte={kernelName:Jo,backendName:"webgl",kernelFunc:dte},hte=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=gt(this.rank),r=fte(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function fte(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 Z_(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, ${c-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(s,a,u,i),p=w.sizeFromShape(a.shape),h=[],f=fe({inputs:{x:s},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=fe({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([s,a])||s.dtype==="string"){let x=n.bufferSync(m),k=n.bufferSync(f),T=N9(k,x,g);return h.forEach(C=>n.disposeIntermediateTensorInfo(C)),n.makeTensorInfo(d.outputShape,T.dtype,T.values)}let b=new hte(f.shape,g),y=n.runWebGLProgram(b,[f,m],f.dtype);h.push(y);let v=fe({inputs:{x:y},backend:n,attrs:{shape:d.outputShape}});return h.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var mte={kernelName:Zo,backendName:"webgl",kernelFunc:Z_},gte="return float(a > b);",bte=`
return vec4(greaterThan(a, b));
`,yte=hn({opSnippet:gte,packedOpSnippet:bte,cpuKernelImpl:_9,dtype:"bool"}),vte={kernelName:ei,backendName:"webgl",kernelFunc:yte},xte="return float(a >= b);",wte=`
return vec4(greaterThanEqual(a, b));
`,kte=hn({opSnippet:xte,packedOpSnippet:wte,dtype:"bool",cpuKernelImpl:E9}),Ite={kernelName:Na,backendName:"webgl",kernelFunc:kte};function Ste(e){let{inputs:t,backend:n}=e,{input:r}=t;return Y_(r,!0,n)}var Cte={kernelName:Ah,backendName:"webgl",kernelFunc:Ste},Tte="return float(!isnan(x) && !isinf(x));",Nte=Ze({opSnippet:Tte,dtype:"bool"}),_te={kernelName:ec,backendName:"webgl",kernelFunc:Nte},Ete="return float(isinf(x));",Ate=Ze({opSnippet:Ete,dtype:"bool"}),$te={kernelName:tc,backendName:"webgl",kernelFunc:Ate},Fte="return float(isnan(x));",Dte=Ze({opSnippet:Fte,dtype:"bool"}),Rte={kernelName:nc,backendName:"webgl",kernelFunc:Dte},Pte="return float(a < b);",Ote=`
return vec4(lessThan(a, b));
`,Mte=hn({opSnippet:Pte,packedOpSnippet:Ote,cpuKernelImpl:A9,dtype:"bool"}),Lte={kernelName:ni,backendName:"webgl",kernelFunc:Mte},Bte="return float(a <= b);",zte=`
return vec4(lessThanEqual(a, b));
`,Wte=hn({opSnippet:Bte,packedOpSnippet:zte,cpuKernelImpl:$9,dtype:"bool"}),Vte={kernelName:ri,backendName:"webgl",kernelFunc:Wte};function Ute(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=F9(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var Gte={kernelName:$h,backendName:"webgl",kernelFunc:Ute},Hte=`if (x < 0.0) return NAN;
return log(x);`,jte=`
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;
`,qte=Ze({opSnippet:Hte,packedOpSnippet:jte,cpuKernelImpl:D9}),Kte={kernelName:Ea,backendName:"webgl",kernelFunc:qte},Xte="return log(1.0 + x);",Yte=Ze({opSnippet:Xte}),Qte={kernelName:rc,backendName:"webgl",kernelFunc:Yte},Zte="return float(a >= 1.0 && b >= 1.0);",Jte=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,ene=hn({opSnippet:Zte,packedOpSnippet:Jte,dtype:"bool"}),tne={kernelName:si,backendName:"webgl",kernelFunc:ene},nne="return float(!(x >= 1.0));",rne=Ze({opSnippet:nne}),sne={kernelName:sc,backendName:"webgl",kernelFunc:rne},ane="return float(a >= 1.0 || b >= 1.0);",one=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,ine=hn({opSnippet:ane,packedOpSnippet:one,dtype:"bool"}),une={kernelName:ed,backendName:"webgl",kernelFunc:ine},cne=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 * ${i};
setOutput(val);
}
`}},lne=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${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);
}
`}},dne=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 lne(s.shape,a,o,i,u):new cne(s.shape,a,o,i,u);return n.runWebGLProgram(l,[s],s.dtype)},pne={kernelName:td,backendName:"webgl",kernelFunc:dne},hne=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 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},fne=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:u,alpha:l,beta:c}=r,d=new hne(s.shape,i,u,l,c);return n.runWebGLProgram(d,[s,a,o],s.dtype)},mne={kernelName:Fh,backendName:"webgl",kernelFunc:fne};function gne(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=fe({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),u=ou(i,e.dtype,"max",r),l=fe({inputs:{x:u},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(u),l}function J_(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=N.getAxesPermutation(l,i),d=c!=null,p=n.shouldExecuteOnCPU([s]),h=s;if(d){if(p){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let C=0;C<x.length;C++)x[C]=s.shape[c[C]];let k=ok(v,s.shape,s.dtype,c,x);h=n.makeTensorInfo(x,s.dtype);let T=n.texData.get(h.dataId);T.values=k}else h=Fm(s,c,n);l=N.getInnerMostAxes(l.length,i)}N.assertAxesAreInnerMostDims("max",l,i);let[f,m]=N.computeOutAndReduceShapes(h.shape,l),g=f;o&&(g=N.expandShapeToKeepDim(f,u));let b;if(p){let v=n.texData.get(h.dataId).values,x=R9(v,w.sizeFromShape(m),g,s.dtype);b=n.makeTensorInfo(g,s.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=gne(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),b}var bne={kernelName:Aa,backendName:"webgl",kernelFunc:J_},yne=b_+`
return max(a, b);
`,vne=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Am+`
return result;
`,xne=hn({opSnippet:yne,packedOpSnippet:vne,cpuKernelImpl:P9}),wne={kernelName:$a,backendName:"webgl",kernelFunc:xne};function kne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Gc(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 lp(c,"max",!1);return n.runWebGLProgram(d,[s],s.dtype)}var Ine={kernelName:Fa,backendName:"webgl",kernelFunc:kne};function Sne(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 Cne={kernelName:nd,backendName:"webgl",kernelFunc:Sne},Tne=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 * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Nne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.front,d=u-1-e.padInfo.top,p=l-1-e.padInfo.left,h=i*u*l-1;this.userCode=`
const ivec3 pads = ivec3(${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 _ne(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 Nne(p),g=n.runWebGLProgram(m,[s,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Ene={kernelName:Rh,backendName:"webgl",kernelFunc:_ne};function Ane(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;Gc([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 lp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Tne(p),b=n.runWebGLProgram(g,[s,m],i.dtype);return n.disposeIntermediateTensorInfo(m),b}var $ne={kernelName:Dh,backendName:"webgl",kernelFunc:Ane};function Fne(e,t,n,r){let s=new lp(n,"max",!1),a=r.runWebGLProgram(s,[e],"float32");s=new lp(n,"max",!0,!0,t);let o=r.runWebGLProgram(s,[e],"float32");return[a,o]}var Dne={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]=Fne(r,i,c,u);return[d,p]}};function Rne(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 Pne={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=Rne(f,g,b,o);for(let v of h)o.disposeIntermediateTensorInfo(v);return y}};function One(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 Mne={kernelName:Ra,backendName:"webgl",kernelFunc:One},Lne=b_+`
return min(a, b);
`,Bne=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Am+`
return result;
`,zne=hn({opSnippet:Lne,packedOpSnippet:Bne,cpuKernelImpl:O9}),Wne={kernelName:Pa,backendName:"webgl",kernelFunc:zne},Vne=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}));
}
`}},Une=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);
}
`}},Gne=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:s,mode:a}=n,o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Une(r.shape,s,a):new Vne(r.shape,s,a);return t.runWebGLProgram(o,[r],r.dtype)},Hne={kernelName:Oa,backendName:"webgl",kernelFunc:Gne},jne=`if (b == 0.0) return NAN;
return mod(a, b);`,qne=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Am+`
return result;
`,Kne=hn({opSnippet:jne,packedOpSnippet:qne}),Xne={kernelName:ac,backendName:"webgl",kernelFunc:Kne},Yne=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}));
}
`}},Qne=`
if (a == b) {
return 1.0;
};
return a / b;`,Zne=`
// 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;
`,eE=hn({opSnippet:Qne,packedOpSnippet:Zne,checkOutOfBounds:!0}),Jne={kernelName:wa,backendName:"webgl",kernelFunc:eE},tE="return a - b;",nE=hn({opSnippet:tE,packedOpSnippet:tE,supportsComplex:!0,cpuKernelImpl:Z9}),ere={kernelName:Qa,backendName:"webgl",kernelFunc:nE};function rE(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=J_({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=nE({inputs:{a:s,b:l},backend:n}),d=q_({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=eE({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 tre={kernelName:Xa,backendName:"webgl",kernelFunc:rE};function nre(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,u=i?s:rE({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),l=u.shape[0],c=u.shape[1],d=new Yne(l,c,a),p=[[o]],h=n.runWebGLProgram(d,[u],"int32",p);return i||n.disposeIntermediateTensorInfo(u),h}var rre={kernelName:Oh,backendName:"webgl",kernelFunc:nre},sE="return -x;";function sre(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.texData.get(r.dataId),[o,i]=L9(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Xc(r.shape,sE):s=new To(r.shape,sE),n.runWebGLProgram(s,[r],r.dtype)}var are={kernelName:ai,backendName:"webgl",kernelFunc:sre},ore=Dr.nonMaxSuppressionV3Impl;function ire(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}=ore(l,c,o,i,u);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var ure={kernelName:ii,backendName:"webgl",kernelFunc:ire},cre=Dr.nonMaxSuppressionV4Impl;function lre(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}=cre(c,d,o,i,u,l);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var dre={kernelName:oc,backendName:"webgl",kernelFunc:lre},pre=Dr.nonMaxSuppressionV5Impl;function hre(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}=pre(c,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var fre={kernelName:ui,backendName:"webgl",kernelFunc:hre},mre=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)));
