human/dist/human.custom.esm.js

8052 lines
1.5 MiB

/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,kr(`Initialization of backend ${e} failed`),kr(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return kr(`Initialization of backend ${e} failed`),kr(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new 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o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),c;r!=null&&(c=_(r,"scale","batchNorm"));let u;return s!=null&&(u=_(s,"offset","batchNorm")),O(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),O(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),O(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&O(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),wu(o,i,l,u,c,a)}var E3=V({batchNorm4d_:JR});function QR(e,t,n){let s=_(e,"x","bincount"),r=_(t,"weights","bincount");O(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),O(n>=0,()=>`size must be non-negative, but got 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${s.shape}`),O(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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a=_(e,"labels","logLoss"),o=_(t,"predictions","logLoss"),i=null;n!=null&&(i=_(n,"weights","logLoss")),zn(a.shape,o.shape,"Error in logLoss: ");let l=Ee(1),c=Ee(s),u=Ot(W(a,Ns(ie(o,c)))),d=W(xe(l,a),Ns(ie(xe(l,o),c))),p=xe(u,d);return Jr(p,i,r)}var EF=V({logLoss_:NF});function RF(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=_(e,"labels","meanSquaredError"),a=_(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=_(n,"weights","meanSquaredError")),zn(r.shape,a.shape,"Error in meanSquaredError: ");let i=t1(r,a);return Jr(i,o,s)}var $F=V({meanSquaredError_:RF});function DF(e,t){let n=_(e,"labels","sigmoidCrossEntropyWithLogits"),s=_(t,"logits","sigmoidCrossEntropyWithLogits");zn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Rr(s),a=W(s,n),o=Zh(Ts(Ot(rn(s))));return ie(xe(r,a),o)}function _F(e,t,n,s=0,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=_(e,"multiClassLabels","sigmoidCrossEntropy"),o=_(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=_(n,"weights","sigmoidCrossEntropy")),zn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let c=Ee(s),u=Ee(1),d=Ee(.5);a=ie(W(a,xe(u,c)),W(d,c))}let l=DF(a,o);return Jr(l,i,r)}var PF=V({sigmoidCrossEntropy_:_F});function FF(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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a={skipEmpty:n},o={input:s,delimiter:r},i=B.runKernel(Nh,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var KF=V({stringSplit_:XF});function ZF(e,t){let n=_(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return B.runKernel(Eh,r,s)}var YF=V({stringToHashBucketFast_:ZF}),JF={fft:af,ifft:gd,rfft:of,irfft:e1},QF={hammingWindow:TP,hannWindow:bv,frame:vv,stft:$P},$e={flipLeftRight:FP,grayscaleToRGB:MP,resizeNearestNeighbor:iF,resizeBilinear:aF,rotateWithOffset:LP,cropAndResize:_P,nonMaxSuppression:WP,nonMaxSuppressionAsync:KP,nonMaxSuppressionWithScore:YP,nonMaxSuppressionWithScoreAsync:QP,nonMaxSuppressionPadded:tF,nonMaxSuppressionPaddedAsync:sF,threshold:cF,transform:pF},Tv={bandPart:fF,gramSchmidt:gF,qr:yF},eO={absoluteDifference:vF,computeWeightedLoss:Jr,cosineDistance:kF,hingeLoss:SF,huberLoss:TF,logLoss:EF,meanSquaredError:$F,sigmoidCrossEntropy:PF,softmaxCrossEntropy:MF},yd={sparseFillEmptyRows:LF,sparseReshape:WF,sparseSegmentMean:UF,sparseSegmentSum:HF},hf={stringNGrams:qF,stringSplit:KF,stringToHashBucketFast:YF},Qr=class extends c3{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else this.applyGradients(r);return Q(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return q3(e,t)}dispose(){this.iterations_!=null&&Q(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ee(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Qr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var ff=class extends Qr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=B.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>tt(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>tt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;j(()=>{let c=ie(W(i,this.rho),W(xt(o),1-this.rho)),u=W(fe(_n(ie(l,this.epsilon)),_n(ie(i,this.epsilon))),o),d=ie(W(l,this.rho),W(xt(u),1-this.rho));i.assign(c),l.assign(d);let p=ie(W(u,-this.learningRate),r);r.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Q(this.accumulatedGrads.map(e=>e.variable)),Q(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};ff.className="Adadelta";vo(ff);var mf=class extends Qr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=B.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>Iu(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=ie(o,xt(a));o.assign(i);let l=ie(W(fe(a,_n(ie(i,B.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Q(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)}};mf.className="Adagrad";vo(mf);var gf=class extends Qr{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=B.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=B.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=ie(W(c,this.beta1),W(l,1-this.beta1)),p=ie(W(u,this.beta2),W(xt(l),1-this.beta2)),h=fe(d,n),f=fe(p,s);c.assign(d),u.assign(p);let m=ie(W(fe(h,ie(_n(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(W(this.accBeta1,this.beta1)),this.accBeta2.assign(W(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Q(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Q(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(Io(this.beta1,this.iterations_+1)),this.accBeta2.assign(Io(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)}};gf.className="Adam";vo(gf);var Af=class extends Qr{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=B.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,ie(W(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)}};Af.className="Adamax";vo(Af);var xd=class extends Qr{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=B.registeredVariables[n];j(()=>{let o=ie(W(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=yn(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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Yt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},fr=class{constructor(e,t,n,s,r,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=o,this.id=iw(),a!=null&&(this.originalName=Jv(a),this.name=Qv(this.originalName)),this.rank=t.length}},EL=0,Ff=class{constructor(e,t){this.callArgs=t,this.id=EL++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},RL=0,nt=class extends le.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=RL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=ta(n)+"_"+Df(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new dr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new q(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return os(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return os(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ea(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new ea(`Layer ${this.name} is not connected, no input to return.`);return os(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new ea(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new ea(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return os(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Ct(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=Ct(this.inputSpec);if(e.length!==t.length)throw new q(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new q(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let l=Number(i),c=r.axes[i],u=l>=0?o[l]:o[o.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],l=s.shape[o];if(i!=null&&l!=null&&i!==l)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=Ct(e),s=!0;for(let a of n)if(!(a instanceof fr)){s=!1;break}let r=!0;for(let a of n)if(a instanceof fr){r=!1;break}if(s===r)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return al(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of Ct(e))a.push(o.shape);this.build(os(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=Ct(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=os(i),this.activityRegularizer!=null)throw new Be("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=$L(e),o=this.computeOutputShape(a),i,l=DL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((c,u)=>new fr(l,c,this,Ct(e),t,this.name,u)):i=new fr(l,o,this,Ct(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Be("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new ea(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new ea(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new dr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Pf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return D1(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=D1(t);for(let r=0;r<s.length;++r){let a=s[r],o=t[r],i=e[r];if(!v.arraysEqual(a.shape,i.shape))throw new q(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);n.push([o,i])}_1(n)})}addWeight(e,t,n,s,r,a,o,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=i!=null?i():$t("zeros"));let l=s.apply(t,n),c=new uw(l,n,e,a,o);return l.dispose(),r!=null&&this.addLoss(()=>r.apply(c.read())),a==null&&(a=!0),a?this._trainableWeights.push(c):this._nonTrainableWeights.push(c),c}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=Ct(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=Ct(e);t=Ct(t),n=Ct(n),s=Ct(s),r=_f(r),a=_f(a);let l=[],c=[],u=[];for(let d of i)l.push(d.sourceLayer),c.push(d.nodeIndex),u.push(d.tensorIndex);new Ff({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function $L(e){e=Ct(e);let t=[];for(let n of e)t.push(n.shape);return os(t)}function DL(e){return"float32"}function cw(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let o=s.inputTensors[a],i=s.inboundLayers[a],l=s.nodeIndices[a],c=cw(o,i,l);for(let u of c)r.indexOf(u)===-1&&r.push(u)}return r}}}var Fu=class extends nt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Df("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new fr(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Ff({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`Cannot pass any input to an InputLayer's apply() method. 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All inputs should only appear once. Found: ${this.inputs.map(A=>A.name)}`);Co(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(A=>A.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let A of this.outputs){let y=A.sourceLayer,x=A.nodeIndex,b=A.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let A of this.inputs){let y=A.sourceLayer,x=A.nodeIndex,b=A.tensorIndex;$r(x===0,"input layer has >1 nodes"),$r(b===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let y=this.inputLayers[A];if(!(y instanceof Fu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${A} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let A of this.outputLayers)this.outputNames.push(A.name);this.internalInputShapes=this.inputs.map(A=>A.shape),this.internalOutputShapes=this.outputs.map(A=>A.shape);let t={},n={},s={},r={},a={},o=[],i=(A,y,x,b,w,k)=>{(b==null||w==null||k==null)&&(b=A.sourceLayer,w=A.nodeIndex,k=A.tensorIndex);let S=b.inboundNodes[w];if(x.indexOf(S)!==-1)throw new dr(`The tensor ${A.name} at layer "${b.name}" is part of a cycle.`);if(y.indexOf(S)!==-1)return;this.containerNodes.add(Pr.nodeKey(b,w)),b.id in a||(a[b.id]=Object.keys(a).length),x.indexOf(S)===-1&&x.push(S);let E=S.inboundLayers.length;for(let $=0;$<E;$++){let F=S.inputTensors[$],R=S.inboundLayers[$],P=S.nodeIndices[$],T=S.tensorIndices[$];i(F,y,x,R,P,T)}for(y.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);o.push(S)},l=[],c=[];for(let A of this.outputs)i(A,l,c);let u=o.slice().reverse();for(let A of u){n[A.id]=A,A.id in t||(t[A.id]=0);let y=t[A.id],x=s[A.outboundLayer.id]==null?0:s[A.outboundLayer.id];y=Math.max(y,x),s[A.outboundLayer.id]=y,r[A.outboundLayer.id]=A.outboundLayer,t[A.id]=y;for(let b=0;b<A.inboundLayers.length;b++){let w=A.inboundLayers[b],k=A.nodeIndices[b],S=w.inboundNodes[k],E=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(y+1,E),n[S.id]=S}}let d={};for(let A in t){let y=t[A];y in d||(d[y]=[]),d[y].push(n[A])}let p={};for(let A in s){let y=s[A];y in p||(p[y]=[]),p[y].push(r[A])}let h=Object.keys(p).map(A=>parseInt(A,10)).sort(bf);this.layers=[];for(let A of h){let y=p[A];y.sort((x,b)=>{let w=a[x.id],k=a[b.id];return w<k?-1:w>k?1:0});for(let x of y)x instanceof Pr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,h=Object.keys(d).map(A=>parseInt(A,10)).sort(bf);let f=this.inputs.slice(),m=[];for(let A of h)for(let y of d[A]){let x=y.outboundLayer;if(x!=null){for(let b of y.inputTensors)if(f.indexOf(b)===-1)throw new dr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of y.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(A=>A.name);for(let A of g){let y=g.filter(x=>x===A).length;if(y!==1)throw new dr(`The name "${A}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Ff({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new q(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new q(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new q(`${a.length} of ${s} weights are not set: ${a}`)}_1(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${V1}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=W1(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=Ct(e);let n=new ll;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Nd(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=Ct(e);let n;return t==null?n=nl(null,e.length):n=Ct(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=_f(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(bf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],A=l.tensorIndices[f],y=`${m.name}_${g}_${A}`,x=n[y];u.push(x)}let d=c.computeOutputShape(os(u)),p=_f(d),h=c.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${c.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],c=this.outputLayersTensorIndices[o],u=`${i.name}_${l}_${c}`;a.push(u)}for(let o=0;o<a.length;o++){let i=a[o];$r(i in n),r.push(n[i])}return os(r)}runInternalGraph(e,t){t==null&&(t=nl(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],c=e[i],u=t[i];n[l.id]=[c,u]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(bf);for(let i of s){let l=this.nodesByDepth[i];for(let c of l){let u=c.outboundLayer,d=c.inputTensors,p=c.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,A,y;if(c.callArgs!=null&&(f=c.callArgs),h.length===1){let[x,b]=h[0];f.mask==null&&(f.mask=b),A=Ct(u.call(x,f)),y=Ct(u.computeMask(x,b)),m=[x],g=[b]}else m=h.map(x=>x[0]),g=h.map(x=>x[1]),f.mask==null&&(f.mask=g),A=Ct(u.call(m,f)),y=Ct(u.computeMask(m,g));if(u.activityRegularizer)throw new Be("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],w=A[x],k=y[x];n[b.id]=[w,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){$r(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,c]=n[i.id];o.push(l.shape),r.push(l),a.push(c)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Pr?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Pr.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new q(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Pr.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let u=0;u<a.inboundNodes.length;u++){let d=a.inboundNodes[u],p=Pr.nodeKey(a,u),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],A=d.nodeIndices[m],y=d.tensorIndices[m],x=Pr.nodeKey(g,A),b=t[x];b==null&&(b=0),f.push([g.name,b,y,h])}l.push(f)}}}let c={};c.name=a.name,c.className=o,c.config=i,c.inboundNodes=l,n.push(c)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=Pr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[a];s.push([o.name,c,u])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=Pr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[a];r.push([o.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let A=[],y;for(let x of g){let b=x[0],w=x[1],k=x[2];if(y=x[3]==null?{}:x[3],!(b in r)){o(m,g);return}let S=r[b];if(S.inboundNodes.length<=w){o(m,g);return}let E=S.inboundNodes[w];A.push(E.outputTensors[k])}A.length>0&&m.apply(os(A),y)}function l(m){let g=m.name,A=mr(m,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(s),r[g]=A,m.inboundNodes.forEach(x=>{if(!(x instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${x}`);o(A,x)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!Bz(a);)for(let m of u){let g=r[m.name];if(g.name in a){let A=a[g.name];delete a[g.name];for(let y of A)i(g,y)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],A=m[1],y=m[2];$r(g in r);let b=r[g].inboundNodes[A].outputTensors;d.push(b[y])}let f=t.outputLayers;for(let m of f){let g=m[0],A=m[1],y=m[2];$r(g in r);let b=r[g].inboundNodes[A].outputTensors;p.push(b[y])}return new e({inputs:d,outputs:p,name:c})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function cB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Ew(e,t){return cB(e,t,"classWeight")}async function Rw(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return or(e);if(e.shape.length===2){if(e.shape[1]>1)return Hs(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Q(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Zt(o,"float32")}else return null}function dB(e,t){return W(e,t)}var pB=32;function $w(e,t){let n,s,r=t;n=r.xs,s=r.ys,v.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let a=Dw("input",e.inputNames,n),o=Dw("output",e.outputNames,s),i=a[0].shape[0];v.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)v.assert(a[l].shape[0]===i,()=>`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<o.length;l++)v.assert(o[l].shape[0]===i,()=>`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 Dw(e,t,n){if(n instanceof Ye)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 hB(e){if(e.length===3)throw new Be("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function fB(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(_w(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=hB(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=Aw(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=yw(u,d,n.epochs,null,null,mB(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.epochs;){let g={};await p.onEpochBegin(f);let A=0,y=0;for(s||(m=await t.iterator());s?A<n.batchesPerEpoch:!0;){let x=await m.next();if(s&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${A} batches; interrupting training. 