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na{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>tt(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>tt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;j(()=>{let c=ue(L(i,this.rho),L(vt(o),1-this.rho)),u=L(fe(Cn(ue(l,this.epsilon)),Cn(ue(i,this.epsilon))),o),d=ue(L(l,this.rho),L(vt(u),1-this.rho));i.assign(c),l.assign(d);let p=ue(L(u,-this.learningRate),r);r.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(ne(this.accumulatedGrads.map(e=>e.variable)),ne(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ff.className="Adadelta";wo(Ff);var Of=class extends na{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>_u(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;j(()=>{let i=ue(o,vt(a));o.assign(i);let l=ue(L(fe(a,Cn(ue(i,W.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&ne(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Of.className="Adagrad";wo(Of);var Mf=class extends na{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=Ee(t).variable(),this.accBeta2=Ee(n).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=xe(1,this.accBeta1),s=xe(1,this.accBeta2);t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>tt(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>tt(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedSecondMoment[a].variable,d=ue(L(c,this.beta1),L(l,1-this.beta1)),p=ue(L(u,this.beta2),L(vt(l),1-this.beta2)),h=fe(d,n),f=fe(p,s);c.assign(d),u.assign(p);let m=ue(L(fe(h,ue(Cn(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&ne(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(ea(this.beta1,this.iterations_+1)),this.accBeta2.assign(ea(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Mf.className="Adam";wo(Mf);var zf=class extends na{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=Ee(0).variable(),this.accBeta1=Ee(t).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=xe(1,this.accBeta1),s=fe(-this.learningRate,ue(L(this.iteration,this.decay),1));t.forEach((r,a)=>{let 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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)}};zf.className="Adamax";wo(zf);var Fd=class extends na{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=W.registeredVariables[n];j(()=>{let o=ue(L(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=An(Ee(-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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r=W.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>tt(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>tt(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>tt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,l=this.accumulatedMoments[s].variable;j(()=>{let c=ue(L(i,this.decay),L(vt(o),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[s].variable,d=ue(L(u,this.decay),L(o,1-this.decay)),p=fe(L(o,this.learningRate),Cn(xe(c,ue(vt(d),this.epsilon)))),h=ue(L(l,this.momentum),p);i.assign(c),u.assign(d),l.assign(h);let f=xe(r,h);r.assign(f)}else{let u=ue(L(i,this.decay),L(vt(o),1-this.decay)),d=ue(L(l,this.momentum),fe(L(o,this.learningRate),Cn(ue(u,this.epsilon))));i.assign(u),l.assign(d);let p=xe(r,d);r.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ne(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ne(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ne(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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(Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function Tw(e,t,n){if(n instanceof Ke)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function xL(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function bL(e,t,n){let s=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.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}`),v.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(Nw(n.validationData))v.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=xL(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=pw(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=hw(u,d,n.epochs,null,null,vL(t,n),null,r,c);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.batchesPerEpoch:x.done){if(r){let b;Nw(n.validationData)?b=Nt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=Nt(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?AL:n.validationBatchSize,verbose:0}));for(let w=0;w0)throw new Ve("Verbose mode is not implemented yet.");v.assert(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let o=wL(t)?t:await t.iterator(),i=0,l=0;for(;s?l{if(c.value){let{xs:u,ys:d}=Cw(e,c.value),p=u.concat(d),h=j(()=>r(p));if(ne(p),l===0)for(let m=0;mue(a[m],L(f,g))),l>0&&ne(y)}ne(h),i+=f,++l}return a}),c.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). 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Need data for each key in: ${t}`);a.push(e[o])}}else if(my(e)){if(e=e,e.length!==t.length)throw new q(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);a=e}else{if(e=e,t.length>1)throw new q(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);a=[e]}if(a=Ew(a),n!=null)for(let o=0;o=0&&c!==u)throw new q(`${r} expected a batch of elements where each example has shape [${n[o].slice(1,n[o].length)}] (i.e.,tensor shape [*,${n[o].slice(1,n[o].length)}]) but the ${r} received an input with ${i.shape[0]} examples, each with shape [${i.shape.slice(1,i.shape.length)}] (tensor shape [${i.shape}])`)}}return a}function TL(e,t,n){let s=To(e.map(a=>a.shape[0]));s.sort();let r=To(t.map(a=>a.shape[0]));if(r.sort(),s.length>1)throw new q(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(a=>a.shape))}`);if(r.length>1)throw new q(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(a=>a.shape))}`);if(s.length>0&&r.length>0&&!v.arraysEqual(s,r))throw new q(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`)}function NL(e,t,n){let s=[ul,im,Vd];for(let r=0;r1)throw new q(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);a=[e]}if(n!=null)for(let o=0;o[]);let n;if(typeof e=="string"||typeof e=="function")n=[e];else if(Array.isArray(e)||typeof e=="object")n=e;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);if(Array.isArray(n))return t.map(s=>n);{let s=[];for(let r of t){let a=n.hasOwnProperty(r)?n[r]:[];Array.isArray(a)||(a=[a]),s.push(a)}return s}}var RL="layers-model",aa=class extends _r{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new q("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");iL(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=oL(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof na))throw new q("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let a in e.loss)if(this.outputNames.indexOf(a)===-1)throw new q(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);for(let a of this.outputNames)e.loss[a]==null&&console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`),t.push(ry(e.loss[a]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new q(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map(o=>ry(o))}else{let a=ry(e.loss);this.outputs.forEach(o=>{t.push(a)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let a=0;a{for(let a=0;a1&&(this.metricsTensors.push([o,a]),this.metricsNames.push(this.outputNames[a]+"_loss"))}});let s=EL(e.metrics,this.outputNames),r=(a,o,i)=>{this.outputNames.length>1&&(o=this.outputNames[a]+"_"+o),this.metricsNames.push(o),this.metricsTensors.push([i,a])};il("metric",()=>{for(let a=0;a{let c="",u,d,p;for(let h of l){if(typeof h=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(h)!==-1){let m=this.internalOutputShapes[a];m[m.length-1]===1||this.lossFunctions[a]===im?["accuracy","acc"].indexOf(h)!==-1?d=ay:["crossentropy","ce"].indexOf(h)!==-1&&(d=gw):this.lossFunctions[a]===om?["accuracy","acc"].indexOf(h)!==-1?d=yw:["crossentropy","ce"].indexOf(h)!==-1&&(d=Aw):["accuracy","acc"].indexOf(h)!==-1?d=oy:["crossentropy","ce"].indexOf(h)!==-1&&(d=iy);let g;["accuracy","acc"].indexOf(h)!==-1?g="acc":["crossentropy","ce"].indexOf(h)!==-1&&(g="ce"),p=d,u=c+g}else p=aL(h),u=c+cm(h);let f;il(u,()=>{f=p}),r(a,u,f)}})(o)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let s=n.batchSize==null?32:n.batchSize;py(s);let r=!0,a=this.standardizeUserDataXY(e,t,r,s);try{let o=a[0].concat(a[1]);this.makeTestFunction();let i=this.testFunction,l=this.testLoop(i,o,s,n.verbose,n.steps);return as(l)}finally{dl(a[0],e),dl(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),kL(this,e,t)}checkNumSamples(e,t,n,s="steps"){let r;if(n!=null){if(r=null,t!=null)throw new q(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?r=e[0].shape[0]:r=e.shape[0];else throw new q(`Either the input data should have a defined shape, or ${s} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new q("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),s=n?t:[t],r=this.retrieveSymbolicTensors(s),a=new cl;if(e instanceof Ke&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new q(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;io.name);for(let o=0;o0){let s=[];throw t.forEach((r,a)=>{r==null&&s.push(e[a])}),new q(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return t}predictLoop(e,t=32,n=!1){return j(()=>{let s=this.checkNumSamples(e);if(n)throw new Ve("Verbose predictLoop() is not implemented yet.");let r=fy(s,t),a=this.outputs.map(o=>[]);for(let o=0;o{let l=r[o][0],c=r[o][1],u=Hd(e,l,c),d=[];if(Array.isArray(u))for(let h=0;ha[c].push(l));return as(a.map(o=>kt(o,0)))})}predict(e,t={}){let n=Ew(e);Dw(n,this.inputNames,this.feedInputShapes,!1);try{let s=t.batchSize==null?32:t.batchSize;return py(s),this.predictLoop(n,s)}finally{dl(n,e)}}predictOnBatch(e){Dw(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,s){if(this.optimizer_==null)throw new hr("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a0&&e[0].shape[0]%s!=0)throw new q(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[o,i]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(s!=null){let c=Iw(s,this.outputNames);l=[];for(let u=0;u{let a=this.checkNumSamples(t,n,r,"steps"),o=[];if(s>0)throw new Ve("Verbose mode is not implemented yet.");if(r!=null)throw new Ve("steps mode in testLoop() is not implemented yet");{let i=fy(a,n),l=Zt(fr(0,a));for(let c=0;c1&&(r+=`_${Lv(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],o=()=>{let u=[];for(let f=0;f1&&f{h=ue(h,f)}),h},i=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(o,l,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>j(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let l=0;lra(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=ra(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[ra(cm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ra(cm(e)));{let e={};for(let t in this.metrics)e[t]=ra(cm(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Ud(e.optimizer_config),n=yr(t),s;if(typeof e.loss=="string")s=al(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>al(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=al(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>al(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=al(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=es.getSaveHandlers(e);if(l.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new q(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await es.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:RL,generatedBy:`TensorFlow.js tfjs-layers v${cy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await es.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=es.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let l=!0;bw(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){bw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};aa.className="Model";de.registerClass(aa);var _w=class extends aa{};_w.className="Functional";de.registerClass(_w);async function $L(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Ud(n),r=yr(s,t);if(e.weightsManifest!=null){let a=await es.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),ne(a)}return r}async function DL(e,t){if(t==null&&(t={}),typeof e=="string"){let n=es.getLoadHandlers(e,t);if(n.length===0)n.push(es.browserHTTPRequest(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return _L(e,void 0,t)}async function _L(e,t,n){if(n==null&&(n={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,o=s.weightData!=null&&s.weightSpecs!=null&&a,i=yr(Ud(r),t,o),l=s.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),s.userDefinedMetadata!=null&&i.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=PL(s.weightData,s.weightSpecs);i.loadWeights(c,a),i.optimizer!=null&&u.length>0&&await i.optimizer.setWeights(u),ne(c),ne(u.map(d=>d.tensor))}return i}function PL(e,t){let n=es.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var qu=class extends aa{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:em("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof qu||e instanceof aa,n;if(t){if(n=e,n.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new q("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=ow({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new q(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=aw(this.outputs[0])}this.inboundNodes=[],new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:rl(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(At(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new aa({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new hr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new hr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new hr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new hr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof qu))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=yr(i,void 0,s);s&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};qu.className="Sequential";de.registerClass(qu);function FL(e){return new aa(e)}function OL(e){return new qu(e)}function ML(e,t){return t==null&&(t={}),DL(e,t)}function Pw(e){return ow(e)}function zL(e,t){Qs.registerCallbackConstructor(e,t)}var is=class extends de.Serializable{getConfig(){return{}}},Fw=class extends is{apply(e,t=1){return cz(e,t)}};Fw.className="elu";de.registerClass(Fw);var Ow=class extends is{apply(e){return vf(e)}};Ow.className="selu";de.registerClass(Ow);var Mw=class extends is{apply(e){return cr(e)}};Mw.className="relu";de.registerClass(Mw);var zw=class extends is{apply(e){return j(()=>Fu(6,cr(e)))}};zw.className="relu6";de.registerClass(zw);var Lw=class extends is{apply(e){return e}};Lw.className="linear";de.registerClass(Lw);var Bw=class extends is{apply(e){return ns(e)}};Bw.className="sigmoid";de.registerClass(Bw);var Ww=class extends is{apply(e){return pz(e)}};Ww.className="hardSigmoid";de.registerClass(Ww);var Vw=class extends is{apply(e){return Ji(e)}};Vw.className="softplus";de.registerClass(Vw);var Uw=class extends is{apply(e){return dz(e)}};Uw.className="softsign";de.registerClass(Uw);var Gw=class extends is{apply(e){return Ki(e)}};Gw.className="tanh";de.registerClass(Gw);var gy=class extends is{apply(e,t=-1){return tl(e,t)}};gy.className="softmax";de.registerClass(gy);var Hw=class extends is{apply(e,t=-1){return ff(e,t)}};Hw.className="logSoftmax";de.registerClass(Hw);var jw=class extends is{apply(e,t=1){return j(()=>L(ns(L(e,t)),e))}};jw.className="swish";de.registerClass(jw);var qw=class extends is{apply(e){return j(()=>L(e,Ki(Ji(e))))}};qw.className="mish";de.registerClass(qw);function $o(e){return e.getClassName()}function yy(e,t={}){return Od(e,de.SerializationMap.getMap().classNameMap,t,"activation")}function Do(e){if(e==null){let t={};return t.className="linear",t.config={},yy(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},yy(t)}else return e instanceof is?e:yy(e)}function Ay(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var Xw=class extends de.Serializable{},jd=class extends Xw{constructor(e){super();Ay(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=jt([1]);return this.hasL1&&(t=ue(t,we(L(this.l1,Kt(e))))),this.hasL2&&(t=ue(t,we(L(this.l2,Bd(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};jd.className="L1L2";de.registerClass(jd);function LL(e){return Ay(e),new jd({l1:e!=null?e.l1:null,l2:0})}function BL(e){return Ay(e),new jd({l2:e!=null?e.l2:null,l1:0})}var Kw={l1l2:"L1L2"};function It(e){return F1(e)}function Zw(e,t={}){return Od(e,de.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Kw?Kw[e]:e,config:{}};return Zw(n)}else return e instanceof Xw?e:Zw(e)}var xy=class extends rt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ge(e);let n=cr(e);return this.maxValue!=null&&(n=ss(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};xy.className="ReLU";de.registerClass(xy);var by=class extends rt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ge(e);return kd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};by.className="LeakyReLU";de.registerClass(by);var vy=class extends rt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Pt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ft(e.alphaRegularizer),this.alphaConstraint=on(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=At(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s(qt(t),t==="channelsFirst"?et(e,[0,2,3,1]):e))}function Yw(e,t){return j(()=>(qt(t),t==="channelsFirst"?et(e,[0,2,3,4,1]):e))}function WL(e,t,n,s=1,r="valid",a,o=1){return j(()=>{if(a==null&&(a=pr()),qt(a),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=et(e,[0,2,1])),r==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=of(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=mr(i,n)),i})}function Jw(e,t,n,s=[1,1],r="valid",a,o,i=null){return j(()=>{if(a==null&&(a=pr()),qt(a),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Sy(e,a);if(r==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Co.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=et(l,[0,3,1,2])),l})}function VL(e,t,n,s=[1,1,1],r="valid",a,o){return j(()=>{if(a==null&&(a=pr()),qt(a),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=Yw(e,a);if(r==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=n1(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=mr(i,n)),a==="channelsFirst"&&(i=et(i,[0,4,1,2,3])),i})}var Cy=class extends rt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Cy.verifyArgs(t),this.rank=e,bn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xu(t.kernelSize,e,"kernelSize"),this.strides=Xu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Fs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,qt(this.dataFormat),this.activation=Do(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=on(t.biasConstraint),this.biasRegularizer=Ft(t.biasRegularizer),this.activityRegularizer=Ft(t.activityRegularizer),this.dilationRate=Xu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`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 q(`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 q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if($r("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!M1(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:$o(this.activation),useBias:this.useBias,biasInitializer:Lt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},qd=class extends Cy{constructor(e,t){super(e,t);this.kernel=null,qd.verifyArgs(t),this.filters=t.filters,bn(this.filters,"filters"),this.kernelInitializer=Pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=on(t.kernelConstraint),this.kernelRegularizer=Ft(t.kernelRegularizer)}build(e){e=At(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,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 j(()=>{e=Ge(e);let n,s=this.bias==null?null:this.bias.read(),r=Wv(this.activation.getClassName());if(r!=null&&this.rank===2)n=Jw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=WL(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Jw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=VL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=At(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},Xd=class extends qd{constructor(e){super(2,e);Xd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!M1(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Xd.className="Conv2D";de.registerClass(Xd);var Kd=class extends qd{constructor(e){super(3,e);Kd.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 q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Kd.className="Conv3D";de.registerClass(Kd);var Ty=class extends Xd{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Ge(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],c=this.kernelSize[0],u=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=Pr(i,d,c,this.padding),f=Pr(l,p,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,1]));let g=lf(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=et(g,[0,3,1,2])),this.bias!=null&&(g=mr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=At(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=Pr(t[s],i,a,this.padding),t[r]=Pr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ty.className="Conv2DTranspose";de.registerClass(Ty);var Ny=class extends Kd{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Ge(e);if(n.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],c=s[a],u=s[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],y=Pr(l,f,d,this.padding),A=Pr(c,m,p,this.padding),x=Pr(u,g,h,this.padding),b=[r,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,4,1]));let w=W3(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=et(w,[0,4,1,2,3])),this.bias!==null&&(w=mr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=At(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=Pr(t[s],c,o,this.padding),t[r]=Pr(t[r],u,i,this.padding),t[a]=Pr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ny.className="Conv3DTranspose";de.registerClass(Ny);var Qw=class extends qd{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 q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("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 q(`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=Pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ft(t.depthwiseRegularizer),this.depthwiseConstraint=on(t.depthwiseConstraint),this.pointwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ft(t.pointwiseRegularizer),this.pointwiseConstraint=on(t.pointwiseConstraint)}build(e){if(e=At(e),e.length{e=Ge(e);let n;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=et(e,[0,2,3,1])),n=b1(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=mr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=et(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=Lt(this.depthwiseInitializer),e.pointwiseInitializer=Lt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseConstraint),e.pointwiseConstraint=an(this.pointwiseConstraint),e}};Qw.className="SeparableConv";var Ey=class extends Qw{constructor(e){super(2,e)}};Ey.className="SeparableConv2D";de.registerClass(Ey);var pm=class extends qd{constructor(e){super(1,e);pm.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"&&!M1(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};pm.className="Conv1D";de.registerClass(pm);var Ry=class extends rt{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 j(()=>{if(e=Ge(e),this.dataFormat==="channelsLast"){let n=Gf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gf(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}};Ry.className="Cropping2D";de.registerClass(Ry);var $y=class extends rt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,sz(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Ge(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=et(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a]);return et(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$y.className="UpSampling2D";de.registerClass($y);function UL(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=pr()),qt(r);let o=Sy(e,r);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=$u(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}var Dy=class extends Cy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=on(e.depthwiseConstraint),this.depthwiseRegularizer=Ft(e.depthwiseRegularizer)}build(e){if(e=At(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,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 j(()=>{e=Ge(e);let n=UL(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=mr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ar(t,this.kernelSize[0],this.padding,this.strides[0]),a=Ar(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Lt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseRegularizer),e}};Dy.className="DepthwiseConv2D";de.registerClass(Dy);function ek(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function tk(e,t,n,s=!1,r,a,o=!1,i=!1){return j(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(fr(2,l));if(t=et(t,c),a!=null)throw new Ve("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."),r!=null&&(r=pe(pe(r,"bool"),"float32"),r.rank===l-1&&(r=Ht(r,-1)),r=et(r,c)),s&&(t=bs(t,0),r!=null&&(r=bs(r,0)));let u=[],d,p=n,h=t.shape[0],f=Wn(t),m;r!=null&&(m=Wn(r));for(let y=0;ye(A,p));if(r==null)d=x[0],p=x[1];else{let b=j(()=>{let w=m[y],k=xe(xs(w),w),S=ue(L(x[0],w),L(p[0],k)),N=p.map((R,P)=>ue(L(x[1][P],w),L(R,k)));return{output:S,newStates:N}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=Tn(u,1)),[d,g,p]})}var Fr=class extends rt{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new mm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Yt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return fr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ey(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}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;no.shape[o.shape.length-1]),a))throw new q(`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 Yt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new q("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(s=>jt([n,s])):this.states_=[jt([n,this.cell.stateSize])];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>jt([n,s])):this.states_[0]=jt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):ne(this.states_);for(let s=0;sAn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=ek(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Yt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof gr){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Ge(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new q(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=tk((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],u=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?u:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return j(()=>{let t=jt(e.shape);return t=we(t,[1,2]),t=Ld(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?H1(t,[1,n]):t):this.cell.stateSize>1?[H1(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Fr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=yr(s,n);return new e(Object.assign(t,{cell:r}))}};Fr.className="RNN";de.registerClass(Fr);var Zd=class extends rt{},hm=class extends Zd{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,bn(this.units,"units"),this.activation=Do(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Uu([1,Eo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,Eo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(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 j(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0xs(e),rate:this.dropout,training:s})),0xs(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Dr(L(e,a),this.kernel.read()):r=Dr(e,this.kernel.read()),this.bias!=null&&(r=mr(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ue(r,Dr(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:$o(this.activation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};hm.className="SimpleRNNCell";de.registerClass(hm);var _y=class extends Fr{constructor(e){e.cell=new hm(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};_y.className="SimpleRNN";de.registerClass(_y);var fm=class extends Zd{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 q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,bn(this.units,"units"),this.activation=Do(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Do(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Uu([1,Eo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,Eo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(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 j(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0xs(e),rate:this.dropout,training:n,count:3})),0xs(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Py.className="GRU";de.registerClass(Py);var Yd=class extends Zd{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,bn(this.units,"units"),this.activation=Do(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Do(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Uu([1,Eo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,Eo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=At(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 s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends Js{apply(i,l){let c=r.apply([a]),u=new jf().apply([a]),d=r.apply([a*2]);return Zv(Zv(c,u),d)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0xs(e),rate:this.dropout,training:n,count:4})),0xs(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,c,u;0{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fy.className="LSTM";de.registerClass(Fy);var mm=class extends Zd{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o{il(`RNNCell_${s}`,()=>{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=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(yr(r,n));return new e({cells:s})}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 ty(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;aJv(t(),n),o=()=>Wd(a,t,s);return!r||r<=1?An(o().clone()):Array(r).fill(void 0).map(o).map(l=>An(l.clone()))}var GL=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=jt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new q("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(()=>jt(r)):this.states_=[jt(r)];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>jt(r)):this.states_[0]=jt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):ne(this.states_);for(let o=0;oAn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],c=e[i?4:3],u=Ar(l,s[0],r,a[0],o[0]),d=Ar(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};nk.className="ConvRNN2D";var gm=class extends Yd{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,bn(this.filters,"filters"),this.kernelSize=Xu(n,2,"kernelSize"),this.kernelSize.forEach(i=>bn(i,"kernelSize")),this.strides=Xu(s||1,2,"strides"),this.strides.forEach(i=>bn(i,"strides")),this.padding=r||"valid",Fs(this.padding),this.dataFormat=a||"channelsLast",qt(this.dataFormat),this.dilationRate=Xu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>bn(i,"dilationRate"))}build(e){var t;e=At(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;i=new(t=class extends Js{apply(d,p){let h=l.apply([c]),f=As([c]),m=l.apply([c*2]);return G1([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0xs(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(ee,J,Q)=>!J||!J[Q]?ee:L(J[Q],ee),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0xs(r),rate:this.recurrentDropout,training:n,count:o}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),y=l(r,h,3),A=3,[x,b,w,k]=xn(this.kernel.read(),o,A),[S,N,R,P]=this.useBias?xn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,x,S,this.padding),u=this.inputConv(u,b,N,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,k,P,this.padding);let[$,D,T,O]=xn(this.recurrentKernel.read(),o,A);f=this.recurrentConv(f,$),m=this.recurrentConv(m,D),g=this.recurrentConv(g,T),y=this.recurrentConv(y,O);let B=this.recurrentActivation.apply(ue(c,f)),H=this.recurrentActivation.apply(ue(u,m)),z=ue(L(H,a),L(B,this.activation.apply(ue(d,g)))),X=L(this.recurrentActivation.apply(ue(p,y)),this.activation.apply(z));return[X,X,z]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=GL(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Qr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?mr(r,n,this.dataFormat):r}recurrentConv(e,t){return Qr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};gm.className="ConvLSTM2DCell";de.registerClass(gm);var Oy=class extends nk{constructor(e){let t=new gm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Oy.className="ConvLSTM2D";de.registerClass(Oy);var ym=class extends rt{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 s=0;s{this.invokeCallHook(e,t);let n=Ge(e);if(0Jv(n,this.rate,r,this.seed),()=>n,s)}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()}};ym.className="Dropout";de.registerClass(ym);var My=class extends ym{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};My.className="SpatialDropout1D";de.registerClass(My);var zy=class extends rt{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,bn(this.units,"units"),this.activation=Do(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=on(e.kernelConstraint),this.biasConstraint=on(e.biasConstraint),this.kernelRegularizer=Ft(e.kernelRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=At(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=At(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e),s=Wv(this.activation.getClassName()),r;return s!