}
`}},gre=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 mre(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},bre={kernelName:li,backendName:"webgl",kernelFunc:gre};function Lm(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=dp({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 pp({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var yre={kernelName:Ni,backendName:"webgl",kernelFunc:Lm};function aE(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=dp({inputs:{input:r},backend:n}),a=aE({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 pp({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var vre={kernelName:ci,backendName:"webgl",kernelFunc:aE};function xre(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=M_({inputs:u,backend:n,attrs:{axis:s}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var wre={kernelName:di,backendName:"webgl",kernelFunc:xre},kre=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}));
}
}
`}},Ire=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);
}
`}},oE=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 pp({backend:n,attrs:{shape:l,value:o,dtype:s.dtype}})}let i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ire(s.shape,a,o):new kre(s.shape,a,o),u=[[o]];return n.runWebGLProgram(i,[s],s.dtype,u)},Sre={kernelName:La,backendName:"webgl",kernelFunc:oE},Cre=`
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);
`,Tre=`
// 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;
`,Nre=hn({opSnippet:Cre,packedOpSnippet:Tre}),_re={kernelName:Ba,backendName:"webgl",kernelFunc:Nre};function Ere(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}=z9(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=yd(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 Are={kernelName:pi,backendName:"webgl",kernelFunc:Ere},iE=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=W9(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},$re={kernelName:ic,backendName:"webgl",kernelFunc:iE},Fre="return 1.0 / x;",Dre=Ze({opSnippet:Fre}),Rre={kernelName:uc,backendName:"webgl",kernelFunc:Dre},Pre=ts+`
return (x < 0.0) ? 0.0 : x;
`,Ore=`
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;
`,Mre=Ze({opSnippet:Pre,packedOpSnippet:Ore}),Lre={kernelName:Wa,backendName:"webgl",kernelFunc:Mre},Bre=ts+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,zre=`
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;
`,Wre=Ze({opSnippet:Bre,packedOpSnippet:zre}),Vre={kernelName:Ua,backendName:"webgl",kernelFunc:Wre},Ure=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);
}
`}},Gre=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 Hre(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 Gre(s.shape,u,l,a,o):new Ure(s.shape,u,l,a,o);return n.runWebGLProgram(c,[s],"float32")}var jre={kernelName:Va,backendName:"webgl",kernelFunc:Hre},qre=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 Kre(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new qre(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Xre={kernelName:Lh,backendName:"webgl",kernelFunc:Kre},Yre=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);
}
`}},Qre=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 < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function Zre(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[u,l]=i,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Qre(s.shape,u,l,a,o):new Yre(s.shape,u,l,a,o);return n.runWebGLProgram(c,[s],s.dtype)}var Jre={kernelName:cc,backendName:"webgl",kernelFunc:Zre},ese=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],d=1/l,p=1/c,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${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),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function tse(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new ese(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var nse={kernelName:Mh,backendName:"webgl",kernelFunc:tse},rse=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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}));
}
`}},sse=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 ase(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 sse(s.shape,i):new rse(s.shape,i);return n.runWebGLProgram(u,[s],s.dtype)}var ose={kernelName:fi,backendName:"webgl",kernelFunc:ase},ise=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 < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},use={kernelName:_i,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,u=new ise(r.shape,a),[l,c]=N.getImageCenter(o,r.shape[1],r.shape[2]),d=[[l,c,Math.sin(s),Math.cos(s)]];return i.runWebGLProgram(u,[r],r.dtype,d)}},cse=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,lse=Ze({opSnippet:cse}),dse={kernelName:mi,backendName:"webgl",kernelFunc:lse},pse="return inversesqrt(x);",hse=Ze({opSnippet:pse,cpuKernelImpl:V9}),fse={kernelName:Ga,backendName:"webgl",kernelFunc:hse},uE=class{constructor(e,t,n,r,s,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=gt(s.length),u=gt(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${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 mse(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 uE(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 gse={kernelName:gi,backendName:"webgl",kernelFunc:mse},bse=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 yse(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new bse(r.shape.length,s.shape,s.shape.length);return n.runWebGLProgram(o,[r,s,a],In(s.dtype,a.dtype))}var vse={kernelName:bi,backendName:"webgl",kernelFunc:yse},xse=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${N.SELU_SCALEALPHA};
float scale = ${N.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,wse=Ze({opSnippet:xse}),kse={kernelName:lc,backendName:"webgl",kernelFunc:wse},cE="return 1.0 / (1.0 + exp(-1.0 * x));",Ise=Ze({opSnippet:cE,packedOpSnippet:cE,cpuKernelImpl:U9}),Sse={kernelName:ja,backendName:"webgl",kernelFunc:Ise},Cse=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Tse=Ze({opSnippet:Cse}),Nse={kernelName:dc,backendName:"webgl",kernelFunc:Tse},_se=k_+`
return sin(x);
`,Ese=Ze({opSnippet:_se}),Ase={kernelName:Ha,backendName:"webgl",kernelFunc:Ese},$se=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Fse=Ze({opSnippet:$se}),Dse={kernelName:vi,backendName:"webgl",kernelFunc:Fse},Rse=`
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;
`,Pse=Ze({opSnippet:Rse}),Ose={kernelName:pc,backendName:"webgl",kernelFunc:Pse},Mse=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=oE({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},Lse={kernelName:xi,backendName:"webgl",kernelFunc:Mse};function Bse(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]=H9(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 zse={kernelName:sd,backendName:"webgl",kernelFunc:Bse};function Wse(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]=j9(i,r.shape,r.dtype,o,u);return[n.makeTensorInfo(c,r.dtype,l),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Vse={kernelName:hc,backendName:"webgl",kernelFunc:Wse};function Use(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]=u_(o,r.shape,r.dtype,i,u,!0);return n.makeTensorInfo(c,r.dtype,l)}var Gse={kernelName:ad,backendName:"webgl",kernelFunc:Use};function Hse(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