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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;f<this.inputs.length;++f)u.push({key:this.inputs[f],value:n[f]});let d=new ll(u),p=Nd(this.outputs,d,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],p[f]);r[f]!=null&&(g=dB(g,r[f]));let A=Wt(g);t.push(A),f===0?h=g:h=ie(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],A=this.metricsTensors[f][1];m=Wt(g(s[A],p[A]))}yn(m),a.push(m)}return h=Wt(h),this.calculateLosses().forEach(f=>{h=ie(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;l<this.inputs.length;++l)a.push({key:this.inputs[l],value:s[l]});let o=new ll(a),i=Nd(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=Wt(c(r[l],i[l]));l===0?n=u:n=ie(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],d=Wt(c(r[u],i[u]));t.push(d)}return t})}async fit(e,t,n={}){return xB(this,e,t,n)}async fitDataset(e,t){return fB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],o=this.makeTrainFunction()(s.concat(r)),i=[];for(let l of o){let c=await l.data();i.push(c[0])}return Q(o),os(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Bh().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Bh().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ta(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ta(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]=ta(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[ta(Vf(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ta(Vf(e)));{let e={};for(let t in this.metrics)e[t]=ta(Vf(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=Td(e.optimizer_config),n=mr(t),s;if(typeof e.loss=="string")s=sl(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>sl(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=sl(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>sl(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=sl(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=ns.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 ns.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:IB,generatedBy:`TensorFlow.js tfjs-layers v${V1}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await ns.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=ns.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let l=!0;Sw(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){Sw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};na.className="Model";le.registerClass(na);var zw=class extends na{};zw.className="Functional";le.registerClass(zw);async function SB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Td(n),r=mr(s,t);if(e.weightsManifest!=null){let a=await ns.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),Q(a)}return r}async function CB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=ns.getLoadHandlers(e,t);if(n.length===0)n.push(ns.browserHTTPRequest(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return TB(e,void 0,t)}async function TB(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=mr(Td(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}=NB(s.weightData,s.weightSpecs);i.loadWeights(c,a),i.optimizer!=null&&u.length>0&&await i.optimizer.setWeights(u),Q(c),Q(u.map(d=>d.tensor))}return i}function NB(e,t){let n=ns.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 X1=class extends na{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Df("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 X1||e instanceof na,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=dw({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=cw(this.outputs[0])}this.inboundNodes=[],new Ff({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:nl(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(mt(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 na({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 dr("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 dr("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 dr("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 dr("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 X1))throw new Be(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=mr(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}}},Gf=X1;Gf.className="Sequential";le.registerClass(Gf);function EB(e){return new na(e)}function RB(e){return new Gf(e)}function $B(e,t){return t==null&&(t={}),CB(e,t)}function Lw(e){return dw(e)}function DB(e,t){P1.registerCallbackConstructor(e,t)}var ls=class extends le.Serializable{getConfig(){return{}}},Bw=class extends ls{apply(e,t=1){return rL(e,t)}};Bw.className="elu";le.registerClass(Bw);var Ww=class extends ls{apply(e){return Z2(e)}};Ww.className="selu";le.registerClass(Ww);var Vw=class extends ls{apply(e){return Rr(e)}};Vw.className="relu";le.registerClass(Vw);var Uw=class extends ls{apply(e){return j(()=>hd(6,Rr(e)))}};Uw.className="relu6";le.registerClass(Uw);var Gw=class extends ls{apply(e){return e}};Gw.className="linear";le.registerClass(Gw);var Hw=class extends ls{apply(e){return hs(e)}};Hw.className="sigmoid";le.registerClass(Hw);var jw=class extends ls{apply(e){return oL(e)}};jw.className="hardSigmoid";le.registerClass(jw);var qw=class extends ls{apply(e){return Cu(e)}};qw.className="softplus";le.registerClass(qw);var Xw=class extends ls{apply(e){return aL(e)}};Xw.className="softsign";le.registerClass(Xw);var Kw=class extends ls{apply(e){return vu(e)}};Kw.className="tanh";le.registerClass(Kw);var K1=class extends ls{apply(e,t=-1){return $u(e,t)}};K1.className="softmax";le.registerClass(K1);var Zw=class extends ls{apply(e,t=-1){return L2(e,t)}};Zw.className="logSoftmax";le.registerClass(Zw);var Yw=class extends ls{apply(e,t=1){return j(()=>W(hs(W(e,t)),e))}};Yw.className="swish";le.registerClass(Yw);var Jw=class extends ls{apply(e){return j(()=>W(e,vu(Cu(e))))}};Jw.className="mish";le.registerClass(Jw);function Ro(e){return e.getClassName()}function Z1(e,t={}){return bd(e,le.SerializationMap.getMap().classNameMap,t,"activation")}function $o(e){if(e==null){let t={};return t.className="linear",t.config={},Z1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Z1(t)}else return e instanceof ls?e:Z1(e)}function Y1(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var Qw=class extends le.Serializable{},Rd=class extends Qw{constructor(e){super();Y1(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=Ht([1]);return this.hasL1&&(t=ie(t,Ie(W(this.l1,rn(e))))),this.hasL2&&(t=ie(t,Ie(W(this.l2,Id(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Rd.className="L1L2";le.registerClass(Rd);function _B(e){return Y1(e),new Rd({l1:e!=null?e.l1:null,l2:0})}function PB(e){return Y1(e),new Rd({l2:e!=null?e.l2:null,l1:0})}var ek={l1l2:"L1L2"};function bt(e){return p1(e)}function tk(e,t={}){return bd(e,le.SerializationMap.getMap().classNameMap,t,"regularizer")}function Dt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in ek?ek[e]:e,config:{}};return tk(n)}else return e instanceof Qw?e:tk(e)}var J1=class extends nt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ve(e);let n=Rr(e);return this.maxValue!=null&&(n=fs(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};J1.className="ReLU";le.registerClass(J1);var Q1=class extends nt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ve(e);return Kh(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Q1.className="LeakyReLU";le.registerClass(Q1);var eA=class extends nt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=$t(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Dt(e.alphaRegularizer),this.alphaConstraint=un(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 ${e.sharedAxes}`)}build(e){e=mt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Yt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ve(e),sf(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Mt(this.alphaInitializer),alphaRegularizer:bt(this.alphaRegularizer),alphaConstraint:ln(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};eA.className="PReLU";le.registerClass(eA);var tA=class extends nt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Be(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ve(e);return dd(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};tA.className="ELU";le.registerClass(tA);var nA=class extends nt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Ve(e);return W(n,he(ms(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};nA.className="ThresholdedReLU";le.registerClass(nA);var sA=class extends nt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new K1().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ve(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};sA.className="Softmax";le.registerClass(sA);function zu(e,t,n){if(typeof e=="number")return nl(e,t);if(e.length!==t)throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!eL(r))throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function gr(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function Fr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+No([n-t,0]);else if(s==="same")e=e*t;else throw new q(`Unsupport padding mode: ${s}.`);return e}function rA(e,t){return j(()=>(jt(t),t==="channelsFirst"?et(e,[0,2,3,1]):e))}function nk(e,t){return j(()=>(jt(t),t==="channelsFirst"?et(e,[0,2,3,4,1]):e))}function FB(e,t,n,s=1,r="valid",a,o=1){return j(()=>{if(a==null&&(a=cr()),jt(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 Be("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=$2(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=hr(i,n)),i})}function sk(e,t,n,s=[1,1],r="valid",a,o,i=null){return j(()=>{if(a==null&&(a=cr()),jt(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=rA(e,a);if(r==="causal")throw new Be("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=So.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 OB(e,t,n,s=[1,1,1],r="valid",a,o){return j(()=>{if(a==null&&(a=cr()),jt(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 ${e.rank}.`);let i=nk(e,a);if(r==="causal")throw new Be("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=P2(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=hr(i,n)),a==="channelsFirst"&&(i=et(i,[0,4,1,2,3])),i})}var aA=class extends nt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",aA.verifyArgs(t),this.rank=e,xn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Be(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(t.kernelSize,e,"kernelSize"),this.strides=zu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,$s(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,jt(this.dataFormat),this.activation=$o(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=$t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=un(t.biasConstraint),this.biasRegularizer=Dt(t.biasRegularizer),this.activityRegularizer=Dt(t.activityRegularizer),this.dilationRate=zu(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"&&!f1(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 ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ro(this.activation),useBias:this.useBias,biasInitializer:Mt(this.biasInitializer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},$d=class extends aA{constructor(e,t){super(e,t);this.kernel=null,$d.verifyArgs(t),this.filters=t.filters,xn(this.filters,"filters"),this.kernelInitializer=$t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=un(t.kernelConstraint),this.kernelRegularizer=Dt(t.kernelRegularizer)}build(e){e=mt(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=Ve(e);let n,s=this.bias==null?null:this.bias.read(),r=jv(this.activation.getClassName());if(r!=null&&this.rank===2)n=sk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=FB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=sk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=OB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Be("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=mt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gr(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Mt(this.kernelInitializer),kernelRegularizer:bt(this.kernelRegularizer),kernelConstraint:ln(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},rk=class extends $d{constructor(e){super(2,e);rk.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!f1(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)}.`)}},Hf=rk;Hf.className="Conv2D";le.registerClass(Hf);var ak=class extends $d{constructor(e){super(3,e);ak.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)}.`)}},jf=ak;jf.className="Conv3D";le.registerClass(jf);var oA=class extends Hf{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 ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Ve(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Fr(i,d,c,this.padding),f=Fr(l,p,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,1]));let g=_2(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=et(g,[0,3,1,2])),this.bias!=null&&(g=hr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=mt(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]=Fr(t[s],i,a,this.padding),t[r]=Fr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};oA.className="Conv2DTranspose";le.registerClass(oA);var iA=class extends jf{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 ${this.padding}`)}build(e){if(e=mt(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Ve(e);if(n.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],A=Fr(l,f,d,this.padding),y=Fr(c,m,p,this.padding),x=Fr(u,g,h,this.padding),b=[r,A,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,4,1]));let w=O3(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=hr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=mt(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]=Fr(t[s],c,o,this.padding),t[r]=Fr(t[r],u,i,this.padding),t[a]=Fr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};iA.className="Conv3DTranspose";le.registerClass(iA);var ok=class extends $d{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Dt(t.depthwiseRegularizer),this.depthwiseConstraint=un(t.depthwiseConstraint),this.pointwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Dt(t.pointwiseRegularizer),this.pointwiseConstraint=un(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Yt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Ve(e);let n;if(this.rank===1)throw new Be("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=et(e,[0,2,3,1])),n=rv(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=hr(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=Mt(this.depthwiseInitializer),e.pointwiseInitializer=Mt(this.pointwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.pointwiseRegularizer=bt(this.pointwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseConstraint),e.pointwiseConstraint=ln(this.pointwiseConstraint),e}};ok.className="SeparableConv";var lA=class extends ok{constructor(e){super(2,e)}};lA.className="SeparableConv2D";le.registerClass(lA);var ik=class extends $d{constructor(e){super(1,e);ik.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"&&!f1(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)}.`)}},uA=ik;uA.className="Conv1D";le.registerClass(uA);var cA=class extends nt{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=Ve(e),this.dataFormat==="channelsLast"){let n=wf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return wf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=wf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return wf(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}};cA.className="Cropping2D";le.registerClass(cA);var dA=class extends nt{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,jt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Yz(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=Ve(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}};dA.className="UpSampling2D";le.registerClass(dA);function MB(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=cr()),jt(r);let o=rA(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 ${t.rank}-D`);return o=cd(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}var pA=class extends aA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=$t(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=un(e.depthwiseConstraint),this.depthwiseRegularizer=Dt(e.depthwiseRegularizer)}build(e){if(e=mt(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=Ve(e);let n=MB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=hr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=mt(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=gr(t,this.kernelSize[0],this.padding,this.strides[0]),a=gr(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=Mt(this.depthwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseRegularizer),e}};pA.className="DepthwiseConv2D";le.registerClass(pA);function lk(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 uk(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 ${l}D.`);let c=[1,0].concat(pr(2,l));if(t=et(t,c),a!=null)throw new Be("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=he(he(r,"bool"),"float32"),r.rank===l-1&&(r=Kt(r,-1)),r=et(r,c)),s&&(t=Rs(t,0),r!=null&&(r=Rs(r,0)));let u=[],d,p=n,h=t.shape[0],f=as(t),m;r!=null&&(m=as(r));for(let A=0;A<h;++A){let y=f[A],x=j(()=>e(y,p));if(r==null)d=x[0],p=x[1];else{let b=j(()=>{let w=m[A],k=xe(Es(w),w),S=ie(W(x[0],w),W(p[0],k)),E=p.map(($,F)=>ie(W(x[1][F],w),W($,k)));return{output:S,newStates:E}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=Pn(u,1)),[d,g,p]})}var ck=class extends nt{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 Kf({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 pr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){$1(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;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Be("Constants support is not implemented in RNN yet.");$1(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Yt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Be("Constants support is not implemented in RNN yet.");this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${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 ea("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=>Ht([n,s])):this.states_=[Ht([n,this.cell.stateSize])];else if(e==null)Q(this.states_),this.keptStates!