=null?r=Dr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Dr(n,this.kernel.read()),this.bias!=null&&(r=mr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:$o(this.activation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Dense";de.registerClass(zy);var Ly=class extends rt{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=At(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],No(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r{this.invokeCallHook(e,t);let n=Ge(e);return this.activation.apply(n)})}getConfig(){let e={activation:$o(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};By.className="Activation";de.registerClass(By);var Wy=class extends rt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Ge(e),iz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="RepeatVector";de.registerClass(Wy);var Vy=class extends rt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Ge(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Vy.className="Reshape";de.registerClass(Vy);var Uy=class extends rt{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=fr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Yt({ndim:this.dims.length+1})]}computeOutputShape(e){e=At(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return et(Ge(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Uy.className="Permute";de.registerClass(Uy);var Gy=class extends rt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Ge(e),s=-1;return Ad(el(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e),s=-1,r=!0,a=Ad(el(n,this.maskValue),s,r);return L(n,pe(a,n.dtype))})}};Gy.className="Masking";de.registerClass(Gy);var Hy=class extends rt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Nt(e.inputLength))}this.inputDim=e.inputDim,bn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,bn(this.outputDim,"outputDim"),this.embeddingsInitializer=Pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ft(e.embeddingsRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.embeddingsConstraint=on(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Ge(e),el(e,tt(e))):null)}computeOutputShape(e){if(e=At(e),this.inputLength==null)return[...e,this.outputDim];let t=Nt(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s{this.invokeCallHook(e,t);let n=Ge(e);n.dtype!=="int32"&&(n=Uf(n,"int32"));let s=Yv(this.embeddings.read(),G(n,[n.size]));return G(s,At(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Lt(this.embeddingsInitializer),embeddingsRegularizer:It(this.embeddingsRegularizer),activityRegularizer:It(this.activityRegularizer),embeddingsConstraint:an(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Hy.className="Embedding";de.registerClass(Hy);var pl=class extends rt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;rr.length);e.indexOf(null)===-1&&To(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Eo(s);for(let a of e){let o=a.rank;for(let i=0;i1){let c=fr(1,l).concat([0]);n.push(et(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=G(et(G(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(fr(0,o-1));a=et(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Ht(s,0));let n=t[0];for(let s=1;s{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>G1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let o;if(s>r){o=s-r;let l=[];for(let c=0;cs){o=r-s;let l=[];for(let c=0;c0){let l;s>r?l=s+r-3:l=s-1;let c=[];for(let u=l;u"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 Ve("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new q(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Jd(r,e[a].shape.length)):s=[Jd(this.axes,t.shape.length),Jd(this.axes,n.shape.length)],this.normalize&&(t=rm(t,s[0]),n=rm(n,s[1])),HL(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Jd(this.axes,e.length),Jd(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Jy.className="Dot";de.registerClass(Jy);var Qy=class extends rt{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 j(()=>{this.invokeCallHook(e,t);let n=Ge(e);return Wd(()=>ue(Hf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Qy.className="GaussianNoise";de.registerClass(Qy);var eA=class extends rt{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 j(()=>{this.invokeCallHook(e,t);let n=Ge(e);return this.rate>0&&this.rate<1?Wd(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,Hf(n.shape,1,r))},()=>n,t.training||!1):n})}};eA.className="GaussianDropout";de.registerClass(eA);var tA=class extends rt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ge(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Wd(()=>{let r=Ge(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Io(Ou(n),this.rate);l=Uf(l,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-c*i*this.rate,d=ue(L(r,l),L(ue(l,-1),i));return ue(L(d,c),u)},()=>Ge(e),t.training||!1)}return e})}};tA.className="AlphaDropout";de.registerClass(tA);function Qd(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=_3(e,t,n,s,r,a);else if(e.rank===3)o=P3(e,t,n,s,r,a);else if(e.rank===4)o=F3(e,t,n,s,r,a);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function jL(e,t,n,s,r=.001){return j(()=>{let a=gf(e,s),o=a.mean,i=a.variance;return[Qd(e,o,i,n,t,r),o,i]})}function qL(e,t,n,s,r=.001){return j(()=>{let a=gf(e,s),o=a.mean,i=a.variance,l=[];for(let f of fr(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let c=G(o,l),u=G(i,l),d=t==null?null:G(t,l),p=n==null?null:G(n,l);return[Qd(e,c,u,p,d,r),o,i]})}function XL(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),fr(0,e.rank-1))?jL(e,t,n,s,r):qL(e,t,n,s,r)}var nA=class extends rt{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=on(e.betaConstraint),this.gammaConstraint=on(e.gammaConstraint),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer)}build(e){e=At(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Yt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Ge(e),r=s.shape,a=r.length,o=fr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=rl(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,fr(0,a).slice(0,a-1)),d=()=>{if(u){let y=G(this.movingMean.read(),l),A=G(this.movingVariance.read(),l),x=this.center?G(this.beta.read(),l):null,b=this.scale?G(this.gamma.read(),l):null;return Qd(s,y,A,x,b,this.epsilon)}else return Qd(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[p,h,f]=XL(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(y,A,x)=>{j(()=>{let b=1-x,w=y.read(),k=L(xe(w,A),b);y.write(xe(w,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Lt(this.betaInitializer),gammaInitializer:Lt(this.gammaInitializer),movingMeanInitializer:Lt(this.movingMeanInitializer),movingVarianceInitializer:Lt(this.movingVarianceInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer),betaConstraint:an(this.betaConstraint),gammaConstraint:an(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};nA.className="BatchNormalization";de.registerClass(nA);var sA=class extends rt{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=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=At(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==To(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Ge(e),s=n.shape,r=s.length;return j(()=>{let a=!0,{mean:o,variance:i}=gf(n,this.axis,a),l=rl(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r&&this.axis!==[r-1]?G(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f{if(e.rank!==4)throw new q(`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 q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=pr()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],ur(e,s)})}var rA=class extends rt{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?pr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=At(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>KL(Ge(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};rA.className="ZeroPadding2D";de.registerClass(rA);function Am(e,t,n,s,r,a){return j(()=>{qt(r),Hv(a),Fs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=pr()),a==null&&(a="max"),e=Sy(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Cd(e,t,n,i):o=bd(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}function sk(e,t,n,s,r,a){return j(()=>{qt(r),Hv(a),Fs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=pr()),a==null&&(a="max"),e=Yw(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=f1(e,t,n,i):o=J2(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,4,1,2,3])),o})}var rk=class extends rt{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 q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(bn(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 q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Fs(this.padding),this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){e=At(e);let t=Ar(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Ld(Ge(e),2);let n=this.poolingFunction(Ge(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return dt(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},aA=class extends rk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),Am(e,t,n,s,r,"max")}};aA.className="MaxPooling1D";de.registerClass(aA);var oA=class extends rk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),Am(e,t,n,s,r,"avg")}};oA.className="AveragePooling1D";de.registerClass(oA);var ak=class extends rt{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 q(`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];bn(this.poolSize,"poolSize"),bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),Fs(this.padding),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ar(t,this.poolSize[0],this.padding,this.strides[0]),n=Ar(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 j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(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}},iA=class extends ak{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),Am(e,t,n,s,r,"max")}};iA.className="MaxPooling2D";de.registerClass(iA);var lA=class extends ak{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),Am(e,t,n,s,r,"avg")}};lA.className="AveragePooling2D";de.registerClass(lA);var ok=class extends rt{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 q(`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];bn(this.poolSize,"poolSize"),bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),Fs(this.padding),this.inputSpec=[new Yt({ndim:5})]}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ar(t,this.poolSize[0],this.padding,this.strides[0]),n=Ar(n,this.poolSize[1],this.padding,this.strides[1]),s=Ar(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(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}},uA=class extends ok{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),sk(e,t,n,s,r,"max")}};uA.className="MaxPooling3D";de.registerClass(uA);var cA=class extends ok{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),sk(e,t,n,s,r,"avg")}};cA.className="AveragePooling3D";de.registerClass(cA);var ik=class extends rt{constructor(e){super(e);this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=yr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?YL:e.mergeMode,ZL(this.mergeMode),e.weights)throw new Ve("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 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o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Td(I("x",e,t,n),o,i)]}case"Sum":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[we(I("x",e,t,n),o,i)]}case"All":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[rf(I("x",e,t,n),o,i)]}case"Any":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Ad(I("x",e,t,n),o,i)]}case"ArgMax":{let o=I("axis",e,t,n);return[_s(I("x",e,t,n),o)]}case"ArgMin":{let o=I("axis",e,t,n);return[G2(I("x",e,t,n),o)]}case"Prod":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[yf(I("x",e,t,n),o,i)]}case"Cumsum":{let o=I("axis",e,t,n),i=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[cf(I("x",e,t,n),o,i,l)]}case"Bincount":let s=I("x",e,t,n),r=I("weights",e,t,n),a=I("size",e,t,n);return[Q2(s,r,a)];case"DenseBincount":{let o=I("x",e,t,n),i=I("weights",e,t,n),l=I("size",e,t,n),c=I("binaryOutput",e,t,n);return[V3(o,i,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},AV=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),r=I("axis",e,t,n),a=I("tensors",e,t,n);return a=a.slice(0,s),[kt(a,r)]}case"Gather":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[Yi(s,pe(r,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),r=I("batchDims",e,t,n),a=I("x",e,t,n),o=I("indices",e,t,n);return[Yi(a,pe(o,"int32"),s,r)]}case"Reverse":{let s=I("dims",e,t,n),r=[];for(let o=0;o{let s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,o=dt(r[0]).shape,i=r.map(l=>{let c=v.arraysEqual(l.shape,a);if(!c&&!v.arraysEqual(dt(l).shape,o))throw new Error("the input tensors shape does not match");return c?l:G(l,a)});return[Tn(i,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return Wn(r,s)}case"Tile":{let s=I("reps",e,t,n);return[Ps(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return xn(a,r,s)}case"ScatterNd":{let s=I("indices",e,t,n),r=I("values",e,t,n),a=I("shape",e,t,n);return[lv(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[uv(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),o=I("defaultValue",e,t,n);return[N1(s,a,r,a.dtype===o.dtype?o:pe(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},xV=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:o}=Pd.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,o]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Pd.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Pd.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Pd.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 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s=I("axis",e,t,n);return[Ht(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[dt(I("x",e,t,n),s)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[m1(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[ur(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[Nd(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[vd(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[s1(I("x",e,t,n),s,r)]}case"BroadcastTo":return[Eu(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[O3(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function qk(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return j(()=>JW(a,o,i));case"basic_math":return j(()=>QW(a,o,i));case"control":return aV(a,o,i);case"convolution":return j(()=>oV(a,o,i));case"creation":return j(()=>iV(a,o,i));case"dynamic":return lV(a,o,i);case"evaluation":return j(()=>uV(a,o,i));case"image":return j(()=>hV(a,o,i));case"graph":return j(()=>cV(a,o,i));case"logical":return j(()=>fV(a,o,i));case"matrices":return j(()=>mV(a,o,i));case"normalization":return j(()=>gV(a,o,i));case"reduction":return j(()=>yV(a,o,i));case"slice_join":return j(()=>AV(a,o,i));case"sparse":return j(()=>xV(a,o,i));case"spectral":return j(()=>bV(a,o,i));case"string":return j(()=>vV(a,o,i));case"transformation":return j(()=>wV(a,o,i));case"hash_table":return pV(a,o,i,s);case"custom":let l=vk(a.op);if(l&&l.customExecutor)return l.customExecutor(new YW(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 v.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Xk=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,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;tt.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 Kk(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,c=Object.keys(e).map(p=>vs(p)[0]),u=[];s!=null&&(u=s.map(p=>vs(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((Zk(p)||TV(p)||NV(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function kV(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>vs(u)[0]).map(u=>e.nodes[u]),i=e.initNodes;o.forEach(u=>{s.has(u.name)&&a.push(u)}),e.weights.forEach(u=>{s.has(u.name)&&a.push(u)}),i!=null&&i.forEach(u=>{s.has(u.name)&&a.push(u)});let l=new Set,c=[];for(;a.length>0;){let u=a.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return c}var IV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],SV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],CV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Zk(e){return IV.indexOf(e.op)>=0}function TV(e){return SV.indexOf(e.op)>=0}function NV(e){return CV.indexOf(e.op)>=0}var PA=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},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 PA(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(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Kk(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return kV(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 s=n.map(u=>this.graph.nodes[vs(u)[0]]),r=t.map(u=>vs(u)[0]),a=r.map(u=>this.graph.nodes[u]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},c={};return j(()=>{let u=new Xk(this.weightMap,l,c,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=vs(f),y=[];y[g]=e[f],d[m]=y});let p=this.getFrozenTensorIds(d),h={};for(let f=0;fUn(f,d,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,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 l=RW(i.name,n,s);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!r.has(c.id)){let u=o[c.id];u===1?(c.dispose(),delete o[c.id]):u!=null&&o[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let a=new Xk(this.weightMap,s,r,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>Un(d,o,a)),l=i.map(d=>d.id),c=Object.keys(e).map(d=>e[d].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(h=>{h&&!h.kept&&!h.isDisposed&&!u.has(h.id)&&h.dispose()})}),this.parent==null&&a.dispose(u),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(A=>this.graph.nodes[vs(A)[0]]),o=n.map(A=>vs(A)[0]),i=o.map(A=>this.graph.nodes[A]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:d}=Kk(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(A=>{let[x,b]=vs(A),w=[];w[b]=e[A],h[x]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let A=this.processStack(a,p,t,h,g,m,o,f,l);await Promise.all(A)}u==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=i.filter(A=>!Zk(A)&&!Un(A.name,h,t)).map(A=>A.name);if(y.length>0){let A="";throw u!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${A}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&I("isConstant",u.node,s,n)&&([d]=oa(u.node.name,n)),s[u.node.name]==null){let p=qk(u.node,s,n,this._resourceManager);d||([d]=oa(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=oa(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=vs(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=vs(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=vs(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},EV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},RV="?tfjs-format=file",$V="model.json",Yk=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new EV}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=es.browserHTTPRequest(e,this.loadOptions);else{let t=es.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(es.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 s=es.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new PA(Wk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Wk.Instance.transformGraph(e.modelInitializer);this.initializer=new PA(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=es.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 Ke)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ut(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${$V}${RV}`);let n=new Yk(e,t);return await n.load(),n}var DV="3.9.0",Jk={};Le(Jk,{CSVDataset:()=>c7,Dataset:()=>Zu,FileDataSource:()=>y7,TextLineDataset:()=>i7,URLDataSource:()=>A7,array:()=>tU,csv:()=>pU,func:()=>hU,generator:()=>fU,microphone:()=>gU,version_data:()=>yU,webcam:()=>mU,zip:()=>nU});var _V=Jo(h5()),PV=Jo(h5());function FV(e,t){return wm(e,t)}function wm(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(Ku(e)){let a=Array.isArray(e)?[]:{};s.add(e);for(let o in e){let i=e[o],l=wm(i,t,n,s);a[o]=l}return s.delete(e),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function OV(e,t=e7){return Qk(e,t)}function Qk(e,t,n=new Set){let s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Ku(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(c=>c[o]),l=Qk(i,t,n);a[o]=l}return n.delete(s),a}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return r.value}function e7(e){return e===null?null:Ku(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function t7(e,t){let n=new Map;wm(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let o=await a;n.set(r,o)}}return wm(e,t,n)}function Ku(e){let t=!1;if(Z().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=f5();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ke)&&!(e instanceof Promise)&&!t)}function MV(e){return e==null||zV(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ke||v.isTypedArray(e)}function zV(e){return e===null||typeof e!="object"&&typeof e!="function"}function LV(e){return FV(e,BV)}function BV(e){return e instanceof Ke?{value:e.clone(),recurse:!1}:Ku(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var n7=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}},FA=class extends n7{constructor(){super(FA.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 s=0;st===!0)}rowMajorBatch(e,t=!0){return new XV(this,e,t)}columnMajorBatch(e,t=!0,n=e7){return this.rowMajorBatch(e,t).map(r=>OV(r,n))}concatenate(e,t){return new a7(s7([this,e]),t)}take(e){return e<0||e==null?this:new qV(this,e)}skip(e){return e<0||e==null?this:new jV(this,e)}prefetch(e){return new o7(this,e)}shuffle(e,t){return new eU(this,e,t)}serial(){return new HV(this)}},UV=class extends vn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:LV(e),done:!1}}},GV=class extends vn{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: ${e.message}`,e}}},HV=class extends vn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},jV=class extends vn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},XV=class extends vn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},KV=class extends vn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;ne(e.value)}}},ZV=class extends vn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ar.getTensorsInContainer(e.value),n=this.transform(e.value),s=ar.getTensorsInContainer(n);for(let r of t)ar.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},YV=class extends vn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},r7=class extends vn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ar.getTensorsInContainer(e.value),n=await this.transform(e.value),s=ar.getTensorsInContainer(n);for(let r of t)ar.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},MA=class extends vn{constructor(){super();this.outputQueue=new FA,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}}},JV=class extends MA{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ar.getTensorsInContainer(e.value),n=this.transform(e.value),s=ar.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)ar.isTensorInList(r,s)||r.dispose();return!0}},a7=class extends vn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries 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t7(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Po.FAIL:throw new Error(`Zipped streams should have the same length. 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Z().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new d7(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 s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[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(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({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((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),nn(n,t)}},p7=class extends vn{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=Zt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=dr([a,r,i,o],[1,4])}else this.cropBox=dr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Z().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new p7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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u=E.computePool3DInfo(a.shape,o,i,1,l,c),d=u.strideDepth,p=u.strideHeight,h=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,y=u.dilationDepth,A=u.dilationHeight,x=u.dilationWidth,b=u.effectiveFilterDepth,w=u.effectiveFilterHeight,k=u.effectiveFilterWidth,S=b-1-u.padInfo.front,N=k-1-u.padInfo.left,R=w-1-u.padInfo.top,P=We(a.shape,"float32"),$=1/(f*m*g),D=n.bufferSync(r);for(let T=0;T=u.outDepth||Math.floor(K)!==K))for(let oe=0;oe=u.outHeight||Math.floor(ce)!==ce))for(let he=0;he=u.outWidth||Math.floor(Ae)!==Ae)continue;Q+=D.get(T,K,ce,Ae,O)}}}P.set(Q*$,T,B,H,z,O)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var aH={kernelName:gh,backendName:"cpu",kernelFunc:rH};function oH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Ne([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=u.strideHeight,p=u.strideWidth,h=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,g=u.dilationWidth,y=u.effectiveFilterHeight,A=u.effectiveFilterWidth,x=A-1-u.padInfo.left,b=y-1-u.padInfo.top,w=We(o.shape,"float32"),k=1/(h*f),S=n.data.get(r.dataId).values,N=We(r.shape,"float32",S);for(let R=0;R=u.outHeight||Math.floor(z)!==z))for(let X=0;X=u.outWidth||Math.floor(ee)!==ee)continue;B+=N.get(R,z,ee,P)}}w.set(B*k,R,$,D,P)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var iH={kernelName:mh,backendName:"cpu",kernelFunc:oH};function lH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;v.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires 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tn(p.outShape,r.dtype),w=v.computeStrides(r.shape),k=v.computeStrides(a.shape),S=w[0],N=x?w[1]:w[2],R=x?w[2]:1,P=x?1:w[1],$=b.strides[0],D=x?b.strides[1]:b.strides[2],T=x?b.strides[2]:1,O=x?1:b.strides[1],B=n.data.get(r.dataId).values,H=n.data.get(a.dataId).values,z=b.values;for(let X=0;X=p.inHeight)continue;let he=oe*k[0],Ae=ee+ce*N;for(let Ie=0;Ie=p.inWidth)continue;let wt=he+Ue*k[1],mt=Ae+ze*R,gt=wt;for(let pt=0;pt=c.inDepth)continue;let X=H*R[0],ee=$+z*N[1];for(let J=0;J=c.inHeight)continue;let ce=X+K*R[1],he=ee+oe*N[2];for(let Ae=0;Ae=c.inWidth)continue;let ze=ce+Oe*R[2],wt=he+Ue*c.inChannels,mt=ze;for(let gt=0;gtMath.cos(e)),PH={kernelName:$a,backendName:"cpu",kernelFunc:_H},FH=xt(Da,e=>Math.cosh(e)),OH={kernelName:Da,backendName:"cpu",kernelFunc:FH};function MH(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,[u,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,y=We([f,m,g,h],"float32"),A=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,w=v.computeStrides(r.shape),k=v.computeStrides(y.shape);for(let S=0;S=u)continue;let O=m>1?($-R)*(d-1)/(m-1):0,B=g>1?(D-P)*(p-1)/(g-1):0;for(let H=0;H1?R*(d-1)+H*O:.5*(R+$)*(d-1);if(z<0||z>d-1){for(let X=0;X1?P*(p-1)+Q*B:.5*(P+D)*(p-1);if(te<0||te>p-1){for(let he=0;he1?P*(p-1)+X*B:.5*(P+D)*(p-1);if(ee<0||ee>p-1){for(let te=0;tey+f-A-1:(y,A)=>y+A;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. 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te=v.locToIndex([B,H,X,J],D,v.computeStrides(P));T[te]=Q}}}return{dataId:l.write(v.toTypedArray(T,s.dtype),P,s.dtype),shape:P,dtype:s.dtype}}},QH={kernelName:Ch,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,c=t,u=v.toNestedArray(s.shape,c.data.get(s.dataId).values),d=v.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:w,filterWidth:k,dilationHeight:S,dilationWidth:N,outShape:R}=E.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);v.assert(a.rank===R.length,()=>`Error in ${Ch}, dy must have the same rank as output ${R.length}, but got ${a.rank}`);let P=v.toNestedArray(R,c.data.get(a.dataId).values),$=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T=0&&K=0&&ceee&&(ee=he,J=te,Q=oe)}}}$[J][Q][X]+=P[T][O][H][X]}}}return{dataId:c.write(v.toTypedArray($,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},ej={kernelName:Sh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,c=t,u=v.toNestedArray(s.shape,c.data.get(s.dataId).values),d=v.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:w,filterWidth:k,dilationHeight:S,dilationWidth:N,outShape:R}=E.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);v.assert(a.rank===R.length,()=>`Error in ${Sh}, dy must have the same rank as output ${R.length}, but got ${a.rank}`);let P=v.toNestedArray(R,c.data.get(a.dataId).values),$=v.makeZerosNestedTypedArray(s.shape,s.dtype);for(let T=0;T=0&&K=0&&ceee&&(ee=he,J=K,Q=ce)}}}$[T][J][Q][X]+=P[T][O][H][X]}}}return{dataId:c.write(v.toTypedArray($,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function ap(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ne(r,"sum");let i;r.dtype==="bool"?i=Fo({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):i=Or({inputs:{x:r},backend:n});let l=i.shape.length,c=v.parseAxisParam(a,i.shape),u=E.getAxesPermutation(c,l),d=c,p=i;u!=null&&(p=Os({inputs:{x:i},backend:n,attrs:{perm:u}}),d=E.getInnerMostAxes(d.length,l)),E.assertAxesAreInnerMostDims("sum",d,p.shape.length);let[h,f]=E.computeOutAndReduceShapes(p.shape,d),m=E.upcastType(p.dtype,"int32"),g=Sm(n,h,m),y=v.sizeFromShape(f),A=n.data.get(g.dataId).values,x=n.data.get(p.dataId).values;for(let b=0;b=0&&(p=ap({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var sj={kernelName:Zc,backendName:"cpu",kernelFunc:nj};function rj(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;Ne([s,r],"eluGrad");let a=new Float32Array(v.sizeFromShape(r.shape)),o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values;for(let l=0;l=1?a[l]=i[l]:a[l]=i[l]*(c+1)}return n.makeTensorInfo(r.shape,"float32",a)}var aj={kernelName:Th,backendName:"cpu",kernelFunc:rj},oj=E.ERF_P,ij=E.ERF_A1,lj=E.ERF_A2,uj=E.ERF_A3,cj=E.ERF_A4,dj=E.ERF_A5,pj=xt(su,e=>{let t=Math.sign(e),n=Math.abs(e),s=1/(1+oj*n);return t*(1-((((dj*s+cj)*s+uj)*s+lj)*s+ij)*s*Math.exp(-n*n))}),hj={kernelName:su,backendName:"cpu",kernelFunc:pj};function Nm(e){let{inputs:t,backend:n,attrs:s}=e,{input:r}=t,{dim:a}=s,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),Et({inputs:{x:r},backend:n,attrs:{shape:i}})}var fj={kernelName:li,backendName:"cpu",kernelFunc:Nm},mj=Jt((e,t)=>e/t),YA=wn(Pa,mj),JA={kernelName:Pa,backendName:"cpu",kernelFunc:YA};function hI(e,t,n){let s=e.shape,r=s[0],a=s[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,c=[r,a],u=v.sizeFromShape(c),d=v.getTypedArrayFromDType("float32",u),p=v.getTypedArrayFromDType("float32",u);for(let g=0;g{let{image:s}=e,r=n,a=v.getTypedArrayFromDType(s.dtype,v.sizeFromShape(s.shape)),[o,i,l,c]=s.shape,u=r.data.get(s.dataId).values;for(let p=0;p=0&&xMath.floor(e/t)),Sj=wn(za,Ij,null,"int32"),Cj={kernelName:za,backendName:"cpu",kernelFunc:Sj};function Tj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=dI({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=sp({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=KA(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var Nj={kernelName:go,backendName:"cpu",kernelFunc:Tj};function Ej(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=pI({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=sp({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=KA(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var Rj={kernelName:yo,backendName:"cpu",kernelFunc:Ej};function $j(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=v.sizeFromShape(s.shape),o=r.shape,i=o[o.length-1],[l,c,u,d]=E.prepareAndValidate(s,r);if(c===0)return n.makeTensorInfo(l,s.dtype,[]);let p=n.data.get(r.dataId).values,h=n.bufferSync(s),f=E7(p,h,s.dtype,c,i,u,d,s.shape,a);return n.makeTensorInfo(l,s.dtype,f.values)}var Dj={kernelName:pi,backendName:"cpu",kernelFunc:$j};function _j(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s;Ne([r,a],"gatherV2");let l=i;i==null&&(l=0);let c=v.sizeFromShape(a.shape),u=v.parseAxisParam(o,r.shape)[0],d=E.segment_util.collectGatherOpShapeInfo(r,a,u,l),p=Et({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Et({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,c/d.batchSize]}}),f=[d.batchSize,d.outerSize,c/d.batchSize,d.sliceSize],m=n.bufferSync(h),g=n.bufferSync(p),y=R7(g,m,f);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.makeTensorInfo(d.outputShape,y.dtype,y.values)}var Pj={kernelName:di,backendName:"cpu",kernelFunc:_j};function Fj(e){let{inputs:t,backend:n}=e,{input:s}=t,r=v.