${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),u=n.readSync(a.dataId),[l,c]=u_(o,r.shape,r.dtype,i,u);return n.makeTensorInfo(c,r.dtype,l)}var jse={kernelName:od,backendName:"webgl",kernelFunc:Hse};function qse(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:u,numUpdates:l,strides:c,outputSize:d}=N.calculateShapes(a,s,i),p=!1,h=new uE(l,u,s.shape.length,a.shape.length,c,[d,1],p),f=n.runWebGLProgram(h,[a,s,o],a.dtype),m=fe({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var Kse={kernelName:id,backendName:"webgl",kernelFunc:qse};function Xse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=w.parseAxisParam(o,s.shape)[0],u=N.prepareSplitSize(s,a,i),l=s.shape.length,c=new Array(l).fill(0),d=s.shape.slice();return u.map(p=>{let h=[...d];h[i]=p;let f=Qc({inputs:{x:s},backend:n,attrs:{begin:c,size:h}});return c[i]+=p,f})}var Yse={kernelName:wi,backendName:"webgl",kernelFunc:Xse},lE="return sqrt(x);",Qse=Ze({opSnippet:lE,packedOpSnippet:lE,cpuKernelImpl:q9}),Zse={kernelName:qa,backendName:"webgl",kernelFunc:Qse},Jse="return x * x;",eae=Ze({opSnippet:Jse}),tae={kernelName:fc,backendName:"webgl",kernelFunc:eae},dE="return (a - b) * (a - b);",nae=hn({opSnippet:dE,packedOpSnippet:dE}),rae={kernelName:Ya,backendName:"webgl",kernelFunc:nae};function sae({inputs:e,attrs:t,backend:n}){let{x:r}=e,s=ts+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new To(r.shape,s);return n.runWebGLProgram(a,[r],r.dtype)}var aae={kernelName:eo,backendName:"webgl",kernelFunc:sae},oae=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 iae(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=Qc({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=K9(h,F,x,y);k=n.makeTensorInfo(f,s.dtype,O.values)}else{let E=new oae(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 uae={kernelName:ki,backendName:"webgl",kernelFunc:iae};function cae(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]=X9(p,h,s,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var lae={kernelName:ud,backendName:"webgl",kernelFunc:cae};function dae(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]=Y9(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 pae={kernelName:Bh,backendName:"webgl",kernelFunc:dae};function hae(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=Q9(o,s);return n.makeTensorInfo(a.shape,"int32",i)}var fae={kernelName:zh,backendName:"webgl",kernelFunc:hae},mae="return tan(x);",gae=Ze({opSnippet:mae}),bae={kernelName:Ii,backendName:"webgl",kernelFunc:gae},yae=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,vae=Ze({opSnippet:yae}),xae={kernelName:Za,backendName:"webgl",kernelFunc:vae},wae=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=kae(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function kae(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]}, ${e[s]})`);return r.join()}function pE(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(s.dtype==="string"||s.shape.length>5){let u=n.readSync(s.dataId),l=s.dtype==="string"?u.map(p=>w.decodeString(p)):u,c=$e(s.shape,s.dtype,l),d=J9(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new wae(s.shape,a);return n.runWebGLProgram(o,[s],s.dtype)}var Iae={kernelName:As,backendName:"webgl",kernelFunc:pE},Sae=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));
}
}
`}},Cae=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 hE(e){let t=1;for(;t<e;)t*=2;return t}function Tae(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]=eQ(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,pp({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=hE(a),y=hE(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(O,D,R)=>{let _=x(),L=new Sae(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 Cae([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=Qc({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),iu(n,T);let C=Z_({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 Nae={kernelName:Si,backendName:"webgl",kernelFunc:Tae},_ae=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 Eae(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 _ae(d,p,o,i,u,g);return n.runWebGLProgram(b,[s,a],"float32")}var Aae={kernelName:Ci,backendName:"webgl",kernelFunc:Eae};function $ae(e){let{inputs:t,attrs:n,backend:r}=e,{axis:s}=n,{x:a}=t;Gc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=r.readSync(a.dataId),{outputValues:i,outputShape:u,indices:l}=tQ(o,s,a.shape,a.dtype);return[r.makeTensorInfo(u,a.dtype,i),r.makeTensorInfo([l.length],"int32",l)]}var Fae={kernelName:Wh,backendName:"webgl",kernelFunc:$ae};function Dae(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=Qc({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 Rae={kernelName:Ti,backendName:"webgl",kernelFunc:Dae},Pae=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 Oae(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=yd(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 Pae(R,k),L=n.compileAndRun(_,[x,T],C);if(u.push(L),L.shape[1]===E)return L;let U=iE({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),j=pE({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 Mae={kernelName:cd,backendName:"webgl",kernelFunc:Oae},Lae=[pne,mne,YQ,ZQ,tZ,sZ,oZ,cZ,dZ,hZ,bZ,vZ,kZ,CZ,FZ,_Z,PZ,BZ,MZ,UZ,HZ,qZ,QZ,sJ,oJ,uJ,fJ,gJ,xJ,IJ,$Q,_J,LJ,zJ,FJ,GJ,jJ,VJ,XJ,ZJ,tee,ree,aee,uee,fee,gee,lee,vee,kee,See,_ee,Fee,Oee,Bee,zee,Wee,Uee,Hee,qee,Xee,Qee,tte,ste,ite,cte,pte,mte,vte,Ite,AQ,Cte,TJ,_te,$te,Rte,DQ,Lte,Vte,Gte,Qte,Kte,tne,sne,une,bne,Cne,Ine,Ene,$ne,Dne,wne,Pne,Mne,Wne,Hne,Xne,rre,LQ,are,ure,dre,fre,lJ,bre,vre,wre,Sre,_re,PQ,Are,$re,dJ,Jne,Rre,Vre,Lre,zQ,jre,Xre,Jre,nse,ose,use,dse,fse,gse,vse,kse,Sse,Nse,Ase,Dse,nJ,tre,Ose,Lse,zse,Vse,Gse,jse,Kse,Yse,Zse,tae,rae,aae,uae,lae,pae,fae,ere,qQ,bae,xae,Iae,Nae,Aae,KQ,Fae,Rae,Mae,yre];for(let e of Lae)gc(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 Bae(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 el(){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 zae(e,t,n,r=!1){let s=`
let workGroupSizeX = ${n.workGroupSize[0]}u;
let workGroupSizeY = ${n.workGroupSize[1]}u;
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(r===!0){let h=gE(t.shape),f=`
[[block]] struct Matrix0 {
numbers: array<${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[fE,f,s,mE,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]=jae(t.shape,n.dispatchLayout),c=gE(t.shape),d=[fE,a.join(`
`),mE,c,u,Wae(t.shape.length)];if(n.atomic||d.push(Vae(t.shape,t.dtype,n.isVec4)),l===t.shape.length){let h=e.map(f=>Uae(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
`)}var fE=`
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);
}
`,mE=`
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 Wae(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 Vae(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}(${o.join(", ")}));
setOutputFlatI32(flatIndex, value);
}
`}return a}function Uae(e,t,n,r){let s=Gae(e,n);return e.shape.length<=t.length&&(s+=Hae(e,t,n,r)),s}function Gae(e,t){let n=e.name,r=e.shape.length,s=on(r),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,r),i=o.map(c=>`${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 Hae(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 jae(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[${p}]);`;else{let f=Bae(h,"uniforms.outShape");o+=`var index${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 gE(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 bE={};Ee(bE,{ArrayBufferToTypedArray:()=>yE,GPUBytesPerElement:()=>bk,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>fk,computeWorkGroupSizeForMatMul:()=>mk,computeWorkPerThreadForConv2d:()=>gk,flatDispatchLayout:()=>ze,isWebGPUSupported:()=>yk,tilesFitEvenlyIntoShape:()=>Us});var tl=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<=tl&&a<=tl&&o<=tl)return[s,a,o];w.assert(s>tl&&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>tl?(i=Math.ceil(Math.cbrt(s)),w.assert(i<=tl,()=>"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 yE(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 qae="return a + b;",Kae="return areal * breal - aimag * bimag;",Xae="return areal * bimag + aimag * breal;",Yae="return a / b;",Qae="return a * b;",Zae="return (a - b) * (a - b);",Jae="return a - b;",eoe="return f32(a == b);",toe="return vec4<f32>(a == b);",noe="return f32(a > b);",roe="return vec4<f32>(a > b);",soe="return f32(a >= b);",aoe="return vec4<f32>(a >= b);",ooe="return f32(a < b);",ioe="return vec4<f32>(a < b);",uoe="return f32(a <= b);",coe="return vec4<f32>(a <= b);",loe="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",doe=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,poe=`
if (isNanCustom(a)) { return a; }
if (isNanCustom(b)) { return b; }
`,vE=`
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;
}
`,hoe=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,foe=`
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);
`,moe="return f32(a != b);",goe="return vec4<f32>(a != b);",boe=`
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);
`,yoe=`
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);
${vE}
return resultTemp;
`,voe="if (a < 0.0) { return b * a; } return a;",xoe=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function xE(e,t){let n=t?vE:poe;return t?`
var resultTemp = vec4<f32>(${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 hp(e,t){switch(e){case 0:return Qae;case 1:return qae;case 2:return Jae;case 3:return Yae;case 4:return t?toe:eoe;case 5:return t?roe:noe;case 6:return t?aoe:soe;case 7:return t?ioe:ooe;case 8:return t?coe:uoe;case 9:return t?doe:loe;case 10:return t?goe:moe;case 11:return Zae;case 12:return t?foe:hoe;case 14:return t?xoe:voe;case 15:return xE("max",t);case 16:return xE("min",t);case 13:return t?yoe:boe;case 17:return Kae;case 18:return Xae;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var bt;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(bt||(bt={}));var woe="return abs(a);",koe="return ceil(a);",Ioe="return cos(a);",Soe=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Coe="return exp(a) - 1.0;",Toe="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Noe=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,_oe="return exp(a);",Eoe="return floor(a);",Aoe="return a;",$oe=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,Foe="return f32(!(a >= 1.0));",Doe="return -a;",Roe="return (a < 0.0) ? b * a : a;",Poe="return max(a, 0.0);",Ooe="return clamp(a, 0.0, 6.0);",Moe="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Loe=`