=null&&(Q(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Ht([n,s])):this.states_[0]=Ht([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()):Q(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>yn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=lk(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 fr){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=Ve(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 ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=uk((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],u=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?u:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return j(()=>{let t=Ht(e.shape);return t=Ie(t,[1,2]),t=kd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?w1(t,[1,n]):t):this.cell.stateSize>1?[w1(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===ck.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=mr(s,n);return new e(Object.assign(t,{cell:r}))}},sa=ck;sa.className="RNN";le.registerClass(sa);var Dd=class extends nt{},qf=class extends Dd{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,xn(this.units,"units"),this.activation=$o(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Dt(e.kernelRegularizer),this.recurrentRegularizer=Dt(e.recurrentRegularizer),this.biasRegularizer=Dt(e.biasRegularizer),this.kernelConstraint=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Pu([1,No([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Pu([1,No([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(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;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Do({ones:()=>Es(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Do({ones:()=>Es(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Dr(W(e,a),this.kernel.read()):r=Dr(e,this.kernel.read()),this.bias!=null&&(r=hr(r,this.bias.read())),o!=null&&(n=W(n,o));let i=ie(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:Ro(this.activation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};qf.className="SimpleRNNCell";le.registerClass(qf);var hA=class extends sa{constructor(e){e.cell=new qf(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(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)}};hA.className="SimpleRNN";le.registerClass(hA);var Xf=class extends Dd{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,xn(this.units,"units"),this.activation=$o(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=$o(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Dt(e.kernelRegularizer),this.recurrentRegularizer=Dt(e.recurrentRegularizer),this.biasRegularizer=Dt(e.biasRegularizer),this.kernelConstraint=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Pu([1,No([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Pu([1,No([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(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],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Do({ones:()=>Es(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Do({ones:()=>Es(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=W(e,r[0]));let c=Dr(e,this.kernel.read());this.useBias&&(c=hr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=W(s,a[0]));let u=this.recurrentKernel.read(),[d,p]=Sn(u,[2*this.units,this.units],u.rank-1),h=Dr(s,d),[f,m,g]=Sn(c,3,c.rank-1),[A,y]=Sn(h,2,h.rank-1);o=this.recurrentActivation.apply(ie(f,A)),i=this.recurrentActivation.apply(ie(m,y));let x=Dr(W(i,s),p);l=this.activation.apply(ie(g,x));let b=ie(W(o,s),W(ie(1,Ot(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ro(this.activation),recurrentActivation:Ro(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Xf.className="GRUCell";le.registerClass(Xf);var fA=class extends sa{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Xf(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(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)}};fA.className="GRU";le.registerClass(fA);var _d=class extends Dd{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,xn(this.units,"units"),this.activation=$o(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=$o(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Dt(e.kernelRegularizer),this.recurrentRegularizer=Dt(e.recurrentRegularizer),this.biasRegularizer=Dt(e.biasRegularizer),this.kernelConstraint=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Pu([1,No([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Pu([1,No([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=mt(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 Zs{apply(o,i){let l=r.apply([a]),c=new If().apply([a]),u=r.apply([a*2]);return tw(tw(l,c),u)}},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],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Do({ones:()=>Es(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Do({ones:()=>Es(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,c,u;0<this.dropout&&this.dropout<1&&(e=W(e,a[0]));let d=Dr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=W(s,o[0])),d=ie(d,Dr(s,this.recurrentKernel.read())),this.useBias&&(d=hr(d,this.bias.read()));let[p,h,f,m]=Sn(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),c=ie(W(l,r),W(i,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=W(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ro(this.activation),recurrentActivation:Ro(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};_d.className="LSTMCell";le.registerClass(_d);var mA=class extends sa{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new _d(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(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)}};mA.className="LSTM";le.registerClass(mA);var Kf=class extends Dd{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<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){$1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{al(`RNNCell_${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{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(mr(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 D1(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}_1(t)}};Kf.className="StackedRNNCells";le.registerClass(Kf);function Do(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):sw(t(),n),i=()=>Sd(o,t,s);return!r||r<=1?yn(i().clone()):Array(r).fill(void 0).map(i).map(c=>yn(c.clone()))}var dk=class extends sa{constructor(e){if(e.unroll)throw new Be("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Be("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Yt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(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=Ht(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new ea("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(()=>Ht(r)):this.states_=[Ht(r)];else if(e==null)Q(this.states_),this.keptStates!=null&&(Q(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ht(r)):this.states_[0]=Ht(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()):Q(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new q(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>yn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],c=e[i?4:3],u=gr(l,s[0],r,a[0],o[0]),d=gr(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};dk.className="ConvRNN2D";var Zf=class extends _d{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super({...e,units:t});this.filters=t,xn(this.filters,"filters"),this.kernelSize=zu(n,2,"kernelSize"),this.kernelSize.forEach(i=>xn(i,"kernelSize")),this.strides=zu(s||1,2,"strides"),this.strides.forEach(i=>xn(i,"strides")),this.padding=r||"valid",$s(this.padding),this.dataFormat=a||"channelsLast",jt(this.dataFormat),this.dilationRate=zu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>xn(i,"dilationRate"))}build(e){var t;e=mt(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 Zs{apply(u,d){let p=l.apply([c]),h=gs([c]),f=l.apply([c*2]);return v1([p,h,f])}},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 ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Do({ones:()=>Es(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(ee,Y,ne)=>!Y||!Y[ne]?ee:W(Y[ne],ee),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Do({ones:()=>Es(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),A=l(r,h,3),y=3,[x,b,w,k]=Sn(this.kernel.read(),o,y),[S,E,$,F]=this.useBias?Sn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,x,S,this.padding),u=this.inputConv(u,b,E,this.padding),d=this.inputConv(d,w,$,this.padding),p=this.inputConv(p,k,F,this.padding);let[R,P,T,M]=Sn(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,P),g=this.recurrentConv(g,T),A=this.recurrentConv(A,M);let U=this.recurrentActivation.apply(ie(c,f)),H=this.recurrentActivation.apply(ie(u,m)),z=ie(W(H,a),W(U,this.activation.apply(ie(d,g)))),X=W(this.recurrentActivation.apply(ie(p,A)),this.activation.apply(z));return[X,X,z]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ko(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?hr(r,n,this.dataFormat):r}recurrentConv(e,t){return ko(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Zf.className="ConvLSTM2DCell";le.registerClass(Zf);var gA=class extends dk{constructor(e){let t=new Zf(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};gA.className="ConvLSTM2D";le.registerClass(gA);var Yf=class extends nt{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.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ve(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Sd(()=>sw(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()}};Yf.className="Dropout";le.registerClass(Yf);var AA=class extends Yf{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};AA.className="SpatialDropout1D";le.registerClass(AA);var yA=class extends nt{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,xn(this.units,"units"),this.activation=$o(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=un(e.kernelConstraint),this.biasConstraint=un(e.biasConstraint),this.kernelRegularizer=Dt(e.kernelRegularizer),this.biasRegularizer=Dt(e.biasRegularizer),this.activityRegularizer=Dt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=mt(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=mt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=jv(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=hr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Ro(this.activation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};yA.className="Dense";le.registerClass(yA);var xA=class extends nt{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=mt(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 ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],To(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ve(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=et(n,s)}return sL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};xA.className="Flatten";le.registerClass(xA);var bA=class extends nt{constructor(e){super(e);this.supportsMasking=!0,this.activation=$o(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ro(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};bA.className="Activation";le.registerClass(bA);var vA=class extends nt{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=Ve(e),tL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};vA.className="RepeatVector";le.registerClass(vA);var wA=class extends nt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let l=s[i];if(this.isUnknown(l))if(a===null)a=i;else throw new q("Can only specifiy one unknown dimension.");else r*=l}let o=To(e);if(a!==null){if(r===0||o%r!=0)throw new q(n);s[a]=o/r}else if(o!==r)throw new q(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ve(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}};wA.className="Reshape";le.registerClass(wA);var kA=class extends nt{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 ${e.dims} instead.`);let t=pr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Yt({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return et(Ve(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};kA.className="Permute";le.registerClass(kA);var IA=class extends nt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Ve(e),s=-1;return Uh(Tu(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=-1,r=!0,a=Uh(Tu(n,this.maskValue),s,r);return W(n,he(a,n.dtype))})}};IA.className="Masking";le.registerClass(IA);var SA=class extends nt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Ct(e.inputLength))}this.inputDim=e.inputDim,xn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,xn(this.outputDim,"outputDim"),this.embeddingsInitializer=$t(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Dt(e.embeddingsRegularizer),this.activityRegularizer=Dt(e.activityRegularizer),this.embeddingsConstraint=un(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=Ve(e),Tu(e,tt(e))):null)}computeOutputShape(e){if(e=mt(e),this.inputLength==null)return[...e,this.outputDim];let t=Ct(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<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ve(e);n.dtype!=="int32"&&(n=vf(n,"int32"));let s=nw(this.embeddings.read(),G(n,[n.size]));return G(s,mt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Mt(this.embeddingsInitializer),embeddingsRegularizer:bt(this.embeddingsRegularizer),activityRegularizer:bt(this.activityRegularizer),embeddingsConstraint:ln(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};SA.className="Embedding";le.registerClass(SA);var cl=class extends nt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Be}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[mt(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=Co(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&Co(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=No(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=kd(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let c=i.shape,u=c[0],d=c.slice(1).concat([u]),p=G(i,[u].concat(To(c.slice(1))));p=et(p,[1,0]),p=G(p,d),n.push(p),r=!0}else if(l>1){let c=pr(1,l).concat([0]);n.push(et(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=G(et(G(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(pr(0,o-1));a=et(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Co(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Kt(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=lr(n,t[s]);return n})}},CA=class extends cl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};CA.className="Add";le.registerClass(CA);var TA=class extends cl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=W(t,e[n]);return t})}};TA.className="Multiply";le.registerClass(TA);var NA=class extends cl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return W(1/e.length,t)})}};NA.className="Average";le.registerClass(NA);var EA=class extends cl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Yr(t,e[n]);return t})}};EA.className="Maximum";le.registerClass(EA);var RA=class extends cl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=hd(t,e[n]);return t})}};RA.className="Minimum";le.registerClass(RA);var $A=class extends cl{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(v.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>v1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(he(Es(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Kt(t[a],-1)):s.push(t[a]);let r=It(s,this.axis);return C2(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};$A.className="Concatenate";le.registerClass($A);function Pd(e,t){for(;e<0;)e+=t;return e}function zB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Be("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 Be("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;c<o;++c)l.push(1);t=G(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let c=0;c<o;++c)l.push(1);e=G(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=Ie(W(e,t),a[0]):i=Ie(W(et(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,c=a[1]===t.shape.length-1;i=He(e,t,l,c)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let c=[];for(let u=l;u<l+o;++u)c.push(u);i=pt(i,c)}return i.shape.length===1&&(i=Kt(i,1)),i})}var DA=class extends cl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(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],n=e[1];if(t.length>3||n.length>3)throw new Be("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)=>Pd(r,e[a].shape.length)):s=[Pd(this.axes,t.shape.length),Pd(this.axes,n.shape.length)],this.normalize&&(t=Of(t,s[0]),n=Of(n,s[1])),zB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Pd(this.axes,e.length),Pd(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 Be("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}};DA.className="Dot";le.registerClass(DA);var _A=class extends nt{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=Ve(e);return Sd(()=>ie(kf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};_A.className="GaussianNoise";le.registerClass(_A);var PA=class extends nt{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=Ve(e);return this.rate>0&&this.rate<1?Sd(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return W(n,kf(n.shape,1,r))},()=>n,t.training||!1):n})}};PA.className="GaussianDropout";le.registerClass(PA);var FA=class extends nt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ve(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 Sd(()=>{let r=Ve(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Yi(Nu(n),this.rate);l=vf(l,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-c*i*this.rate,d=ie(W(r,l),W(ie(l,-1),i));return ie(W(d,c),u)},()=>Ve(e),t.training||!1)}return e})}};FA.className="AlphaDropout";le.registerClass(FA);function Fd(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=T3(e,t,n,s,r,a);else if(e.rank===3)o=N3(e,t,n,s,r,a);else if(e.rank===4)o=E3(e,t,n,s,r,a);else throw new Be(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function LB(e,t,n,s,r=.001){return j(()=>{let a=tf(e,s),o=a.mean,i=a.variance;return[Fd(e,o,i,n,t,r),o,i]})}function BB(e,t,n,s,r=.001){return j(()=>{let a=tf(e,s),o=a.mean,i=a.variance,l=[];for(let f of pr(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let c=G(o,l),u=G(i,l),d=t==null?null:G(t,l),p=n==null?null:G(n,l);return[Fd(e,c,u,p,d,r),o,i]})}function WB(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),pr(0,e.rank-1))?LB(e,t,n,s,r):BB(e,t,n,s,r)}var OA=class extends nt{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(e.gammaInitializer||"ones"),this.movingMeanInitializer=$t(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=$t(e.movingVarianceInitializer||"ones"),this.betaConstraint=un(e.betaConstraint),this.gammaConstraint=un(e.gammaConstraint),this.betaRegularizer=Dt(e.betaRegularizer),this.gammaRegularizer=Dt(e.gammaRegularizer)}build(e){e=mt(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=Ve(e),r=s.shape,a=r.length,o=pr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=nl(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,pr(0,a).slice(0,a-1)),d=()=>{if(u){let A=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),x=this.center?G(this.beta.read(),l):null,b=this.scale?G(this.gamma.read(),l):null;return Fd(s,A,y,x,b,this.epsilon)}else return Fd(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]=WB(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,y,x)=>{j(()=>{let b=1-x,w=A.read(),k=W(xe(w,y),b);A.