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=Et({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=hI(i,!0,n),c=Et({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var Oj={kernelName:Eh,backendName:"cpu",kernelFunc:Fj},Mj=xt(au,e=>Number.isFinite(e)?1:0,"bool"),zj={kernelName:au,backendName:"cpu",kernelFunc:Mj},Lj=xt(ou,e=>Math.abs(e)===1/0?1:0,"bool"),Bj={kernelName:ou,backendName:"cpu",kernelFunc:Lj},Wj=xt(iu,e=>Number.isNaN(e)?1:0,"bool"),Vj={kernelName:iu,backendName:"cpu",kernelFunc:Wj};function Uj(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=F7(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var Gj={kernelName:Rh,backendName:"cpu",kernelFunc:Uj},Hj=xt(lu,e=>Math.log1p(e)),jj={kernelName:lu,backendName:"cpu",kernelFunc:Hj},qj=Jt((e,t)=>e&&t),Xj=wn(yi,qj,null,"bool"),Kj={kernelName:yi,backendName:"cpu",kernelFunc:Xj},Zj=xt(uu,e=>e?0:1,"bool"),Yj={kernelName:uu,backendName:"cpu",kernelFunc:Zj},Jj=Jt((e,t)=>e||t),Qj=wn(Jc,Jj,null,"bool"),eq={kernelName:Jc,backendName:"cpu",kernelFunc:Qj};function tq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s;Ne(r,"LRN");let c=r.shape[3],u=c-1,d=n.data.get(r.dataId).values,p=v.sizeFromShape(r.shape),h=new Float32Array(p);function f(m){let g=m%c,y=m-g+Math.max(0,g-a),A=m-g+Math.min(g+a,u),x=0;for(;y<=A;y++){let b=d[y];x+=b*b}return x}for(let m=0;m`Error in maxPool: Either strides or dilations must be 1. 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s=this.gl;_m(s,e,this.framebuffer),this.debug&&cp(s),this.outputTexture=e,ke(s,()=>s.viewport(0,0,t,n)),ke(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),ke(this.gl,()=>this.gl.scissor(e,t,n,s))}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 EZ(e){let t=0;for(;t`${e}.${n}`)}function Hn(e,t){return t===1?[e]:A4(e,t)}function fY(e,t){if(e===1)return"rc";let n="";for(let s=0;s ${t[0]}`;let s="";for(let r=e-2;r= ${t[r]}`,r= ${t}; bool rEdge = rp1 >= ${n}; `}function xY(e,t){let n=e.length,s=gY(n,t);return n===1?`getA(rc), rc + 1 >= ${e[0]} ? 0. : getA(rc + 1), 0, 0`:`getA(${s[0]}), cEdge ? 0. : getA(${s[1]}), rEdge ? 0. : getA(${s[2]}), rEdge || cEdge ? 0. : getA(${s[3]})`}var x4=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=` ${r} ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); result[${s}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${s>0?"}":""} `}this.userCode=` ${bY(t,this.enableShapeUniforms)} ${this.enableShapeUniforms?lx():ix(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 bY(e,t){return` 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Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Nn.PACKED_2X2_FLOAT32:Nn.UNPACKED_FLOAT32:e?Nn.PACKED_2X2_FLOAT16:Nn.UNPACKED_FLOAT16}function v4(e,t){if(e===Ms.UPLOAD)return Nn.PACKED_2X2_FLOAT32;if(e===Ms.RENDER||e==null)return IY(t);if(e===Ms.DOWNLOAD||e===Ms.PIXELS)return Nn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function w4(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Mo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},br="if (isnan(x)) return x;",SY="return x;",k4="return abs(x);",CY="return (x >= 0.0) ? x : (exp(x) - 1.0);",TY=br+` return (x < 0.0) ? 0.0 : x; `,NY=br+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Bm="return x;",EY="return 1.0 / (1.0 + exp(-1.0 * x));",RY="return x;",$Y=` vec4 result; result.r = (x.r >= 0.0) ? x.r : 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Error("WebGL is not supported on this device");if(e==null){let t=Mr(Z().getNumber("WEBGL_VERSION"));this.binaryCache=LY(Z().getNumber("WEBGL_VERSION")),this.gpgpu=new Lm(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 vY(this.gpgpu),this.numMBBeforeWarning=VY(),this.texData=new Vc(this,ts())}nextDataId(){return ic.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Z().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Z().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. 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t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new oc(s,Bm):h=new Mo(s,Bm);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Z().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(a!=="complex64"&&Z().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...$m(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];u=E.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(s);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let h=this.gpgpu.gl;ke(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ts().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.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 Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Z().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 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o.values=v.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. 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int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${s}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${s}; i++) { int inIdx = ${i}; float candidate = getA(batch, inIdx); if (candidate ${o} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}},$J=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=St(i),c=Hn("coords",i),u,d;if(a===1){d=i+1;let S=St(d);u=` ${S} sourceLocR = ${S}(${c.join()}, 0); ++${c[i-1]}; ${S} sourceLocG = ${S}(${c.join()}, 0); ++${c[i-2]}; ${S} sourceLocA = ${S}(${c.join()}, 0); --${c[i-1]}; ${S} sourceLocB = ${S}(${c.join()}, 0); --${c[i-2]};`}else d=i,u=` ${l} sourceLocR = coords; 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const ivec2 pads = ivec2(${p}, ${h}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; float avgValue = 0.0; for (int wR = 0; wR < ${u}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC += ${c}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float value = getX(batch, xR, xC, d); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${S} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(a/4)*4,w=a%4,k=` if (${f}) { avgValue += dot(values, ones); } else { minMaxValue = ${A}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${o}, ${i}); const ivec2 pads = ivec2(${p}, ${h}); const float initializationValue = ${y}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xR, int xC, int d) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${y}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${u}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${b}; wC += 4) { int xC = xCCorner + wC * ${c}; vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), getValue(batch, xR, xC + 3 * ${c}, d) ); ${k} } int xC = xCCorner + ${b}; if (${w===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${k} } else if (${w===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); ${k} } else if (${w===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), initializationValue ); ${k} } } setOutput(${x}); } `}},yx=class{constructor(e,t,n,s=!1,r=!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,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),n){let R=">=";this.userCode=` const ivec3 strides = ivec3(${o}, ${i}, ${l}); const ivec3 pads = ivec3(${m}, ${g}, ${y}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${p}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC += ${d}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float value = getX(batch, xD, xR, xC, ch); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${R} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} + wR * ${f} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let k=Math.floor(a/4)*4,S=a%4,N=` if (${A}) { avgValue += dot(values, ones); } else { minMaxValue = ${b}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${o}, ${i}, ${l}); const ivec3 pads = ivec3(${m}, ${g}, ${y}); const float initializationValue = ${x}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xD, int xR, int xC, int ch) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${x}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${p}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${k}; 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) ); ${N} } int xC = xCCorner + ${k}; if (${S===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${N} } else if (${S===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${d}, ch), initializationValue, initializationValue ); ${N} } else if (${S===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 ); ${N} } } setOutput(${w}); } } `}};function JJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tc(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Is({inputs:{x:r},backend:n});let d=new fp(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var QJ={kernelName:Sa,backendName:"webgl",kernelFunc:JJ};function eQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new yx(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var tQ={kernelName:Hc,backendName:"webgl",kernelFunc:eQ},nQ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.top,u=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${c}, ${u}); const float avgMultiplier = float(${d}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${i}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${s}.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 >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},sQ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=` const ivec3 pads = ivec3(${h}, ${f}, ${m}); const float avgMultiplier = float(${g}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${u}; wD += ${i}) { float dyD = float(dyDCorner + wD) / ${r}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${d}; wR += ${l}) { float dyR = float(dyRCorner + wR) / ${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 += ${c}) { 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 rQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new sQ(p);return n.runWebGLProgram(h,[r],o.dtype)}var aQ={kernelName:gh,backendName:"webgl",kernelFunc:rQ};function oQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;tc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=new nQ(u);return n.runWebGLProgram(d,[r],o.dtype)}var iQ={kernelName:mh,backendName:"webgl",kernelFunc:oQ};function lQ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return jm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var uQ={kernelName:Ca,backendName:"webgl",kernelFunc:lQ},cQ=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),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))); } `}},dQ=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),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); } `}},pQ=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[s,r,a],u=null;o!=null&&(u=o.shape,c.push(o));let d=null;i!=null&&(d=i.shape,c.push(i));let p=Z().getBool("WEBGL_PACK_NORMALIZATION")?new dQ(s.shape,r.shape,a.shape,u,d,l):new cQ(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},hQ={kernelName:La,backendName:"webgl",kernelFunc:pQ},fQ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=St(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=mQ(this.rank),s,r=e.map((a,o)=>`sourceLoc.${Ax[o]} = start[${o}] + coords.${Ax[o]};`);s=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` `)} `,this.userCode=` void main() { ${s} setOutput(getSource(${n})); } `}},Ax=["x","y","z","w","u","v"];function mQ(e){if(e===1)return"sourceLoc";if(e<=6)return Ax.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var gQ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=St(this.rank),n=Hn("coords",this.rank),s=Hn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=` result.x = ${a}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${s[this.rank-1]}; result.y = ${a}; --${s[this.rank-1]}; } `,i=this.rank===1?"":` --${n[this.rank-1]}; if (++${n[this.rank-2]} < ${e[this.rank-2]}) { ++${s[this.rank-2]}; result.z = ${a}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${s[this.rank-1]}; 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= to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${d}; wR++) { int xR = xRCorner + wR * ${c}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${p}; wC++) { int xC = xCCorner + wC * ${u}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${m}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), getX(batch, xR, xC, d1 + 2), getX(batch, xR, xC, d1 + 3) ); dotProd += dot(xValues, wValues); } else { vec4 xValues = vec4( getX(batch, d1, xR, xC), getX(batch, d1 + 1, xR, xC), getX(batch, d1 + 2, xR, xC), getX(batch, d1 + 3, xR, xC) ); dotProd += dot(xValues, wValues); } } if (${f===1}) { if (${m}) { dotProd += getX(batch, xR, xC, ${h}) * getW(wR, wC, ${h}, d2); } else { dotProd += getX(batch, ${h}, xR, xC) * getW(wR, wC, ${h}, d2); } } else if (${f===2}) { vec2 wValues = vec2( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2) ); if (${m}) { vec2 xValues = vec2( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${h}, xR, xC), getX(batch, ${h} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${f===3}) { vec3 wValues = vec3( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2), getW(wR, wC, ${h} + 2, d2) ); if (${m}) { vec3 xValues = vec3( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1), getX(batch, xR, xC, ${h} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${h}, xR, xC), getX(batch, ${h} + 1, xR, xC), getX(batch, ${h} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${w} ${b} setOutput(result); } `}},UQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${a}, ${o}); const ivec3 pads = ivec3(${t}, ${n}, ${s}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d2 = coords.u; ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xFCorner = xFRCCorner.x; int xRCorner = xFRCCorner.y; int xCCorner = xFRCCorner.z; // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; for (int wF = 0; wF < ${u}; wF++) { int xF = xFCorner + wF * ${i}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR++) { int xR = xRCorner + wR * ${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 < ${h}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${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); } `}},GQ=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=Ls(this.outputShape.length);let{dataFormat:n}=t,s=Gn(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let c=0;c<=1;c++)for(let u=0;u<=1;u++)l+=` blockIndex = rc.y + ${u}; pos = rc.x + ${c}; ${i} offsetY = int(blockIndex / outWidth) * stride[0] - pad[0]; d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow); if(d0 < inputShape[${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[${o}] && d1 >= 0) { ch = imod(pos, inChannels); if (${r}) { innerDims = vec2(d1, ch); result[${c*2+u}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${c*2+u}] = getChannel( getA(ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec2 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${l} ${s.output} = result; } `}};function q4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=s.texData.get(e.dataId),u=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((d===1||p===1)&&u>O4)&&c.isPacked&&h&&c.texture!=null&&l[2]%2!=0&&v.arraysEqual(c.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(dp(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let S=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let N=jm({a:w,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=s.texData.get(N.dataId);v.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=k,R.shape=n.outShape,g=Is({inputs:{x:N},backend:s}),g.shape=n.outShape,y.push(N)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=be({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=jm({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=be({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),y.push(w),y.push(k),y.push(S)}for(let b of y)s.disposeIntermediateTensorInfo(b);return g}function X4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*c*u,g=p*d,y=[m,g],A=!0,x=!1,b=[],w=be({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let S=new GQ(y,n),N=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(S,[w],"float32",N),P=be({inputs:{x:R},backend:s,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(P);let $=r!=null,D=a!=null,T=i==="leakyrelu",O=i?Um(i,!0):null,B=new $4(P.shape,k.shape,[1,g,n.outChannels],A,x,$,O,D,T),H=[P,k];if(r&&H.push(r),D&&H.push(a),T){let J=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(J),b.push(J)}let z=s.runWebGLProgram(B,H,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],ee=be({inputs:{x:z},backend:s,attrs:{shape:X}});b.push(z);for(let J of b)s.disposeIntermediateTensorInfo(J);return ee}function HQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=q4({x:r,filter:a,convInfo:p,backend:n});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=X4({x:r,filter:a,convInfo:p,backend:n});else{let m=new j4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=be({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var jQ={kernelName:Ea,backendName:"webgl",kernelFunc:HQ},qQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=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} - ${s}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } if (${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); } `}},XQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,c=a?2:3,u=a?3:1;this.userCode=` const ivec2 pads = ivec2(${o}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${u}]; ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - 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) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { if (${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); } `}},KQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=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} - ${r}; 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 * ${s} - ${o}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},ZQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${i}, ${l}, ${c}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${t}; wF++) { float dyF = float(dyFCorner + wF) / ${r}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); int wFPerm = ${t} - 1 - wF; for (int wR = 0; wR < ${n}; wR++) { float dyR = float(dyRCorner + wR) / ${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 < ${s}; 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 = ${s} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function YQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),h=new qQ(p);return n.runWebGLProgram(h,[r,a],"float32")}var JQ={kernelName:Ah,backendName:"webgl",kernelFunc:YQ};function QQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=new XQ(p);return n.runWebGLProgram(h,[r,a],"float32")}var eee={kernelName:Ra,backendName:"webgl",kernelFunc:QQ};function tee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeConv3DInfo(r.shape,a.shape,o,l,i),u=new UQ(c);return n.runWebGLProgram(u,[r,a],"float32")}var nee={kernelName:Xc,backendName:"webgl",kernelFunc:tee};function see(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,c=E.computeConv3DInfo(r.shape,l,o,1,i),u=new KQ(c);return n.runWebGLProgram(u,[r,a],"float32")}var ree={kernelName:xh,backendName:"webgl",kernelFunc:see};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=E.computeConv3DInfo(l,a.shape,i,1,o),u=new ZQ(c);return n.runWebGLProgram(u,[r,a],"float32")}var oee={kernelName:bh,backendName:"webgl",kernelFunc:aee},iee=R4+` return cos(x); `,lee=it({opSnippet:iee}),uee={kernelName:$a,backendName:"webgl",kernelFunc:lee},cee=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,dee=it({opSnippet:cee}),pee={kernelName:Da,backendName:"webgl",kernelFunc:dee},hee=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,y]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[A,x,b]=d>1?[`${(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(${A}); 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 = ${x}; float in_y = ${y}; if( in_y < 0.0 || in_y > ${h} ) { setOutput(float(${r})); return; } float in_x = ${b}; if( in_x < 0.0 || in_x > ${f} ) { setOutput(float(${r})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${p} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},fee=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new hee(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},mee={kernelName:ai,backendName:"webgl",kernelFunc:fee},K4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${Z4(s,"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() { ${St(s)} coords = getOutputCoords(); int end = ${Y4(s,"coords")}; float val = ${r}; int pow2 = int(pow(2.0, index)); if (${o}) { int idx = ${i}; ${Y4(s,"coords")} = idx; val += getX(${Z4(s,"coords")}); } setOutput(val); } `}};function Z4(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 ${e} is not yet supported`)}function Y4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function gee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=E.getAxesPermutation([a],l),u=r;c!=null&&(u=jn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=E.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=Is({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new K4(u.shape,!1,i),g=[[f]],y=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(o){let f=new K4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=E.getUndoAxesPermutation(c),m=jn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var yee={kernelName:ri,backendName:"webgl",kernelFunc:gee};function Aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=m4(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=$Z(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var xee={kernelName:vh,backendName:"webgl",kernelFunc:Aee},bee=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 vee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;v.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new bee(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var wee={kernelName:oi,backendName:"webgl",kernelFunc:vee},J4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ls(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",c="";n&&(s?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:l=` float activation(float x) { ${n} } `,c="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${l} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${i}; int q = d2 - d1 * ${i}; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. for (int wR = 0; wR < ${a}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } for (int wC = 0; wC < ${o}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${u} ${c} setOutput(result); } `}},Q4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ls(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { `;for(let y=0;y<(d+1)/2;y++){let A=y*2;if(p+=` xC = xCCorner + ${A*l}; `,i===1){if(A= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${A}.zw = vec2(0.0); } xTexelC${A}Ready = 1; } `,l===1&&A>0?p+=` xC${A} = vec4(xTexelC${A-2}.zw, xTexelC${A}.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${A} = vec4(previous.zw, xTexelC${A}.xy); } else { xC${A} = vec4(0.0, 0.0, xTexelC${A}.xy); } `):p+=` if (xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${A}.zw = vec2(0.0); } xTexelC${A}Ready = 1; } xC${A} = xTexelC${A}; `,A+1= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) { xTexelC${A+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${A+1}.zw = vec2(0.0); } xTexelC${A+1}Ready = 1; } `,l>1&&(p+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xCOffset, d1); xTexelC${A}Ready = 1; } `),p+=` xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.xy); `):x===1?p+=` xC${A+1} = xTexelC${A}; `:p+=` xCOffset = xC + ${x}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) { xTexelC${A+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${A+1}.zw = vec2(0.0); } xTexelC${A+1}Ready = 1; } xC${A+1} = xTexelC${A+1}; `}}else A= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${A}.zw = vec2(0.0); } xTexelC${A}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${A+1}Ready == 0) { xTexelC${A+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${A+1}.zw = vec2(0.0); } xTexelC${A+1}Ready = 1; } xC${A} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw); `,A+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${A+1} = vec4(xTexelC${A+1}.xy, final.xy); `)):(p+=` if(xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${A}.zw = vec2(0.0); } xTexelC${A}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) { xTexelC${A+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${A+1}.zw = vec2(0.); } xTexelC${A+1}Ready = 1; } xC${A} = vec4( xTexelC${A}.xy, xTexelC${A+1}.xy); `,A+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new Q4(d):p=new J4(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,[r,a],"float32",h)}var Iee={kernelName:_a,backendName:"webgl",kernelFunc:kee},See=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=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} - ${s}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},Cee=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${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) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; // TO DO: Vec4 over the channelMul for (int dm = 0; dm < ${i}; dm++) { int d2 = d1 * ${i} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function Tee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=E.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new See(d);return n.runWebGLProgram(p,[r,a],"float32")}var Nee={kernelName:wh,backendName:"webgl",kernelFunc:Tee};function Eee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Cee(d);return n.runWebGLProgram(p,[r,a],"float32")}var Ree={kernelName:kh,backendName:"webgl",kernelFunc:Eee},$ee=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 Dee(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=be({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new $ee(a),l=n.runWebGLProgram(i,[o],o.dtype),c=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var _ee={kernelName:Ih,backendName:"webgl",kernelFunc:Dee},Pee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=` const ivec2 strides = ivec2(${r}, ${a}); const ivec2 pads = ivec2(${u}, ${d}); const float neg_infinity = -3.4e38; void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.w; ivec2 outTopLeftCorner = coords.yz * strides - pads; int hBeg = outTopLeftCorner.x; int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; for (int h = 0; h < ${o}; h++) { int hIn = hBeg + h * ${l}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${i}; w++) { int wIn = wBeg + w * ${c}; 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 Fee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),u,d=new Pee(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=be({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var Oee={kernelName:Kc,backendName:"webgl",kernelFunc:Fee};function Mee(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m=0&&(p=Hm({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var zee={kernelName:Zc,backendName:"webgl",kernelFunc:Mee},Lee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Bee=` 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; `,Wee=it({opSnippet:Lee,packedOpSnippet:Bee}),Vee={kernelName:Fa,backendName:"webgl",kernelFunc:Wee},Uee="return (b >= 1.0) ? a : a * (b + 1.0);",Gee=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,Hee=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(Gee,s.shape,r.shape):new lc(Uee,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},jee={kernelName:Th,backendName:"webgl",kernelFunc:Hee},qee=` return vec4(equal(a, b)); `,Xee="return float(a == b);",Kee=En({opSnippet:Xee,packedOpSnippet:qee,dtype:"bool",cpuKernelImpl:PZ}),Zee={kernelName:ii,backendName:"webgl",kernelFunc:Kee},Yee=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${E.ERF_P}; 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vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}},yte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.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; } `}},Ate={kernelName:ad,backendName:"webgl",kernelFunc:xte},dc;function xte(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],d=[c,l,a];(i||o)&&(dc==null&&(dc=document.createElement("canvas").getContext("2d")),dc.canvas.width=l,dc.canvas.height=c,dc.drawImage(r,0,0,l,c),r=dc.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=Ms.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Z().getBool("WEBGL_PACK")?new yte(d):new gte(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function bte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=q4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=X4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,k=h==="leakyrelu",S=h?Um(h,!1):null,N=new j4(g,b,S,w,k),R=[r,a];if(o&&R.push(o),i&&R.push(i),k){let P=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(P),A.push(P)}y=n.runWebGLProgram(N,R,"float32")}let x=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var vte={kernelName:go,backendName:"webgl",kernelFunc:bte};function wte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=u;m==null&&(m=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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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; `,ine=it({opSnippet:ane,packedOpSnippet:one,cpuKernelImpl:HZ}),lne={kernelName:Va,backendName:"webgl",kernelFunc:ine},une="return log(1.0 + x);",cne=it({opSnippet:une}),dne={kernelName:lu,backendName:"webgl",kernelFunc:cne},pne="return float(a >= 1.0 && b >= 1.0);",hne=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,fne=En({opSnippet:pne,packedOpSnippet:hne,dtype:"bool"}),mne={kernelName:yi,backendName:"webgl",kernelFunc:fne},gne="return float(!