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;
`,Boe="return 1.0/sqrt(a);",zoe="return 1.0 / (1.0 + exp(-1.0 * a));",Woe="return sin(a);",Voe=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,Uoe="return sqrt(a);",Goe="return a * a;",Hoe=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,joe="return f32(i32((a)));";function nl(e,t){switch(e){case 0:return woe;case 2:return Ioe;case 3:return Soe;case 1:return koe;case 4:return t?Noe:Toe;case 5:return _oe;case 6:return Coe;case 7:return Eoe;case 8:return Aoe;case 9:return $oe;case 10:return Foe;case 11:return Doe;case 12:return Roe;case 13:return t?Loe:Poe;case 14:return t?Moe:Ooe;case 15:return Boe;case 18:return zoe;case 16:return Woe;case 17:return Voe;case 19:return Uoe;case 20:return Goe;case 21:return Hoe;case 22:return joe;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Gs(e,t=!1){if(e===null)return null;if(e==="linear")return nl(bt.LINEAR);if(e==="relu")return nl(bt.RELU,t);if(e==="elu")return nl(bt.ELU,t);if(e==="relu6")return nl(bt.RELU6,t);if(e==="prelu")return hp(Mt.PRELU,t);if(e==="sigmoid")return nl(bt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function wE(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};
${el()} {
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 qoe(e){return`
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
let tileSize = ${e[0]*4};
${el()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
// Without this initialization strange values show up in acc.
var acc = vec4<f32>(0.0);
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * tileSize / 4 + tileCol;
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < tileSize / 4; k = k + 1) {
let rowB = t * tileSize + k * 4;
let BCached0 = mm_readB(rowB, globalCol, globalId);
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
let ACached = mm_Asub[k];
acc = acc + BCached0 * ACached.x;
acc = acc + BCached1 * ACached.y;
acc = acc + BCached2 * ACached.z;
acc = acc + BCached3 * ACached.w;
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var Koe=class{constructor(e,t,n,r=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=mk(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=r!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=s,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${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?wE([this.vecSize,this.workPerThread,1],this.workGroupSize):qoe(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}>;
${el()} {
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 Xoe(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${el()} {
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 kE=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):Xoe(this.workGroupSize)}
`}};function Yoe(){return`
var<workgroup> sumValues : array<f32, workGroupSizeX>;
${el()} {
let coords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
let batch = coords[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
let dataA = mm_readA(batch, row, k);
let dataB = mm_readB(batch, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`}var Qoe=class{constructor(e,t=!1,n=!1,r=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=r!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=s,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${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);
}
${Yoe()}
`}};function Zoe(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.
${el()} {
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 >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var Joe=class{constructor(e,t,n,r=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=r!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=s,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${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);
}
}
${Zoe(this.workGroupSize)}
`}};function We(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:s}=n,a=w.sizeFromShape(r.shape),o=w.inferFromImplicitShape(s,a),i=w.sizeFromShape(o);return w.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${r.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:o,dtype:r.dtype}}var eie={kernelName:hi,backendName:"webgpu",kernelFunc:We};function xk({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,d=n?e.shape[l-2]:e.shape[l-1],p=r?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Ri.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let k=n?[b,d,h]:[b,h,d],T=r?[y,f,p]:[y,p,f],C=We({inputs:{x:e},backend:s,attrs:{shape:k}}),E=We({inputs:{x:t},backend:s,attrs:{shape:T}}),F=[C,E],O=Math.max(b,y),D=d%4==0&&f%4==0&&!n&&!r&&f>=32,R;h*f<=32?R=new Qoe([O,h,f],n,r,a,u,o):!n&&!r&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?R=new Joe(k,T,[O,h,f],a,u,o):D?R=new Koe(k,[O,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,o):R=new kE(k,[O,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,r,a,u,o);let _=[C,E];a&&_.push(a),o&&_.push(o);let L=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],U=s.runWebGPUProgram(R,_,e.dtype,L),j=We({inputs:{x:U},backend:s,attrs:{shape:x}});F.push(U);for(let K of F)s.disposeData(K.dataId);return j}function tie(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:d}=r;return xk({a:s,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var nie={kernelName:to,backendName:"webgpu",kernelFunc:tie},IE=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${hp(this.op,!1)}
}
${Ue()}
if(index < uniforms.size) {
let areal = getARealAtOutCoordsByGlobalIndex(index);
let aimag = getAImagAtOutCoordsByGlobalIndex(index);
let breal = getBRealAtOutCoordsByGlobalIndex(index);
let bimag = getBImagAtOutCoordsByGlobalIndex(index);
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},rie=class{constructor(e,t,n,r){this.variableNames=["A","B"],this.size=!0;let s=256;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.lastDimensionSize=r?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=r,this.op=e,this.shaderKey=`binaryShared_${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 {
${hp(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);
${t}
setOutputFlat(flatIndex, binaryOperation(a, b));
}
}
}
`}},sie=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${hp(this.op,this.isVec4)}
}
${Ue()}
if (index < uniforms.size) {
let a = getAAtOutCoordsByGlobalIndex(index);
let b = getBAtOutCoordsByGlobalIndex(index);
setOutputFlat(index, binaryOperation(a, b));
}
}
`}},SE=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${hp(this.op,!1)}
}
${Ue()}
if (index < uniforms.size) {
let a = getAAtOutCoordsByGlobalIndex(index);
let b = getBAtOutCoordsByGlobalIndex(index);
setOutputFlat(index, binaryOperation(a, b));
}
}
`}};function CE(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4==0)return new sie(e,t,n);let s=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return s||a?new rie(e,t,n,a):new SE(e,t,n)}function Lr(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var aie={kernelName:_a,backendName:"webgpu",kernelFunc:Lr};function rl(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=Lr({inputs:{x:r},backend:n}),u=Lr({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var oie={kernelName:Kl,backendName:"webgpu",kernelFunc:rl},Wm=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${nl(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=CE(e,o.shape,i.shape);return u.runWebGPUProgram(k,[v,x],In(b.dtype,y.dtype))});else{let g=new IE(Mt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),b=new IE(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=rl({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=CE(e,o.shape,i.shape);return u.runWebGPUProgram(c,[o,i],l)}}var{addImpl:iie,ceilImpl:uie,concatImpl:cie,equalImpl:lie,expImpl:die,expm1Impl:pie,floorImpl:hie,gatherNdImpl:fie,gatherV2Impl:mie,greaterEqualImpl:gie,greaterImpl:bie,lessEqualImpl:yie,lessImpl:vie,logImpl:xie,maxImpl:wie,maximumImpl:kie,minimumImpl:Iie,multiplyImpl:Sie,negImpl:Cie,notEqualImpl:Tie,prodImpl:Nie,rangeImpl:_ie,rsqrtImpl:Eie,simpleAbsImpl:Aie,sliceImpl:$ie,stridedSliceImpl:Fie,stringNGramsImpl:Die,subImpl:Rie,tileImpl:Pie,topKImpl:Oie,transposeImpl:Mie,uniqueImpl:ibe}=fm,Lie=fn({opType:bt.ABS,cpuKernelImpl:Aie}),Bie={kernelName:Vo,backendName:"webgpu",kernelFunc:Lie},zie=Rn({opSnippet:Mt.ADD,cpuKernelImpl:iie,supportsComplex:!0}),Wie={kernelName:_s,backendName:"webgpu",kernelFunc:zie},Vie=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, ${t});
}
}
}
`}};function Uie(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Lr({inputs:{x:r[0]},backend:n});let s=r.map(i=>i.dtype).reduce((i,u)=>In(i,u)),a=r.map(i=>i.shape),o=new Vie(a);return n.runWebGPUProgram(o,r,s)}var Gie={kernelName:la,backendName:"webgpu",kernelFunc:Uie},TE=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let r=[t];N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,e.length),this.op=n==="min"?"<":">";let[s]=N.computeOutAndReduceShapes(e,r);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${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]);