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:Mt(this.betaInitializer),gammaInitializer:Mt(this.gammaInitializer),movingMeanInitializer:Mt(this.movingMeanInitializer),movingVarianceInitializer:Mt(this.movingVarianceInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer),betaConstraint:ln(this.betaConstraint),gammaConstraint:ln(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};OA.className="BatchNormalization";le.registerClass(OA);var MA=class extends nt{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 ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(e.gammaInitializer||"ones"),this.betaRegularizer=Dt(e.betaRegularizer),this.gammaRegularizer=Dt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=mt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Co(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=Ve(e),s=n.shape,r=s.length;return j(()=>{let a=!0,{mean:o,variance:i}=tf(n,this.axis,a),l=nl(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r?G(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=js(o,p),i=js(i,p),u=js(u,h),d=js(d,h),Fd(n,o,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Mt(this.betaInitializer),gammaInitializer:Mt(this.gammaInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};MA.className="LayerNormalization";le.registerClass(MA);function VB(e,t,n){return j(()=>{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=cr()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],qs(e,s)})}var zA=class extends nt{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?cr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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=mt(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(()=>VB(Ve(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};zA.className="ZeroPadding2D";le.registerClass(zA);function Jf(e,t,n,s,r,a){return j(()=>{jt(r),Zv(a),$s(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=cr()),a==null&&(a="max"),e=rA(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Qh(e,t,n,i):o=Hh(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}function pk(e,t,n,s,r,a){return j(()=>{jt(r),Zv(a),$s(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=cr()),a==null&&(a="max"),e=nk(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=U2(e,t,n,i):o=E2(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,4,1,2,3])),o})}var hk=class extends nt{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(xn(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 ${JSON.stringify(e.strides)}`);xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,$s(this.padding),this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){e=mt(e);let t=gr(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=kd(Ve(e),2);let n=this.poolingFunction(Ve(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return pt(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},LA=class extends hk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),Jf(e,t,n,s,r,"max")}};LA.className="MaxPooling1D";le.registerClass(LA);var BA=class extends hk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),Jf(e,t,n,s,r,"avg")}};BA.className="AveragePooling1D";le.registerClass(BA);var fk=class extends nt{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 ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];xn(this.poolSize,"poolSize"),xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),$s(this.padding),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(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(Ve(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}},WA=class extends fk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),Jf(e,t,n,s,r,"max")}};WA.className="MaxPooling2D";le.registerClass(WA);var VA=class extends fk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),Jf(e,t,n,s,r,"avg")}};VA.className="AveragePooling2D";le.registerClass(VA);var mk=class extends nt{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 ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];xn(this.poolSize,"poolSize"),xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),$s(this.padding),this.inputSpec=[new Yt({ndim:5})]}computeOutputShape(e){e=mt(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=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(n,this.poolSize[1],this.padding,this.strides[1]),s=gr(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(Ve(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 mk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),pk(e,t,n,s,r,"max")}};UA.className="MaxPooling3D";le.registerClass(UA);var GA=class extends mk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),pk(e,t,n,s,r,"avg")}};GA.className="AveragePooling3D";le.registerClass(GA);var gk=class extends nt{constructor(e){super(e);this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Be}},HA=class extends gk{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Ve(e);return Wt(n,1)})}};HA.className="GlobalAveragePooling1D";le.registerClass(HA);var jA=class extends gk{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Ve(e);return rs(n,1)})}};jA.className="GlobalMaxPooling1D";le.registerClass(jA);var Ak=class extends nt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Be}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},qA=class extends Ak{call(e,t){return j(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?Wt(n,[1,2]):Wt(n,[2,3])})}};qA.className="GlobalAveragePooling2D";le.registerClass(qA);var XA=class extends Ak{call(e,t){return j(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?rs(n,[1,2]):rs(n,[2,3])})}};XA.className="GlobalMaxPooling2D";le.registerClass(XA);var yk=class extends nt{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=mr(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},KA=class extends yk{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=mt(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=mt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Ve(e),uk((a,o)=>[Ve(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};KA.className="TimeDistributed";le.registerClass(KA);function UB(e){rl(Zz,"BidirectionalMergeMode",e)}var GB="concat",ZA=class extends yk{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=mr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=mr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?GB:e.mergeMode,UB(this.mergeMode),e.weights)throw new Be("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):os(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=lk(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(u=>new Yt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),o.push(...c)}if(s!=null)throw new Be("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof fr;for(let l of a)if(l instanceof fr!==i)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){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.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Rs(r,1));let o;return this.mergeMode==="concat"?o=v1([s,r]):this.mergeMode==="sum"?o=ie(s,r):this.mergeMode==="ave"?o=W(.5,ie(s,r)):this.mergeMode==="mul"?o=W(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){al(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),al(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=mr(t.layer);if(delete t.layer,t.numConstants!=null)throw new Be("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};ZA.className="Bidirectional";le.registerClass(ZA);function HB(e){return new Fu(e)}function jB(e){return new tA(e)}function qB(e){return new J1(e)}function XB(e){return new Q1(e)}function KB(e){return new eA(e)}function ZB(e){return new sA(e)}function YB(e){return new nA(e)}function JB(e){return new uA(e)}function QB(e){return new Hf(e)}function eW(e){return new oA(e)}function 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s=I("elementShape",e,t,n),r=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=I(a,e,t,n),i=KV(s,r,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let s=I("tensorListId",e,t,n),r=I("indices",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,o,a)]}case"TensorListStack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=I("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,o)]}case"TensorListFromTensor":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=XV(s,r,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let s=I("tensorListId",e,t,n),r=n.getTensorList(s.id),a=I("dtype",e,t,n),o=I("elementShape",e,t,n);return[r.concat(a,o)]}case"TensorListPushBack":{let s=I("tensorListId",e,t,n),r=I("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let 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implemented`)}},fU=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=hf.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=hf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[hf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mU=(e,t,n)=>{switch(e.op){case"Cast":return[he(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[Kt(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[pt(I("x",e,t,n),s)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[Q3(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[qs(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[nf(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[jh(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[z3(I("x",e,t,n),s,r)]}case"BroadcastTo":return[ud(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[R3(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function n7(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return j(()=>HV(a,o,i));case"basic_math":return j(()=>jV(a,o,i));case"control":return JV(a,o,i);case"convolution":return j(()=>QV(a,o,i));case"creation":return j(()=>eU(a,o,i));case"dynamic":return tU(a,o,i);case"evaluation":return j(()=>nU(a,o,i));case"image":return j(()=>oU(a,o,i));case"graph":return j(()=>sU(a,o,i));case"logical":return j(()=>iU(a,o,i));case"matrices":return j(()=>lU(a,o,i));case"normalization":return j(()=>uU(a,o,i));case"reduction":return j(()=>cU(a,o,i));case"slice_join":return j(()=>dU(a,o,i));case"sparse":return j(()=>pU(a,o,i));case"spectral":return j(()=>hU(a,o,i));case"string":return j(()=>fU(a,o,i));case"transformation":return j(()=>mU(a,o,i));case"hash_table":return aU(a,o,i,s);case"custom":let l=Rk(a.op);if(l&&l.customExecutor)return l.customExecutor(new GV(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 s7=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;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function r7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,c=Object.keys(e).map(p=>As(p)[0]),u=[];s!=null&&(u=s.map(p=>As(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((a7(p)||bU(p)||vU(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 gU(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>As(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 AU=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],yU=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],xU=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function a7(e){return AU.indexOf(e.op)>=0}function bU(e){return yU.indexOf(e.op)>=0}function vU(e){return xU.indexOf(e.op)>=0}var fy=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 fy(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=r7(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 gU(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[As(u)[0]]),r=t.map(u=>As(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 s7(this.weightMap,l,c,this.functionExecutorMap),d={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=As(f),A=[];A[g]=e[f],d[m]=A});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=n7(m,d,u,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,u,p,r,h)}}return this.parent==null&&u.dispose(p),t.map(f=>Un(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=kV(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 s7(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(y=>this.graph.nodes[As(y)[0]]),o=n.map(y=>As(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:d}=r7(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[x,b]=As(y),w=[];w[b]=e[y],h[x]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let y=this.processStack(a,p,t,h,g,m,o,f,l);await Promise.all(y)}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 A=i.filter(y=>!a7(y)&&!Un(y.name,h,t)).map(y=>y.name);if(A.length>0){let y="";throw u!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${y}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&I("isConstant",u.node,s,n)&&([d]=ra(u.node.name,n)),s[u.node.name]==null){let p=n7(u.node,s,n,this._resourceManager);d||([d]=ra(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=ra(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=As(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=As(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=As(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},wU=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},kU="?tfjs-format=file",IU="model.json",o7=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new wU}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=ns.browserHTTPRequest(e,this.loadOptions);else{let t=ns.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(ns.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let s=ns.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new fy(Zk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Zk.Instance.transformGraph(e.modelInitializer);this.initializer=new fy(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=ns.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ye)&&!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 st(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},w7=class extends bn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(K().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new w7(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),Gt(n,t)}},k7=class extends bn{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=ur([a,r,i,o],[1,4])}else this.cropBox=ur([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(K().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 k7(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}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Gs.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=Kt(he(e,"float32"),0),n;n=$e.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},I7=class{},S7=class extends bn{split(e){return new JU(this,e)}},JU=class extends S7{constructor(e,t){super();this.upstream=e,this.impl=new QU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},QU=class extends gy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},eG=class extends bn{decodeUTF8(){return new tG(this)}},tG=class extends S7{constructor(e){super();this.upstream=e,this.impl=new nG(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},nG=class extends gy{constructor(e){super();if(this.upstream=e,K().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=e5();this.decoder=new 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I7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return T7(this.url)?new N7(this.url,this.fileOptions).iterator():sG(this.url,this.fileOptions)}};function aG(e,t={}){return new v7(new E7(e),t)}function oG(e){let t=my(e);return ys(async()=>t)}function iG(e){return ys(async()=>{let t=await e();return my(()=>t.next())})}async function lG(e,t){return k7.create(e,t)}async function uG(e){return w7.create(e)}var cG="0.0.0";function Ne(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var dG=Xs.whereImpl,R7=class extends Ll{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Dc(this,ss())}nextDataId(){return R7.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,K().get("IS_NODE")&&N.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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u=N.computePool3DInfo(a.shape,o,i,1,l,c),d=u.strideDepth,p=u.strideHeight,h=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,A=u.dilationDepth,y=u.dilationHeight,x=u.dilationWidth,b=u.effectiveFilterDepth,w=u.effectiveFilterHeight,k=u.effectiveFilterWidth,S=b-1-u.padInfo.front,E=k-1-u.padInfo.left,$=w-1-u.padInfo.top,F=Le(a.shape,"float32"),R=1/(f*m*g),P=n.bufferSync(r);for(let T=0;T<u.batchSize;++T)for(let M=0;M<u.inChannels;++M)for(let U=0;U<u.inDepth;++U)for(let H=0;H<u.inHeight;++H)for(let z=0;z<u.inWidth;++z){let X=U-S,ee=H-$,Y=z-E,ne=0;for(let se=0;se<b;se+=A){let J=(X+se)/d;if(!