(x >= 1.0));",yne=it({opSnippet:gne}),Ane={kernelName:uu,backendName:"webgl",kernelFunc:yne},xne="return float(a >= 1.0 || b >= 1.0);",bne=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,vne=En({opSnippet:xne,packedOpSnippet:bne,dtype:"bool"}),wne={kernelName:Jc,backendName:"webgl",kernelFunc:vne},kne=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${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 * ${i}; setOutput(val); } `}},Ine=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; int r = coords.y; int c = coords.z; int d = coords.w; bool hasNextCol = d < ${this.outputShape[3]}; bool hasNextRow = c < ${this.outputShape[2]}; vec4 sum = vec4(0.); vec4 xFragAtOutputCoords = getX(b, r, c, d); vec4 xAtOutputCoords = vec4( getChannel(xFragAtOutputCoords, vec2(c, d)), hasNextCol ? getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, hasNextRow ? getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, (hasNextRow && hasNextCol) ? getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); int firstChannel = d - ${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); } `}},Sne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Ine(r.shape,a,o,i,l):new kne(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Cne={kernelName:Qc,backendName:"webgl",kernelFunc:Sne},Tne=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { int depthBegin = int(max(0.0, float(d - ${t}))); int depthEnd = int(min(float(${this.depth}), float(d + ${t} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${s}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${s}) * float(${r}) * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${r}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}},Nne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s,d=new Tne(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},Ene={kernelName:$h,backendName:"webgl",kernelFunc:Nne};function Rne(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=bl(i,e.dtype,"max",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function iS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let x=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Is({inputs:{x:r},backend:n});let d=new fp(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Mne={kernelName:Ha,backendName:"webgl",kernelFunc:One};function zne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,c,l),p=new yx(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Lne={kernelName:ed,backendName:"webgl",kernelFunc:zne},Bne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*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 < ${r}; wR += ${s}) { 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 = ${l} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},Wne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=` const ivec3 pads = ivec3(${u}, ${d}, ${p}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${i}; wD += ${r}) { float dyD = float(dyDCorner + wD) / ${t}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${l}; wR += ${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 < ${c}; wC += ${o}) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); int maxPosValue = ${h} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wD * ${l} * ${c} + wR * ${c} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function Vne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new yx(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Wne(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Une={kernelName:_h,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;tc([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=E.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new fp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Bne(p),y=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var Hne={kernelName:Dh,backendName:"webgl",kernelFunc:Gne};function jne(e,t,n,s){let r=new fp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new fp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var qne={kernelName:Ph,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let c=[1,1];v.assert(E.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let u=E.computePool2DInfo(s.shape,r,a,c,o),[d,p]=jne(s,i,u,l);return[d,p]}};function Xne(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=bl(i,"float32","mean",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var Kne={kernelName:ja,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let N=0;Nc[0]+e[u]+c[1]);let s=e.length,r=St(s),a=t.map(c=>c[0]).join(","),o=t.map((c,u)=>c[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=` int start = ${a}; int end = ${o}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${l}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${l}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${r} start = ${r}(${a}); ${r} end = ${r}(${o}); void main() { ${r} outC = getOutputCoords(); for (int i = 0; i < ${s}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${l}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; } } ${r} coords = outC - start; setOutput(getX(${i})); } `}},sse=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 s=e.length,r=St(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${d}; } else if (source >= end) { source = (end - 1) * 2 - source + ${d}; } source -= start; `;p=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${u}); ${i[s-1]} += 1; if(${c}) { ${h} result[1] = getChannel(getX(${l.join()}), ${u}); } `}else{let h=` ${r} source = rc; ${r} lt = ${r}(lessThan(source, start)); ${r} gte = ${r}(greaterThanEqual(source, end)); ${r} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${d}) + gte * ((end - 1) * 2 - source + ${d}); source -= start; `;p=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${u}); ${i[s-1]} += 1; if(${c}) { ${h} result[1] = getChannel(getX(${l.join()}), ${u}); } rc = outputLoc; ${i[s-2]} += 1; if(${i[s-2]} < ${this.outputShape[s-2]}) { ${h} result[2] = getChannel(getX(${l.join()}), ${u}); ${i[s-1]} += 1; if(${c}) { ${h} result[3] = getChannel(getX(${l.join()}), ${u}); } } `}this.userCode=` const ${r} start = ${r}(${a}); const ${r} end = ${r}(${o}); void main() { ${r} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${p} setOutput(result); } `}},rse=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sse(s.shape,r,a):new nse(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},ase={kernelName:Ka,backendName:"webgl",kernelFunc:rse},ose=`if (b == 0.0) return NAN; return mod(a, b);`,ise=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+Vm+` return result; `,lse=En({opSnippet:ose,packedOpSnippet:ise}),use={kernelName:cu,backendName:"webgl",kernelFunc:lse},cse=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})); } `}},dse=` if (a == b) { return 1.0; }; return a / b;`,pse=` // 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; `,lS=En({opSnippet:dse,packedOpSnippet:pse,checkOutOfBounds:!0}),hse={kernelName:Pa,backendName:"webgl",kernelFunc:lS},uS="return a - b;",cS=En({opSnippet:uS,packedOpSnippet:uS,supportsComplex:!0,cpuKernelImpl:cY}),fse={kernelName:co,backendName:"webgl",kernelFunc:cS};function dS(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=iS({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=be({inputs:{x:i},backend:n,attrs:{shape:l}}),u=cS({inputs:{a:r,b:c},backend:n}),d=tS({inputs:{x:u},backend:n}),p=Hm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=be({inputs:{x:p},backend:n,attrs:{shape:l}}),f=lS({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var mse={kernelName:lo,backendName:"webgl",kernelFunc:dS};function gse(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:dS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new cse(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var yse={kernelName:Fh,backendName:"webgl",kernelFunc:gse},pS="return -x;";function Ase(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=ZZ(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new oc(s.shape,pS):r=new Mo(s.shape,pS),n.runWebGLProgram(r,[s],s.dtype)}var xse={kernelName:Ai,backendName:"webgl",kernelFunc:Ase},bse=Zs.nonMaxSuppressionV3Impl;function vse(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=bse(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var wse={kernelName:bi,backendName:"webgl",kernelFunc:vse},kse=Zs.nonMaxSuppressionV4Impl;function Ise(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=kse(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Sse={kernelName:du,backendName:"webgl",kernelFunc:Ise},Cse=Zs.nonMaxSuppressionV5Impl;function Tse(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=Cse(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Nse={kernelName:vi,backendName:"webgl",kernelFunc:Tse},Ese=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${s}), float(${n}), float(index == coords.y))); } `}},Rse=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=v.sizeFromShape(r.shape),c=new Ese(l,a,o,i),u=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=be({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},$se={kernelName:ki,backendName:"webgl",kernelFunc:Rse};function Zm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=mp({inputs:{input:s},backend:n}),a=Zm({inputs:{x:r},backend:n}),o=Km({inputs:{input:s},backend:n}),i=Zm({inputs:{x:o},backend:n}),l=zo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return gp({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Dse={kernelName:Li,backendName:"webgl",kernelFunc:Zm};function hS(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=mp({inputs:{input:s},backend:n}),a=hS({inputs:{x:r},backend:n}),o=Km({inputs:{input:s},backend:n}),i=Zm({inputs:{x:o},backend:n}),l=zo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return gp({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var _se={kernelName:wi,backendName:"webgl",kernelFunc:hS};function Pse(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return bx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=bx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=H4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var Fse={kernelName:Ii,backendName:"webgl",kernelFunc:Pse},Ose=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=St(s),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,s);if(s===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=` ${r} start = ${r}(${a}); ${r} end = ${r}(${o}); void main() { ${r} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { ${r} coords = outC - start; setOutput(getX(${i})); } } `}},Mse=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 s=e.length,r=St(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1; if(${c}) { `,s===1?"":`} rc = outputLoc; ${i[s-2]} += 1; if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1; if(${c}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return gp({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Mse(r.shape,a,o):new Ose(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},zse={kernelName:Ya,backendName:"webgl",kernelFunc:fS},Lse=` 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); `,Bse=` // 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)); `+Vm+` return result; `,Wse=En({opSnippet:Lse,packedOpSnippet:Bse}),Vse={kernelName:Ja,backendName:"webgl",kernelFunc:Wse};function Use(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],c=v.parseAxisParam(a,r.shape),u=c,d=E.getAxesPermutation(u,i),p=r;d!=null&&(p=jn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=E.getInnerMostAxes(u.length,i),l.push(p)),E.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=JZ(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=E.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),y=be({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),A=fd(r.dtype),x=bl(y,A,"prod",n);h=be({inputs:{x},backend:n,attrs:{shape:f}}),l.push(y),l.push(x)}if(o){l.push(h);let f=E.expandShapeToKeepDim(h.shape,c);h=be({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Gse={kernelName:Si,backendName:"webgl",kernelFunc:Use},mS=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=QZ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Hse={kernelName:pu,backendName:"webgl",kernelFunc:mS},jse="return 1.0 / x;",qse=it({opSnippet:jse}),Xse={kernelName:hu,backendName:"webgl",kernelFunc:qse},Kse=br+` return (x < 0.0) ? 0.0 : x; `,Zse=` 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; `,Yse=it({opSnippet:Kse,packedOpSnippet:Zse}),Jse={kernelName:eo,backendName:"webgl",kernelFunc:Yse},Qse=br+` return (x < 0.0) ? 0.0 : min(6.0, x); `,ere=` 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; `,tre=it({opSnippet:Qse,packedOpSnippet:ere}),nre={kernelName:no,backendName:"webgl",kernelFunc:tre},sre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/u[0]}, ${c[1]/u[1]}); const vec2 inputShapeRC = vec2(${o}.0, ${i}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${d}; // Compute the four integer indices. ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); ivec2 sourceCeilRC = ivec2( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d); float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d); float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d); float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d); vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC); float top = topLeft + (topRight - topLeft) * fracRC.y; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; float newValue = top + (bottom - top) * fracRC.x; setOutput(newValue); } `}},rre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/u[0]}, ${c[1]/u[1]}, ${c[1]/u[1]}); const vec3 inputShapeRC = vec3(${o}.0, ${i}.0, ${i}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${d}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); ivec3 sourceCeilRC = ivec3( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; // In parallel, construct four corners for all four components in // packed 2x2 cell. vec4 topLeft = vec4( getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 bottomLeft = vec4( getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 topRight = vec4( getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0); vec4 bottomRight = vec4( getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0); vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC); vec4 top = mix(topLeft, topRight, fracRC.yyzz); vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz); vec4 newValue = mix(top, bottom, fracRC.x); setOutput(newValue); } `}};function are(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new rre(r.shape,l,c,a,o):new sre(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var ore={kernelName:to,backendName:"webgl",kernelFunc:are},ire=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${u}); const float invHeightScale = float(${d}); const float invWidthScale = float(${p}); const int winHeight = int(${h}); const int winWidth = int(${f}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(startRLerp - float(winHeight / 2)); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(startCLerp - float(winWidth / 2)); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${o}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0)); float dxRLerp = dxR - float(topDxRIndex); float inverseDxRLerp = 1.0 - dxRLerp; float dxC = float(dyC) * widthScale; int leftDxCIndex = int(floor(dxC)); int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutput(accumulator); } `}};function lre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new ire(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var ure={kernelName:Mh,backendName:"webgl",kernelFunc:lre},cre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/u[0]}, ${c[1]/u[1]}); const vec2 inputShapeRC = vec2(${o}.0, ${i}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${p}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}},dre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/u[0]}, ${c[1]/u[1]}, ${c[1]/u[1]}); const vec3 inputShapeRC = vec3(${o}.0, ${i}.0, ${i}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${p}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function pre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new dre(r.shape,l,c,a,o):new cre(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var hre={kernelName:fu,backendName:"webgl",kernelFunc:pre},fre=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${u}); const float invHeightScale = float(${d}); const float invWidthScale = float(${p}); const int winHeight = int(${h}); const int winWidth = int(${f}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(floor(startRLerp - float(winHeight / 2))); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(floor(startCLerp - float(winWidth / 2))); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${o}) { continue; } float sourceFracRow = float(${i[0]}) * (float(dyR) / float(${l[0]})); float sourceFracCol = float(${i[1]}) * (float(dyC) / float(${l[1]})); int sourceNearestRow = int(min( float(int(${s}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${r}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function mre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new fre(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var gre={kernelName:Oh,backendName:"webgl",kernelFunc:mre},yre=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=St(n);this.userCode=` void main() { ${a} coords = getOutputCoords(); setOutput(getX(${r})); } `}},Are=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-${n} tensor is not yet supported`);this.outputShape=e;let s=Hn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=St(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${e[0]} - rc - 1), ${e[0]} - rc - 1); if(${r}){ result.g = getChannel(getX(${e[0]} - (rc + 1) - 1), ${e[0]} - (rc + 1) - 1); } setOutput(result); } `:this.userCode=` void main() { ${o} rc = getOutputCoords(); vec4 result = vec4(0.); result.r = ${i(s.slice())}; if(${r}){ result.g = ${l(s.slice())}; } if(${a}) { result.b = ${c(s.slice())}; if(${r}) { result.a = ${u(s.slice())}; } } setOutput(result); } `;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((y,A)=>p(A,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function xre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return Is({inputs:{x:r},backend:n});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Are(r.shape,i):new yre(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var bre={kernelName:Ti,backendName:"webgl",kernelFunc:xre},vre=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int y = coords[1]; float coordXFloat = (float(x) - params[0]) * params[3] - (float(y) - params[1]) * params[2]; float coordYFloat = (float(x) - params[0]) * params[2] + (float(y) - params[1]) * params[3]; int coordX = int(round(coordXFloat + params[0])); int coordY = int(round(coordYFloat + params[1])); ${r} if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}},wre={kernelName:Bi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new vre(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},kre=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,Ire=it({opSnippet:kre}),Sre={kernelName:Ni,backendName:"webgl",kernelFunc:Ire},Cre="return inversesqrt(x);",Tre=it({opSnippet:Cre,cpuKernelImpl:eY}),Nre={kernelName:so,backendName:"webgl",kernelFunc:Tre},gS=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=St(r.length),l=St(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=` ${i} strides = ${i}(${r}); void main() { ${l} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${t}; j++) { int index = round(${u}); flattenedIndex += index * ${h}; } if (flattenedIndex == coords[0]) { sum += ${p}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function Ere(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=be({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new gS(l,i,h.shape.length,f.shape.length,u,p),y=n.runWebGLProgram(g,[f,h,m],f.dtype),A=be({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),A}var Rre={kernelName:Ei,backendName:"webgl",kernelFunc:Ere},$re=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let c=0;c= 1.0) { setOutput(getA(${r})); } else { setOutput(getB(${r})); } } `}};function Dre(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new $re(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var _re={kernelName:Ri,backendName:"webgl",kernelFunc:Dre},Pre=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${E.SELU_SCALEALPHA}; float scale = ${E.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,Fre=it({opSnippet:Pre}),Ore={kernelName:mu,backendName:"webgl",kernelFunc:Fre},yS="return 1.0 / (1.0 + exp(-1.0 * x));",Mre=it({opSnippet:yS,packedOpSnippet:yS,cpuKernelImpl:tY}),zre={kernelName:ao,backendName:"webgl",kernelFunc:Mre},Lre=` if (isnan(x)) { return 0.0; } return sign(x); `,Bre=it({opSnippet:Lre}),Wre={kernelName:gu,backendName:"webgl",kernelFunc:Bre},Vre=R4+` return sin(x); `,Ure=it({opSnippet:Vre}),Gre={kernelName:ro,backendName:"webgl",kernelFunc:Ure},Hre=` float e2x = exp(x); 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// 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)); } } `}},Lae=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 vl(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function vS(e){let t=1;for(;tl){let P=n.readSync(r.dataId),[$,D]=pY(P,c,r.dtype,a,o);return[n.makeTensorInfo($.shape,$.dtype,$.values),n.makeTensorInfo(D.shape,D.dtype,D.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,gp({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=v.sizeFromShape(c)/u,g=be({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&vl(n,h);let y=vS(a),A=vS(u),x=null,b=()=>x===null?[g,g]:[g,x],w=(P,$,D)=>{let T=b(),O=new zae(D),H=[[u],[x===null?1:0],[Number.NEGATIVE_INFINITY],[P],[$]],z=x;x=n.runWebGLProgram(O,T,"int32",H),vl(n,z)};for(let P=1;P=1;D/=2)w($,D,[m,A])}for(let P=A;P>y;P/=2){let $=b(),D=new Lae([m,P/2]),O=[[u],[x===null?1:0],[y]],B=x;x=n.runWebGLProgram(D,$,"int32",O),vl(n,B);let H=y/2,z=H*2;for(let X=H;X>=1;X/=2)w(z,X,x.shape)}let k=x;x=uc({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),vl(n,k);let S=oS({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});vl(n,g);let N=c.slice(0,-1);N.push(a),k=x,x=be({inputs:{x},attrs:{shape:N},backend:n}),vl(n,k);let R=S;return S=be({inputs:{x:S},attrs:{shape:N},backend:n}),vl(n,R),[S,x]}var Wae={kernelName:xu,backendName:"webgl",kernelFunc:Bae},Vae=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){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 < ${t}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${r}); } return outputValue; } void main() { ivec4 coords = getOutputCoords(); float outputValue; int batch = coords[0]; int x = coords[2]; int y = coords[1]; int channel = coords[3]; float xf = float(x); float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${r}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${t})); float mapY = mapCoord(inY, float(${e})); if (${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 Uae(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=new Vae(d,p,o,i,l,g);return n.runWebGLProgram(y,[r,a],"float32")}var Gae={kernelName:Mi,backendName:"webgl",kernelFunc:Uae};function Hae(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;tc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:c}=hY(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var jae={kernelName:Gh,backendName:"webgl",kernelFunc:Hae};function qae(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;mn.disposeIntermediateTensorInfo(m)),f}var Xae={kernelName:zi,backendName:"webgl",kernelFunc:qae},Kae=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,d=` sumValue += dot(values, segFilter); `,p="";r%n>0&&(p=` if (inIdx < 0 || inIdx >= ${r}) { return initializationValue; } `);let h="";r%n>0&&(h=` if (inIdx < 0 || inIdx >= ${r}) { return -1.0; } `),this.userCode=` const float initializationValue = ${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 < ${c}; 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 + ${c}; if (${u===1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); int inIdxSeg = int(getSegmentIdAtIndex(inIdx)); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, 0, 0, 0 ); ${d} } else if (${u===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, 0, 0 ); ${d} } else if (${u===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, 0 ); ${d} } setOutput(${l}); } `}};function Zae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],c=0,u=E.getAxesPermutation([c],i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=E.getInnerMostAxes(1,i)[0]);let p=E.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=be({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=fd(r.dtype),g=(b,w,k,S,N)=>{let R=b.shape[0],P=b.shape[1],$=E.segment_util.segOpComputeOptimalWindowSize(P,N),D={windowSize:$,inSize:P,batchSize:R,numSegments:N},T=new 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Yae={kernelName:rd,backendName:"webgl",kernelFunc:Zae},Jae=[Cne,Ene,pJ,fJ,yJ,bJ,wJ,SJ,TJ,EJ,_J,FJ,zJ,WJ,XJ,GJ,YJ,tQ,QJ,aQ,iQ,uQ,hQ,bQ,wQ,NQ,RQ,PQ,MQ,qY,VQ,JQ,eee,jQ,ree,oee,nee,uee,pee,mee,yee,xee,wee,Nee,Ree,Iee,_ee,Oee,zee,Vee,jee,Zee,Qee,ete,tte,ste,ate,ite,ute,dte,mte,Ate,vte,kte,Cte,Ete,_te,Mte,jY,Lte,BQ,Vte,Hte,Xte,KY,Jte,nne,rne,dne,lne,mne,Ane,wne,$ne,Lne,Mne,Une,Hne,qne,Fne,Kne,Yne,tse,ase,use,yse,eJ,xse,wse,Sse,Nse,IQ,$se,_se,Fse,zse,Vse,YY,Gse,Hse,SQ,hse,Xse,nre,Jse,nJ,ore,ure,hre,gre,bre,wre,Sre,Nre,Rre,_re,Ore,zre,Wre,Gre,qre,AQ,mse,Zre,Jre,eae,nae,rae,oae,lae,cae,pae,mae,yae,xae,wae,Iae,Cae,Nae,fse,uJ,$ae,Pae,Mae,Wae,Gae,cJ,jae,Xae,Yae,Dse];for(let e of Jae)Yr(e);var ls;(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"})(ls||(ls={}));var yp;(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"})(yp||(yp={}));var wS;function Qae(e){wS=e.wasm.cwrap(mo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function eoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let N=n.dataIdMap.get(o.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);f=N.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=yp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],A=c?a.shape[1]:a.shape[2],x=r.shape[0],b=n.makeOutput([x,y,A],r.dtype),w=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return wS(p,k,r.shape.length,h,S,a.shape.length,l,c,g,f,m,d||0,w),b}var toe={kernelName:mo,backendName:"wasm",setupFunc:Qae,kernelFunc:eoe};function Rn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function s(r){let{backend:a,inputs:{x:o}}=r,i=a.dataIdMap.get(o.dataId).id,l=a.makeOutput(o.shape,o.dtype),c=a.dataIdMap.get(l.dataId).id;return v.sizeFromShape(l.shape)===0||t(i,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:s}}var noe=Rn(ti);function qn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:c,b:u}=l,d=i.dataIdMap.get(c.dataId).id,p=i.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,f=E.assertAndGetBroadcastShape(c.shape,u.shape),m=i.makeOutput(f,h);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),A=i.dataIdMap.get(m.dataId).id,x=()=>s(d,g,c.shape.length,p,y,u.shape.length,ls[c.dtype],A);if(t&&c.dtype==="float32")return x(),m;let b=E.getBroadcastDims(c.shape,f),w=E.getBroadcastDims(u.shape,f),k=b.every((N,R)=>N===R),S=w.every((N,R)=>N===R);if(k&&S)return x(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var soe=!0,roe=qn(qr,soe),kS;function aoe(e){kS=e.wasm.cwrap(ka,null,["array","number","number","number"])}function ooe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return kS(a,r.length,ls[s.dtype],o),s}var ioe={kernelName:ka,backendName:"wasm",setupFunc:aoe,kernelFunc:ooe};function Ym(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var loe={kernelName:Wa,backendName:"wasm",kernelFunc:Ym},IS;function uoe(e){IS=e.wasm.cwrap(ho,null,["number","array","number","number","number","array","number"])}function pc(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=doe(t.x.shape,s.perm),o=!0;for(let f=0;f=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var poe={kernelName:ho,backendName:"wasm",kernelFunc:pc,setupFunc:uoe};function Lo(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=E.getAxesPermutation(o,r),l=null,c=!1;if(i!=null){let u=new Array(r);for(let h=0;h`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Soe={kernelName:Ci,backendName:"wasm",kernelFunc:us},ES;function Coe(e){ES=e.wasm.cwrap(Ca,null,["number","array","number","number","array","number","number","number","number"])}function Toe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=g===y||g===1||y===1;v.assert(l>=2&&c>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(g>y?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let w=o?[g,u,p]:[g,p,u],k=i?[y,h,d]:[y,d,h],S=us({inputs:{x:r},backend:n,attrs:{shape:w}}),N=us({inputs:{x:a},backend:n,attrs:{shape:k}}),R=n.dataIdMap.get(S.dataId).id,P=n.dataIdMap.get(N.dataId).id,$=o?S.shape[2]:S.shape[1],D=i?N.shape[1]:N.shape[2],T=Math.max(g,y),O=n.makeOutput([T,$,D],S.dtype),B=n.dataIdMap.get(O.dataId).id,H=new Uint8Array(new Int32Array(S.shape).buffer),z=new Uint8Array(new Int32Array(N.shape).buffer);return ES(R,H,S.shape.length,P,z,N.shape.length,o,i,B),n.disposeData(S.dataId),n.disposeData(N.dataId),O.shape=b,O}var Noe={kernelName:Ca,backendName:"wasm",setupFunc:Coe,kernelFunc:Toe};function Ap(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=yn.parseSliceParams(t,n,s),i=yn.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),c=r.makeOutput(o,t.dtype),u=v.computeStrides(t.shape),d=r.dataIdMap.get(c.dataId);if(i){let f=yn.computeFlatOffset(a,u);return t.dtype==="string"?d.stringBytes=l.slice(f,f+v.sizeFromShape(o)):r.typedArrayFromHeap(c).set(l.subarray(f,f+v.sizeFromShape(o))),c}if(t.dtype==="string"){let f=Tm(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)Eoe(l,u[0],p,a,o);else if(h===3)Roe(l,u[0],u[1],p,a,o);else if(h===4)$oe(l,u[0],u[1],u[2],p,a,o);else{let f=Tm(l,a,o,t.shape,t.dtype);p.set(f)}return c}function Eoe(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let c=o;cy*A),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=us({inputs:{x:r},backend:n,attrs:{shape:l}}),f=pc({inputs:{x:h},backend:n,attrs:{perm:c}}),m=us({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Ap({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Poe={kernelName:ni,backendName:"wasm",kernelFunc:_oe};function Jm(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Foe={kernelName:Ta,backendName:"wasm",kernelFunc:Jm},Ooe=Rn(Na),RS;function Moe(e){RS=e.wasm.cwrap(Xr,null,["number","number","number","number"])}function zoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return RS(i,a,o,c),l}var Loe={kernelName:Xr,backendName:"wasm",setupFunc:Moe,kernelFunc:zoe};function $S(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return Ym({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(E.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(x=>{let b=v.sizeFromShape(x.shape.slice(s));return us({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=E.computeOutShape(h.map(x=>x.shape),1);let m=h[0].shape[0]===1,g=UA(f,r,t[0].dtype,m),y=E.computeOutShape(a.map(x=>x.shape),s);o.shape=y;let A=n.dataIdMap.get(o.dataId);return A.stringBytes=E.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=v.sizeFromShape(a[0].shape.slice(0,s)),c=0,u=a.map(h=>{let f=v.sizeFromShape(h.shape.slice(s));return c+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=E.getAxesPermutation([a],l),u=r;c!==null&&(u=pc({inputs:{x:r},attrs:{perm:c},backend:n}));let d=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[d],l);let p=n.makeOutput(u.shape,u.dtype),h=u.shape[d],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(p.dataId).id;FS(f,o?1:0,i?1:0,h,m,ls[r.dtype]);let g=p;if(c!==null){let y=E.getUndoAxesPermutation(c);g=pc({inputs:{x:p},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var eie={kernelName:ri,backendName:"wasm",setupFunc:Joe,kernelFunc:Qoe},OS;function tie(e){OS=e.wasm.cwrap(oi,null,["number","number","number","array","number","array","array","number","number"])}function nie(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s;v.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=t.makeOutput(f,"float32"),y=t.dataIdMap.get(r.dataId).id,A=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return OS(y,a,o==="NHWC"?1:0,A,r.shape.length-1,x,b,f.length,w),m}var sie={kernelName:oi,backendName:"wasm",setupFunc:tie,kernelFunc:nie},MS;function rie(e){MS=e.wasm.cwrap(_a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function aie(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d}=n,p=c==null?[1,1]:c,h=E.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,A=h.padInfo.bottom,x=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,S=h.strideWidth,N=h.inChannels,R=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let $=s.makeOutput(h.outShape,"float32"),D=s.dataIdMap.get($.dataId).id;return MS(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,y,A,x,P,b,w,k,S,N,R,D),$}var oie={kernelName:_a,backendName:"wasm",setupFunc:rie,kernelFunc:aie},iie=Rn(Fa),lie=!1,uie=qn(ii,lie,"bool"),cie=Rn(Oa);function wx(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),us({inputs:{x:r},backend:s,attrs:{shape:i}})}var die={kernelName:li,backendName:"wasm",kernelFunc:wx};function zS(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var pie={kernelName:ru,backendName:"wasm",kernelFunc:zS},LS;function hie(e){LS=e.wasm.cwrap(ci,null,["number","number","number","number","number","number"])}function fie(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return LS(a,i,l,c,u,o),r}var mie={kernelName:ci,backendName:"wasm",kernelFunc:fie,setupFunc:hie},gie=Rn(Ma),yie=!1,Aie=qn(za,yie),BS;function xie(e){BS=e.wasm.cwrap(La,null,["number","number","number","number","number","number","number"])}function bie(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:c}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return BS(u,d,p,h,f,r,g),m}var vie={kernelName:La,backendName:"wasm",setupFunc:xie,kernelFunc:bie},WS;function wie(e){WS=e.wasm.cwrap(go,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kie(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=yp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=s.dataIdMap.get(r.dataId).id,A=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let K=s.dataIdMap.get(o.dataId);if(K.