}
}
`}},Hie=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]}>;
${zm()}
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
[[builtin(workgroup_id)]] workgroupId : vec3<u32>) {
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] =
A.numbers[y * width + x];
}
workgroupBarrier();
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputFlat((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},jie=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=on(this.outputShape.length),t=qie(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 qie(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=Mie(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 Hie(s.shape,a);return o.runWebGPUProgram(c,[s],s.dtype)}let l=new jie(s.shape,a);return o.runWebGPUProgram(l,[s],s.dtype)}var Kie={kernelName:Ja,backendName:"webgpu",kernelFunc:cu};function Xie(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 TE(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 Yie={kernelName:da,backendName:"webgpu",kernelFunc:Xie};function Qie(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 TE(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 Zie={kernelName:ju,backendName:"webgpu",kernelFunc:Qie},NE=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});
}
}
`}},_E=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 Jie(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 _E(c):(d=new NE(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 eue={kernelName:pa,backendName:"webgpu",kernelFunc:Jie};function tue(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 nue={kernelName:ha,backendName:"webgpu",kernelFunc:tue},rue=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 : ${on(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=on(this.rank),t=sue(this.rank),n;return this.start.length===1?n=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((s,a)=>`sourceLoc.${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 sue(e){if(e===1)return"sourceLoc";if(e<=6)return wk.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function sl(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,u]=$t.parseSliceParams(s,a,o);if($t.assertParamsValid(s,i,u),n.shouldExecuteOnCPU([s])||s.dtype==="string"){let d=n.tensorMap.get(s.dataId),p=$ie(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 rue(i,u),c=[{type:"int32",data:i}];return n.runWebGPUProgram(l,[s],s.dtype,c)}var aue={kernelName:yi,backendName:"webgpu",kernelFunc:sl},oue=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=sl({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},iue={kernelName:Uo,backendName:"webgpu",kernelFunc:oue},EE=Rn({opSnippet:Mt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Tie}),uue={kernelName:oi,backendName:"webgpu",kernelFunc:EE};function fp(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 cue={kernelName:rd,backendName:"webgpu",kernelFunc:fp};function lue(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=rl({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),u}if(s.dtype==="complex64"){let o=fp({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 lue(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=EE({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 due={kernelName:fa,backendName:"webgpu",kernelFunc:kk},pue=fn({opType:bt.CEIL,cpuKernelImpl:uie}),hue={kernelName:ma,backendName:"webgpu",kernelFunc:pue},fue=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);
}
}
`}},mue=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`
${Ue()}
if(index < uniforms.size) {
let value = getAAtOutCoordsByGlobalIndex(index);
if (isNanCustom(value)) {
setOutputFlat(index, value);
return;
}
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function gue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i,u=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return w.sizeFromShape(s.shape)%4==0?i=new fue(s.shape):i=new mue(s.shape),n.runWebGPUProgram(i,[s],s.dtype,u)}var bue={kernelName:Es,backendName:"webgpu",kernelFunc:gue},yue=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${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 vue={kernelName:Jl,backendName:"webgpu",kernelFunc:Vm};function Ik(e,t,n){let r=e[0].dtype;if(r==="complex64"){let h=e.map(y=>fp({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=rl({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=cie(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}=xue(e,t,n),i=a.map(h=>h.shape),u=new yue(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 xue(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 AE(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 wue={kernelName:Go,backendName:"webgpu",kernelFunc:AE},kue=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 $E({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 Iue({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 kue(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 kE(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 FE=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=wE([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);
}
}
${t}
`}},DE=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=fk(this.dispatchLayout,this.outputShape),this.elementsPerThread=gk(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=r,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${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;
}
${e}
`}},RE=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=r,this.shaderKey=`conv2DNaive_${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 Sue(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 $E({x:s,filter:a,convInfo:p,backend:r});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&s.shape[0]===1)return Iue({x:s,filter:a,convInfo:p,backend:r});let h,f=[p.padInfo.top,p.padInfo.left],m=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]}],g=X().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new RE(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new FE(p):h=new DE(p),!g){let b=p.outShape[1]*p.outShape[2],y=p.outShape[3],v=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}return r.runWebGPUProgram(h,[s,a],s.dtype,m)}var Cue={kernelName:ga,backendName:"webgpu",kernelFunc:Sue},Tue=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=fk(this.dispatchLayout,this.outputShape),this.elementsPerThread=gk(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${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;
}
${vk(this.elementsPerThread,this.workGroupSize)}
`}},Nue=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${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 (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutputFlat(index, dotProd);
}
}
`}};function _ue(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=r,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(o,a.shape,i,1,u,c,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Nue(p);else{f=new Tue(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],b=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[s,a],"float32",h)}var Eue={kernelName:ba,backendName:"webgpu",kernelFunc:_ue},Aue=fn({opType:bt.COS}),$ue={kernelName:ya,backendName:"webgpu",kernelFunc:Aue},Fue=fn({opType:bt.COSH}),Due={kernelName:va,backendName:"webgpu",kernelFunc:Fue},Rue=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);
}
}
}
`}},Pue=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 Rue(s.shape[3],a.shape,i,u),d=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[s,a,o],"float32",d)},Oue={kernelName:jo,backendName:"webgpu",kernelFunc:Pue},Mue=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 = ${this.getInputSamplingString()};
setOutputFlat(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Lue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],u=o==="NHWC"?s.shape[1]:s.shape[2],l=o==="NHWC"?s.shape[2]:s.shape[3],c=o==="NHWC"?s.shape[3]:s.shape[1],d=u*a,p=l*a,h=c/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new Mue(f,o);return n.runWebGPUProgram(g,[s],s.dtype,m)}var Bue={kernelName:qo,backendName:"webgpu",kernelFunc:Lue},PE=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=r,this.shaderKey=`depthwise3x3_${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}
${t}
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},OE=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=r,this.shaderKey=`depthwise_${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}
${t}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function zue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=r,c=u;c==null&&(c=[1,1]);let d=N.computeConv2DInfo(s.shape,a.shape,o,c,i,l,!0),p=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}],h;return d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?h=new PE(d):(h=new OE(d),p.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.outChannels/d.inChannels]})),n.runWebGPUProgram(h,[s,a],s.dtype,p)}var Wue={kernelName:xa,backendName:"webgpu",kernelFunc:zue},ME=Rn({opSnippet:Mt.MUL,cpuKernelImpl:Sie,supportsComplex:!0}),Vue={kernelName:Ma,backendName:"webgpu",kernelFunc:ME},Uue=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=ze(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${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) {