(J<0||J>=u.outDepth||Math.floor(J)!==J))for(let te=0;te<w;te+=y){let ue=(ee+te)/p;if(!(ue<0||ue>=u.outHeight||Math.floor(ue)!==ue))for(let ce=0;ce<k;ce+=x){let be=(Y+ce)/h;if(be<0||be>=u.outWidth||Math.floor(be)!==be)continue;ne+=P.get(T,J,ue,be,M)}}}F.set(ne*R,T,U,H,z,M)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var QH={kernelName:th,backendName:"cpu",kernelFunc:JH};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var sj={kernelName:za,backendName:"cpu",kernelFunc:nj};function rj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ne([r],"batchToSpaceND");let i=a.reduce((A,y)=>A*y),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=Tt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Ds({inputs:{x:h},backend:n,attrs:{perm:c}}),m=Tt({inputs:{x:f},backend:n,attrs:{shape:u}}),g=pl({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var aj={kernelName:si,backendName:"cpu",kernelFunc:rj};function oj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=by(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var ij={kernelName:nh,backendName:"cpu",kernelFunc:oj};function lj(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=N.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var uj={kernelName:sh,backendName:"cpu",kernelFunc:lj},cj=gt(jr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),dj={kernelName:jr,backendName:"cpu",kernelFunc:cj},pj=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let c=0;c<i.length;c++){let u=i[c],d=l[c];s[c]=Math.hypot(u,d)}return n.makeOutput(s,t.shape,"float32")},hj={kernelName:Mc,backendName:"cpu",kernelFunc:pj};function 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$j(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,A=Le([f,m,g,h],"float32"),y=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(A.shape);for(let S=0;S<f;S++){let E=S*4,$=y[E],F=y[E+1],R=y[E+2],P=y[E+3],T=x[S];if(T>=u)continue;let M=m>1?(R-$)*(d-1)/(m-1):0,U=g>1?(P-F)*(p-1)/(g-1):0;for(let H=0;H<m;H++){let z=m>1?$*(d-1)+H*M:.5*($+R)*(d-1);if(z<0||z>d-1){for(let X=0;X<g;X++)for(let ee=0;ee<h;ee++){let Y=ee+X*k[2]+H*k[1]+S*k[0];A.values[Y]=c}continue}if(l==="bilinear"){let X=Math.floor(z),ee=Math.ceil(z),Y=z-X;for(let ne=0;ne<g;ne++){let se=g>1?F*(p-1)+ne*U:.5*(F+P)*(p-1);if(se<0||se>p-1){for(let ce=0;ce<h;ce++){let be=ce+ne*k[2]+H*k[1]+S*k[0];A.values[be]=c}continue}let J=Math.floor(se),te=Math.ceil(se),ue=se-J;for(let ce=0;ce<h;ce++){let 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new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Oj={kernelName:ih,backendName:"cpu",kernelFunc:Fj};function Mj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;v.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Wj={kernelName:lh,backendName:"cpu",kernelFunc:Bj};function Vj(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s;Ne([r,a],"depthwiseConv2DNativeBackpropInput");let d=v.computeStrides(r.shape),p=v.computeStrides(a.shape),h=N.computeConv2DInfo(u,a.shape,o,i,l,c,!0),f=new sn(h.inShape,"float32"),m=f.values,[g,A,y]=f.strides,x=n.data.get(r.dataId).values,[b,w,k]=d,S=n.data.get(a.dataId).values,[E,$,F]=p,{batchSize:R,filterHeight:P,filterWidth:T,inChannels:M,inHeight:U,inWidth:H,outChannels:z,outHeight:X,outWidth:ee,strideHeight:Y,strideWidth:ne}=h,se=P-1-h.padInfo.top,J=T-1-h.padInfo.left,te=z/M;for(let ue=0;ue<R;++ue)for(let ce=0;ce<M;++ce)for(let be=0;be<U;++be){let we=be-se,Ce=Math.max(0,Math.ceil(we/Y)),Oe=Math.min(X,(P+we)/Y);for(let Ue=0;Ue<H;++Ue){let Xe=Ue-J,rt=Math.max(0,Math.ceil(Xe/ne)),ft=Math.min(ee,(T+Xe)/ne),ut=0;for(let ct=Ce;ct<Oe;++ct){let At=ct*Y-we;for(let yt=rt;yt<ft;++yt){let 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eS(e){if(xm==null){let t=Mr(e);xm=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,xm)}function tS(e){if(e===0)return 0;let t,n=Mr(e);return Ps(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Ps(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Ps(e,t){return e.getExtension(t)!=null}function My(e){try{if(Mr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function nS(e){if(e===0)return!1;let t=Mr(e);if(e===1){if(!Ps(t,"OES_texture_float"))return!1}else if(!Ps(t,"EXT_color_buffer_float"))return!1;return zy(t)}function sS(e){if(e===0)return!1;let t=Mr(e);if(e===1){if(!Ps(t,"OES_texture_float")||!Ps(t,"WEBGL_color_buffer_float"))return!1}else{if(Ps(t,"EXT_color_buffer_float"))return zy(t);let s="EXT_color_buffer_half_float";if(Ps(t,s)){let r=t.getExtension(s);return EZ(t,r)}return!1}return zy(t)}function zy(e){let t=Py(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function EZ(e,t){let n=Py(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function rS(e){return e!==2?!1:Mr(e).fenceSync!=null}function Hu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var De=K();De.registerFlag("HAS_WEBGL",()=>De.getNumber("WEBGL_VERSION")>0);De.registerFlag("WEBGL_VERSION",()=>My(2)?2:My(1)?1:0);De.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);De.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>De.get("WEBGL_VERSION")===2);De.registerFlag("WEBGL_CPU_FORWARD",()=>!0);De.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);De.registerFlag("WEBGL_PACK",()=>De.getBool("HAS_WEBGL"));De.registerFlag("WEBGL_PACK_NORMALIZATION",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_CLIP",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_REDUCE",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_LAZILY_UNPACK",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_CONV_IM2COL",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>QI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>eS(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=De.getNumber("WEBGL_VERSION");return e===0?0:tS(e)});De.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>De.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!gu.isMobile());De.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>nS(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>De.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:De.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));De.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>sS(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_FENCE_API_ENABLED",()=>rS(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>De.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);De.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});De.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>gu.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});De.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);De.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);De.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);De.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Gn(){let e,t,n,s,r,a,o,i,l,c;return K().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,l="",c=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,c=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:c}}function gl(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function bm(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function RZ(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function $Z(e,t,n="index"){let s=e.map((a,o)=>o),r=RZ(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function Ly(e){let t=v.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function By(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var aS=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,{getBroadcastDims:oS}=N;function DZ(e,t,n){let s=[];if(e.forEach(h=>{let f=v.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Wy(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
`),a=e.map(h=>_Z(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=Gn(),l=OZ(i),c,u,d=LZ(i);return t.isPacked?(c=PZ(t.logicalShape,o,n.enableShapeUniforms),u=zZ(i)):(c=FZ(t.logicalShape,o,n.enableShapeUniforms),u=MZ(i)),n.packedInputs&&(d+=UZ),[d,l,u,r,c,a,n.userCode].join(`
`)}function ju(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return tY(e,t);case 1:return sY(e,t);case 2:return aY(e,t);case 3:return iY(e,t);case 4:return uY(e,t);case 5:return cY(e);case 6:return dY(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function iS(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return eY(e);case 1:return nY(e,t);case 2:return rY(e,t);case 3:return oY(e,t);default:return lY(e,t)}}function _Z(e,t,n=!1,s){let r="";n?r+=iS(e,s):r+=ju(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=pY(e,t):r+=hY(e,t)),r}function PZ(e,t,n){switch(e.length){case 0:return lS();case 1:return GZ(e,t,n);case 2:return JZ(e,t,n);case 3:return jZ(e,t,n);default:return XZ(e,t,n)}}function FZ(e,t,n){switch(e.length){case 0:return lS();case 1:return HZ(e,t,n);case 2:return QZ(e,t,n);case 3:return qZ(e,t,n);case 4:return KZ(e,t,n);case 5:return ZZ(e,t);case 6:return YZ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function OZ(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function MZ(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function zZ(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function LZ(e){return`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${BZ}
${WZ}
${VZ}
`}var BZ=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,WZ=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,VZ=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,UZ=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function lS(){return`
int getOutputCoords() {
return 0;
}
`}function GZ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${s[1]}.0);
}
`:s[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${s[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
}
`}function HZ(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function jZ(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function qZ(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${bm(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let s=gl(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec3(r, c, d);
}
`}function XZ(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
int b${c} = index / ${o};
index -= b${c} * ${o};
`+i,l=`b${c}, `+l;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${l});
}
`}function KZ(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${bm(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let s=gl(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec4(r, c, d, d2);
}
`}function ZZ(e,t){let n=gl(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function YZ(e,t){let n=gl(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function JZ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
}
`;let r=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function QZ(e,t,n){return v.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Al(e){return`offset${e}`}function eY(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Gn();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function tY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
float ${s}() {
return sampleTexture(${n}, halfCR);
}
`;let o=Al(n);if(t)return`
float ${s}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
return sampleTexture(${n}, uv);
}
`;let[i,l]=e.shapeInfo.texShape;return`
float ${s}() {
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
return sampleTexture(${n}, uv);
}
`}function nY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Gn();if(t)return`
vec4 ${s}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
vec4 ${s}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function sY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${s}(int index) {
${qu(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
float ${s}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let i=Al(n);return o===1?t?`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${s}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
return sampleTexture(${n}, uv);
}
`}function rY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Gn();if(a!=null&&v.arraysEqual(n,a))return t?`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return ${l.texture2D}(${s}, uv);
}
`:`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
return ${l.texture2D}(${s}, uv);
}
`;if(t)return`
vec4 ${r}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${s}, uv);
}
`;let c=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(n[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${u}, ${c[0]}, ${c[1]}, row, col);
return ${l.texture2D}(${s}, uv);
}
`}function aY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`;let p=a[0],h=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let p=Xu(e,l),h=["row","col"];return`
${ju(p,t)}
float ${r}(int row, int col) {
return ${r}(${Ku(h,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${qu(e)}
}
`;let c=a[0],u=a[1],d=Al(s);return u===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
return sampleTexture(${s}, uv);
}
`:c===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
return sampleTexture(${s}, uv);
}
`:t?`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s}Shape[1] + col + ${d};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${d};
vec2 uv = uvFromFlat(${c}, ${u}, index);
return sampleTexture(${s}, uv);
}
`}function oY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=Xu(e,p),m=["b","row","col"];return`
${iS(f,t)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${Ku(m,h)});
}
`}let i=Gn();if(t)return`
vec4 ${r}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`;let l=o[0],c=o[1],u=Math.ceil(n[2]/2),d=u*Math.ceil(n[1]/2);return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${c}, ${d}, ${u}, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`}function iY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),c=i;if(c.length<n.length){let m=Xu(e,c),g=["row","col","depth"];return`
${ju(m,t)}
float ${r}(int row, int col, int depth) {
return ${r}(${Ku(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${o}, 1)));
${qu(e)}
}
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
float ${r}(int row, int col, int depth) {
int stride1 = ${s}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;if(p===o&&h==null)return t?`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;let f=Al(s);return t?`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${s}Shape[1] * ${s}Shape[2];
int stride1 = ${s}Shape[2];
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${s}, uv);
}
`}function lY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Gn();if(t)return`
vec4 ${s}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=l[0],u=l[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
vec4 ${s}(${h}) {
int index = ${f};
int texR = index / ${u};
int texC = index - texR * ${u};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${c});
return ${r.texture2D}(${n}, uv);
}
`}function uY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(n);if(l.length<n.length){let y=Xu(e,l),x=["row","col","depth","depth2"];return`
${ju(y,t)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${Ku(x,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, 1)));
${qu(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&u==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;if(h===a&&u==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;let A=Al(s);return t?`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${A});
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${p}, ${h}, index + ${A});
return sampleTexture(${s}, uv);
}
`}function cY(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=Xu(e,l),g=["row","col","depth","depth2","depth3"];return`
${ju(m)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${Ku(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, ${r})) +
depth3;
${qu(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&u==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${o}, ${a}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&u==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let f=Al(n);return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} + depth * ${a} +
depth2 * ${r} + depth3 + ${f};
vec2 uv = uvFromFlat(${p}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function dY(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=Xu(e,r),A=["row","col","depth","depth2","depth3","depth4"];return`
${ju(g)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${Ku(A,a)});
}
`}let o=t[5],i=t[4]*o,l=t[3]*i,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${o}, 1)));
${qu(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===u&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${i}, ${o})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===o&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=Al(n);return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${u} + col * ${c} + depth * ${l} +
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function qu(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function pY(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=oS(e.shapeInfo.logicalShape,t.logicalShape),l=vt(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(y=>`coords.${d[y+c]} = 0;`).join(`
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((y,x)=>`coords.${d[x+c]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,A=v.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!A)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!A)o===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(i.length){let y=a-2,x=a-1;i.indexOf(y)>-1&&i.indexOf(x)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(x)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${s}(${p});
${h}
}
`}function hY(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(o,a))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let c=vt(l),u=oS(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,p,h=["x","y","z","w","u","v"];i===0?p="":l<2&&u.length>=1?p="coords = 0;":p=u.map(m=>`coords.${h[m+d]} = 0;`).join(`