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${K.shape.length}.`);if(K.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${K.shape}) does not match the number of output channels (${x})`);b=K.id}let w=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,$=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,O=m.strideWidth,B=m.inChannels,H=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,ee=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),Q=s.dataIdMap.get(J.dataId).id,te=i==null?0:s.dataIdMap.get(i.dataId).id;return WS(y,z,X,ee,A,w,k,b,S,N,R,P,H,$,D,T,O,B,x,g,te,f||0,Q),J}var Iie={kernelName:go,backendName:"wasm",setupFunc:wie,kernelFunc:kie},VS;function Sie(e){VS=e.wasm.cwrap(yo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Cie(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!0),g=yp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=s.dataIdMap.get(r.dataId).id,A=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let K=s.dataIdMap.get(o.dataId);if(K.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${K.shape.length}.`);if(K.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${K.shape}) does not match the number of output channels (${x})`);b=K.id}let w=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,$=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,O=m.strideWidth,B=m.inChannels,H=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,ee=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),Q=s.dataIdMap.get(J.dataId).id,te=i==null?0:s.dataIdMap.get(i.dataId).id;return VS(y,z,X,ee,A,w,k,b,S,N,R,P,H,$,D,T,O,B,x,g,te,f||0,Q),J}var Tie={kernelName:yo,backendName:"wasm",setupFunc:Sie,kernelFunc:Cie},US;function Nie(e){US=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","array","number"])}function Eie(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=_2.prepareAndValidate(s,r),c=t.makeOutput(a,s.dtype);if(o===0)return c;let u=r.shape,d=u[u.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),y=t.dataIdMap.get(c.dataId).id;return US(h,ls[s.dtype],m,o,d,i,g,y),c}var Rie={kernelName:pi,backendName:"wasm",setupFunc:Nie,kernelFunc:Eie},GS;function $ie(e){GS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Die(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),u=us({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),d=v.sizeFromShape(a.shape),p=us({inputs:{x:a},attrs:{shape:[c.batchSize,d/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,d/c.batchSize,c.sliceSize],f=t.makeOutput(h,r.dtype);if(v.sizeFromShape(r.shape)===0)return f;let m=u.shape.length-1,y=t.dataIdMap.get(u.dataId).id,x=t.dataIdMap.get(p.dataId).id,b=t.dataIdMap.get(f.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(u.shape)).buffer),k=new Uint8Array(new Int32Array(v.computeStrides(h)).buffer);return GS(y,ls[r.dtype],w,m,x,c.batchSize,k,b),t.disposeData(u.dataId),t.disposeData(p.dataId),f.shape=c.outputShape,f}var _ie={kernelName:di,backendName:"wasm",setupFunc:$ie,kernelFunc:Die},Pie=!1,Fie=qn(hi,Pie,"bool"),Oie=!1,Mie=qn(Ba,Oie,"bool"),HS;function zie(e){HS=e.wasm.cwrap(fi,null,["number","number","number"])}function Lie(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,t.dtype);if(v.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;HS(r,n,o)}return a}var Bie={kernelName:fi,backendName:"wasm",setupFunc:zie,kernelFunc:Lie},Wie=!1,Vie=qn(mi,Wie,"bool"),Uie=!1,Gie=qn(gi,Uie,"bool"),Hie=Rn(Va),jie=!1,qie=qn(yi,jie,"bool"),jS;function Xie(e){jS=e.wasm.cwrap(Ua,null,["number, number, number"])}function Kie(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;c=u,l=x}let f=c.shape.length;E.assertAxesAreInnerMostDims("max",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),y=v.sizeFromShape(g),A=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;jS(l,y,x)}if(h&&t.disposeData(u.dataId),a){let x=E.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Zie={kernelName:Ua,backendName:"wasm",setupFunc:Xie,kernelFunc:Kie},Yie=!1,Jie=qn(Ga,Yie),qS;function Qie(e){qS=e.wasm.cwrap(Ha,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ele(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,A=u.dilationWidth,x=u.strideHeight,b=u.strideWidth,w=u.inChannels,k=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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} if (val < 0.0) { return false; } if (val == 0.0) { return false; } return true; } fn isNanCustomVec4F32(val : vec4) -> vec4 { var res = vec4 (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, shape : vec4) -> bool { return all(coord >= vec4(0)) && all(coord < shape); } fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool { return all(coord >= vec3(0)) && all(coord < shape); } fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool { return all(coord >= vec2(0)) && all(coord < shape); } `,kC=` fn getFlatIndex1D(coord : i32, shape : i32) -> i32 { return coord; } fn getFlatIndex2D(coords : vec2, shape : vec2) -> i32 { return i32(dot(vec2(coords), vec2(f32(shape.y), 1.0))); } fn getFlatIndex3D(coords : vec3, shape : vec3) -> i32 { return i32(dot(vec3(coords), vec3(f32(shape.y) * f32(shape.z), f32(shape.z), 1.0))); } fn getFlatIndex4D(coords : vec4, shape : vec4) -> i32 { return i32(dot(vec4(coords), vec4( f32(shape.y) * f32(shape.z) * f32(shape.w), f32(shape.z) * f32(shape.w), f32(shape.w), 1.0))); } // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex(globalId : vec3, localId : vec3) -> i32 { if (uniforms.dispatchSize.y == 1u && uniforms.dispatchSize.z == 1u) { return i32(globalId.x); } let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY + localId.y * workGroupSizeX + localId.x; let workGroupID = (globalId - localId)/vec3( workGroupSizeX, workGroupSizeY, workGroupSizeZ); return i32((workGroupID.z * uniforms.dispatchSize.x * uniforms.dispatchSize.y + workGroupID.y * uniforms.dispatchSize.x + workGroupID.x) * (workGroupSizeX * workGroupSizeY * workGroupSizeZ) + localInvocationIndex); } `;function pce(e,t,n){let s=e.length,r=e0(t,n),a;if(n?a=`fn setOutputFlat(flatIndex : i32, value : vec4) { result.numbers[flatIndex] = ${r}(value); } fn setOutputFlatI32(flatIndex : i32, value : vec4) { result.numbers[flatIndex] = ${r}(value); }`:a=`fn setOutputFlat(flatIndex : i32, value : f32) { result.numbers[flatIndex] = ${r}(value); } fn setOutputFlatI32(flatIndex : i32, value : i32) { result.numbers[flatIndex] = ${r}(value); }`,s>=2){switch(s){case 2:a+=` fn getOutputFlatIndex(coords : vec2) -> i32 { return i32(dot(vec2(coords), vec2(f32(uniforms.outShapeStrides), 1.0))); } `;break;case 3:a+=` fn getOutputFlatIndex(coords : vec3) -> i32 { return i32(dot(vec3(coords), vec3(f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), 1.0))); } `;break;case 4:a+=` fn getOutputFlatIndex(coords : vec4) -> i32 { return i32(dot(vec4(coords), vec4( f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), f32(uniforms.outShapeStrides.z), 1.0))); } `;break;default:v.assert(!1,()=>`Unsupported ${s}D shape`);break}let o=["d0","d1","d2","d3"].slice(0,s),i=ln(s);n?a+=` fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")})); setOutputFlat(flatIndex / 4, value); } fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")})); setOutputFlatI32(flatIndex / 4, value); } `:a+=` fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : f32) { let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")})); setOutputFlat(flatIndex, value); } fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : i32) { let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")})); setOutputFlatI32(flatIndex, value); } `}return a}function hce(e,t,n,s){let r=fce(e,n);return e.shape.length<=t.length&&(r+=mce(e,t,n,s)),r}function fce(e,t){let n=e.name,s=e.shape.length,r=ln(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${u} : i32`).join(", ");if(s<1)return t?` fn ${a}() -> vec4 { return vec4(${n}.numbers[0]); } `:` fn ${a}() ->f32 { return f32(${n}.numbers[0]); } `;let l=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,c=`${s}D`;return s===0&&(c="1D"),t?` fn ${a}(${i}) -> vec4 { return vec4(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}), ${l}) / 4]); } `:` fn ${a}(${i}) -> f32 { return f32(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}), ${l})]); } `}function mce(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"AtOutCoords",i=e.shape.length,l=t.length,c=ln(l);if(v.arraysEqual(e.shape,t)&&s)return n?` fn ${o}ByGlobalId(globalId : vec3, globalIndex : i32) -> vec4 { return vec4(${r}.numbers[globalIndex]); } fn ${o}ByCoords(coords : ${c}) -> vec4 { return vec4(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"} / 4]); } `:` fn ${o}ByGlobalId(globalId : vec3, globalIndex : i32) -> f32 { return f32(${r}.numbers[globalIndex]); } fn ${o}ByCoords(coords : ${c}) -> f32 { return f32(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"}]); } `;let u=E.getBroadcastDims(e.shape,t),d=l-i,p="";if(i===0)return n?` fn ${o}ByGlobalId(globalId : vec3, globalIndex : i32) -> vec4 { return get${a}(); } fn ${o}ByCoords(coords : ${c}) -> vec4 { return get${a}(); } `:` fn ${o}ByGlobalId(globalId : vec3, globalIndex : i32) -> f32{ return get${a}(); } fn ${o}ByCoords(coords : ${c}) -> f32{ return get${a}(); } `;l<2&&u.length>=1?p="coords = 0;":p=u.map(g=>`coords[${g+d}] = 0;`).join(` `);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=ln(i),y=e.shape.map((A,x)=>`coords[${x+d}]`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?` fn ${o}ByGlobalId(globalId : vec3, globalIndex : i32) -> vec4 { var coords = getOutputCoords(globalId, globalIndex); ${p} return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4]; } fn ${o}ByCoords(coordsIn : ${c}) -> vec4 { var coords = coordsIn; ${p} return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4]; } `:` fn ${o}ByGlobalId(globalId : vec3, globalIndex : i32) -> f32 { var coords = getOutputCoords(globalId, globalIndex); ${p} return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]); } fn ${o}ByCoords(coordsIn : ${c}) -> f32 { var coords = coordsIn; ${p} return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]); } `}function gce(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords(globalId : vec3, globalIndex : i32) -> ${ln(a)}{ return getCoordsFromFlatIndex(i32(globalIndex)); } `,a];let o="",i=[n,s,r],l=0;for(let p=0;p, globalIndex : i32) -> ${u} { ${o} `;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function IC(e){let t=e.length;if(t<=1)return"fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=ln(t),r=[];for(let o=0;o vec2 { let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides; return vec2(d0, d1); }`;let a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides[${i}]`,c=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`;return`${l}; ${c};`}).join("");return` fn getCoordsFromFlatIndex(index : i32) -> ${s} { ${a} return ${s}(${r.join(",")}); } `}var SC={};Le(SC,{ArrayBufferToTypedArray:()=>CC,GPUBytesPerElement:()=>Rx,computeDispatch:()=>Be,computeWorkGroupSizeForConv2d:()=>Tx,computeWorkGroupSizeForMatMul:()=>Nx,computeWorkPerThreadForConv2d:()=>Ex,flatDispatchLayout:()=>at,isWebGPUSupported:()=>$x,tilesFitEvenlyIntoShape:()=>ua});var hc=65535,wl=e=>{let t=1;for(let n=0;nn%e[s]==0)}function Be(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(wl(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(wl(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(wl(e.z.map(l=>t[l]))/(n[2]*s[2])):1];if(r<=hc&&a<=hc&&o<=hc)return[r,a,o];v.assert(r>hc&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let i=Math.ceil(Math.sqrt(r));return i>hc?(i=Math.ceil(Math.cbrt(r)),v.assert(i<=hc,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function Tx(e,t){let n=wl(e.x.map(r=>t[r])),s=wl(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function Nx(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Ex(e,t){let n=wl(e.x.map(r=>t[r])),s=wl(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function at(e){return{x:e.map((t,n)=>n)}}function Rx(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function CC(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),s=new ArrayBuffer(n.length),r=new Uint8Array(s);for(let a=0;a(a >= vec4(1.0)) * vec4(b >= vec4(1.0)));`,Oce=` if (isNanCustom(a)) { return a; } if (isNanCustom(b)) { return b; } `,TC=` 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; } `,Mce=` let s = sign(a) * sign(b); let ia = i32(round(a)); let ib = i32(round(b)); return f32(idiv(ia, ib, s)); `,zce=` let ia = vec4(round(a)); let ib = vec4(round(b)); let cond = ib != vec4(0); var resultTemp = vec4(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(resultTemp); `,Lce="return f32(a != b);",Bce="return vec4(a != b);",Wce=` 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); `,Vce=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(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(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(a < vec4(0.0)) * vec4(floor(b) < b); ${TC} return resultTemp; `,Uce="if (a < 0.0) { return b * a; } return a;",Gce=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function NC(e,t){let n=t?TC:Oce;return t?` var resultTemp = vec4(${e}(a, b)); let isNaN = min(vec4(isNanCustomVec4F32(a)) + vec4(isNanCustomVec4F32(b)), vec4(1.0)); `+n+` return resultTemp; `:n+` return ${e}(a, b); `}function wp(e,t){switch(e){case je.MUL:return vce;case je.ADD:return yce;case je.SUB:return kce;case je.DIV:return bce;case je.EQUAL:return t?Sce:Ice;case je.GREATER:return t?Tce:Cce;case je.GREATER_EQUAL:return t?Ece:Nce;case je.LESS:return t?$ce:Rce;case je.LESS_EQUAL:return t?_ce:Dce;case je.LOGICAL_AND:return t?Fce:Pce;case je.NOT_EQUAL:return t?Bce:Lce;case je.SQUARED_DIFFERENCE:return wce;case je.INT_DIV:return t?zce:Mce;case je.PRELU:return t?Gce:Uce;case je.MAX:return NC("max",t);case je.MIN:return NC("min",t);case je.POW:return t?Vce:Wce;case je.COMPLEX_MULTIPLY_REAL:return Ace;case je.COMPLEX_MULTIPLY_IMAG:return xce;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Fe;(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"})(Fe||(Fe={}));var Hce="return abs(a);",jce="return ceil(a);",qce="return cos(a);",Xce=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; `,Kce="return exp(a) - 1.0;",Zce="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Yce=` var resFloat = exp(a) - vec4(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; `,Jce="return exp(a);",Qce="return floor(a);",ede="return a;",tde=`if (a < 0.0) { return 1.0/0.0; } return log(a);`,nde="return f32(!(a >= 1.0));",sde="return -a;",rde="return (a < 0.0) ? b * a : a;",ade="return max(a, 0.0);",ode="return clamp(a, 0.0, 6.0);",ide="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",lde=` var resFloat = a * vec4(a >= vec4(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; `,ude="return 1.0/sqrt(a);",cde="return 1.0 / (1.0 + exp(-1.0 * a));",dde="return sin(a);",pde=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; `,hde="return sqrt(a);",fde="return a * a;",mde=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); `,gde="return f32(i32((a)));";function fc(e,t){switch(e){case Fe.ABS:return Hce;case Fe.COS:return qce;case Fe.COSH:return Xce;case Fe.CEIL:return jce;case Fe.ELU:return t?Yce:Zce;case Fe.EXP:return Jce;case Fe.EXPM1:return Kce;case Fe.FLOOR:return Qce;case Fe.LINEAR:return ede;case Fe.LOG:return tde;case Fe.LOGICAL_NOT:return nde;case Fe.NEG:return sde;case Fe.PRELU:return rde;case Fe.RELU:return t?lde:ade;case Fe.RELU6:return t?ide:ode;case Fe.RSQRT:return ude;case Fe.SIGMOID:return cde;case Fe.SIN:return dde;case Fe.SINH:return pde;case Fe.SQRT:return hde;case Fe.SQUARE:return fde;case Fe.TANH:return mde;case Fe.TO_INT:return gde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Bo(e,t=!1){if(e===null)return null;if(e==="linear")return fc(Fe.LINEAR);if(e==="relu")return fc(Fe.RELU,t);if(e==="elu")return fc(Fe.ELU,t);if(e==="relu6")return fc(Fe.RELU6,t);if(e==="prelu")return wp(je.PRELU,t);if(e==="sigmoid")return fc(Fe.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function EC(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 mm_Asub : array, ${n.TileInner/n.ColPerThread}>, ${n.TileAOuter}>; var mm_Bsub : array, ${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}; ${Me()} { 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, ${n.RowPerThread}>; var ACached : vec4; var BCached : array, 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 yde(e){return` var mm_Asub : array, ${e[0]}>; let tileSize = ${e[0]*4}; ${Me()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); let numTiles = (uniforms.dimInner - 1) / tileSize + 1; // Without this initialization strange values show up in acc. var acc = vec4(0.0); // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. let colA = t * tileSize / 4 + tileCol; mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId); workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < tileSize / 4; k = k + 1) { let rowB = t * tileSize + k * 4; let BCached0 = mm_readB(rowB, globalCol, globalId); let BCached1 = mm_readB(rowB + 1, globalCol, globalId); let BCached2 = mm_readB(rowB + 2, globalCol, globalId); let BCached3 = mm_readB(rowB + 3, globalCol, globalId); let ACached = mm_Asub[k]; acc = acc + BCached0 * ACached.x; acc = acc + BCached1 * ACached.y; acc = acc + BCached2 * ACached.z; acc = acc + BCached3 * ACached.w; } workgroupBarrier(); } if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) { mm_write(globalRow, globalCol, acc, globalId); } } `}var Ade=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=Nx(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${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],s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.vecSize,a=r,o=[s,a],i=[a,r];return[ua(o,this.aShape.slice(1)),ua(i,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2(row, col * 4), vec2(uniforms.dimAOuter, uniforms.dimInner))) { return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]; } return vec4(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2(row, col * 4), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]; } return vec4(0.0)`,n="",s="";if(this.activation){let o=Bo(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4, outCoord : vec3) -> vec4 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${o} }`:n=` fn activation(a : vec4, outCoord : vec3) -> vec4 { ${o} }`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${n} fn mm_readA(row : i32, col : i32, globalId : vec3) -> vec4 { 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) -> vec4 { 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, globalId : vec3) { if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2]) { var value = valueIn; let batch = i32(globalId.z); let outCoord = vec3(batch, row, col * 4); ${r} ${s} setOutput(outCoord[0], outCoord[1], outCoord[2], value); } } ${this.outputShape[1]>1?EC([this.vecSize,this.workPerThread,1],this.workGroupSize):yde(this.workGroupSize)} `}};function Dx(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return` var mm_Asub : array, ${n}>; var mm_Bsub : array, ${r}>; ${Me()} { 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) / ${r} + 1; var acc : array, ${e[1]}>; var ACached : f32; var BCached : array; // 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 = ${r} / ${t[0]}; let tileColA = i32(localId.x) * ColPerThreadA; let RowPerThreadB = ${r} / ${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 * ${r} + 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 * ${r} + inputRow, globalCol + innerCol, globalId); } } workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < ${r}; 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 xde(e){return` let TileSize = ${e[0]*4}; var mm_Asub : array, ${e[0]}>; ${Me()} { 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(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(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 RC=class{constructor(e,t,n,s=!1,r=!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 l=s?e[1]:e[2];this.workGroupSize=Nx(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let c=a!=null,u=i!=null;c&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=u;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,l]:[this.outputShape[0],l,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),v.assert(s%this.workGroupSize[0]==0&&s%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[ua(r,this.aShape.slice(1)),ua(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(row, col), vec2(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(row, col), vec2(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(row, col), vec2(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(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return B.numbers[batch * batchBSize + col * uniforms.dimInner + row]; } return 0.0;`;let n="",s="";if(this.activation){let o=Bo(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3) -> f32 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${o} }`:n=` fn activation(a : f32, outCoord : vec3) -> f32 { ${o} } `,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${n} fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { let batchASize = uniforms.aShape[1] * uniforms.aShape[2]; let batch = i32(globalId.z); ${e} } fn mm_readB(row : i32, col : i32, globalId : vec3) -> 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) { var value = valueIn; let batch = i32(globalId.z); let outCoord = vec3(batch, row, col); ${r} ${s} setOutput(batch, row, col, value); } ${this.outputShape[1]>1?Dx([this.workPerThread,this.workPerThread,1],this.workGroupSize):xde(this.workGroupSize)} `}};function bde(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return` var mm_Asub1 : array, ${t}>; var mm_Bsub1 : array, ${s}>; var mm_Asub2 : array, ${t}>; var mm_Bsub2 : array, ${s}>; // 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. ${Me()} { 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) / ${s} + 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 + ${s}; mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId); globalRowB = globalRowB + ${s}; } } 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 + ${s}; mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId); globalRowB = globalRowB + ${s}; } else { // Compute acc values for a single thread. for (var k = 0; k < ${s}; 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 + ${s}; mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId); globalRowB = globalRowB + ${s}; } else { // Compute acc values for a single thread. for (var k = 0; k < ${s}; 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 >= ${t} && writeCol >= 0) { mm_write(writeCol, globalCol, acc, globalId); } } `}var vde=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimInner))) { return A.numbers[batch * batchASize + row * uniforms.dimInner + col]; } return 0.0;`,t=`if (coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col]; } return 0.0;`,n="",s="";if(this.activation){let o=Bo(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3) -> f32 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${o} }`:n=`fn activation(a : f32, outCoord : vec3) -> f32 { ${o} }`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${n} fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { let batchASize = uniforms.aShape[1] * uniforms.aShape[2]; let batch = i32(globalId.z); ${e} } fn mm_readB(row : i32, col : i32, globalId : vec3) -> 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) { if (coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimBOuter))) { let batch = i32(globalId.z); let outCoord = vec3(batch, row, col); var value = valueIn; ${r} ${s} setOutput(batch, row, col, value); } } ${bde(this.workGroupSize)} `}};function nt(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var wde={kernelName:Ci,backendName:"webgpu",kernelFunc:nt};function _x({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=y===A||y===1||A===1;v.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let w=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[y,d,h]:[y,h,d],S=s?[A,f,p]:[A,p,f],N=nt({inputs:{x:e},backend:r,attrs:{shape:k}}),R=nt({inputs:{x:t},backend:r,attrs:{shape:S}}),P=[N,R],$=Math.max(y,A),D=d%4==0&&f%4==0&&!n&&!s&&f>=32,T;!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?T=new vde(k,S,[$,h,f],a,l,o):D?T=new Ade(k,[$,h,f],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):T=new RC(k,[$,h,f],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let O=[N,R];a&&O.push(a),o&&O.push(o);let B=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],H=r.runWebGPUProgram(T,O,e.dtype,B),z=nt({inputs:{x:H},backend:r,attrs:{shape:w}});P.push(H);for(let X of P)r.disposeData(X.dataId);return z}function kde(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return _x({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Ide={kernelName:mo,backendName:"webgpu",kernelFunc:kde},$C=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { ${wp(this.op,!1)} } ${Me()} { ${He()} if(index < uniforms.size) { let areal = getARealAtOutCoordsByGlobalId(globalId, index); let aimag = getAImagAtOutCoordsByGlobalId(globalId, index); let breal = getBRealAtOutCoordsByGlobalId(globalId, index); let bimag = getBImagAtOutCoordsByGlobalId(globalId, index); setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag)); } } `}},Sde=class{constructor(e,t,n,s){this.variableNames=["A","B"];let r=256;this.workGroupSize=[r,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%(this.workGroupSize[0]*this.workPerThread)==0,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}_${this.sizeFit}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAAtOutCoordsByCoords(coords); let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}]; let b = getBAtOutCoordsByCoords(coords);`,n=this.sizeFit?`let coords = getCoordsFromFlatIndex(flatIndex); ${t} setOutputFlat(flatIndex, binaryOperation(a, b));`:`if(flatIndex < uniforms.size) { let coords = getCoordsFromFlatIndex(flatIndex); ${t} setOutputFlat(flatIndex, binaryOperation(a, b)); }`;return` fn binaryOperation(a : f32, b : f32) -> f32 { ${wp(this.op,!1)} } var sharedBuf : array; ${Me()} { ${He()} // 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; ${n} } } `}},Cde=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.fitShape=this.size%this.workGroupSize[0]==0,this.shaderKey=`binaryVec4_${e}_${this.fitShape}`,this.size=v.sizeFromShape(this.outputShape)/this.workPerThread}getUserCode(){let e,n=`fn binaryOperation(a : vec4, b : vec4) -> vec4 { ${wp(this.op,this.isVec4)} }`;return this.fitShape?e=` ${n} ${Me()} { ${He()} let a = vec4(A.numbers[index]); let b = vec4(B.numbers[index]); setOutputFlat(index, binaryOperation(a, b)); } `:e=` ${n} ${Me()} { ${He()} if (index < uniforms.size) { let a = vec4(A.numbers[index]); let b = vec4(B.numbers[index]); setOutputFlat(index, binaryOperation(a, b)); } } `,e}},DC=class{constructor(e,t,n){this.variableNames=["A","B"];let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%s==0,this.shapesFit=v.arraysEqual(t,n)&&this.sizeFit,this.workPerThread=this.sizeFit||this.shapesFit?1:2,this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=`binary_${e}_${this.sizeFit}_${this.shapesFit}`,this.op=e}getUserCode(){let e,n=` fn binaryOperation(a : f32, b : f32) -> f32 { ${wp(this.op,!1)} }`;return this.shapesFit?e=` ${n} ${Me()} { ${He()} let a = f32(A[index]); let b = f32(B[index]); setOutputFlat(index, binaryOperation(a, b)); } `:this.sizeFit?e=` ${n} ${Me()} { ${He()} let coords = getCoordsFromFlatIndex(index); let a = getAAtOutCoordsByCoords(coords); let b = getBAtOutCoordsByCoords(coords); setOutputFlat(index, binaryOperation(a, b)); } `:e=` ${n} ${Me()} { ${He()} for (var i = 0; i < ${this.workPerThread}; i = i + 1 ) { let flatIndex = index * ${this.workPerThread} + i; if(flatIndex < uniforms.size) { let coords = getCoordsFromFlatIndex(flatIndex); let a = getAAtOutCoordsByCoords(coords); let b = getBAtOutCoordsByCoords(coords); setOutputFlat(flatIndex, binaryOperation(a, b)); } } } `,e}};function _C(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new Cde(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Sde(e,t,n,a):new DC(e,t,n)}function tr(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Tde={kernelName:Wa,backendName:"webgpu",kernelFunc:tr};function mc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=tr({inputs:{x:s},backend:n}),l=tr({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var Nde={kernelName:jc,backendName:"webgpu",kernelFunc:mc},t0=class{constructor(e,t){this.variableNames=["A"];let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.size=v.sizeFromShape(this.outputShape),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { ${fc(this.op,!1)} } ${Me()} { ${He()} if (index < uniforms.size) { let a = getAAtOutCoordsByGlobalId(globalId, index); setOutputFlat(index, unaryOperation(a)); } } `}};function $n({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let c=o.tensorMap.get(a.dataId),u=t(c.values,i);return o.makeTensorInfo(a.shape,i,u)}let l=new t0(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function Xn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let d=l.tensorMap.get(o.dataId),p=l.tensorMap.get(i.dataId),h,f;if(e!==je.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},b={dataId:A.dataId,dtype:A.dtype,shape:i.shape},w=_C(e,o.shape,i.shape);return l.runWebGPUProgram(w,[x,b],Ln(y.dtype,A.dtype))});else{let g=new $C(je.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),y=new $C(je.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),A=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=mc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||Ln(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let d=l.tensorMap.get(o.dataId).values,p=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?E.fromUint8ToStringArray(d):d,f=o.dtype==="string"?E.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,c);return l.makeTensorInfo(g,c,m)}let u=_C(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:Ede,ceilImpl:Rde,concatImpl:$de,equalImpl:Dde,expImpl:_de,expm1Impl:Pde,floorImpl:Fde,gatherNdImpl:Ode,gatherV2Impl:Mde,greaterEqualImpl:zde,greaterImpl:Lde,lessEqualImpl:Bde,lessImpl:Wde,logImpl:Vde,maxImpl:Ude,maximumImpl:Gde,minimumImpl:Hde,multiplyImpl:jde,negImpl:qde,notEqualImpl:Xde,prodImpl:Kde,rangeImpl:Zde,rsqrtImpl:Yde,simpleAbsImpl:Jde,sliceImpl:Qde,stridedSliceImpl:epe,stringNGramsImpl:tpe,subImpl:npe,tileImpl:spe,transposeImpl:rpe,uniqueImpl:age}=BA,ape=$n({opType:Fe.ABS,cpuKernelImpl:Jde}),ope={kernelName:ti,backendName:"webgpu",kernelFunc:ape},ipe=Xn({opSnippet:je.ADD,cpuKernelImpl:Ede,supportsComplex:!0}),lpe={kernelName:qr,backendName:"webgpu",kernelFunc:ipe},upe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}AtOutCoordsByCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return` ${Me()} { ${He()} 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, ${t}); } } } `}};function cpe(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return tr({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Ln(i,l)),a=s.map(i=>i.shape),o=new upe(a);return n.runWebGPUProgram(o,s,r)}var dpe={kernelName:ka,backendName:"webgpu",kernelFunc:cpe},PC=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="axis : i32;";let s=[t];E.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r,a]=E.