${n}
}
}
`}};function mp(e,t,n,r,s){let a=e.shape.length,o=[],i=w.parseAxisParam(t,e.shape),u=i,l=N.getAxesPermutation(u,a),c=e;l!=null&&(c=cu({inputs:{x:e},attrs:{perm:l},backend:s}),u=N.getInnerMostAxes(u.length,a),o.push(c)),N.assertAxesAreInnerMostDims(r,u,a);let[d,p]=N.computeOutAndReduceShapes(c.shape,u),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let f;if((r==="max"||r==="prod")&&s.shouldExecuteOnCPU([c])){let m=s.tensorMap.get(c.dataId).values;switch(r){case"max":let g=wie(m,w.sizeFromShape(p),h,e.dtype);f=s.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=Nie(c.shape,c.dtype,m,u);f=s.makeTensorInfo(y,v,b);break;default:throw new Error(`${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":yd(e.dtype),x=[{type:"int32",data:[m]}],k=new Uue(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 mp(s,a,o,"sum",n)}var Gue={kernelName:Ka,backendName:"webgpu",kernelFunc:Sk};function Hue(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=ME({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 jue={kernelName:Zl,backendName:"webgpu",kernelFunc:Hue},que=fn({opType:bt.ELU}),Kue={kernelName:ka,backendName:"webgpu",kernelFunc:que},Xue=Rn({opSnippet:Mt.EQUAL,dtype:"bool",cpuKernelImpl:lie}),Yue={kernelName:Ko,backendName:"webgpu",kernelFunc:Xue},LE=fn({opType:bt.EXP,cpuKernelImpl:die,dtype:"float32"}),Que={kernelName:Ia,backendName:"webgpu",kernelFunc:LE};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 Zue={kernelName:Xo,backendName:"webgpu",kernelFunc:Ck},Jue=fn({opType:bt.EXPM1,cpuKernelImpl:pie}),ece={kernelName:Yo,backendName:"webgpu",kernelFunc:Jue},tce=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 al(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 tce(r),i=[{type:"float32",data:[s]}];return t.runWebGPUProgram(o,[],a,i)}}var nce={kernelName:Ju,backendName:"webgpu",kernelFunc:al},rce=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);
}
}
`}},sce={kernelName:Qo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new rce(n.shape);return r.runWebGPUProgram(s,[n],n.dtype)}},ace=fn({opType:bt.FLOOR,cpuKernelImpl:hie}),oce={kernelName:Sa,backendName:"webgpu",kernelFunc:ace},ice=Rn({opSnippet:Mt.INT_DIV,dtype:"int32"}),uce={kernelName:Ca,backendName:"webgpu",kernelFunc:ice},cce=(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}))})},BE=(e,t,n,r,s,a=!1)=>{let o={dtype:s.dtype,shape:s.shape},i=zae(r,o,t,a),u=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"}})};function zE(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 WE(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=zE(c,d,p),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>BE(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 lce={kernelName:ld,backendName:"webgpu",kernelFunc:dce},ol;function dce(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 WE({externalImage:s,backend:n,attrs:r,outShape:p,useImport:!0});if((o||i)&&(ol==null&&(ol=document.createElement("canvas").getContext("2d")),ol.canvas.width=c,ol.canvas.height=d,ol.drawImage(s,0,0,c,d),s=ol.canvas),l||u||o||i)return WE({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 pce=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)));
}
}
`}},hce={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 pce(r.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,r.dtype,f)}};function fce(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 $E({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 RE(g,b,h,y);else{k?v=new FE(g,b,h,y):v=new DE(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 mce={kernelName:no,backendName:"webgpu",kernelFunc:fce};function gce(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 PE(f,g,p,b):(v=new OE(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 bce={kernelName:ro,backendName:"webgpu",kernelFunc:gce},yce=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 vce(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=fie(y,v,r.dtype,l,o,c,d,r.shape,i);return n.makeTensorInfo(u,r.dtype,x.values)}let f=new yce(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 xce={kernelName:Jo,backendName:"webgpu",kernelFunc:vce},wce=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=kce(this.aShape,"i32");return`
${Ue()}
if (index < uniforms.size) {
let resRC = getCoordsFromFlatIndex(index);
setOutputFlat(index, getA(${e}));
}
}
`}};function kce(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 VE(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=mie(C,x,f);return d.forEach(F=>n.disposeData(F.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new wce(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 Ice={kernelName:Zo,backendName:"webgpu",kernelFunc:VE},Sce=Rn({opSnippet:Mt.GREATER,cpuKernelImpl:bie,dtype:"bool"}),Cce={kernelName:ei,backendName:"webgpu",kernelFunc:Sce},Tce=Rn({opSnippet:Mt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:gie}),Nce={kernelName:Na,backendName:"webgpu",kernelFunc:Tce},_ce=Rn({opSnippet:Mt.LESS,dtype:"bool",cpuKernelImpl:vie}),Ece={kernelName:ni,backendName:"webgpu",kernelFunc:_ce},Ace=Rn({opSnippet:Mt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:yie}),$ce={kernelName:ri,backendName:"webgpu",kernelFunc:Ace},Fce=fn({opType:bt.LOG,cpuKernelImpl:xie}),Dce={kernelName:Ea,backendName:"webgpu",kernelFunc:Fce},Rce=Rn({opSnippet:Mt.LOGICAL_AND,dtype:"bool"}),Pce={kernelName:si,backendName:"webgpu",kernelFunc:Rce},Oce=fn({opType:bt.LOGICAL_NOT}),Mce={kernelName:sc,backendName:"webgpu",kernelFunc:Oce};function UE(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r;return mp(s,a,o,"max",n)}var Lce={kernelName:Aa,backendName:"webgpu",kernelFunc:UE},Bce=Rn({opSnippet:Mt.MAX,cpuKernelImpl:kie}),zce={kernelName:$a,backendName:"webgpu",kernelFunc:Bce};function Wce(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 _E(c),p.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else d=new NE(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 Vce={kernelName:Fa,backendName:"webgpu",kernelFunc:Wce};function Uce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{keepDims:a,axis:o}=r;return mp(s,o,a,"mean",n)}var Gce={kernelName:Da,backendName:"webgpu",kernelFunc:Uce};function Hce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return mp(s,a,o,"min",n)}var jce={kernelName:Ra,backendName:"webgpu",kernelFunc:Hce},qce=Rn({opSnippet:Mt.MIN,cpuKernelImpl:Iie}),Kce={kernelName:Pa,backendName:"webgpu",kernelFunc:qce},Xce=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}));
}
}
`}},Yce={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 Xce(r.shape,s,a);return o.runWebGPUProgram(u,[r],r.dtype,i)}};function Qce(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.tensorMap.get(r.dataId),[o,i]=Cie(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 Zce={kernelName:ai,backendName:"webgpu",kernelFunc:Qce};function Jce(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 ele={kernelName:ii,backendName:"webgpu",kernelFunc:Jce};function tle(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 nle={kernelName:ui,backendName:"webgpu",kernelFunc:tle};function Um(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=fp({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=rl({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 al({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var rle={kernelName:Ni,backendName:"webgpu",kernelFunc:Um};function GE(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=fp({inputs:{input:r},backend:n}),a=GE({inputs:{x:s},backend:n}),o=Vm({inputs:{input:r},backend:n}),i=Um({inputs:{x:o},backend:n}),u=rl({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 al({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var sle={kernelName:ci,backendName:"webgpu",kernelFunc:GE};function ale(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=AE({inputs:u,backend:n,attrs:{axis:s}});return i.forEach(c=>n.disposeData(c.dataId)),l}var ole={kernelName:di,backendName:"webgpu",kernelFunc:ale},ile=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}));
}
}
}
`}},HE=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 al({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 ile(s.shape,a);return n.runWebGPUProgram(u,[s],s.dtype,i)},ule={kernelName:La,backendName:"webgpu",kernelFunc:HE},cle=Rn({opSnippet:Mt.POW}),lle={kernelName:Ba,backendName:"webgpu",kernelFunc:cle};function dle(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=new SE(Mt.PRELU,r.shape,s.shape);return n.runWebGPUProgram(a,[r,s],"float32")}var ple={kernelName:za,backendName:"webgpu",kernelFunc:dle};function hle(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return mp(s,a,o,"prod",n)}var fle={kernelName:pi,backendName:"webgpu",kernelFunc:hle},mle=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=_ie(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},gle={kernelName:ic,backendName:"webgpu",kernelFunc:mle},jE=Rn({opSnippet:Mt.DIV}),ble={kernelName:wa,backendName:"webgpu",kernelFunc:jE},yle=fn({opType:bt.RELU}),vle={kernelName:Wa,backendName:"webgpu",kernelFunc:yle},xle=fn({opType:bt.RELU6}),wle={kernelName:Ua,backendName:"webgpu",kernelFunc:xle},kle=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 Ile(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 kle(s.shape,u,l);return n.runWebGPUProgram(f,[s],"float32",h)}var Sle={kernelName:Va,backendName:"webgpu",kernelFunc:Ile},Cle=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 Tle(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 Cle(s.shape,u,l,o);return n.runWebGPUProgram(f,[s],s.dtype,h)}var Nle={kernelName:cc,backendName:"webgpu",kernelFunc:Tle},_le=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));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputFlat(index, outputValue);