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
float ${r}() {
${c} coords = getOutputCoords();
${p}
return get${s}(${f});
}
`}function vt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Wy(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function Xu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Ku(e,t){return t.map(n=>e[n]).join(", ")}function fY(e,t,n,s){let r=n.map((x,b)=>{let w={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(w.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:w}}),a=r.map(x=>x.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=DZ(r,o,t),l=e.createProgram(i),c=null,u=e.getUniformLocation(l,"NAN",!1);K().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(l,"INFINITY",!1));let d=!1,p={},h={},f={};for(let x=0;x<t.variableNames.length;x++){let b=t.variableNames[x];p[b]=e.getUniformLocation(l,b,d),p[`offset${b}`]=e.getUniformLocation(l,`offset${b}`,d),t.enableShapeUniforms&&(h[`${b}Shape`]=e.getUniformLocation(l,`${b}Shape`,d),f[`${b}TexShape`]=e.getUniformLocation(l,`${b}TexShape`,d))}let m,g,A;t.enableShapeUniforms&&(m=e.getUniformLocation(l,"outShape",d),A=e.getUniformLocation(l,"outShapeStrides",d),g=e.getUniformLocation(l,"outTexShape",d));let y=[];return t.customUniforms&&t.customUniforms.forEach((x,b)=>{y[b]=e.getUniformLocation(l,x.name,d)}),{program:t,source:i,webGLProgram:l,uniformLocations:p,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:c,nanLoc:u,inShapesLocations:h,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:A,outTexShapeLocation:g}}function uS(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!v.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!v.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function mY(e,t,n,s,r){t.program.enableShapeUniforms||(uS(t.inShapeInfos,n),uS([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),K().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=Wy(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,c)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,c)=>{let u=t.customUniformLocations[c],d=r[c];if(l.type==="float")e.gl.uniform1fv(u,d);else if(l.type==="vec2")e.gl.uniform2fv(u,d);else if(l.type==="vec3")e.gl.uniform3fv(u,d);else if(l.type==="vec4")e.gl.uniform4fv(u,d);else if(l.type==="int")e.gl.uniform1iv(u,d);else if(l.type==="ivec2")e.gl.uniform2iv(u,d);else if(l.type==="ivec3")e.gl.uniform3iv(u,d);else if(l.type==="ivec4")e.gl.uniform4iv(u,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function gY(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=Wy(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(u.length===2&&!e.packedInputs)h=`${u[0]>1}_${u[1]>1}`;else if(u.length>2&&!e.packedInputs){let w=v.computeStrides(u);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=u.length===2&&v.arraysEqual(o.shape,l),A=v.sizeFromShape(o.shape)===1,y=N.getBroadcastDims(o.shape,n.shape),x=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||u.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${x}_${c?d:""}_${u.length}_${A}_${y}_${g}_${p}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${K().getNumber("WEBGL_VERSION")}`,a}function Fs(e){return K().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var AY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Ud.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?bm(["r","c","d"],e):gl(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}},yY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Ud.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?bm(["r","c","d"],e):gl(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}},xY=class{constructor(e){this.variableNames=["A"],this.outTexUsage=_s.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
${aS}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},bY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=_s.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
${aS}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},vY=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?By():Ly(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${s}, 0., 0., 0.);
}
`}},wY=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
localCoords = coords;
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${o};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${i}] = values[0];
} else if (offset == 1) {
result[${i}] = values[1];
} else if (offset == 2) {
result[${i}] = values[2];
} else {
result[${i}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?By():Ly(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${s}
${n.output} = ${r};
}
`}},cS={};ze(cS,{bindVertexProgramAttributeStreams:()=>xS,createBufferFromOutputTexture:()=>wS,createFloat16MatrixTexture:()=>mS,createFloat16PackedMatrixTexture:()=>yS,createFloat32MatrixTexture:()=>fS,createIndexBuffer:()=>hS,createPackedMatrixTexture:()=>AS,createUnsignedBytesMatrixTexture:()=>gS,createVertexBuffer:()=>pS,createVertexShader:()=>dS,downloadByteEncodedFloatMatrixFromOutputTexture:()=>IS,downloadFloat32MatrixFromBuffer:()=>kS,downloadMatrixFromPackedOutputTexture:()=>CS,downloadPackedMatrixFromBuffer:()=>SS,getInternalFormatForFloat16MatrixTexture:()=>Uy,getInternalFormatForFloat16PackedMatrixTexture:()=>jy,getInternalFormatForFloat32MatrixTexture:()=>Vy,getInternalFormatForPackedMatrixTexture:()=>Hy,getInternalFormatForUnsignedBytesMatrixTexture:()=>Gy,uploadDenseMatrixToTexture:()=>bS,uploadPixelDataToTexture:()=>vS});function dS(e){let t=Gn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return MI(e,n)}function pS(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return WI(e,t)}function hS(e){let t=new Uint16Array([0,1,2,2,1,3]);return VI(e,t)}function Xd(e,t,n,s,r,a){GI(t,n);let o=UI(e),i=e.TEXTURE_2D;return Se(e,()=>e.bindTexture(i,o)),Se(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Se(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Se(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Se(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Se(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function Vy(e){return e.internalFormatFloat}function fS(e,t,n,s){let[r,a]=Gd(t,n);return Xd(e,r,a,Vy(s),s.textureFormatFloat,e.FLOAT)}function Uy(e){return e.internalFormatHalfFloat}function mS(e,t,n,s){let[r,a]=Gd(t,n);return Xd(e,r,a,Uy(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function Gy(e){return e.downloadTextureFormat}function gS(e,t,n,s){let[r,a]=Gd(t,n);return Xd(e,r,a,Gy(s),e.RGBA,e.UNSIGNED_BYTE)}function Hy(e){return e.internalFormatPackedFloat}function AS(e,t,n,s){let[r,a]=Gu(t,n);return Xd(e,r,a,Hy(s),e.RGBA,e.FLOAT)}function jy(e){return e.internalFormatPackedHalfFloat}function yS(e,t,n,s){let[r,a]=Gu(t,n);return Xd(e,r,a,jy(s),e.RGBA,s.textureTypeHalfFloat)}function xS(e,t,n){let s=0,r=3*4,a=3*4+2*4;return Se(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Fy(e,t,"clipSpacePos",n,3,a,s)&&Fy(e,t,"uv",n,2,a,r)}function bS(e,t,n,s,r,a){Se(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),Se(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function vS(e,t,n){Se(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Se(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Se(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function wS(e,t,n,s){let r=e.createBuffer();Se(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Se(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Se(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Se(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function kS(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function IS(e,t,n,s){let[r,a]=Gd(t,n),o=4,i=new Uint8Array(AZ(t*n,o));return Se(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function SS(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(yZ(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function CS(e,t,n){let s=new Float32Array(t*n*4);return Se(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var vm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=K().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,pm(t,e)):this.gl=Mr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(K().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Hd(this.gl,r),Ps(this.gl,a))this.textureHalfFloatExtension=Hd(this.gl,a);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ps(this.gl,s))this.colorBufferHalfFloatExtension=Hd(this.gl,s);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Ps(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ps(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=pS(this.gl),this.indexBuffer=hS(this.gl),this.framebuffer=HI(this.gl),this.textureConfig=Py(this.gl,this.textureHalfFloatExtension)}get debug(){return K().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. 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this.throwIfDisposed(),yS(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),AS(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Oy(this.gl,this.framebuffer),this.outputTexture=null),Se(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>IS(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return SS(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return kS(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=wS(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(K().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>CS(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=zI(t,e);this.vertexShader==null&&(this.vertexShader=dS(t));let s=LI(t);return Se(t,()=>t.attachShader(s,this.vertexShader)),Se(t,()=>t.attachShader(s,n)),BI(t,s),this.debug&&fm(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=xS(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Se(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&fm(this.gl,this.program),Se(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?qI(this.gl,e,t):XI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Se(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return 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this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Hd(this.gl,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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e=kY(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),mm(this.gl,e,this.framebuffer),this.debug&&jd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(mm(this.gl,this.outputTexture,this.framebuffer),this.debug&&jd(this.gl)):Oy(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;mm(s,e,this.framebuffer),this.debug&&jd(s),this.outputTexture=e,Se(s,()=>s.viewport(0,0,t,n)),Se(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Se(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 kY(e){let t=0;for(;t<e.length&&e[t]();++t);return 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uJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=Hn("rc",t),s=vt(t),r=dJ(t,e,n),a=pJ(t,e[e.length-1],e[e.length-2],n),o=hJ(e,n);this.userCode=`
void main() {
${s} rc = getOutputCoords();
if(${r}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${o}));
}
}
`}}};function cJ(e,t){let n=[];for(let s=0;s<=1;s++)for(let r=0;r<=1;r++){let a=`${s===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function dJ(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let r=e-2;r<e;r++)s+=`${n[r]} >= ${t[r]}`,r<e-1&&(s+="||");return s}function pJ(e,t,n,s){if(e===1)return"";let r=s.slice(-2);return`
int r = ${r[0]};
int c = ${r[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function hJ(e,t){let n=e.length,s=cJ(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 $S=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Fs(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}] =
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${s>0?"}":""}
`}this.userCode=`
${fJ(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?By():Ly(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 fJ(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?$Z(["r","c","d"],"inputShape"):gl(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var mJ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=_S(t,n),r=PS(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=DS(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===Cn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Cn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Cn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=_S(n,s),a=PS(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=DS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function gJ(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function DS(e,t,n,s,r){let a=AJ(t,s),o;if(r){let[l,c]=Gu(e[0],e[1]);o=l*c}else{let[l,c]=Gd(e[0],e[1]);o=l*c}let i=gJ(n,a);return o*i}function AJ(e,t){switch(e){case Cn.PACKED_2X2_FLOAT32:return Hy(t);case Cn.PACKED_2X2_FLOAT16:return jy(t);case Cn.UNPACKED_FLOAT32:return Vy(t);case Cn.UNPACKED_FLOAT16:return Uy(t);case Cn.PACKED_4X1_UNSIGNED_BYTE:return Gy(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function yJ(e){return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Cn.PACKED_2X2_FLOAT32:Cn.UNPACKED_FLOAT32:e?Cn.PACKED_2X2_FLOAT16:Cn.UNPACKED_FLOAT16}function _S(e,t){if(e===_s.UPLOAD)return Cn.PACKED_2X2_FLOAT32;if(e===_s.RENDER||e==null)return yJ(t);if(e===_s.DOWNLOAD||e===_s.PIXELS)return Cn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function PS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Fo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},yr="if (isnan(x)) return x;",xJ="return x;",FS="return abs(x);",bJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",vJ=yr+`
return (x < 0.0) ? 0.0 : x;
`,wJ=yr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,wm="return x;",kJ="return 1.0 / (1.0 + exp(-1.0 * x));",IJ="return x;",SJ=`
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;
`,CJ=`
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;
`,TJ=`
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;
`,NJ="return 1.0 / (1.0 + exp(-1.0 * x));",Zu=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},EJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Hn("rc",t),s=vt(t),r=lJ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${o}));
}
`}},RJ=Xs.whereImpl,$J=1e-7,DJ=1e-4,km={};function _J(e){return e in km||(km[e]={}),km[e]}var PJ=K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),FJ=600;function OJ(){return K().global.screen==null?1024:K().global.screen.height*K().global.screen.width*window.devicePixelRatio*FJ/1024/1024}var OS=class extends Ll{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!K().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Mr(K().getNumber("WEBGL_VERSION"));this.binaryCache=_J(K().getNumber("WEBGL_VERSION")),this.gpgpu=new vm(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 mJ(this.gpgpu),this.numMBBeforeWarning=OJ(),this.texData=new Dc(this,ss())}nextDataId(){return OS.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((K().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||K().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:_s.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(K().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:_s.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new Zu(o,wm):d=new Fo(o,wm);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);u=N.mergeRealAndImagArrays(d,p)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new Zu(s,wm):h=new Fo(s,wm);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(!K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&K().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"&&K().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...hm(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=N.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;Se(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)&&ss().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 Le(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!FI(n))throw K().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...hm(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=K().getBool("WEBGL_PACK")&&s===!0,o=a?gm(t):t,i=a?new bY(o):new xY(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${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 K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=PJ){return K().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return RJ(e.shape,t)}packedUnaryOp(e,t,n){let s=new Zu(e.shape,t),r=this.compileAndRun(s,[e],n);return ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=NS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(K().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,FS,e.dtype);let t=new Fo(e.shape,FS),n=this.compileAndRun(t,[e]);return ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}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 this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new EJ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new uJ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[fl(e.shape),...ml(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[fl(t),...ml(t)],a=new $S(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=gm(s),o,i=hm(a);n?o=new yY(a):o=new AY(a);let l=!0,c=[i],u=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,c,l);return{dtype:r,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Ud.DENSE){let m=hm(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(a.shape)===0)return 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. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=K().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!qd(g.shape,m.shape)){let A=m,y=m.shape;m.shape=g.shape,m=this.packedReshape(m,y),i.push(m),g=this.texData.get(m.dataId),A.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:o,isUniform:!1},u=gY(e,l,c),d=this.getAndSaveBinary(u,()=>fY(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),mY(this.gpgpu,d,l,c,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=K().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!K().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(K().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!K().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=K().getBool("DEBUG");K().set("DEBUG",!1);let t=this.abs(Ee(1e-8)).dataSync()[0];if(K().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?$J:DJ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=JI(n,i),t.texShape=u),r!=null){let d=gm(n),p,h=u[1],f=u[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;i?([h,f]=Gu(u[0],u[1]),p=new wY(d,m)):p=new vY(d,m);let g=this.makeTensorInfo([f,h],s);m?this.texData.get(g.dataId).usage=_s.PIXELS:this.texData.get(g.dataId).usage=_s.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let A=[[f,h]],y=!0,x=this.runWebGLProgram(p,[g],s,A,y),b=this.texData.get(x.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(x.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let d=this.acquireTexture(u,o,s,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=MJ(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}},Kd=OS;Kd.nextDataId=0;function MJ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var zJ="0.0.0";function MS(){K().set("WEBGL_FORCE_F16_TEXTURES",!0)}gu.isBrowser()&&Ki("webgl",()=>new Kd,2);var LJ={forceHalfFloat:MS},zS=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Yu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Im=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`,Zd=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Fs(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${vt(r)} coords = getOutputCoords();