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r;let o=v.sizeFromShape(a);this.reductionFactor=2;let i=256,l=Math.min(Math.ceil(o/this.reductionFactor),i);this.workGroupSize=[l,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((c,u)=>u)},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=this.workGroupSize[0]>1,t=` var xBestIndices : array; var xBestValues : array; `,n=` xBestIndices[localId.x] = bestIndex; xBestValues[localId.x] = bestValue; for(var currentSize = WorkGroupSize; currentSize > 1; currentSize = DIV_CEIL(currentSize, ${this.reductionFactor})) { workgroupBarrier(); for (var w = 0; w < ${this.reductionFactor}; w = w + 1) { let i = i32(localId.x) * ${this.reductionFactor} + w; if (i < currentSize) { let candidateIndex = xBestIndices[i]; let candidate = xBestValues[i]; if(candidate ${this.op} bestValue && !isNanCustom(candidate)) { bestValue = candidate; bestIndex = candidateIndex; } } } xBestIndices[localId.x] = bestIndex; xBestValues[localId.x] = bestValue; } if (localId.x == 0u) { setOutputFlatI32(flatOutputIndex, i32(bestIndex)); } `,s=ln(this.outputShape.length),r=(i,l)=>this.outputShape.length===1?i:`${i}[${l}]`,a=i=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${i}]`;return` fn DIV_CEIL(a : i32, b : i32) -> i32 { return ((a - 1) / b + 1); } let WorkGroupSize = ${this.workGroupSize[0]}; ${e?t:""} // 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(globalId : vec3, globalIndex : i32) -> vec2{ let outputCoords : ${s} = getOutputCoords(globalId, globalIndex); 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 = ${a(`${this.inputShape.length} - r`)}; if (${this.inputShape.length} - r == uniforms.axis) { inputStride = stride; } else { offset = offset + ${r("outputCoords","i")} * stride; i = i - 1; } stride = stride * length; } return vec2(offset, inputStride); } fn getInputIndex(coordInfo : vec2, index : i32) -> i32{ return coordInfo[0] + coordInfo[1] * index; } ${Me()} { ${He()} let coordInfo = getInputCoordInfo(globalId, index); var bestIndex = 0; var bestValue = x.numbers[getInputIndex(coordInfo, bestIndex)]; let Length = ${a("uniforms.axis")}; let WorkPerThread = DIV_CEIL(Length, WorkGroupSize); for (var w = 0; w < WorkPerThread; w = w + 1) { let i = i32(globalId.x) * WorkPerThread + w; if (i < Length) { let candidate = x.numbers[getInputIndex(coordInfo, i)]; if (candidate ${this.op} bestValue && !isNanCustom(f32(candidate))) { bestValue = candidate; 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pad : vec2; dilation : vec2; convDims : vec2; filterDims : vec2;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${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"),` ${Me()} { ${He()} let coords = getOutputCoords(globalId, index); if (coordsInBounds4D(coords, uniforms.outShape)) { let batch = coords[0]; let xRCCorner = vec2(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} } } setOutput(batch, coords[1], coords[2], coords[3], ${t}); } } `}},OC=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` ${Me()} { ${He()} let coords = getOutputCoords(globalId, index); let batch = coords[0]; let d = coords[3]; if (all(coords < uniforms.outShape)) { let xRCCorner = coords.yz * uniforms.stride; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; let value = getX(batch, xRCorner, xCCorner, d); setOutput(batch, coords[1], coords[2], d, value); } } `}};function bpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return tr({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new OC(u):(d=new FC(u,"avg"),p.push({type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]})),n.runWebGPUProgram(d,[r],r.dtype,p)}var vpe={kernelName:Sa,backendName:"webgpu",kernelFunc:bpe};function wpe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return _x({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var kpe={kernelName:Ca,backendName:"webgpu",kernelFunc:wpe},Ipe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=t,this.rank=t.length,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ln(e.length)}; `,this.shaderKey="slice",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=ln(this.rank),t=Spe(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Px[a]} = uniforms.start[${a}] + coords.${Px[a]};`),` ${Me()} { ${He()} if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getOutputCoords(globalId, index); ${n.join(` `)} setOutputFlat(index, getSource(${t})); } } `}},Px=["x","y","z","w","u","v"];function Spe(e){if(e===1)return"sourceLoc";if(e<=6)return Px.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function kp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=yn.parseSliceParams(r,a,o);if(yn.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=Qde(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let c=new Ipe(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var Cpe={kernelName:$i,backendName:"webgpu",kernelFunc:kp},Tpe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=nt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=kl({inputs:{x:f},backend:n,attrs:{perm:c}}),g=nt({inputs:{x:m},backend:n,attrs:{shape:u}}),y=kp({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(A=>n.disposeData(A.dataId)),y},Npe={kernelName:ni,backendName:"webgpu",kernelFunc:Tpe},MC=Xn({opSnippet:je.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Xde}),Epe={kernelName:xi,backendName:"webgpu",kernelFunc:MC};function Ip(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return tr({inputs:{x:r.complexTensorInfos.real},backend:n})}var Rpe={kernelName:td,backendName:"webgpu",kernelFunc:Ip};function $pe(e,t){let n=new t0(e.shape,Fe.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Fx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return tr({inputs:{x:r},backend:n});let o=jt(r.shape),i=Fx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=mc({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=Ip({inputs:{input:r},backend:n}),i=Fx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=tr({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return $pe(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=MC({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Dpe={kernelName:Ta,backendName:"webgpu",kernelFunc:Fx},_pe=$n({opType:Fe.CEIL,cpuKernelImpl:Rde}),Ppe={kernelName:Na,backendName:"webgpu",kernelFunc:_pe},Fpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4",this.size=v.sizeFromShape(this.outputShape)/4}getUserCode(){return` ${Me()} { ${He()} if(index < uniforms.size) { let value = getAAtOutCoordsByGlobalId(globalId, index); var clampedValue : vec4; 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); } } `}},Ope=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return` ${Me()} { ${He()} if(index < uniforms.size) { let value = getAAtOutCoordsByGlobalId(globalId, index); if (isNanCustom(value)) { setOutputFlat(index, value); return; } setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } `}};function Mpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new Fpe(r.shape):i=new Ope(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var zpe={kernelName:Xr,backendName:"webgpu",kernelFunc:Mpe},Lpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shapes=e,this.shaderKey=`concat${e}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=new Array(this.shapes.length-1),t=[];if(e.length>0){e[0]=this.shapes[0][1];for(let a=1;aIp({inputs:{input:m},backend:n})),d=e.map(m=>n0({inputs:{input:m},backend:n})),p=Ox(u,t,n),h=Ox(d,t,n),f=mc({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeData(m.dataId)),d.forEach(m=>n.disposeData(m.dataId)),n.disposeData(p.dataId),n.disposeData(h.dataId),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(y=>{let A=v.sizeFromShape(y.shape.slice(t));return nt({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),d=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),p=E.computeOutShape(u.map(y=>y.shape),1),h=u[0].shape[0]===1,f=$de(d,p,s,h),m=E.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(y=>n.disposeData(y.dataId)),g}let{tensors2D:a,outShape:o}=Wpe(e,t,n),i=new Lpe(a.map(u=>u.shape)),l=n.runWebGPUProgram(i,a,a[0].dtype);a.forEach(u=>n.disposeData(u.dataId));let c=nt({inputs:{x:l},backend:n,attrs:{shape:o}});return n.disposeData(l.dataId),c}function Wpe(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>nt({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function zC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return tr({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),Ox(i,a,n)}var Vpe={kernelName:si,backendName:"webgpu",kernelFunc:zC},Upe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2; stride : vec2; dilation : vec2; outWidth : i32; itemsPerBlockRow : i32; inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return` ${Me()} { ${He()} for(var i = 0; i<${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[${t}] && d1 >= 0) { value = getA(d0, d1, ch); } } setOutputFlat(flatIndex, value); } } } `}};function LC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=n.dataFormat==="channelsLast",u=!1,d=!1,p=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],h=nt({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=nt({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=_x({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=nt({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Gpe({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=n,x=A==="channelsLast",b=l*c*u,w=m*f,k=[w,b],S=!1,N=!1,R=[],P=nt({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),$=nt({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(P),R.push($);let D=new Upe(k,x),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],O=s.runWebGPUProgram(D,[P],P.dtype,T),B=nt({inputs:{x:O},backend:s,attrs:{shape:[1,k[0],k[1]]}});R.push(O),R.push(B);let H=[1,k[0],k[1]],z=new RC(H,[1,w,n.outChannels],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,N),X=H[1],ee=H[2],J=n.outChannels,Q=[{type:"int32",data:[X]},{type:"int32",data:[J]},{type:"int32",data:[ee]}],te=s.runWebGPUProgram(z,[B,$],B.dtype,Q),K=x?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],oe=nt({inputs:{x:te},backend:s,attrs:{shape:K}});R.push(te);for(let ce of R)s.disposeData(ce.dataId);return oe}var BC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2; pad : vec2; stride : vec2; dilation : vec2; dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.isVec4=!0,this.outputShape=e.outShape,v.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=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,a),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,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}_${this.fitB}`}getShapeFit(e){let t=this.workGroupSize[1]*e[1],n=this.workGroupSize[0]*e[0],s=n,r=[t,s],a=[s,n],o=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],l=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ua(r,[o,l]),ua(a,[l,i])]}getSampleAWithRemainder(e){return`let flatIndex${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${e} == 1) { temp = vec4(curData${e}.yzw, nextData${e}.x); } elseif (divBy4Remainder${e} == 2) { temp = vec4(curData${e}.zw, nextData${e}.xy); } elseif (divBy4Remainder${e} == 3) { temp = vec4(curData${e}.w, nextData${e}.xyz); } } `}getUserCode(){let t=EC([4,4,1],this.workGroupSize),r=`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( 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(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(0.0); }`:`var temp = vec4(0.0); ${this.getSampleAWithRemainder(1)} resData = temp; if (WCol == (uniforms.filterDims[1] - 1)) { coord = vec4( coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0); ${this.getSampleAWithRemainder(2)} if (inChCoord == 0) { resData = vec4(resData.xyz, temp.x); } elseif (inChCoord == 1) { resData = vec4(resData.xy, temp.xy); } else { resData = vec4(resData.x, temp.xyz); } } `} return resData;`,a=this.fitA?`${r}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) { ${r} } return vec4(0.0); `,o=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2(row, col * 4), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return W.numbers[row * uniforms.dimBOuter / 4 + col]; } return vec4(0.0); `,i="",l="";if(this.activation){let d=Bo(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4, outCoord : vec4) -> vec4 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${d} }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(a: vec4) -> vec4 { let b = getLeakyreluAlphaAtOutCoords(); ${d} }`,new Error("Leakyrelu is not supported.");i=` fn activation(a : vec4, outCoord : vec4) -> vec4 { ${d} }`}l="value = activation(value, outCoord);"}let c=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${i} fn mm_readA(row : i32, col : i32, globalId : vec3) -> vec4 { let r = row; let c = col * 4; var batch = i32(globalId.z); ${a} } fn mm_readB(row : i32, col : i32, globalId : vec3) -> vec4 { ${o} } fn mm_write(row : i32, col : i32, valueInput : vec4, globalId : vec3) { var batch = i32(globalId.z); var value = valueInput; if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter) { let outCoord = vec4( batch, row / uniforms.outShape[2], row % uniforms.outShape[2], col * 4); ${c} ${l} setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3], value); } } ${t} `}},WC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2; pad : vec2; stride : vec2; dilation : vec2; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ex(this.dispatchLayout,this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ua(s,[a,i]),ua(r,[i,o])]}getUserCode(){let e=Dx(this.elementsPerThread,this.workGroupSize),t=` let outRow = row / uniforms.outShape[2]; let outCol = row % uniforms.outShape[2]; let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]); let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1]; let coord = vec4( batch, outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0], outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1], col % uniforms.xShape[3]); // The bounds checking is always needed since we use it to pad zero for the // 'same' padding type. if(coordsInBounds4D(coord, uniforms.xShape)) { return x.numbers[getFlatIndex4D(coord, uniforms.xShape)]; } return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${t} } return 0.0; `,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return W.numbers[row * uniforms.dimBOuter + col]; } return 0.0; `,r="",a="";if(this.activation){let l=Bo(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4) -> f32 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${l} }`:r=` fn activation(a : f32, outCoord : vec4) -> f32 { ${l} } `,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${r} fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { var batch = i32(globalId.z); ${n} } fn mm_readB(row : i32, col : i32, globalId : vec3) -> f32 { ${s} } fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3) { var batch = i32(globalId.z); var value = valueInput; let outCoord = vec4( batch, row / uniforms.outShape[2], row % uniforms.outShape[2], col); ${o} ${a} result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value; } ${e} `}},VC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2; pad : vec2; stride : vec2; dilation : vec2;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Bo(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4) -> f32{ let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${r} }`:e=` fn activation(a : f32, outCoord : vec4) -> f32{ ${r} } `,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(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(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(batch, row, col, chan); if (coordsInBounds4D(coord, uniforms.outShape)) { ${n} ${t} setOutput(batch, row, col, chan, value); } } ${Me()} { ${He()} let coords = getOutputCoords(globalId, index); 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 Hpe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=n,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!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 LC({x:r,filter:a,convInfo:p,backend:s});if(Z().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return Gpe({x:r,filter:a,convInfo:p,backend:s});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=Z().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new VC(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new BC(p):h=new WC(p),!g){let y=p.outShape[1]*p.outShape[2],A=p.outShape[3],x=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[y]},{type:"int32",data:[A]},{type:"int32",data:[x]})}return s.runWebGPUProgram(h,[r,a],r.dtype,m)}var jpe={kernelName:Ea,backendName:"webgpu",kernelFunc:Hpe},qpe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2; pads : vec2; stride : vec2; outBackprop : vec4; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ex(this.dispatchLayout,this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return` fn mm_readA(row : i32, col : i32, globalId : vec3) -> 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( 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) -> 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(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) { var batch = i32(globalId.z); var value = valueInput; let outCoord = vec4( batch, row / uniforms.outShape[2], row % uniforms.outShape[2], col); result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value; } ${Dx(this.elementsPerThread,this.workGroupSize)} `}},Xpe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2; pads : vec2; stride : vec2; outBackprop : vec4;",this.workGroupSize=[64,1,1],this.outputShape=e.inShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return` ${Me()} { ${He()} let coords = getOutputCoords(globalId, index); if (coordsInBounds4D(coords, uniforms.outShape)) { let batch = coords[0]; let d1 = coords[${n}]; let dyCorner = vec2(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 (${this.isChannelsLast}) { let xValue = getDy(batch, idyR, idyC, d2); let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } else { let xValue = getDy(batch, d2, idyR, idyC); let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } } } } setOutput(coords[0], coords[1], coords[2], coords[3], dotProd); } } `}};function Kpe(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Z().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Xpe(p);else{f=new qpe(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Zpe={kernelName:Ra,backendName:"webgpu",kernelFunc:Kpe},Ype=$n({opType:Fe.COS}),Jpe={kernelName:$a,backendName:"webgpu",kernelFunc:Ype},Qpe=$n({opType:Fe.COSH}),ehe={kernelName:Da,backendName:"webgpu",kernelFunc:Qpe},the=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1];let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${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` fn writeResult(coords : vec4, value : f32) { if (coordsInBounds4D(coords, uniforms.outShape)) { setOutput(coords[0], coords[1], coords[2], coords[3], value); } } ${Me()} { ${He()} let height_ratio = f32(${n}); let width_ratio = f32(${a}); let coords = getOutputCoords(globalId, index); 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 = ${s}; let width_scale = ${o}; let in_y = ${r}; if( in_y < 0.0 || in_y > ${e} ) { writeResult(coords, uniforms.extrapolationValue); return; } let in_x = ${i}; if( in_x < 0.0 || in_x > ${t} ) { writeResult(coords, uniforms.extrapolationValue); return; } let sourceFracIndexCR = vec2(in_x,in_y); if(${this.methodId} == 1) { // Compute the four integer indices. let sourceFloorCR = vec2(sourceFracIndexCR); let sourceCeilCR = vec2(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(sourceFloorCR); let top = topLeft + (topRight - topLeft) * fracCR.x; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; let newValue = top + (bottom - top) * fracCR.y; writeResult(coords, newValue); } else { // Compute the coordinators of nearest neighbor point. let sourceNearestCR = vec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); let newValue = getImage( bInd, sourceNearestCR.y, sourceNearestCR.x, d); writeResult(coords,newValue); } } `}},nhe=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new the(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},she={kernelName:ai,backendName:"webgpu",kernelFunc:nhe},rhe=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.size=v.sizeFromShape(this.outputShape),this.dataFormat=t}getUserCode(){return` ${Me()} { ${He()} if (index < uniforms.size) { let coords = getOutputCoords(globalId, 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 = ${this.getInputSamplingString()}; setOutputFlat(index, rlt); } }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ahe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new rhe(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var ohe={kernelName:oi,backendName:"webgpu",kernelFunc:ahe},UC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2; stride : vec2; dilation : vec2; inDims : vec2;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Bo(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4, globalId : vec3, globalIndex : i32) -> vec4 { let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, globalIndex); ${r} }`:e=` fn activation(a : vec4, globalId : vec3, globalIndex : i32) -> vec4 { ${r} } `,t="dotProd[i] = activation(dotProd[i], globalId, index);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);":"";return` ${e} ${Me()} { ${He()} let batch = 0; let r = i32(globalId.x); let c = i32(globalId.y) * 4; let d2 = i32(globalId.z) * 4; let xRCCorner = vec2(r, c) * uniforms.stride - uniforms.pad; let d1 = d2; let q = 0; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; var wVals : array, 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, 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(0.0); } else { xVals[wR][wC] = getX(batch, xR, xC, d1); } } } var dotProd : array, 4>; dotProd[0] = vec4(0.0); dotProd[1] = vec4(0.0); dotProd[2] = vec4(0.0); dotProd[3] = vec4(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(batch, r, c + i, d2); if (coordsInBounds4D(coords, uniforms.outShape)) { ${n} ${t} setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]); } } } `}},GC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2; stride : vec2; dilation : vec2; inDims : vec2;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.activation}_${this.convInfo.outChannels/this.convInfo.inChannels}`}getUserCode(){let e=this.convInfo.outChannels/this.convInfo.inChannels,t="",n="";if(this.activation){let a=Bo(this.activation,!1);this.hasPreluActivation?t=`fn activation(a : f32, globalId : vec3, index : i32) -> f32 { let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, index); ${a} }`:t=` fn activation(a : f32, globalId : vec3, index : i32) -> f32 { ${a} } `,n="dotProd = activation(dotProd, globalId, index);"}let s=this.addBias?"dotProd = dotProd + getBiasAtOutCoordsByGlobalId(globalId, index);":"";return` ${t} fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) { let coord = vec4(batch, row, col, chan); if (coordsInBounds4D(coord, uniforms.outShape)) { setOutput(batch, row, col, chan, value); } } ${Me()} { ${He()} let coords = getOutputCoords(globalId, index); let batch = coords[0]; let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad; let d2 = coords[3]; let d1 = d2 / ${e}; let q = d2 - d1 * ${e}; let inputRowStart = xRCCorner.x; let inputColStart = xRCCorner.y; let inputRowEnd = inputRowStart + ${this.convInfo.filterHeight} * uniforms.dilation[0]; let inputColEnd = inputColStart + ${this.convInfo.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 < ${this.convInfo.filterHeight}; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilation[0]; for (var wC = 0; wC < ${this.convInfo.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 < ${this.convInfo.filterHeight}; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilation[0]; if (xR < 0 || xR >= uniforms.inDims[0]) { continue; } for (var wC = 0; wC < ${this.convInfo.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; } } } ${s} ${n} writeResult(batch, coords[1], coords[2], d2, dotProd); } `}};function ihe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?p=new UC(d):p=new GC(d);let h=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}];return n.runWebGPUProgram(p,[r,a],r.dtype,h)}var lhe={kernelName:_a,backendName:"webgpu",kernelFunc:ihe},HC=Xn({opSnippet:je.MUL,cpuKernelImpl:jde,supportsComplex:!0}),uhe={kernelName:Za,backendName:"webgpu",kernelFunc:HC},che=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.inputShape=[e.batchSize,e.inSize];let[s]=E.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=s.length===0?[1]:s,this.reductionFactor=2;let r=256,a=Math.min(Math.ceil(e.inSize/this.reductionFactor),r);this.workGroupSize=[a,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((o,i)=>i)},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.reduceType=t,this.shaderKey=`reduce_${t}_${n}`}getUserCode(){let e=this.workGroupSize[0]>1,t="",n="0.0";this.reduceType==="min"||this.reduceType==="max"?(t=` if (isNanCustom(candidate)) { bestValue = uniforms.NAN; } elseif (candidate ${this.reduceType==="min"?"<":">"} bestValue) { bestValue = candidate; }`,n="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?t=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(t=" bestValue = bestValue * candidate; ",n="1.0");let s=this.reduceType==="mean"?"setOutputFlat(flatOutputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(flatOutputIndex, bestValue);",r=` var xBestValues : array; `,a=` xBestValues[localId.x] = bestValue; ${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`bestValue = ${n};`:" "} var currentSize = WorkGroupSize; for(; currentSize > 1;) { workgroupBarrier(); for (var w = 0; w < ${this.reductionFactor}; w = w + 1) { let i = i32(localId.x) * ${this.reductionFactor} + w; if (i < currentSize) { let candidate = xBestValues[i]; ${t} } } workgroupBarrier(); xBestValues[localId.x] = bestValue; currentSize = DIV_CEIL(currentSize, ${this.reductionFactor}); ${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`if(currentSize > 1) { bestValue = ${n}; }`:""} } if (localId.x == 0u) { ${s} } `;return` fn DIV_CEIL(a : i32, b : i32) -> i32 { return ((a - 1) / b + 1); } let WorkGroupSize = ${this.workGroupSize[0]}; ${e?r:""} fn getOffset(globalId : vec3, index : i32) -> i32 { let outputCoords = getOutputCoords(globalId, index); let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize; return offset; } ${Me()} { ${He()} let offset= getOffset(globalId, index); var bestValue = ${n}; let Length = uniforms.reduceSize; let WorkPerThread = DIV_CEIL(Length, WorkGroupSize); for (var w = 0; w < WorkPerThread; w = w + 1) { let i = i32(globalId.x) * WorkPerThread + w; if (i < Length) { let candidate = f32(x.numbers[offset + i]); ${t} } } let flatOutputIndex = i32(globalId.y); ${e?a:s} } `}};function Sp(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=E.getAxesPermutation(l,a),u=e;c!=null&&(u=kl({inputs:{x:e},attrs:{perm:c},backend:r}),l=E.getInnerMostAxes(l.length,a),o.push(u)),E.assertAxesAreInnerMostDims(s,l,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=E.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=Ude(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=Kde(u.shape,u.dtype,m,l);f=r.makeTensorInfo(A,x,y);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),y=v.sizeFromShape(u.shape)/m,A={windowSize:m,inSize:m,batchSize:y,outSize:1},x=s==="mean"?"float32":fd(e.dtype),b=[{type:"int32",data:[m]}],w=new che(A,s,x),k=r.runWebGPUProgram(w,[u],x,b);o.push(k),f=nt({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Mx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sp(r,a,o,"sum",n)}var dhe={kernelName:io,backendName:"webgpu",kernelFunc:Mx};function phe(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m=0&&(p=Mx({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var hhe={kernelName:Zc,backendName:"webgpu",kernelFunc:phe},fhe=$n({opType:Fe.ELU}),mhe={kernelName:Fa,backendName:"webgpu",kernelFunc:fhe},ghe=Xn({opSnippet:je.EQUAL,dtype:"bool",cpuKernelImpl:Dde}),yhe={kernelName:ii,backendName:"webgpu",kernelFunc:ghe},jC=$n({opType:Fe.EXP,cpuKernelImpl:_de,dtype:"float32"}),Ahe={kernelName:Oa,backendName:"webgpu",kernelFunc:jC};function zx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),nt({inputs:{x:a},backend:s,attrs:{shape:i}})}var xhe={kernelName:li,backendName:"webgpu",kernelFunc:zx},bhe=$n({opType:Fe.EXPM1,cpuKernelImpl:Pde}),vhe={kernelName:ui,backendName:"webgpu",kernelFunc:bhe},whe=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workPerThread=4,this.workGroupSize=[16,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="fill",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return` ${Me()} { ${He()} for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { setOutputFlat(flatIndex, uniforms.value); } } } `}};function s0(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new whe(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var khe={kernelName:ru,backendName:"webgpu",kernelFunc:s0},Ihe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return` ${Me()} { ${He()} if (index < uniforms.size) { let coords = getOutputCoords(globalId, index); let coordX = uniforms.xShape[2] - coords[2] - 1; let outputValue = getX(coords[0], coords[1], coordX, coords[3]); setOutputFlat(index, outputValue); } } `}},She={kernelName:ci,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Ihe(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Che=$n({opType:Fe.FLOOR,cpuKernelImpl:Fde}),The={kernelName:Ma,backendName:"webgpu",kernelFunc:Che},Nhe=Xn({opSnippet:je.INT_DIV,dtype:"int32"}),Ehe={kernelName:za,backendName:"webgpu",kernelFunc:Nhe},Rhe=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},qC=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=dce(s,o,t,a),l=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"}})};function XC(e,t,n,s="",r=""){return(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r+e.shaderKey}function KC(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=v.sizeFromShape(r),l=v.computeStrides(r),c=n.makeTensorInfo(r,"int32"),u=n.getFromPixelsProgram(a?"import":"copyExternal");u.updateOutputShape(r);let d=[c.shape],p=[c.dtype,a?"import":"copyExternal"],h=XC(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>qC(n.device,u,f.pipelineLayout,[],c,!0));u.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:u.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(c.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let y=[i,o,...l,...u.dispatch];u.setUniform(n.device,y);let A;if(a){let x={source:t};A=n.device.importExternalTexture(x)}else A=u.inputTexture.createView();return n.runFromPixelsProgram(u,g.bufferInfo.buffer,f,A,c.dataId),c}var $he={kernelName:ad,backendName:"webgpu",kernelFunc:Dhe},gc;function Dhe(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement,c=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[u,d]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[d,u,a];if(Z().getBool("WEBGPU_USE_IMPORT")&&o)return KC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((o||i)&&(gc==null&&(gc=document.createElement("canvas").getContext("2d")),gc.canvas.width=u,gc.canvas.height=d,gc.drawImage(r,0,0,u,d),r=gc.canvas),c||l||o||i)return KC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let y=h.length,A=0;for(let x=0;x(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } `}},Phe={kernelName:La,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,c=n,u=[s,o,i],d=null;a!=null&&(d=a.shape,u.push(a));let p=null;r!=null&&(p=r.shape,u.push(r));let h=new _he(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function Fhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),y=o!=null,A=i!=null,x;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 LC({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=Z().getBool("WEBGPU_USE_NAIVE_CONV2D"),w=g.inChannels%4==0&&g.outChannels%4==0,k=[g.padInfo.top,g.padInfo.left],S=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...k]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(b)x=new VC(g,y,h,A);else{w?x=new BC(g,y,h,A):x=new WC(g,y,h,A);let R=g.outShape[1]*g.outShape[2],P=g.outShape[3],$=g.filterHeight*g.filterWidth*g.inShape[3];S.push({type:"int32",data:[R]},{type:"int32",data:[P]},{type:"int32",data:[$]})}let N=[r,a];return y&&N.push(o),A&&N.push(i),n.runWebGPUProgram(x,N,r.dtype,S)}var Ohe={kernelName:go,backendName:"webgpu",kernelFunc:Fhe};function Mhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p}=s,h=u;h==null&&(h=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let f=E.