}
}
`}},Ele={kernelName:_i,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,u=new _le(r.shape,a),[l,c]=N.getImageCenter(o,r.shape[1],r.shape[2]),d=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(s)]},{type:"float32",data:[Math.cos(s)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(u,[r],r.dtype,d)}},Ale=fn({opType:bt.RSQRT,cpuKernelImpl:Eie}),$le={kernelName:Ga,backendName:"webgpu",kernelFunc:Ale},Fle=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 Dle(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=al({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 Fle(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 Rle={kernelName:gi,backendName:"webgpu",kernelFunc:Dle},Ple=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 Ole(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new Ple(r.shape.length,s.shape,s.shape.length);return n.runWebGPUProgram(o,[r,s,a],In(s.dtype,a.dtype))}var Mle={kernelName:bi,backendName:"webgpu",kernelFunc:Ole},Lle=fn({opType:bt.SIGMOID}),Ble={kernelName:ja,backendName:"webgpu",kernelFunc:Lle},zle=fn({opType:bt.SIN}),Wle={kernelName:Ha,backendName:"webgpu",kernelFunc:zle},Vle=fn({opType:bt.SINH}),Ule={kernelName:vi,backendName:"webgpu",kernelFunc:Vle},qE=Rn({opSnippet:Mt.SUB,cpuKernelImpl:Rie,supportsComplex:!0}),Gle={kernelName:Qa,backendName:"webgpu",kernelFunc:qE};function Hle(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=UE({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=qE({inputs:{a:s,b:l},backend:n}),d=LE({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=jE({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 jle={kernelName:Xa,backendName:"webgpu",kernelFunc:Hle},qle=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=HE({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},Kle={kernelName:xi,backendName:"webgpu",kernelFunc:qle},Xle=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 Yle(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 Xle(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 Qle={kernelName:id,backendName:"webgpu",kernelFunc:Yle};function Zle(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=sl({inputs:{x:s},backend:n,attrs:{begin:c,size:h}});return c[i]+=p,f})}var Jle={kernelName:wi,backendName:"webgpu",kernelFunc:Zle},ede=fn({opType:bt.SQRT}),tde={kernelName:qa,backendName:"webgpu",kernelFunc:ede},nde={kernelName:fc,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)}},rde=Rn({opSnippet:Mt.SQUARED_DIFFERENCE}),sde={kernelName:Ya,backendName:"webgpu",kernelFunc:rde},ade=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 ode(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=sl({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=Fie(h,E,x,y);k=n.makeTensorInfo(f,s.dtype,F.values)}else{let C=new ade(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 ide={kernelName:ki,backendName:"webgpu",kernelFunc:ode};function ude(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]=Die(p,h,s,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var cde={kernelName:ud,backendName:"webgpu",kernelFunc:ude},lde=fn({opType:bt.TANH}),dde={kernelName:Za,backendName:"webgpu",kernelFunc:lde},pde=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=hde(this.rank,"uniforms.");return`
${Ue()}
if (index < uniforms.size) {
let resRC = getCoordsFromFlatIndex(index);
setOutputFlat(index, getA(${e}));
}
}
`}};function hde(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 fde(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=Pie(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new pde(s.shape,a);return n.runWebGPUProgram(o,[s],s.dtype)}var mde={kernelName:As,backendName:"webgpu",kernelFunc:fde},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; 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));
}
}
}
`}},bde=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 il(e,t){t!==null&&e.disposeData(t.dataId)}function KE(e){let t=1;for(;t<e;)t*=2;return t}function yde(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]=Oie(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,al({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=KE(a),h=KE(u),f=null,m=()=>f===null?[d,d]:[d,f],g=(k,T,C)=>{let E=m(),F=new gde(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),il(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 bde([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),il(n,O);let D=p/2,R=D*2;for(let _=D;_>=1;_/=2)g(R,_,f.shape)}let b=f;f=sl({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),il(n,b);let y=VE({inputs:{x:d,indices:f},backend:n,attrs:{axis:1,batchDims:1}});il(n,d);let v=i.slice(0,-1);v.push(a),b=f,f=We({inputs:{x:f},attrs:{shape:v},backend:n}),il(n,b);let x=y;return y=We({inputs:{x:y},attrs:{shape:v},backend:n}),il(n,x),[y,f]}var vde={kernelName:Si,backendName:"webgpu",kernelFunc:yde},xde=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 wde(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 xde(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 kde={kernelName:Ci,backendName:"webgpu",kernelFunc:wde};function Ide(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=sl({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 Sde={kernelName:Ti,backendName:"webgpu",kernelFunc:Ide},Cde=[nie,Bie,Wie,Gie,Yie,Zie,eue,nue,iue,due,hue,bue,oie,wue,Cue,Eue,$ue,Due,Oue,Bue,Wue,jue,Kue,Yue,Zue,Que,ece,nce,sce,lce,oce,uce,hce,mce,bce,xce,Ice,Cce,Nce,aie,vue,Ece,$ce,Dce,Pce,Mce,Lce,zce,Vce,Gce,jce,Kce,Yce,Vue,Zce,ele,nle,uue,sle,ole,ule,ple,fle,lle,gle,cue,ble,vle,wle,eie,Sle,Nle,Ele,$le,Rle,Mle,Ble,Wle,Ule,aue,ide,cde,jle,Kle,Jle,Qle,tde,nde,sde,Gle,Gue,dde,mde,vde,kde,Kie,Sde,rle];for(let e of Cde)gc(e);var Tde=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=XE(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=XE(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 XE(e,t){return`${e}_${t}`}var YE=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}}},Nde=class extends YE{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}}},_de=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),QE=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 Tde(this.device),this.tensorMap=new Gl(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 QE.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 YE),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Nde),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=yE(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=zE(e,o,h,g,m),{bindGroupLayout:y,pipelineLayout:v}=this.getCachedOrCreateLayout(e.variableNames.length),x=this.getAndSavePipeline(b,()=>BE(this.device,e,v,p,s)),k=this.activeTimers!=null,T=cce(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=_de){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=QE;Tk.nextDataId=0;var ZE={};Ee(ZE,{WebGPUBackend:()=>Tk,webgpu_util:()=>bE});yc.isBrowser()&&yk()&&kd("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 gp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(gp||(gp={}));var JE;function Ede(e){JE=e.wasm.cwrap(to,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Ade(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 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ype(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 ll=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 ll(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 tge(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 wp(e){return 1/(1+Math.exp(-e))}function nge(e){return Math.log(e/(1-e))}var dl=class extends dt{constructor(t,n,r,s,a=!1){super({x:t,y:n,width:r,height:s},a)}};var rge=.5,sge=.43,age=.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/age),u=hu(t),l=Math.floor(Math.max(0,u.x-rge*i)),c=Math.floor(Math.max(0,u.y-sge*i));return new dl(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 s$=class extends Sr{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],hu([t[3],t[4]])]}};var pl=class extends Sr{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(hu)}};var kp=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${pu(this.distance)})`:""}`}};var Ip=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 a$=class extends Ip{static assertIsValidPredictedBox(t,n){if(Ip.assertIsValidLabeledBox(t,n),!cl(t.score)||!cl(t.classScore))throw new Error(`${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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Hk()?qk(Uk()):jk()?qk(Gk()):null}function ige(e){if(un||Kk(),!