`,r===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=Hn("coords",r);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= outShape[${r} - 2];
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= outShape[${r} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function bs(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var BJ={kernelName:Ba,backendName:"webgl",kernelFunc:bs};function Oo(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=bs({inputs:{x:s},backend:n}),l=bs({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var WJ={kernelName:Oc,backendName:"webgl",kernelFunc:Oo},LS="return (a < 0.) ? b * a : a;",BS=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function VJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zd(BS,r.shape,o.shape):new Yu(LS,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var UJ={kernelName:mi,backendName:"webgl",kernelFunc:VJ},WS="return (a < 0.) ? b * a : a;",VS=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function GJ(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zd(VS,s.shape,r.shape):new Yu(WS,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var HJ={kernelName:Ja,backendName:"webgl",kernelFunc:GJ},US="if (isnan(x)) return x;",jJ=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,qJ=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function ot({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let c=K().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Zu(o.shape,t):u=new Fo(o.shape,e),i.runWebGLProgram(u,[o],l)}}function Tn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:c}=o,u=i;if(s&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[g,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,w]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:w.dataId,dtype:w.dtype,shape:c.shape},E=new Yu(e,l.shape,c.shape);return u.runWebGLProgram(E,[k,S],Bn(b.dtype,w.dtype))}),y=Oo({inputs:{real:g,imag:A},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(A),y}let d=a||Bn(l.dtype,c.dtype);if((l.dtype==="string"||c.dtype==="string"||u.shouldExecuteOnCPU([l,c]))&&r!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(c.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,A=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[y,x]=r(l.shape,c.shape,g,A,d),b=u.makeTensorInfo(x,d),w=u.texData.get(b.dataId);return w.values=y,b}let p=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new Zd(t,l.shape,c.shape,n):h=new Yu(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function Sm(e,t=!1){if(e==="linear")return t?IJ:xJ;if(e==="relu")return t?CJ:vJ;if(e==="elu")return t?SJ:bJ;if(e==="relu6")return t?TJ:wJ;if(e==="prelu")return t?VS:WS;if(e==="leakyrelu")return t?BS:LS;if(e==="sigmoid")return t?NJ:kJ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var GS=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Fs(this.outputShape.length);let c=s?e[1]:e[2],u=Math.ceil(c/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:l?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:m=`vec4 activation(vec4 x) {
${o}
}`,g="result = activation(result);");let A=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let y="rc.x",x="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${y};
int batchB = ${x};
vec4 a = getMatrixA(batchA, ${d});
vec4 b = getMatrixB(batchB, ${p});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${f[0]});
result += (${h[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${A}
${g}
setOutput(result);
}
`}},HS={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},jS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},qS="return a * b;";function Xy(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new jS(HS.REAL,s.shape,r.shape),u=new jS(HS.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Oo({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=UY(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Zd(qS,s.shape,r.shape):o=new Yu(qS,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var XJ={kernelName:Ka,backendName:"webgl",kernelFunc:Xy};function KJ(e,t,n){let s=[fl(e.shape),...ml(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[fl(t),...ml(t)],o=new $S(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function ve(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let u=o.texData.get(r.dataId);return u.isPacked&&!qd(r.shape,l)&&!(u.texture!==null&&qd(u.shape,l))?KJ(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var ZJ={kernelName:Ti,backendName:"webgl",kernelFunc:ve},XS=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${r}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${o}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${o};
if (${i===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${i===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${i===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},YJ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,d=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${i}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${i}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,p="vec4";t==="all"?(o="1.0",d=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(o="0.0",d=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${o};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${o});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${d}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${d}
} else if (${u===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${d}
} else if (${u===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${d}
}
setOutput(${l});
}
`}};function JJ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function yl(e,t,n,s){let r=JJ(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:c}=r[o],u,d;n==="mean"?u=o===0?new XS({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},i):new XS({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c}):u=new YJ({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},n),d=a,a=s.runWebGLProgram(u,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var QJ=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=vt(this.rank),r=eQ(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function eQ(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var tQ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=vt(this.rank),r=RS("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${i}) {
result[1] = ${l};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${i}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Cm(e,t,n){let s=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tQ(e.shape,t):new QJ(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function nQ(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=N.getAxesPermutation(i,a),c=l!=null,u=e;c&&(u=Cm(e,l,s),i=N.getInnerMostAxes(i.length,a)),N.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=N.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=N.expandShapeToKeepDim(d,o));let f=v.sizeFromShape(p),g=v.sizeFromShape(e.shape)/f,A=ve({inputs:{x:u},attrs:{shape:[g,f]},backend:s}),y=rd(e.dtype),x=yl(A,y,"sum",s),b=ve({inputs:{x},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(x),c&&s.disposeIntermediateTensorInfo(u),b}function Tm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return nQ(r,a,o,n)}var sQ={kernelName:oo,backendName:"webgl",kernelFunc:Tm};function jn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];let c;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=qy(d,r.shape,r.dtype,a,l);c=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(c.dataId);h.values=p}else c=Cm(r,a,o);return c}var rQ={kernelName:po,backendName:"webgl",kernelFunc:jn},KS=1e3;function Nm({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),y=v.sizeFromShape(g),x=A===y||A===1||y===1;v.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let w=(A>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[A,d,h]:[A,h,d],S=s?[y,f,p]:[y,p,f],E=ve({inputs:{x:e},backend:r,attrs:{shape:k}}),$=ve({inputs:{x:t},backend:r,attrs:{shape:S}}),F=[E,$],R=Math.max(A,y),P=n?E.shape[1]:E.shape[2],T=a!=null,M=o!=null,U=l==="leakyrelu",H=l!=null?Sm(l,!0):null,z=T||M||U||H!=null,X;if((h===1||f===1)&&P>KS&&z===!1){let Y=E,ne=$;n&&(Y=jn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),F.push(Y)),s&&(ne=jn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),F.push(ne));let se=f!==1,J=f===1,te=Y;se&&(te=ve({inputs:{x:Y},backend:r,attrs:{shape:[R,P,1]}}),F.push(te));let ue=f===1?2:1,ce=ne;J&&(ce=ve({inputs:{x:ne},backend:r,attrs:{shape:[R,1,P]}}),F.push(ce));let be=Xy({inputs:{a:te,b:ce},backend:r});X=Tm({inputs:{x:be},backend:r,attrs:{axis:ue,keepDims:!0}}),F.push(be)}else{let Y=Bn(e.dtype,t.dtype),ne=new GS(k,S,[R,h,f],n,s,T,H,M,U),se=[E,$];if(a!=null&&se.push(a),M&&se.push(o),U){let J=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));se.push(J),F.push(J)}X=r.runWebGLProgram(ne,se,Y)}let ee=ve({inputs:{x:X},backend:r,attrs:{shape:w}});F.push(X);for(let Y of F)r.disposeIntermediateTensorInfo(Y);return ee}function aQ(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 Nm({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var oQ={kernelName:fo,backendName:"webgl",kernelFunc:aQ},ZS="return abs(x);";function iQ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=NS(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zu(s.shape,ZS):r=new Fo(s.shape,ZS),n.runWebGLProgram(r,[s],s.dtype)}var lQ={kernelName:ni,backendName:"webgl",kernelFunc:iQ},uQ=yr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,cQ=ot({opSnippet:uQ}),dQ={kernelName:Vl,backendName:"webgl",kernelFunc:cQ},pQ=yr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,hQ=ot({opSnippet:pQ}),fQ={kernelName:Ul,backendName:"webgl",kernelFunc:hQ},YS="return a + b;",mQ=Tn({opSnippet:YS,packedOpSnippet:YS,supportsComplex:!0,cpuKernelImpl:IY}),gQ={kernelName:Hr,backendName:"webgl",kernelFunc:mQ},AQ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${s};
setOutput(result);
}
`}},yQ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${s};
setOutput(result);
}
`}};function Em(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return bs({inputs:{x:s[0]},backend:n});if(s.length>K().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),c=Em({inputs:s.slice(0,l),backend:n}),u=Em({inputs:s.slice(l),backend:n});return Em({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>Bn(l,c)),a=s.map(l=>l.shape),i=K().getBool("WEBGL_PACK")?new yQ(s[0].shape,a):new AQ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var xQ={kernelName:wa,backendName:"webgl",kernelFunc:Em};function bQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,i)),N.assertAxesAreInnerMostDims("all",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=yl(m,m.dtype,"all",n),A;if(o){let y=N.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var vQ={kernelName:Gl,backendName:"webgl",kernelFunc:bQ};function wQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,i)),N.assertAxesAreInnerMostDims("any",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=yl(m,m.dtype,"any",n),A;if(o){let y=N.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var kQ={kernelName:Hl,backendName:"webgl",kernelFunc:wQ},IQ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${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 ${o} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},SQ=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${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=vt(i),c=Hn("coords",i),u,d;if(a===1){d=i+1;let S=vt(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=`
${l} sourceLocR = coords;
++${c[i-1]};
${l} sourceLocG = coords;
++${c[i-2]};
${l} sourceLocA = coords;
--${c[i-1]};
${l} sourceLocB = coords;
--${c[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(S=>"int "+S),m=Hn("sourceLocR",d-1).concat("inIdx.r"),g=Hn("sourceLocG",d-1).concat("inIdx.g"),A=Hn("sourceLocB",d-1).concat("inIdx.b"),y=Hn("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",b=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${y.join()})));`,w=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${A.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,k=s?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${p.join()}),
vec2(${p.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${p.join()}),
vec2(${p.slice(-2).join()}));
}
${k}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[i-1]} < ${o[i-1]-1};
bool hasNextRow = ${c[i-2]} < ${o[i-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${w};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${w};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function JS(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=N.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new IQ(i,n,s==null),c=[t];s!=null&&c.push(s);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let d=JS(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function QS(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=N.computeOptimalWindowSize(a),i=new SQ(r,o,n,s==null),l=s==null?[t]:[t,s],c=e.runWebGLProgram(i,l,"int32");if(c.shape.length===t.shape.length){let u=QS(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function e4(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!K().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[c,u]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(u),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=JS(e,p,s);a.push(h);let f=ve({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return QS(e,t,s)}function CQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=e4(n,l,o[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var TQ={kernelName:ka,backendName:"webgl",kernelFunc:CQ};function NQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=e4(n,l,o[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var EQ={kernelName:jl,backendName:"webgl",kernelFunc:NQ},RQ=yr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,$Q=ot({opSnippet:RQ}),DQ={kernelName:ql,backendName:"webgl",kernelFunc:$Q},_Q=yr+"return log(x + sqrt(x * x + 1.0));",PQ=ot({opSnippet:_Q}),FQ={kernelName:Xl,backendName:"webgl",kernelFunc:PQ},OQ=yr+`
return atan(x);
`,MQ=ot({opSnippet:OQ}),zQ={kernelName:Kl,backendName:"webgl",kernelFunc:MQ},LQ=jJ+`
return atan(a, b);
`,BQ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+qJ+`
return result;
`,WQ=Tn({opSnippet:LQ,packedOpSnippet:BQ}),VQ={kernelName:Yl,backendName:"webgl",kernelFunc:WQ},UQ=yr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,GQ=ot({opSnippet:UQ}),HQ={kernelName:Zl,backendName:"webgl",kernelFunc:GQ},Yd=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.strideHeight,i=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${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 * ${d} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let y="max",x=`${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 = ${y}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
const float initializationValue = ${A};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${A});
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});
}
`}},Ky=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,A=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${g}, ${A});
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 ${$} 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,E=`
if (${y}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${g}, ${A});
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)
);
${E}
}
int xC = xCCorner + ${k};
if (${S===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${E}
} else if (${S===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
initializationValue,
initializationValue
);
${E}
} else if (${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
);
${E}
}
}
setOutput(${w});
}
}