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),m=[r,a],g=o!=null,y=i!=null;g&&m.push(o),y&&m.push(i);let A;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?A=new UC(f,g,p,y):A=new GC(f,g,p,y);let x=[{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]}];return n.runWebGPUProgram(A,m,"float32",x)}var zhe={kernelName:yo,backendName:"webgpu",kernelFunc:Mhe},Lhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.size=v.sizeFromShape(this.outputShape),this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${ln(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` ${Me()} { ${He()} let coords = getOutputCoords(globalId, 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; } if (index < uniforms.size) { setOutputFlat(index, getA(flattenIndex, coords[1])); } } `}};function Bhe(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=E.prepareAndValidate(s,r),p=nt({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=nt({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),x=n.bufferSync(s),b=Ode(A,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new Lhe(o,[c,u]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),y=nt({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),y}var Whe={kernelName:pi,backendName:"webgpu",kernelFunc:Bhe},Vhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=Uhe(this.aShape,"i32");return` ${Me()} { ${He()} let resRC = getOutputCoords(globalId, index); if (index < uniforms.size) { setOutputFlat(index, getA(${e})); } } `}};function Uhe(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=nt({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=nt({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let w=n.tensorMap.get(m.dataId).values,k=We(m.shape,m.dtype,w),N=n.tensorMap.get(f.dataId).values,R=We(f.shape,f.dtype,N),P=Mde(R,k,g);return h.forEach($=>n.disposeData($.dataId)),n.makeTensorInfo(d.outputShape,P.dtype,P.values)}let y=new Vhe(f.shape,g),A=n.runWebGPUProgram(y,[f,m],f.dtype);h.push(A);let x=nt({inputs:{x:A},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeData(b.dataId)),x}var Hhe={kernelName:di,backendName:"webgpu",kernelFunc:Ghe},jhe=Xn({opSnippet:je.GREATER,cpuKernelImpl:Lde,dtype:"bool"}),qhe={kernelName:hi,backendName:"webgpu",kernelFunc:jhe},Xhe=Xn({opSnippet:je.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:zde}),Khe={kernelName:Ba,backendName:"webgpu",kernelFunc:Xhe},Zhe=Xn({opSnippet:je.LESS,dtype:"bool",cpuKernelImpl:Wde}),Yhe={kernelName:mi,backendName:"webgpu",kernelFunc:Zhe},Jhe=Xn({opSnippet:je.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Bde}),Qhe={kernelName:gi,backendName:"webgpu",kernelFunc:Jhe},efe=$n({opType:Fe.LOG,cpuKernelImpl:Vde}),tfe={kernelName:Va,backendName:"webgpu",kernelFunc:efe},nfe=Xn({opSnippet:je.LOGICAL_AND,dtype:"bool"}),sfe={kernelName:yi,backendName:"webgpu",kernelFunc:nfe},rfe=$n({opType:Fe.LOGICAL_NOT}),afe={kernelName:uu,backendName:"webgpu",kernelFunc:rfe};function ZC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return Sp(r,a,o,"max",n)}var ofe={kernelName:Ua,backendName:"webgpu",kernelFunc:ZC},ife=Xn({opSnippet:je.MAX,cpuKernelImpl:Gde}),lfe={kernelName:Ga,backendName:"webgpu",kernelFunc:ife};function ufe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l),d,p=[];if(u.filterHeight===1&&u.filterWidth===1){if(v.arraysEqual(u.inShape,u.outShape))return tr({inputs:{x:r},backend:n});d=new OC(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new FC(u,"max"),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]});return n.runWebGPUProgram(d,[r],r.dtype,p)}var cfe={kernelName:Ha,backendName:"webgpu",kernelFunc:ufe};function dfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return Sp(r,o,a,"mean",n)}var pfe={kernelName:ja,backendName:"webgpu",kernelFunc:dfe};function hfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sp(r,a,o,"min",n)}var ffe={kernelName:qa,backendName:"webgpu",kernelFunc:hfe},mfe=Xn({opSnippet:je.MIN,cpuKernelImpl:Hde}),gfe={kernelName:Xa,backendName:"webgpu",kernelFunc:mfe},yfe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,c)=>`uniforms.pad${c}[0]`).join(","),n=this.xShape.map((l,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=ln(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${Me()} { ${He()} let start = ${o}(${t}); let end = ${o}(${n}); var outC = getOutputCoords(globalId, index); if (index < uniforms.size) { for (var i = 0; i < ${e}; i = i + 1) { if (${a} < ${s}) { ${a} = ${s} * 2 - ${a} - ${this.offset}; } elseif(${a} >= ${r}) { ${a} = (${r} - 1) * 2 - ${a} + ${this.offset}; } } let coords = outC - start; setOutputFlat(index, getX(${i})); } } `}},Afe={kernelName:Ka,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new yfe(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function xfe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=qde(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new t0(s.shape,Fe.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var bfe={kernelName:Ai,backendName:"webgpu",kernelFunc:xfe};function vfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Zs.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var wfe={kernelName:bi,backendName:"webgpu",kernelFunc:vfe};function kfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=Zs.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Ife={kernelName:vi,backendName:"webgpu",kernelFunc:kfe};function r0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Ip({inputs:{input:s},backend:n}),a=r0({inputs:{x:r},backend:n}),o=n0({inputs:{input:s},backend:n}),i=r0({inputs:{x:o},backend:n}),l=mc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return s0({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Sfe={kernelName:Li,backendName:"webgpu",kernelFunc:r0};function YC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Ip({inputs:{input:s},backend:n}),a=YC({inputs:{x:r},backend:n}),o=n0({inputs:{input:s},backend:n}),i=r0({inputs:{x:o},backend:n}),l=mc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return s0({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Cfe={kernelName:wi,backendName:"webgpu",kernelFunc:YC};function Tfe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return zx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=zx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=zC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var Nfe={kernelName:Ii,backendName:"webgpu",kernelFunc:Tfe},Efe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2;`}),this.xShape=e,this.shaderKey="pad",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.xShape.length,t=ln(e),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),s=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${Me()} { ${He()} let start = ${r}; let end = ${a}; if (index < uniforms.size) { let outC = getOutputCoords(globalId, index); if (${o} || ${i}) { setOutputFlat(index, uniforms.constantValue); } else { let coords = outC - start; setOutputFlat(index, getX(${l})); } } } `}},JC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(c=>v.arraysEqual(c,[0,0])))return tr({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return s0({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(c=>i.push({type:"int32",data:[c[0],c[1]]}));let l=new Efe(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Rfe={kernelName:Ya,backendName:"webgpu",kernelFunc:JC},$fe=Xn({opSnippet:je.POW}),Dfe={kernelName:Ja,backendName:"webgpu",kernelFunc:$fe};function _fe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new DC(je.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Pfe={kernelName:Qa,backendName:"webgpu",kernelFunc:_fe};function Ffe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sp(r,a,o,"prod",n)}var Ofe={kernelName:Si,backendName:"webgpu",kernelFunc:Ffe},Mfe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Zde(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},zfe={kernelName:pu,backendName:"webgpu",kernelFunc:Mfe},QC=Xn({opSnippet:je.DIV}),Lfe={kernelName:Pa,backendName:"webgpu",kernelFunc:QC},Bfe=$n({opType:Fe.RELU}),Wfe={kernelName:eo,backendName:"webgpu",kernelFunc:Bfe},Vfe=$n({opType:Fe.RELU6}),Ufe={kernelName:no,backendName:"webgpu",kernelFunc:Vfe},Gfe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeBilinear_${s}_${r}_${this.outputShape[1]>1}_${this.outputShape[2]>1}`}getUserCode(){let e=this.alignCorners&&this.outputShape[1]>1,t=this.alignCorners&&this.outputShape[2]>1;return` ${Me()} { ${He()} let coords = getOutputCoords(globalId, index); if (all(coords < uniforms.outShape)) { let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( ${e?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"}, ${t?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"}); let effectiveOutSize = vec2( ${e?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"}, ${t?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"}); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = ${this.halfPixelCenters?"(vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(rc) * effectiveInputOverOutputRatioRC"}; // Compute the four integer indices. let sourceFloorRC = vec2(sourceFracIndexRC); let sourceCeilRC = vec2( min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC))); let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d); let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d); let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d); let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d); let fracRC = sourceFracIndexRC - vec2(sourceFloorRC); let top = topLeft + (topRight - topLeft) * fracRC.y; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; let newValue = top + (bottom - top) * fracRC.x; setOutput(b, coords[1], coords[2], d, newValue); } } `}};function Hfe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=new Gfe(r.shape,l,c,a,i);return n.runWebGPUProgram(u,[r],"float32")}var jfe={kernelName:to,backendName:"webgpu",kernelFunc:Hfe},qfe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${s}_${this.outputShape[1]>1}_${this.outputShape[2]>1}_${r}`}getUserCode(){let e=this.alignCorners?"0.5":"0.0",t;this.halfPixelCenters?t="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":t="vec2(rc) * effectiveInputOverOutputRatioRC";let n=this.alignCorners&&this.outputShape[1]>1,s=this.alignCorners&&this.outputShape[2]>1;return` ${Me()} { ${He()} let coords = getOutputCoords(globalId, index); if (all(coords < uniforms.outShape)) { let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( ${n?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"}, ${s?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"}); let effectiveOutSize = vec2( ${n?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"}, ${s?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"}); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = ${t}; // Compute the coordinators of nearest neighbor point. let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z)); let sourceNearestRC = vec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${e}))); let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(b, coords[1], coords[2], d, newValue); } } `}};function Xfe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=new qfe(r.shape,l,c,a,o);return n.runWebGPUProgram(u,[r],r.dtype)}var Kfe={kernelName:fu,backendName:"webgpu",kernelFunc:Xfe},Zfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32; cosRadians : f32;`,this.shaderKey="rotate",this.size=v.sizeFromShape(this.outputShape),this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` ${Me()} { ${He()} if (index < uniforms.size) { let coords = getOutputCoords(globalId, 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)); ${this.fillSnippet} if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 && coordY < uniforms.xShape[1]) { outputValue = getX(coords[0], coordY, coordX, coords[3]); } setOutputFlat(index, outputValue); } } `}},Yfe={kernelName:Bi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Zfe(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},Jfe=$n({opType:Fe.RSQRT,cpuKernelImpl:Yde}),Qfe={kernelName:so,backendName:"webgpu",kernelFunc:Jfe},e6=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.outputShape=a,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`,this.size=v.sizeFromShape(this.outputShape);let l=ln(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let c="";n===1?c="i":n===2&&(c="i, j"),this.indicesSnippet=`getIndices(${c})`;let u="";s===1?u="i":s===2&&(u="i, coords[1]"),this.updatesSnippet=`getUpdates(${u})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return` ${Me()} { ${He()} let globalIndex = index * ${this.workPerThread}; if (globalIndex < uniforms.size) { var sum = vec4(0.0); var found = vec4(false); for (var i = 0; i < uniforms.updateSize; i = i + 1) { var flattenedIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexInside = i32(round(${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 eme(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=nt({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=nt({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=[{type:"int32",data:[l]},{type:"int32",data:[i]},{type:"int32",data:u}],y=new e6(l,i,h.shape.length,f.shape.length,u,p),A=n.runWebGPUProgram(y,[f,h,m],f.dtype,g),x=nt({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(A.dataId),n.disposeData(m.dataId),x}var tme={kernelName:Ei,backendName:"webgpu",kernelFunc:eme},nme=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o= 1.0) { setOutputFlat(index, getA(${t})); } else { setOutputFlat(index, getB(${t})); } } } `}};function sme(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new nme(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var rme={kernelName:Ri,backendName:"webgpu",kernelFunc:sme},ame=$n({opType:Fe.SIGMOID}),ome={kernelName:ao,backendName:"webgpu",kernelFunc:ame},ime=$n({opType:Fe.SIN}),lme={kernelName:ro,backendName:"webgpu",kernelFunc:ime},ume=$n({opType:Fe.SINH}),cme={kernelName:Di,backendName:"webgpu",kernelFunc:ume},t6=Xn({opSnippet:je.SUB,cpuKernelImpl:npe,supportsComplex:!0}),dme={kernelName:co,backendName:"webgpu",kernelFunc:t6};function pme(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=ZC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=nt({inputs:{x:i},backend:n,attrs:{shape:l}}),u=t6({inputs:{a:r,b:c},backend:n}),d=jC({inputs:{x:u},backend:n}),p=Mx({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=nt({inputs:{x:p},backend:n,attrs:{shape:l}}),f=QC({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(c.dataId),n.disposeData(u.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var hme={kernelName:lo,backendName:"webgpu",kernelFunc:pme},fme=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,A)=>y*A),l=[[0,0]];l.push(...o);for(let y=1+a.length;yn.disposeData(y.dataId)),g},mme={kernelName:_i,backendName:"webgpu",kernelFunc:fme};function gme(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new e6(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=nt({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var yme={kernelName:nd,backendName:"webgpu",kernelFunc:gme};function Ame(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=kp({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var xme={kernelName:Pi,backendName:"webgpu",kernelFunc:Ame},bme=$n({opType:Fe.SQRT}),vme={kernelName:oo,backendName:"webgpu",kernelFunc:bme},wme={kernelName:Au,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new t0(n.shape,Fe.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},kme=Xn({opSnippet:je.SQUARED_DIFFERENCE}),Ime={kernelName:uo,backendName:"webgpu",kernelFunc:kme},Sme=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=ln(this.outputShape.length);this.uniforms=`begin : ${t}; strides : ${t}; `,this.shaderKey="stridedSlice",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` ${Me()} { ${He()} if (index < uniforms.size) { let coords = getOutputCoords(globalId, index); setOutputFlat(index, getX(${t})); } } `}};function Cme(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:y,outShape:A}=yn.sliceInfo(r.shape,a,o,i,l,c,u,d,p),x=nt({inputs:{x:r},backend:n,attrs:{shape:y}}),b;if(h){let k=kp({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=nt({inputs:{x:k},backend:n,attrs:{shape:A}}),n.disposeData(k.dataId)}else if(A.some(k=>k===0))b=n.makeTensorInfo(A,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let N=n.tensorMap.get(x.dataId).values,R=We(x.shape,x.dtype,N),P=epe(A,R,m,f);b=n.makeTensorInfo(A,x.dtype,P.values)}else{let S=new Sme(A),N=[{type:"int32",data:f},{type:"int32",data:m}];b=n.runWebGPUProgram(S,[x],x.dtype,N)}let w=nt({inputs:{x:b},backend:n,attrs:{shape:A}});return n.disposeData(x.dataId),n.disposeData(b.dataId),w}var Tme={kernelName:Fi,backendName:"webgpu",kernelFunc:Cme};function Nme(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=tpe(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Eme={kernelName:sd,backendName:"webgpu",kernelFunc:Nme},Rme=$n({opType:Fe.TANH}),$me={kernelName:po,backendName:"webgpu",kernelFunc:Rme},Dme=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1];let n=new Array(e.length);for(let s=0;s=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"],s=[];for(let r=0;r=5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=We(r.shape,r.dtype,c),d=spe(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Dme(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var Fme={kernelName:Kr,backendName:"webgpu",kernelFunc:Pme},Ome=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(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; } ${Me()} { ${He()} let coords = getOutputCoords(globalId, index); if (coordsInBounds4D(coords, uniforms.outShape)) { 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; } } setOutput(coords[0], coords[1], coords[2], coords[3], outputValue); } } `}};function Mme(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=new Ome(g),A=o==="nearest"?1:2,x;switch(i){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return n.runWebGPUProgram(y,[r,a],"float32",b)}var zme={kernelName:Mi,backendName:"webgpu",kernelFunc:Mme};function Lme(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;mn.disposeData(m.dataId)),f}var Bme={kernelName:zi,backendName:"webgpu",kernelFunc:Lme},Wme=[Ide,ope,lpe,dpe,ype,xpe,vpe,kpe,Npe,Dpe,Ppe,zpe,Nde,Vpe,jpe,Zpe,Jpe,ehe,she,ohe,lhe,hhe,mhe,yhe,xhe,Ahe,vhe,khe,She,$he,The,Ehe,Phe,Ohe,zhe,Whe,Hhe,qhe,Khe,Tde,Bpe,Yhe,Qhe,tfe,sfe,afe,ofe,lfe,cfe,pfe,ffe,gfe,Afe,uhe,bfe,wfe,Ife,Epe,Cfe,Nfe,Rfe,Pfe,Ofe,Dfe,zfe,Rpe,Lfe,Wfe,Ufe,wde,jfe,Kfe,Yfe,Qfe,tme,rme,ome,lme,cme,Cpe,Tme,Eme,hme,mme,xme,yme,vme,wme,Ime,dme,dhe,$me,Fme,zme,mpe,Bme,Sfe];for(let e of Wme)Yr(e);var Vme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=n6(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=n6(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function n6(e,t){return`${e}_${t}`}var s6=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){v.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` [[binding(1), group(0)]] var src: ${this.useImport?"texture_external":"texture_2d"}; ${Me()} { ${He()} let flatIndexBase = index * uniforms.numChannels; let coords = getCoordsFromFlatIndex(flatIndexBase); let values = ${e}; for (var i = 0; i < uniforms.numChannels; i = i + 1) { let flatIndex = flatIndexBase + i; if (flatIndex < uniforms.size) { 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,s)=>n===this.lastUniformData[s])||(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}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Ume=class extends s6{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}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Gme=Z().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),a0=class extends Gl{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,!$x())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 Vme(this.device),this.tensorMap=new Vc(this,ts()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Z().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 a0.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:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.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 s={id:this.nextDataId()},r=v.sizeFromShape(t)*Rx(n);return n==="bool"&&e instanceof Uint8Array&&(e=Int32Array.from(e)),this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=v.sizeFromShape(n)*Rx(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}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 s6),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Ume),this.fromPixelImportProgram;default:v.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),Z().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.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 ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=E.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=CC(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values&&this.queue.writeBuffer(t.bufferInfo.buffer,0,t.values))}makeUniformsDataView(e){let t=this.acquireBuffer(e.byteLength,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(t,0,e),{offset:0,size:e.byteLength,buffer:t}}arrayToDataView(e,t){let n=4,s=new DataView(new ArrayBuffer(t*n)),r=0;return e.forEach(a=>{let o=a.data;if(a.type!=="int32"&&a.type!=="float32"&&a.type!=="uint32")throw new Error(`${a.type} not supported!`);a.type==="int32"?o.forEach(i=>{s.setInt32(r*n,i,!0),r++}):a.type==="uint32"?o.forEach(i=>{s.setUint32(r*n,i,!0),r++}):o.forEach(i=>{s.setFloat32(r*n,i,!0),r++})}),s}computePadding(e){let t=0,n=0,s=0,r=[];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:v.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}n=Math.ceil(t/i)*i-t;for(let l=0;lR.shape),l="int32";i.map(R=>{o.push({type:l,data:R})});let c=v.computeStrides(r.shape);o.push({type:l,data:c}),e.size!=null&&o.push({type:l,data:[e.size]}),o.push({type:"uint32",data:e.dispatch}),s&&(o=[...o,...s]);let u=null,d=this.computePadding(o),p=d.byteLength;u=this.makeUniformsDataView(d);let h=t.map((R,P)=>{if(R.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(R.dataId),{dtype:this.tensorMap.get(R.dataId).dtype,shape:R.shape,name:e.variableNames[P]}});this.uploadToGPU(r.dataId);let f=h.map(R=>R.dtype).concat(r.dtype),m=h.map(R=>E.getBroadcastDims(R.shape,r.shape)),g=h.map(R=>v.arraysEqual(R.shape,r.shape)).join("_"),y=m.map(R=>R.join("_")).join(";"),A=XC(e,i,f,y,g),{bindGroupLayout:x,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),w=this.getAndSavePipeline(A,()=>qC(this.device,e,b,h,r)),k=this.activeTimers!=null,S=Rhe(this.device,x,t.map(R=>this.tensorToBinding(R)),this.tensorToBinding(r),u);this.ensureCommandEncoderReady();let N=this.getComputePass();if(k&&this.supportTimeQuery&&N.writeTimestamp(this.querySet,0),N.setPipeline(w),N.setBindGroup(0,S),N.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),k&&this.supportTimeQuery&&N.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(R=>{this.commandQueueOwnedIds.add(R.dataId)}),this.commandQueueOwnedIds.add(r.dataId),u){let R={byteSize:p,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:u.buffer};this.uniformDisposalQueue.push(R)}return Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),k&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{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(r),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 s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[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),r/1e6}shouldExecuteOnCPU(e,t=Gme){return Z().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&v.sizeFromShape(n.shape)a0,webgpu_util:()=>SC});wu.isBrowser()&&$x()&&qi("webgpu",async()=>{Z().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Z().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has("timestamp-query");s?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. 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r0e=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],a0e=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],o0e=[33,133,362,263,1,78,308],yge=r0e.map(e=>Tp[e]),Age=a0e.map(e=>Tp[e]),xge=o0e.map(e=>Tp[e]);var l6=e=>({startPoint:_e(e,[0,0],[-1,2]),endPoint:_e(e,[0,2],[-1,2])});var Np=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],l0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2],jx=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],qx=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],u6=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:s}},Xx=(e,t,n)=>{let s=t.shape[1],r=t.shape[2];return $e.cropAndResize(t,[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]],[0],n)},Ep=(e,t=1.5)=>{let n=l0(e),s=Np(e),r=[t*s[0]/2,t*s[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks}},Rp=e=>{let t=l0(e),n=Np(e),s=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-s),Math.round(t[1]-s)],endPoint:[Math.round(t[0]+s),Math.round(t[1]+s)],landmarks:e.landmarks}},u0=e=>{let t=e.map(s=>s[0]),n=e.map(s=>s[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},c0=[[1,0,0],[0,1,0],[0,0,1]],i0e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),l0e=(e,t)=>i0e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var c6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Cl=(e,t)=>{let n=0;for(let s=0;s{let n=[];for(let s=0;s{let n=[],s=e.length;for(let r=0;r{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=c6(t[0],t[1]),o=d6(a,r),i=c6(-t[0],-t[1]);return d6(o,i)},c0e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Cl(t[0],n),-Cl(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},d0e=(e,t)=>[Cl(e,t[0]),Cl(e,t[1])];function h6(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s[a[0]/r*(d[0]-r/2),a[1]/r*(d[1]-r/2),d[2]||0]),i=n!==0?p6(n,[0,0]):c0,l=n!==0?o.map(d=>[...d0e(d,i),d[2]]):o,c=n!==0?c0e(s):c0,u=[...l0({startPoint:t.startPoint,endPoint:t.endPoint}),1];return l.map(d=>[Math.round(d[0]+Cl(u,c[0])),Math.round(d[1]+Cl(u,c[1])),Math.round(d[2]||0)])}function Kx(e,t,n){let[s,r]=e.landmarks.length>=Gx.count?Gx.symmetryLine:Cp.symmetryLine,a=l0e(e.landmarks[s],e.landmarks[r]),o=l0({startPoint:e.startPoint,endPoint:e.endPoint}),i=[o[0]/t.shape[2],o[1]/t.shape[1]],l=$e.rotateWithOffset(t,a,0,i),c=p6(-a,o),u=Xx({startPoint:e.startPoint,endPoint:e.endPoint},l,[n,n]),d=fe(u,255);return ne(u),ne(l),[a,c,d]}var m6=6,Ws,Zx=[],g6=null,Vs=0,$p=()=>Vs;async function y6(e){var t;return ie.initial&&(Ws=null),Ws?e.debug&&ae("cached model:",Ws.modelUrl):(Ws=await ut(ct(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Ws||!Ws.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",Ws.modelUrl)),Vs=Ws.inputs[0].shape?Ws.inputs[0].shape[2]:0,Vs===-1&&(Vs=64),Zx=h6(Vs),g6=dr(Zx),Ws}function p0e(e){let t=_e(e,[0,1],[-1,2]),n=ue(t,g6),s=_e(e,[0,3],[-1,2]),r=fe(s,Vs),a=fe(n,Vs),o=fe(r,2),i=xe(a,o),l=ue(a,o),c=L(i,Vs),u=L(l,Vs);return Ru([c,u],1)}async function A6(e,t){var c,u,d,p;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return{boxes:[]};let[n,s,r]=j(()=>{let h=$e.resizeBilinear(e,[Vs,Vs]),f=xe(fe(h,127.5),.5),m=Ws==null?void 0:Ws.execute(f),g;if(Array.isArray(m)){let b=m.sort((N,R)=>N.size-R.size),w=kt([b[0],b[2]],2),k=kt([b[1],b[3]],2),S=kt([k,w],1);g=dt(S,0)}else g=dt(m);let y=p0e(g),A=_e(g,[0,0],[-1,1]),x=dt(ns(A));return[g,y,x]}),a=await $e.nonMaxSuppressionAsync(s,r,((c=t.face.detector)==null?void 0:c.maxDetected)||0,((u=t.face.detector)==null?void 0:u.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0),o=await a.array();ne(a);let i=[],l=await r.data();for(let h=0;h(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let m=_e(s,[o[h],0],[1,-1]),g=j(()=>G(dt(_e(n,[o[h],m6-1],[1,-1])),[m6,-1]));i.push({box:l6(m),landmarks:g,anchor:Zx[o[h]],confidence:f}),ne(m)}}return ne(n),ne(s),ne(r),{boxes:i,scaleFactor:[e.shape[2]/Vs,e.shape[1]/Vs]}}var nr,Wo=0,h0e=2.3,Yx=Lr.leftEyeLower0,Jx=Lr.rightEyeLower0,Ac={leftBounds:[Yx[0],Yx[Yx.length-1]],rightBounds:[Jx[0],Jx[Jx.length-1]]},xc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function x6(e){var t;return ie.initial&&(nr=null),nr?e.debug&&ae("cached model:",nr.modelUrl):(nr=await ut(ct(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!nr||!nr.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load 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m=_s(c.find(b=>b.shape[1]===100),1),g=(await m.data())[0];ne(m);let y=await c.find(b=>b.shape[1]===100).data();u.age=Math.round(y[g-1]>y[g+1]?10*g-100*y[g-1]:10*g+100*y[g+1])/10;let x=await c.find(b=>b.shape[1]===1024).data();u.descriptor=[...x],c.forEach(b=>ne(b))}p0[n]=u,E6=s,i(u)})):null}var m0e=["angry","disgust","fear","happy","sad","surprise","neutral"],un,h0=[],$6=0,sb=Number.MAX_SAFE_INTEGER,rb=[.2989,.587,.114];async function D6(e){var t;return ie.initial&&(un=null),un?e.debug&&ae("cached model:",un.modelUrl):(un=await ut(ct(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!un||!un.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",un.modelUrl)),un}async function ab(e,t,n,s){var r;return un?sb<(((r=t.face.emotion)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&$6===s&&h0[n]&&h0[n].length>0?(sb++,h0[n]):(sb=0,new Promise(async a=>{var g,y;let o=$e.resizeBilinear(e,[(un==null?void 0:un.inputs[0].shape)?un.inputs[0].shape[2]:0,(un==null?void 0:un.inputs[0].shape)?un.inputs[0].shape[1]:0],!1),[i,l,c]=xn(o,3,3);ne(o);let u=L(i,rb[0]),d=L(l,rb[1]),p=L(c,rb[2]);ne(i),ne(l),ne(c);let h=sf([u,d,p]);ne(u),ne(d),ne(p);let f=j(()=>L(xe(h,.5),2));ne(h);let m=[];if((g=t.face.emotion)==null?void 0:g.enabled){let A=await(un==null?void 0:un.predict(f)),x=await A.data();ne(A);for(let b=0;b(((y=t.face.emotion)==null?void 0:y.minConfidence)||0)&&m.push({score:Math.min(.99,Math.trunc(100*x[b])/100),emotion:m0e[b]});m.sort((b,w)=>w.score-b.score)}ne(f),h0[n]=m,$6=s,a(m)})):null}var Dp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],g0e=Dp.length,_p=Dp.reduce((e,t,n)=>(e[t]=n,e),{}),y0e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Yge=y0e.map(([e,t])=>[_p[e],_p[t]]),_6=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function P6(e){let t=e.reduce(({maxX:n,maxY:s,minX:r,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(s,i),minX:Math.min(r,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function F6(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(c,u)=>({id:u,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/s,c.box[2]/r,c.box[3]/s],box:[Math.trunc(c.box[0]*o),Math.trunc(c.box[1]*a),Math.trunc(c.box[2]*o),Math.trunc(c.box[3]*a)],keypoints:c.keypoints.map(({score:d,part:p,position:h})=>({score:d,part:p,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/s,h.y/s]}))});return e.map((c,u)=>i(c,u))}var ob=class{constructor(t,n){Te(this,"priorityQueue");Te(this,"numberOfElements");Te(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(nn?n:e}function O6(e,t,n,s){let r=n-e,a=s-t;return r*r+a*a}function cb(e,t){return{x:e.x+t.x,y:e.y+t.y}}var Cs,A0e=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],f0=1,bc=16,x0e=50**2;function M6(e,t,n,s,r,a,o=2){let i=y=>({y:a.get(y.y,y.x,e),x:a.get(y.y,y.x,a.shape[2]/2+e)}),l=(y,A,x)=>({y:ub(Math.round(y.y/bc),0,A-1),x:ub(Math.round(y.x/bc),0,x-1)}),[c,u]=s.shape,d=l(t.position,c,u),p=i(d),f=cb(t.position,p);for(let y=0;y[_p[p],_p[h]]),o=a.map(([,p])=>p),i=a.map(([p])=>p),l=t.shape[2],c=o.length,u=new Array(l),d=lb(e.part,bc,n);u[e.part.id]={score:e.score,part:Dp[e.part.id],position:d};for(let p=c-1;p>=0;--p){let h=o[p],f=i[p];u[h]&&!u[f]&&(u[f]=M6(p,u[h],f,t,n,r))}for(let p=0;pt){i=!1;break}if(!i)break}return i}function w0e(e,t){let[n,s,r]=t.shape,a=new ob(n*s*r,({score:o})=>o);for(let o=0;o{var o;let a=(o=r[s])==null?void 0:o.position;return a?O6(n,t,a.y,a.x)<=x0e:!1})}function k0e(e,t){return t.reduce((s,{position:r,score:a},o)=>(z6(e,r,o)||(s+=a),s),0)/t.length}function I0e(e,t,n,s,r,a){let o=[],i=w0e(a,t);for(;o.lengthh.score>a);let d=k0e(o,u),p=P6(u);d>a&&o.push({keypoints:u,box:p,score:Math.round(100*d)/100})}return o}async function db(e,t){let n=j(()=>{if(!Cs.inputs[0].shape)return[];let 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l.palmLandmarks.array();ne(l.box),ne(l.palmLandmarks),i.push(W6({startPoint:u,endPoint:d,palmLandmarks:p,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function S0e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function U6(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return S0e(n)}var G6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Vo(e,t){let n=0;for(let s=0;so[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>fb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return g0(y0(r),T0e)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=g0(y0(n),q6);s.palmLandmarks=[];for(let r=0;r[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=hb(s,[0,0]),c=i.map(h=>[...fb(h,l),h[2]]),u=j6(r),d=[...Pp(n),1],p=[Vo(d,u[0]),Vo(d,u[1])];return c.