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:oge,setEnv:qk,initialize:Kk,createBrowserEnv:Uk,createFileSystem:Km,createNodejsEnv:Gk,monkeyPatch:ige,isBrowser:Hk,isNodejs:jk};Kk();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 Xk=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 Xk(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 $o([l],{x:a-s/2,y:o},this.options.drawLabelOptions).draw(t)}};function uge(e,t){(Array.isArray(t)?t:[t]).forEach(r=>{let s=r instanceof St?r.score:ws(r)?r.detection.score:void 0,a=r instanceof St?r.box:ws(r)?r.detection.box:new dt(r),o=s?`${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 Yk(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 Qk(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 loading yet");let{width:r,height:s}=t||gu(e),a=bu({width:r,height:s});return e instanceof n?Xn(a).putImageData(e,0,0):Xn(a).drawImage(e,0,0,r,s),a}async function Zk(e,t){let n=t||rt.getEnv().createCanvasElement(),[r,s,a]=e.shape.slice(Ir(e)?1:0),o=M(()=>e.as3D(r,s,a).toInt());return await Pi.toPixels(o,n),o.dispose(),n}function Ym(e){let{Image:t,Canvas:n,Video:r}=rt.getEnv();return e instanceof t||e instanceof n||e instanceof r}function Jk(e,t,n=!1){let{Image:r,Canvas:s}=rt.getEnv();if(!(e instanceof r||e instanceof s))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return bu({width:1,height:1});let a=gu(e),o=t/Math.max(a.height,a.width),i=o*a.width,u=o*a.height,l=bu({width:t,height:t}),c=e instanceof s?e:Tp(e),d=Math.abs(i-u)/2,p=n&&i<u?d:0,h=n&&u<i?d:0;return c.width>0&&c.height>0&&Xn(l).drawImage(c,p,h,i,u),l}var 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 ${o} passed, but not supported in input array`);this._imageTensors[s]=r,this._inputDimensions[s]=r.shape.slice(1);return}let a=r instanceof rt.getEnv().Canvas?r:Tp(r);this._canvases[s]=a,this._inputDimensions[s]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return 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$ge(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 Fge(e,t){let{sizes:n,centers:r}=$ge(e),s=vt(Oe(t,[1,0])),a=me(V(Tn(me(s[2],5)),n[0]),2),o=Z(V(me(s[0],10),n[0]),r[0]),i=me(V(Tn(me(s[3],5)),n[1]),2),u=Z(V(me(s[1],10),n[1]),r[1]);return Oe(Ut([he(o,a),he(u,i),Z(o,a),Z(u,i)]),[1,0])}function _$(e,t,n){return M(()=>{let r=e.shape[0],s=Fge(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=$r(Ve(t,[0,0,1],[-1,-1,-1])),o=Ve(a,[0,0,0],[-1,-1,1]);o=G(o,[r,o.shape[1]]);let i=vt(s),u=vt(o);return{boxes:i,scores:u}})}function xu(e,t){return M(()=>{let n=e.shape[0],r=G(yu(e,t.box_encoding_predictor),[n,-1,1,4]),s=G(yu(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:r,classPrediction:s}})}function E$(e,t,n){return M(()=>{let 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L$(e,t,n,r){let{extractWeights:s,getRemainingWeights:a}=Ln(e),o=[],{extractConvParams:i,extractConvWithBatchNormParams:u,extractSeparableConvParams:l}=Rge(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: ${a().length}`);return{params:c,paramMappings:o}}function Pge(e,t){let n=lr(e,t);function r(i){let u=n(`${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(`${i}/bn`);return{conv:u,bn:l}}let o=bl(n);return{extractConvParams:s,extractConvWithBatchNormParams:a,extractSeparableConvParams:o}}function B$(e,t){let n=[],{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}=Pge(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 hI=class extends gn{constructor(t){super("TinyYolov2");pI(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?wl(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 B$(t,this.config)}extractParams(t){let n=this.config.filterSizes||hI.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 ${r} filterSizes in config`);return L$(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=wp(m[b][y][v][0]);if(!r||x>r){let k=(y+wp(g[b][y][v][0]))/l*i,T=(b+wp(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 ll(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)}},kl=hI;kl.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Il=class extends kl{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:F$,classes:["face"],...t?{anchors:R$,meanRgb:P$}:{anchors:D$,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?M$:O$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Oge(e,t=!0){let n=new Il(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 fl(t,a):await hl(t,a)),i=await n(o);return o.forEach(u=>u instanceof Ae&&u.dispose()),i}async function Sl(e,t,n,r,s){return ku([e],t,async a=>n(a[0]),r,s)}var z$=.4,W$=[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)],V$=[117.001,114.697,97.404];var Cl=class extends kl{constructor(){let t={withSeparableConvs:!0,iouThreshold:z$,classes:["face"],anchors:W$,meanRgb:V$,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 Cl,tinyYolov2:new Il,faceLandmark68Net:new vl,faceLandmark68TinyNet:new ug,faceRecognitionNet:new xl,faceExpressionNet:new ag,ageGenderNet:new ig},U$=(e,t)=>st.ssdMobilenetv1.locateFaces(e,t),Mge=(e,t)=>st.tinyFaceDetector.locateFaces(e,t),Lge=(e,t)=>st.tinyYolov2.locateFaces(e,t),G$=e=>st.faceLandmark68Net.detectLandmarks(e),Bge=e=>st.faceLandmark68TinyNet.detectLandmarks(e),zge=e=>st.faceRecognitionNet.computeFaceDescriptor(e),Wge=e=>st.faceExpressionNet.predictExpressions(e),Vge=e=>st.ageGenderNet.predictAgeAndGender(e),H$=e=>st.ssdMobilenetv1.load(e),Uge=e=>st.tinyFaceDetector.load(e),Gge=e=>st.tinyYolov2.load(e),Hge=e=>st.faceLandmark68Net.load(e),jge=e=>st.faceLandmark68TinyNet.load(e),qge=e=>st.faceRecognitionNet.load(e),Kge=e=>st.faceExpressionNet.load(e),Xge=e=>st.ageGenderNet.load(e),Yge=H$,Qge=U$,Zge=G$;var fI=class extends Wr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},Tl=class extends fI{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 _l(this,this.input)}},Nl=class extends fI{async run(){let t=await this.parentTask;if(!t)return;let n=await Sl(t,this.input,r=>st.faceExpressionNet.predictExpressions(r),this.extractedFaces);return og(t,n)}withAgeAndGender(){return new El(this,this.input)}},Iu=class extends Tl{withAgeAndGender(){return new Cu(this,this.input)}withFaceDescriptors(){return new Do(this,this.input)}},Su=class extends Nl{withAgeAndGender(){return new Tu(this,this.input)}withFaceDescriptor(){return new Ro(this,this.input)}};var mI=class extends Wr{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},_l=class extends mI{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 Tl(this,this.input)}},El=class extends mI{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:r,genderProbability:s}=await Sl(t,this.input,a=>st.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return dg(pg(t,r,s),n)}withFaceExpressions(){return new Nl(this,this.input)}},Cu=class extends _l{withFaceExpressions(){return new Iu(this,this.input)}withFaceDescriptors(){return new Do(this,this.input)}},Tu=class extends El{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 Sl(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 fl(this.input,n):await hl(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)=>yl(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 fl(this.input,[n]):await hl(this.input,[n]),s=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ae&&a.dispose()),yl(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 Tl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new _l(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 Nl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new El(this.runAndExtendWithFaceDetection(),this.input)}};function Jge(e,t=new zr){return new xg(e,t)}function wg(e,t=new zr){return new Dp(e,t)}async function j$(e,t){return wg(e,new zr(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function ebe(e,t={}){return wg(e,new Is(t)).withFaceLandmarks().withFaceDescriptors()}var tbe=j$;function gI(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 ${s++}`;this._labeledDescriptors=r.map(o=>{if(o instanceof qs)return o;if(o instanceof Float32Array)return new qs(a(),[o]);if(o.descriptor&&o.descriptor instanceof Float32Array)return new qs(a(),[o.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(r=>gI(r,t)).reduce((r,s)=>r+s,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:r})=>new kp(r,this.computeMeanDistance(t,n))).reduce((n,r)=>n.distance<r.distance?n:r)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this._distanceThreshold?n:new kp("unknown",n.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(r=>qs.fromJSON(r));return new kg(n,t.distanceThreshold)}};function nbe(e){let t=new Cl;return t.extractWeights(e),t}function q$(e,t){let{width:n,height:r}=new On(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(s=>q$(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 yl(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 rbe=typeof process!="undefined",K$=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",sbe={faceapi:d$,node:rbe,browser:K$};K$&&(Wu.set("CHECK_COMPUTATION_FOR_ERRORS",!1),Wu.set("WEBGL_CPU_FORWARD",!0),Wu.set("WEBGL_USE_SHAPES_UNIFORMS",!0));return abe;})();
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use backend file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
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