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(s=!0));let a=[];for(let o=0;o=n.hand.minConfidence/4){let x=G(y,[-1,3]),b=await x.array();ne(y),ne(x);let w=this.transformRawCoords(b,h,l,p),k=this.getBoxForHandLandmarks(w);this.storedBoxes[o]={...k,confidence:A};let S={landmarks:w,confidence:A,boxConfidence:i.confidence,fingerConfidence:A,box:{topLeft:k.startPoint,bottomRight:k.endPoint}};a.push(S)}else this.storedBoxes[o]=null;ne(y)}else{let 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R0e=.7,Tl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function Z6(e,t,n,s){let r=(t-s)/(e-n),a=Math.atan(r)*180/Math.PI;return a<=0?a=-a:a>0&&(a=180-a),a}function Y6(e,t){if(!e||!t)return[0,0];let n=Z6(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=Z6(e[1],e[2],t[1],t[2]);return[n,s]}function J6(e,t=1){let n=0,s=0,r=0;return e>=75&&e<=105?n=1*t:e>=25&&e<=155?s=1*t:r=1*t,[n,s,r]}function $0e(e,t,n){let s=e[0]-t[0],r=e[0]-n[0],a=t[0]-n[0],o=e[1]-t[1],i=e[1]-n[1],l=t[1]-n[1],c=e[2]-t[2],u=e[2]-n[2],d=t[2]-n[2],p=Math.sqrt(s*s+o*o+c*c),h=Math.sqrt(r*r+i*i+u*u),f=Math.sqrt(a*a+l*l+d*d),m=(f*f+p*p-h*h)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Tl.NO_CURL_START_LIMIT?y=cs.none:g>Tl.HALF_CURL_START_LIMIT?y=cs.half:y=cs.full,y}function Q6(e,t,n,s){let r;return s===Math.abs(e)?e>0?r=Ze.horizontalLeft:r=Ze.horizontalRight:s===Math.abs(t)?t>0?r=Ze.horizontalLeft:r=Ze.horizontalRight:n>0?r=Ze.horizontalLeft:r=Ze.horizontalRight,r}function e8(e,t,n,s){let r;return s===Math.abs(e)?e<0?r=Ze.verticalDown:r=Ze.verticalUp:s===Math.abs(t)?t<0?r=Ze.verticalDown:r=Ze.verticalUp:n<0?r=Ze.verticalDown:r=Ze.verticalUp,r}function D0e(e,t,n,s,r,a,o,i){let l,c=e8(e,t,n,s),u=Q6(r,a,o,i);return c===Ze.verticalUp?u===Ze.horizontalLeft?l=Ze.diagonalUpLeft:l=Ze.diagonalUpRight:u===Ze.horizontalLeft?l=Ze.diagonalDownLeft:l=Ze.diagonalDownRight,l}function _0e(e,t,n,s){let r=e[0]-t[0],a=e[0]-n[0],o=t[0]-n[0],i=e[1]-t[1],l=e[1]-n[1],c=t[1]-n[1],u=Math.max(Math.abs(r),Math.abs(a),Math.abs(o)),d=Math.max(Math.abs(i),Math.abs(l),Math.abs(c)),p=0,h=0,f=0,m=d/(u+1e-5);m>1.5?p+=Tl.DISTANCE_VOTE_POWER:m>.66?h+=Tl.DISTANCE_VOTE_POWER:f+=Tl.DISTANCE_VOTE_POWER;let g=Math.sqrt(r*r+i*i),y=Math.sqrt(a*a+l*l),A=Math.sqrt(o*o+c*c),x=Math.max(g,y,A),b=e[0],w=e[1],k=n[0],S=n[1];x===g?(k=n[0],S=n[1]):x===A&&(b=t[0],w=t[1]);let P=Y6([b,w],[k,S]),$=J6(P,Tl.TOTAL_ANGLE_VOTE_POWER);p+=$[0],h+=$[1],f+=$[2];for(let T of s){let O=J6(T,Tl.SINGLE_ANGLE_VOTE_POWER);p+=O[0],h+=O[1],f+=O[2]}let D;return p===Math.max(p,h,f)?D=e8(l,i,c,d):f===Math.max(h,f)?D=Q6(a,r,o,u):D=D0e(l,i,c,d,a,r,o,u),D}function t8(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Je.all){let o=Je.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=Y6(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of Je.all){let o=a===Je.thumb?1:0,i=Je.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=$0e(l,c,u),p=_0e(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function x0(e){if(!e||e.length===0)return null;let t=t8(e),n={};for(let s of Je.all)n[Je.getName(s)]={curl:cs.getName(t.curls[s]),direction:Ze.getName(t.directions[s])};return n}function n8(e){let t=[];if(!e||e.length===0)return t;let n=t8(e);for(let s of K6){let r=s.matchAgainst(n.curls,n.directions);r>=R0e&&t.push({name:s.name,confidence:r})}return t}var s8={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},ca,da,r8;async function gb(e,t){let n=await r8.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;rn[r].landmarks[d]);let o=n[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let u of o)u[0]i[2]&&(i[2]=u[0]),u[1]>i[3]&&(i[3]=u[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=x0(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:c})}return s}async function yb(e){var n,s,r,a,o,i;ie.initial&&(ca=null,da=null),!ca||!da?([ca,da]=await Promise.all([e.hand.enabled?ut(ct(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((s=e.hand.detector)==null?void 0:s.modelPath)||"").includes("tfhub.dev")}):null,e.hand.landmarks?ut(ct(e.modelBasePath,((r=e.hand.skeleton)==null?void 0:r.modelPath)||""),{fromTFHub:(((a=e.hand.skeleton)==null?void 0:a.modelPath)||"").includes("tfhub.dev")}):null]),e.hand.enabled&&(!ca||!ca.modelUrl?ae("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&ae("load model:",ca.modelUrl),!da||!da.modelUrl?ae("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&ae("load model:",da.modelUrl))):(e.debug&&ae("cached model:",ca.modelUrl),e.debug&&ae("cached model:",da.modelUrl));let t=new pb(ca);return r8=new mb(t,da),[ca,da]}function Ab(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function a8(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function vc(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}function Fp(e){return[Math.max(0,e[1]),Math.max(0,e[0]),Math.min(1,e[3]+e[1]),Math.min(1,e[2]+e[0])]}var Rt=[null,null],P0e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Go=[[0,0],[0,0]],F0e=["hand","fist","pinch","point","face","tip","pinchtip"],o8=1.6,O0e=512,M0e=1.2,Op=0,pa=[0,0],Xt={boxes:[],hands:[]},i8={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]};async function l8(e){var t;if(ie.initial&&(Rt[0]=null),Rt[0])e.debug&&ae("cached model:",Rt[0].modelUrl);else{wc(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Rt[0]=await ut(ct(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let n=Object.values(Rt[0].modelSignature.inputs);Go[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Go[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Rt[0]||!Rt[0].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",Rt[0].modelUrl)}return Rt[0]}async function u8(e){var t;if(ie.initial&&(Rt[1]=null),Rt[1])e.debug&&ae("cached model:",Rt[1].modelUrl);else{Rt[1]=await ut(ct(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let n=Object.values(Rt[1].modelSignature.inputs);Go[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Go[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Rt[1]||!Rt[1].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",Rt[1].modelUrl)}return Rt[1]}async function z0e(e,t){let n=[];if(!e||!Rt[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,O0e),o=Math.round(a*r/8)*8;s.resize=$e.resizeBilinear(e,[a,o]),s.cast=pe(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Rt[0].executeAsync(s.cast,P0e),s.boxes=dt(s.rawBoxes,[0,2]),s.scores=dt(s.rawScores,[0]);let i=Wn(s.scores,1);i.splice(4,1),s.filtered=Tn(i,1),ne(...i),s.max=Bn(s.filtered,1),s.argmax=_s(s.filtered,1);let l=0;s.nms=await $e.nonMaxSuppressionAsync(s.boxes,s.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data(),u=await s.max.data(),d=await s.argmax.data();for(let p of Array.from(c)){let h=_e(s.boxes,p,1),f=await h.data();ne(h);let m=Math.max(f[3]-f[1],f[2]-f[0]),g=vc([f[1],f[0],m,m],M0e),y=Fp(g),A=[Math.trunc(f[1]*pa[0]),Math.trunc(f[0]*pa[1]),Math.trunc((f[3]-f[1])*pa[0]),Math.trunc((f[2]-f[0])*pa[1])],x=u[p],b=F0e[d[p]],w={id:l++,score:x,box:A,boxRaw:g,boxCrop:y,label:b};n.push(w)}return Object.keys(s).forEach(p=>ne(s[p])),n.sort((p,h)=>h.score-p.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function c8(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Rt[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=$e.cropAndResize(e,[t.boxCrop],[0],[Go[1][0],Go[1][1]],"bilinear"),r.cast=pe(r.crop,"float32"),r.div=fe(r.cast,255),[r.score,r.keypoints]=Rt[1].execute(r.div);let a=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(o>=(n.hand.minConfidence||0)){s.fingerScore=o,r.reshaped=G(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(u=>[u[0]/Go[1][1],u[1]/Go[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);console.log(pa,t.box),s.keypoints=c.map(u=>[pa[0]*u[0]+t.box[0],pa[1]*u[1]+t.box[1],u[2]||0]),s.landmarks=x0(s.keypoints);for(let u of Object.keys(i8))s.annotations[u]=i8[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>ne(r[i]))}return s}var Nl=0;async function xb(e,t){var n,s;return Nl++,!Rt[0]||!Rt[1]||!((n=Rt[0])==null?void 0:n.inputs[0].shape)||!((s=Rt[1])==null?void 0:s.inputs[0].shape)?[]:(pa=[e.shape[2]||0,e.shape[1]||0],Op++,t.skipFrame&&Op<=(t.hand.skipFrames||0)?(console.log(Nl,"SKIP",{results:Xt.hands.length}),Xt.hands):new Promise(async r=>{console.log(Nl,"DETECT",{skipped:Op,hands:Xt.hands.length,boxes:Xt.boxes.length}),t.skipFrame&&Op<=10*(t.hand.skipFrames||0)&&Xt.hands.length>0?(Xt.hands=await Promise.all(Xt.boxes.map(o=>c8(e,o,t))),console.log(Nl,"HANDS",{hands:Xt.hands.length})):(Xt.boxes=await z0e(e,t),console.log(Nl,"BOXES",{hands:Xt.boxes.length}),Xt.hands=await Promise.all(Xt.boxes.map(o=>c8(e,o,t))),console.log(Nl,"HANDS",{hands:Xt.hands.length}),Op=0);let a=[...Xt.boxes];if(Xt.boxes.length=0,t.cacheSensitivity>0){for(let o=0;o.05&&i.box[3]/(e.shape[1]||1)>.05&&Xt.hands[o].fingerScore&&Xt.hands[o].fingerScore>(t.hand.minConfidence||0)){let l=vc(i.box,o8),c=vc(i.boxRaw,o8),u=Fp(c);Xt.boxes.push({...a[o],box:l,boxRaw:c,boxCrop:u})}}console.log(Nl,"CACHED",{hands:Xt.boxes.length})}r(Xt.hands)}))}var wb={};Mc(wb,{connected:()=>vb,kpt:()=>bb});var bb=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftThumb","leftHand","rightThumb","rightHand"],vb={leftLeg:["leftHip","leftKnee","leftAnkle","leftHeel","leftFoot"],rightLeg:["rightHip","rightKnee","rightAnkle","rightHeel","rightFoot"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist","leftPalm"],rightArm:["rightShoulder","rightElbow","rightWrist","rightPalm"],leftHand:[],rightHand:[],head:[]};var d8={initial:!0},cn=[null,null],Ho=[[0,0],[0,0]],kb=Number.MAX_SAFE_INTEGER,Ib,Sb=null,jo=[[0,0],[0,0],[0,0],[0,0]];async function p8(e){var t,n;if(d8.initial&&(cn[0]=null),!cn[0]&&((t=e.body.detector)==null?void 0:t.modelPath)){cn[0]=await ut(ct(e.modelBasePath,((n=e.body.detector)==null?void 0:n.modelPath)||""));let s=Object.values(cn[0].modelSignature.inputs);Ho[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,Ho[0][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!cn[0]||!cn[0].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",cn[0].modelUrl)}else e.debug&&cn[0]&&ae("cached model:",cn[0].modelUrl);return cn[0]}async function h8(e){var t;if(d8.initial&&(cn[1]=null),cn[1])e.debug&&ae("cached model:",cn[1].modelUrl);else{cn[1]=await ut(ct(e.modelBasePath,e.body.modelPath||""));let n=Object.values(cn[1].modelSignature.inputs);Ho[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Ho[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,((t=e.body.modelPath)==null?void 0:t.includes("lite"))?Ib=["ld_3d","output_segmentation","output_heatmap","world_3d","output_poseflag"]:Ib=["Identity","Identity_2","Identity_3","Identity_4","Identity_1"],!cn[1]||!cn[1].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",cn[1].modelUrl)}return cn[1]}function L0e(e,t){let n=e.map(o=>o.position[0]),s=e.map(o=>o.position[1]),r=[Math.min(...n),Math.min(...s),Math.max(...n)-Math.min(...n),Math.max(...s)-Math.min(...s)],a=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{keypointsBox:r,keypointsBoxRaw:a}}async function B0e(e){let t={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;jo=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],t.pad=ur(e,jo),t.resize=$e.resizeBilinear(t.pad,[Ho[1][0],Ho[1][1]]);let n=fe(t.resize,255);return Object.keys(t).forEach(s=>ne(t[s])),n}function W0e(e,t){for(let n of e)n.position=[n.position[0]*(t[0]+jo[2][0]+jo[2][1])/t[0]-jo[2][0],n.position[1]*(t[1]+jo[1][0]+jo[1][1])/t[1]-jo[1][0],n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],n.position[2]];return e}async function V0e(e,t,n){var d;let s={};s.input=await B0e(e),[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=await((d=cn[1])==null?void 0:d.execute(s.input,Ib));let r=await s.ld.data(),a=[],o=5;for(let p=0;pp+=h.score,0)/a.length)/100;if(i<(t.body.minConfidence||0))return null;let l=W0e(a,n),c=L0e(l,[n[0],n[1]]);Object.keys(s).forEach(p=>ne(s[p]));let u={};for(let[p,h]of Object.entries(vb)){let f=[];for(let m=0;mA.part===h[m]),y=l.find(A=>A.part===h[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}u[p]=f}return{id:0,score:i,box:c.keypointsBox,boxRaw:c.keypointsBoxRaw,keypoints:l,annotations:u}}async function Cb(e,t){let n=[e.shape[2]||0,e.shape[1]||0];return kb<(t.body.skipFrames||0)&&t.skipFrame?kb++:(Sb=await V0e(e,t,n),kb=0),Sb?[Sb]:[]}var Eb={};Mc(Eb,{connected:()=>Nb,kpt:()=>Tb});var Tb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Nb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var dn,Zn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Rb=Number.MAX_SAFE_INTEGER;async function $b(e){return ie.initial&&(dn=null),dn?e.debug&&ae("cached model:",dn.modelUrl):(dn=await ut(ct(e.modelBasePath,e.body.modelPath||"")),!dn||!dn.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",dn.modelUrl)),dn}function U0e(e,t){let[n,s]=e.shape;return j(()=>{let r=(i,l)=>xe(i,L(fe(i,Ee(l,"int32")),Ee(l,"int32"))),a=G(e,[s*n]),o=Bn(a,0).dataSync()[0];if(o>t){let i=_s(a,0),l=r(i,n).dataSync()[0],c=fe(i,Ee(n,"int32")).dataSync()[0];return[l,c,o]}return[0,0,o]})}async function Db(e,t){var n;return Rb<(((n=t.body)==null?void 0:n.skipFrames)||0)&&t.skipFrame&&Object.keys(Zn.keypoints).length>0?(Rb++,[Zn]):(Rb=0,new Promise(async s=>{var u;let r=j(()=>{if(!(dn==null?void 0:dn.inputs[0].shape))return null;let d=$e.resizeBilinear(e,[dn.inputs[0].shape[2],dn.inputs[0].shape[1]],!1);return L(d,2).sub(1)}),a;if(t.body.enabled&&(a=await(dn==null?void 0:dn.predict(r))),ne(r),a){Zn.keypoints.length=0;let d=a.squeeze();ne(a);let p=d.unstack(2);ne(d);for(let h=0;h(((u=t.body)==null?void 0:u.minConfidence)||0)&&Zn.keypoints.push({score:Math.round(100*g)/100,part:Tb[h],positionRaw:[f/dn.inputs[0].shape[2],m/dn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/dn.inputs[0].shape[2]),Math.round(e.shape[1]*m/dn.inputs[0].shape[1])]})}p.forEach(h=>ne(h))}Zn.score=Zn.keypoints.reduce((d,p)=>p.score>d?p.score:d,0);let o=Zn.keypoints.map(d=>d.position[0]),i=Zn.keypoints.map(d=>d.position[1]);Zn.box=[Math.min(...o),Math.min(...i),Math.max(...o)-Math.min(...o),Math.max(...i)-Math.min(...i)];let l=Zn.keypoints.map(d=>d.positionRaw[0]),c=Zn.keypoints.map(d=>d.positionRaw[1]);Zn.boxRaw=[Math.min(...l),Math.min(...c),Math.max(...l)-Math.min(...l),Math.max(...c)-Math.min(...c)];for(let[d,p]of Object.entries(Nb)){let h=[];for(let f=0;fy.part===p[f]),g=Zn.keypoints.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}Zn.annotations[d]=h}s([Zn])}))}var Pb={};Mc(Pb,{connected:()=>b0,kpt:()=>Mp,pairs:()=>_b});var Mp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_b=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],b0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var pn,El=0,G0e=1.5,Dn={boxes:[],bodies:[]},Fb=Number.MAX_SAFE_INTEGER,ds=[];async function f8(e){return ie.initial&&(pn=null),pn?e.debug&&ae("cached model:",pn.modelUrl):(wc(["size"],e),pn=await ut(ct(e.modelBasePath,e.body.modelPath||"")),!pn||!pn.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",pn.modelUrl)),El=pn.inputs[0].shape?pn.inputs[0].shape[2]:0,El===-1&&(El=256),pn}function m8(){for(let e of _b){let t=ds.find(s=>s.part===e[0]),n=ds.find(s=>s.part===e[1]);if(t&&n&&t.position[0]>n.position[0]){let s=t;t=n,n=s}}}async function g8(e,t,n,s){let r=e[0][0];ds.length=0;let a=0;for(let c=0;ct.body.minConfidence){let u=[(s[3]-s[1])*r[c][1]+s[1],(s[2]-s[0])*r[c][0]+s[0]];ds.push({score:Math.round(100*a)/100,part:Mp[c],positionRaw:u,position:[Math.round((n.shape[2]||0)*u[0]),Math.round((n.shape[1]||0)*u[1])]})}m8(),a=ds.reduce((c,u)=>u.score>c?u.score:c,0);let o=[],i=Ab(ds.map(c=>c.position),[n.shape[2],n.shape[1]]),l={};for(let[c,u]of Object.entries(b0)){let d=[];for(let p=0;pm.part===u[p]),f=ds.find(m=>m.part===u[p+1]);h&&f&&h.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&d.push([h.position,f.position])}l[c]=d}return o.push({id:0,score:a,box:i.box,boxRaw:i.boxRaw,keypoints:ds,annotations:l}),o}async function y8(e,t,n,s){let r=[];for(let a=0;at.body.minConfidence){ds.length=0;for(let u=0;u<17;u++){let d=o[3*u+2];if(d>t.body.minConfidence){let p=[(s[3]-s[1])*o[3*u+1]+s[1],(s[2]-s[0])*o[3*u+0]+s[0]];ds.push({part:Mp[u],score:Math.round(100*d)/100,positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}}m8();let l=Ab(ds.map(u=>u.position),[n.shape[2],n.shape[1]]),c={};for(let[u,d]of Object.entries(b0)){let p=[];for(let h=0;hg.part===d[h]),m=ds.find(g=>g.part===d[h+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}c[u]=p}r.push({id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...ds],annotations:c})}}return r.sort((a,o)=>o.score-a.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function Ob(e,t){return!pn||!(pn==null?void 0:pn.inputs[0].shape)?[]:(t.skipFrame||(Dn.boxes.length=0),Fb++,t.skipFrame&&Fb<=(t.body.skipFrames||0)?Dn.bodies:new Promise(async n=>{let s={};if(Fb=0,Dn.bodies=[],Dn.boxes.length>=(t.body.maxDetected||0))for(let r=0;rne(s[i]))}if(Dn.bodies.length!==t.body.maxDetected){s.resized=$e.resizeBilinear(e,[El,El],!1),s.cast=pe(s.resized,"int32"),s.res=await(pn==null?void 0:pn.predict(s.cast));let r=await s.res.array();Dn.bodies=s.res.shape[2]===17?await g8(r,t,e,[0,0,1,1]):await y8(r,t,e,[0,0,1,1]),Object.keys(s).forEach(a=>ne(s[a]))}Dn.boxes.length=0;for(let r=0;rMp.length/2){let a=vc(Dn.bodies[r].boxRaw,G0e),o=Fp(a);Dn.boxes.push(o)}n(Dn.bodies)}))}var kc=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop 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drier"},{class:80,label:"toothbrush"}];var Ts,v0=[],Mb=Number.MAX_SAFE_INTEGER,w0=2.5;async function A8(e){if(!Ts||ie.initial){Ts=await ut(ct(e.modelBasePath,e.object.modelPath||""));let t=Object.values(Ts.modelSignature.inputs);if(Ts.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Ts.inputSize)throw new Error(`cannot determine model inputSize: ${e.object.modelPath}`);!Ts||!Ts.modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",Ts.modelUrl)}else e.debug&&ae("cached model:",Ts.modelUrl);return Ts}async function H0e(e,t,n,s){let r=0,a=[];for(let c of[1,2,4])j(async()=>{var g,y;let u=c*13,d=(g=e.find(A=>A.shape[1]===u**2&&A.shape[2]===kc.length))==null?void 0:g.squeeze(),p=(y=e.find(A=>A.shape[1]===u**2&&A.shape[2]s.object.minConfidence&&x!==61){let w=(.5+Math.trunc(A%u))/u,k=(.5+Math.trunc(A/u))/u,S=f[A].map(B=>B*(u/c/t)),[N,R]=[w-w0/c*S[0],k-w0/c*S[1]],[P,$]=[w+w0/c*S[2]-N,k+w0/c*S[3]-R],D=[N,R,P,$];D=D.map(B=>Math.max(0,Math.min(B,1)));let T=[D[0]*n[0],D[1]*n[1],D[2]*n[0],D[3]*n[1]],O={id:r++,score:Math.round(100*b)/100,class:x+1,label:kc[x].label,box:T.map(B=>Math.trunc(B)),boxRaw:D};a.push(O)}}});e.forEach(c=>ne(c));let o=a.map(c=>[c.boxRaw[1],c.boxRaw[0],c.boxRaw[3],c.boxRaw[2]]),i=a.map(c=>c.score),l=[];if(o&&o.length>0){let c=await $e.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await c.data(),ne(c)}return a=a.filter((c,u)=>l.includes(u)).sort((c,u)=>u.score-c.score),a}async function zb(e,t){return Mb<(t.object.skipFrames||0)&&t.skipFrame&&v0.length>0?(Mb++,v0):(Mb=0,!ie.kernels.includes("mod")||!ie.kernels.includes("sparsetodense")?v0:new Promise(async n=>{let s=[e.shape[2],e.shape[1]],r=$e.resizeBilinear(e,[Ts.inputSize,Ts.inputSize],!1),a=fe(r,255),o=a.transpose([0,3,1,2]);ne(a),ne(r);let i;t.object.enabled&&(i=await Ts.predict(o)),ne(o);let l=await H0e(i,Ts.inputSize,s,t);v0=l,n(l)}))}var rr,Rl=0,k0=[],Lb=Number.MAX_SAFE_INTEGER;async function x8(e){if(ie.initial&&(rr=null),rr)e.debug&&ae("cached model:",rr.modelUrl);else{wc(["floormod"],e),rr=await ut(ct(e.modelBasePath,e.object.modelPath||""));let t=Object.values(rr.modelSignature.inputs);Rl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!rr||!rr.modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",rr.modelUrl)}return rr}async function j0e(e,t,n){if(!e)return[];let s=[],r=await e.array(),a=dt(e);ne(e);let o=xn(a,6,1);ne(a);let i=Tn([o[1],o[0],o[3],o[2]],1),l=dt(i);ne(i);let c=dt(o[4]),u=dt(o[5]);o.forEach(f=>ne(f));let d=await $e.nonMaxSuppressionAsync(l,c,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);ne(l),ne(c),ne(u);let 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function b8(e,t,n){var m,g;if(Wb)return{data:[],canvas:null,alpha:null};Wb=!0,Us||await Vb(n);let s=yc(e,n),r=((m=s.canvas)==null?void 0:m.width)||0,a=((g=s.canvas)==null?void 0:g.height)||0;if(!s.tensor)return{data:[],canvas:null,alpha:null};let o={};o.resize=$e.resizeBilinear(s.tensor,[Us.inputs[0].shape?Us.inputs[0].shape[1]:0,Us.inputs[0].shape?Us.inputs[0].shape[2]:0],!1),ne(s.tensor),o.norm=fe(o.resize,255),o.res=Us.predict(o.norm),o.squeeze=dt(o.res,0),o.squeeze.shape[2]===2?(o.softmax=tl(o.squeeze),[o.bg,o.fg]=Wn(o.softmax,2),o.expand=Ht(o.fg,2),o.pad=Ht(o.expand,0),o.crop=$e.cropAndResize(o.pad,[[0,0,.5,.5]],[0],[r,a]),o.data=dt(o.crop,0)):o.data=$e.resizeBilinear(o.squeeze,[a,r]);let i=Array.from(await o.data.data());if(ie.node&&!ie.Canvas&&typeof ImageData=="undefined")return n.debug&&ae("canvas support missing"),Object.keys(o).forEach(y=>ne(o[y])),{data:i,canvas:null,alpha:null};let l=Bs(r,a);await Xs.toPixels(o.data,l);let 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s=fn(ha,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round";for(let o=0;o0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*(o[2]||0)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,Gb(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 0.5)`:s.color,r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4))};r.font=s.font,o(a.annotations.index,"index"),o(a.annotations.middle,"middle"),o(a.annotations.ring,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palm,"palm")}if(s.drawPolygons&&a.annotations){let o=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}async function Kb(e,t,n){let s=fn(ha,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Lp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}async function T8(e,t,n){let s=fn(ha,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round",r.font=s.font;for(let a=0;a{let t=(d,p)=>Math.atan2(d[1]-p[1],d[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],c=Math.sqrt(l[0]**2+l[1]**2);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},R8=(e,t)=>{let n=g=>{let y=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=y,g[1]/=y,g[2]/=y,g},s=(g,y)=>{let A=g[0]-y[0],x=g[1]-y[1],b=g[2]-y[2];return[A,x,b]},r=(g,y)=>{let A=g[1]*y[2]-g[2]*y[1],x=g[2]*y[0]-g[0]*y[2],b=g[0]*y[1]-g[1]*y[0];return[A,x,b]},a=g=>{let[y,A,x,b,w,k,S,N,R]=g,P,$,D;return b<1?b>-1?(D=Math.asin(b),$=Math.atan2(-S,y),P=Math.atan2(-k,w)):(D=-Math.PI/2,$=-Math.atan2(N,R),P=0):(D=Math.PI/2,$=Math.atan2(N,R),P=0),isNaN(P)&&(P=0),isNaN($)&&($=0),isNaN(D)&&(D=0),{pitch:2*-P,yaw:2*-$,roll:2*-D}},o=g=>{let y=(x,b,w,k)=>Math.atan2(k-b,w-x);return{pitch:y(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:y(g[33][0],g[33][2],g[263][0],g[263][2]),roll:y(g[33][0],g[33][1],g[263][0],g[263][1])}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),u=n(s(c[1],c[0])),d=n(s(c[3],c[2])),p=n(r(d,u));d=r(u,p);let h=[d[0],d[1],d[2],u[0],u[1],u[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?K0e(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var Zb=async(e,t)=>{var d,p,h,f;let n,s,r,a,o,i,l,c=[];e.state="run:face",n=ot();let u=await S6(t,e.config);if(e.performance.face=Math.trunc(ot()-n),!t.shape||t.shape.length!==4)return[];if(!u)return[];for(let m=0;m0&&u[m].annotations.rightEyeIris.length>0&&u[m].annotations.leftEyeIris[0]!==null&&u[m].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(u[m].annotations.leftEyeIris[3][0]-u[m].annotations.leftEyeIris[1][0]),Math.abs(u[m].annotations.rightEyeIris[4][1]-u[m].annotations.rightEyeIris[2][1]))/t.shape[2]:0,A=e.config.face.detector.return?dt(u[m].tensor):null;ne(u[m].tensor),u[m].tensor&&delete u[m].tensor,c.push({...u[m],id:m,age:l.age,gender:l.gender,genderScore:l.genderScore,embedding:l.descriptor,emotion:o,iris:y!==0?Math.trunc(500/y/11.7)/100:0,rotation:g,tensor:A}),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),c};var $8=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},D8=e=>{if(!e)return[];let t=[];for(let n=0;n450){let s=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(s)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let 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J0e(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(C0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(T0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&ae("Warmup tfjs-node not loaded");return s}async function L8(e,t){let n=ot();if(e.state="warmup",t&&(e.config=fn(e.config,t)),!e.config.warmup||e.config.warmup==="none")return{error:"null"};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await Z0e(e):typeof Image!="undefined"||ie.Canvas!==void 0?s=await Y0e(e):s=await J0e(e);let a=ot();e.config.debug&&ae("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var Sc,Bp,Wp,N0,W8=class{constructor(t){Te(this,"version");Te(this,"config");Te(this,"result");Te(this,"state");Te(this,"process");Te(this,"tf");Te(this,"env");Te(this,"draw");Te(this,"models");Te(this,"events");Te(this,"faceTriangulation");Te(this,"faceUVMap");Te(this,"performance");Lc(this,Sc,void 0);Lc(this,Bp,void 0);Lc(this,Wp,void 0);Te(this,"gl");Te(this,"analyze",(...t)=>{if(!zc(this,Bp))return;let n=this.tf.engine().state.numTensors,s=zc(this,Sc);Bc(this,Sc,n);let r=n-s;r!==0&&ae(...t,r)});Lc(this,N0,t=>{if(!zc(this,Wp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof Ke))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Te(this,"similarity",O8);Te(this,"distance",S0);Te(this,"match",M8);Te(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});o0(),this.env=ie,ba.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Qh}/dist/`,ba.modelBasePath=this.env.browser?"../models/":"file://models/",ba.backend=this.env.browser?"humangl":"tensorflow",this.version=Lx,Object.defineProperty(this,"version",{value:Lx}),this.config=JSON.parse(JSON.stringify(ba)),Object.seal(this.config),t&&(this.config=fn(this.config,t)),this.tf=Il,this.state="idle",Bc(this,Sc,0),Bc(this,Bp,!1),Bc(this,Wp,!1),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new zp,this.draw={options:ha,canvas:(n,s)=>N8(n,s),face:(n,s,r)=>jb(n,s,r),body:(n,s,r)=>qb(n,s,r),hand:(n,s,r)=>Xb(n,s,r),gesture:(n,s,r)=>Hb(n,s,r),object:(n,s,r)=>Kb(n,s,r),person:(n,s,r)=>T8(n,s,r),all:(n,s,r)=>E8(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=T6,this.faceUVMap=N6,this.gl=Bt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ba)),this.config.backend=t}validate(t){return n2(ba,t||this.config)}image(t,n=!0){return yc(t,this.config,n)}async segmentation(t,n){return b8(t,n,this.config)}enhance(t){return tb(t)}async init(){await I0(this,!0),await this.tf.ready(),o6(this.env)}async load(t){this.state="load";let n=ot(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=fn(this.config,t)),ie.initial&&(this.config.debug&&ae(`version: ${this.version}`),this.config.debug&&ae(`tfjs version: ${this.tf.version_core}`),await I0(this)||ae("error: backend check failed"),await tf(),this.env.browser&&(this.config.debug&&ae("configuration:",this.config),this.config.debug&&ae("tf flags:",this.tf.ENV.flags))),await w8(this),ie.initial&&this.config.debug&&ae("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ie.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await k8(this),this.emit("load"));let a=Math.trunc(ot()-n);a>(this.performance.load||0)&&(this.performance.load=a)}next(t=this.result){return F8(t,this.config)}async warmup(t){return L8(this,t)}async detect(t,n){return this.state="detect",new Promise(async s=>{var y,A,x,b,w,k,S,N,R,P,$,D,T,O,B,H,z,X,ee,J,Q,te;this.state="config";let r,a;this.config=fn(this.config,n),this.state="check";let o=zc(this,N0).call(this,t);o&&(ae(o,t),s({error:o}));let i=ot();await I0(this),await this.load(),r=ot(),this.state="image";let l=yc(t,this.config);if(this.process=l,this.performance.image=Math.trunc(ot()-r),this.analyze("Get Image:"),!l.tensor){this.config.debug&&ae("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=ot(),this.config.skipFrame=await i6(this.config,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(ot()-r),this.analyze("Check Changed:");let c=[],u=[],d=[],p=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?Zb(this,l.tensor):[],this.performance.face&&delete this.performance.face):(r=ot(),c=this.config.face.enabled?await Zb(this,l.tensor):[],a=Math.trunc(ot()-r),a>0&&(this.performance.face=a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let h=this.config.body.maxDetected===-1?fn(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?(((y=this.config.body.modelPath)==null?void 0:y.includes("posenet"))?u=this.config.body.enabled?db(l.tensor,h):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("blazepose"))?u=this.config.body.enabled?Cb(l.tensor,h):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?u=this.config.body.enabled?Db(l.tensor,h):[]:((b=this.config.body.modelPath)==null?void 0:b.includes("movenet"))&&(u=this.config.body.enabled?Ob(l.tensor,h):[]),this.performance.body&&delete this.performance.body):(r=ot(),((w=this.config.body.modelPath)==null?void 0:w.includes("posenet"))?u=this.config.body.enabled?await db(l.tensor,h):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("blazepose"))?u=this.config.body.enabled?await Cb(l.tensor,h):[]:((S=this.config.body.modelPath)==null?void 0:S.includes("efficientpose"))?u=this.config.body.enabled?await Db(l.tensor,h):[]:((N=this.config.body.modelPath)==null?void 0:N.includes("movenet"))&&(u=this.config.body.enabled?await Ob(l.tensor,h):[]),a=Math.trunc(ot()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?fn(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?(((P=(R=this.config.hand.detector)==null?void 0:R.modelPath)==null?void 0:P.includes("handdetect"))?d=this.config.hand.enabled?gb(l.tensor,f):[]:((D=($=this.config.hand.detector)==null?void 0:$.modelPath)==null?void 0:D.includes("handtrack"))&&(d=this.config.hand.enabled?xb(l.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=ot(),((O=(T=this.config.hand.detector)==null?void 0:T.modelPath)==null?void 0:O.includes("handdetect"))?d=this.config.hand.enabled?await gb(l.tensor,f):[]:((H=(B=this.config.hand.detector)==null?void 0:B.modelPath)==null?void 0:H.includes("handtrack"))&&(d=this.config.hand.enabled?await xb(l.tensor,f):[]),a=Math.trunc(ot()-r),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((z=this.config.object.modelPath)==null?void 0:z.includes("nanodet"))?p=this.config.object.enabled?zb(l.tensor,this.config):[]:((X=this.config.object.modelPath)==null?void 0:X.includes("centernet"))&&(p=this.config.object.enabled?Bb(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ot(),((ee=this.config.object.modelPath)==null?void 0:ee.includes("nanodet"))?p=this.config.object.enabled?await zb(l.tensor,this.config):[]:((J=this.config.object.modelPath)==null?void 0:J.includes("centernet"))&&(p=this.config.object.enabled?await Bb(l.tensor,this.config):[]),a=Math.trunc(ot()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,u,d,p]=await Promise.all([c,u,d,p])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=ot(),m=[...D8(c),...$8(u),...P8(d),..._8(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(ot()-r)),this.performance.total=Math.trunc(ot()-i);let g=((te=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:te.shape)||[];this.result={face:c,body:u,hand:d,gesture:m,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return z8(c,u,d,m,g)}},ne(l.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Sc=new WeakMap,Bp=new WeakMap,Wp=new WeakMap,N0=new WeakMap;return Q0e;})(); /** * @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. */