`}};function jQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Hu(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(N.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return bs({inputs:{x:r},backend:n});let d=new Yd(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var qQ={kernelName:Ia,backendName:"webgl",kernelFunc:jQ};function XQ(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=N.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new Ky(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var KQ={kernelName:Fc,backendName:"webgl",kernelFunc:XQ},ZQ=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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},YQ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${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 JQ(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=N.computePool3DInfo(o.shape,i,l,d,c,u),h=new YQ(p);return n.runWebGLProgram(h,[r],o.dtype)}var QQ={kernelName:th,backendName:"webgl",kernelFunc:JQ};function eee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Hu([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=N.computePool2DInfo(o.shape,i,l,1,c),d=new ZQ(u);return n.runWebGLProgram(d,[r],o.dtype)}var tee={kernelName:eh,backendName:"webgl",kernelFunc:eee};function nee(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Nm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var see={kernelName:Sa,backendName:"webgl",kernelFunc:nee},ree=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(N.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)));
}
`}},aee=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(N.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);
}
`}},oee=({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=K().getBool("WEBGL_PACK_NORMALIZATION")?new aee(s.shape,r.shape,a.shape,u,d,l):new ree(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},iee={kernelName:za,backendName:"webgl",kernelFunc:oee},lee=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=vt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=uee(this.rank),s,r=e.map((a,o)=>`sourceLoc.${Zy[o]} = start[${o}] + coords.${Zy[o]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${r.join(`
`)}
`,this.userCode=`
void main() {
${s}
setOutput(getSource(${n}));
}
`}},Zy=["x","y","z","w","u","v"];function uee(e){if(e===1)return"sourceLoc";if(e<=6)return Zy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var cee=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=vt(this.rank),n=Hn("coords",this.rank),s=Hn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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]};
result.w = ${a};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${s[u]} = ${n[u]} + start[${u}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${o}
${i}
setOutput(result);
}
`}};function dee(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=An.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function Ju(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=An.parseSliceParams(r,a,o);if(An.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=ZY(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),u=An.isSliceContinous(r.shape,i,l);if(c||!u){let d=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new cee(l):new lee(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),dee(r,i,l,n)}var pee={kernelName:Di,backendName:"webgl",kernelFunc:Ju},hee=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 WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:u}}),A=Ju({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},fee={kernelName:si,backendName:"webgl",kernelFunc:hee};function mee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),c=TS(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var gee={kernelName:nh,backendName:"webgl",kernelFunc:mee};function Aee(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=N.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var yee={kernelName:sh,backendName:"webgl",kernelFunc:Aee},xee="return float(a != b);",t4=Tn({opSnippet:xee,cpuKernelImpl:HY,dtype:"bool"}),bee={kernelName:bi,backendName:"webgl",kernelFunc:t4};function Jd(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return bs({inputs:{x:r.complexTensorInfos.real},backend:n})}var vee={kernelName:Hc,backendName:"webgl",kernelFunc:Jd},wee="return float(int(x));";function kee(e,t){let n=new Fo(e.shape,wee),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Yy(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return bs({inputs:{x:r},backend:n});let o=Ht(r.shape),i=Yy({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Oo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Jd({inputs:{input:r},backend:n}),i=Yy({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=bs({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return kee(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=t4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Iee={kernelName:Ca,backendName:"webgl",kernelFunc:Yy},n4="return ceil(x);",See=ot({opSnippet:n4,packedOpSnippet:n4,cpuKernelImpl:CY}),Cee={kernelName:Ta,backendName:"webgl",kernelFunc:See},Tee=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}},Nee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function Eee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;K().getBool("WEBGL_PACK_CLIP")?i=new Nee(r.shape):i=new Tee(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var Ree={kernelName:jr,backendName:"webgl",kernelFunc:Eee},$ee=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function s4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Dee(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new $ee(s.shape),o=[s4(s,r.complexTensorInfos.real),s4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var _ee={kernelName:Mc,backendName:"webgl",kernelFunc:Dee},Pee=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},Fee=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=vt(s),a=Hn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],c=o.slice(-2),u=o.join(),d=`if (${l} < ${i[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
return getChannel(
getT${f}(${Rm(o,l,m)}),
vec2(${Rm(c,l,m)}));
}`}let p=i.length,h=i[i.length-1];d+=`
return getChannel(
getT${p}(${Rm(o,l,h)}),
vec2(${Rm(c,l,h)}));`,this.userCode=`
float getValue(${o.map(f=>"int "+f)}) {
${d}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[s-1]} = ${a[s-1]} + 1;
if (${a[s-1]} < ${n[s-1]}) {
result.g = getValue(${a});
}
${a[s-2]} = ${a[s-2]} + 1;
if (${a[s-2]} < ${n[s-2]}) {
result.a = getValue(${a});
}
${a[s-1]} = ${a[s-1]} - 1;
if (${a[s-2]} < ${n[s-2]} &&
${a[s-1]} < ${n[s-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function Rm(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function $m(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return bs({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Oee={kernelName:Wc,backendName:"webgl",kernelFunc:$m};function Qu(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>Jd({inputs:{input:m},backend:n})),d=e.map(m=>$m({inputs:{input:m},backend:n})),p=Qu(u,t,n),h=Qu(d,t,n),f=Oo({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(A=>{let y=v.sizeFromShape(A.shape.slice(t));return ve({inputs:{x:A},backend:n,attrs:{shape:[-1,y]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=N.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=TY(d,p,s,h),m=N.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),g}if(e.length>K().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=Qu(e.slice(0,u),t,n),p=Qu(e.slice(u),t,n),h=Qu([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new Fee(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=Mee(e,t,n),i=new Pee(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function Mee(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function r4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=N.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 bs({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return N.assertParamsConsistent(l,a),Qu(i,a,n)}var zee={kernelName:ri,backendName:"webgl",kernelFunc:r4},a4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,A=m?2:3,y=m?3:1,x="",b="";n&&(s?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:x=`
float activation(float x) {
${n}
}
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${y}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${A}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${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}
${b}
setOutput(result);
}
`}},Lee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${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);
}
`}},Bee=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=Fs(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 o4({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,A=[];if(!((d===1||p===1)&&u>KS)&&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(qd(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let S=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(S);let E=Nm({a:w,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),$=s.texData.get(E.dataId);v.assert($.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=k,$.shape=n.outShape,g=bs({inputs:{x:E},backend:s}),g.shape=n.outShape,A.push(E)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=ve({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Nm({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),A.push(w),A.push(k),A.push(S)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function i4({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,A=[m,g],y=!0,x=!1,b=[],w=ve({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let S=new Bee(A,n),E=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],$=s.runWebGLProgram(S,[w],"float32",E),F=ve({inputs:{x:$},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push($),b.push(F);let R=r!=null,P=a!=null,T=i==="leakyrelu",M=i?Sm(i,!0):null,U=new GS(F.shape,k.shape,[1,g,n.outChannels],y,x,R,M,P,T),H=[F,k];if(r&&H.push(r),P&&H.push(a),T){let Y=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(Y),b.push(Y)}let z=s.runWebGLProgram(U,H,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],ee=ve({inputs:{x:z},backend:s,attrs:{shape:X}});b.push(z);for(let Y of b)s.disposeIntermediateTensorInfo(Y);return ee}function Wee(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=N.convertConv2DDataFormat(l),p=N.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=o4({x:r,filter:a,convInfo:p,backend:n});else if(K().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=i4({x:r,filter:a,convInfo:p,backend:n});else{let m=new a4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=ve({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var Vee={kernelName:Na,backendName:"webgl",kernelFunc:Wee},Uee=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);
}
`}},Gee=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);
}
`}},Hee=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},jee=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${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 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function qee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),h=new Uee(p);return n.runWebGLProgram(h,[r,a],"float32")}var Xee={kernelName:rh,backendName:"webgl",kernelFunc:qee};function Kee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=new Gee(p);return n.runWebGLProgram(h,[r,a],"float32")}var Zee={kernelName:Ea,backendName:"webgl",kernelFunc:Kee};function Yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=N.computeConv3DInfo(r.shape,a.shape,o,l,i),u=new Lee(c);return n.runWebGLProgram(u,[r,a],"float32")}var Jee={kernelName:zc,backendName:"webgl",kernelFunc:Yee};function Qee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,c=N.computeConv3DInfo(r.shape,l,o,1,i),u=new Hee(c);return n.runWebGLProgram(u,[r,a],"float32")}var ete={kernelName:ah,backendName:"webgl",kernelFunc:Qee};function tte(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=N.computeConv3DInfo(l,a.shape,i,1,o),u=new jee(c);return n.runWebGLProgram(u,[r,a],"float32")}var nte={kernelName:oh,backendName:"webgl",kernelFunc:tte},ste=US+`
return cos(x);
`,rte=ot({opSnippet:ste}),ate={kernelName:Ra,backendName:"webgl",kernelFunc:rte},ote=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,ite=ot({opSnippet:ote}),lte={kernelName:$a,backendName:"webgl",kernelFunc:ite},ute=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,A]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,b]=d>1?[`${(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(${y});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${g};
float width_scale = ${x};
float in_y = ${A};
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(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},cte=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new ute(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},dte={kernelName:oi,backendName:"webgl",kernelFunc:cte},l4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${u4(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() {
${vt(s)} coords = getOutputCoords();
int end = ${c4(s,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${o}) {
int idx = ${i};
${c4(s,"coords")} = idx;
val += getX(${u4(s,"coords")});
}
setOutput(val);
}
`}};function u4(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 ${e} is not yet supported`)}function c4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function pte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=N.getAxesPermutation([a],l),u=r;c!=null&&(u=jn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=N.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=bs({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new l4(u.shape,!1,i),g=[[f]],A=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let f=new l4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=N.getUndoAxesPermutation(c),m=jn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var hte={kernelName:ai,backendName:"webgl",kernelFunc:pte};function fte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=TS(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=SY(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var mte={kernelName:ih,backendName:"webgl",kernelFunc:fte},gte=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 Ate(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=new gte(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var yte={kernelName:ii,backendName:"webgl",kernelFunc:Ate},d4=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=Fs(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}
${c}
setOutput(result);
}
`}},p4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Fs(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;p+=`
for (int r = 0; r < ${c}; r++) {
`;for(let g=0;g<u;g++)p+=`
xTexelC${g*2} = vec4(0.0);
xTexelC${g*2}Ready = 0;
xTexelC${g*2+1} = vec4(0.0);
xTexelC${g*2+1}Ready = 0;
xC${g} = vec4(0.0);`;p+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let g=0;g<(d+1)/2;g++){let A=g*2;if(p+=`
xC = xCCorner + ${A*l};
`,i===1){if(A<u&&(o%2==1?(p+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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<u)){let y=o%2==0?v.nearestLargerEven(l):l;l%2==0&&o%2==1||l%2!=0&&o%2!=1?(p+=`
xCOffset = xC + imod(pads[1], 2) + ${y};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${A+1}.xy);
`):y===1?p+=`
xC${A+1} = xTexelC${A};
`:p+=`
xCOffset = xC + ${y};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${A+1};
`}}else A<u&&(o%2==1?(p+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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<u&&(p+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 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${A+1}.xy);
`,A+1<u&&(p+=`
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
`)));A<u&&(p+=`
wTexel = getW(r, ${A}, d1, q);
dotProd += xC${A} * vec4(wTexel.xz, wTexel.xz);
`,A+1<u&&(p+=`
wTexel = getW(r, ${A+1}, d1, q);
dotProd += xC${A+1} * vec4(wTexel.xz, wTexel.xz);
`))}p+=`
}
`,p+=`
}
`;let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${p}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function xte(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]),v.assert(N.eitherStridesOrDilationsAreOne(o,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=N.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;K().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new p4(d):p=new d4(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 bte={kernelName:Da,backendName:"webgl",kernelFunc:xte},vte=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},wte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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 * ${i} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function kte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=N.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new vte(d);return n.runWebGLProgram(p,[r,a],"float32")}var Ite={kernelName:lh,backendName:"webgl",kernelFunc:kte};function Ste(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=N.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new wte(d);return n.runWebGLProgram(p,[r,a],"float32")}var Cte={kernelName:uh,backendName:"webgl",kernelFunc:Ste},Tte=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function Nte(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=ve({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new Tte(a),l=n.runWebGLProgram(i,[o],o.dtype),c=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var Ete={kernelName:ch,backendName:"webgl",kernelFunc:Nte},Rte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
const ivec2 strides = ivec2(${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 $te(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=N.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),u,d=new Rte(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=ve({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var Dte={kernelName:Lc,backendName:"webgl",kernelFunc:$te};function _te(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=N.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:A,expandDims:y}=N.getEinsumPermutation(h,l[g]),x;N.isIdentityPermutation(A)?x=a[g]:(x=jn({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(x));let b=x.shape.slice();for(let w=0;w<y.length;++w)b.splice(y[w],0,1);v.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=Xy({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Tm({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 Pte={kernelName:Bc,backendName:"webgl",kernelFunc:_te},Fte="return (x >= 0.0) ? x : (exp(x) - 1.0);",Ote=`
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;
`,Mte=ot({opSnippet:Fte,packedOpSnippet:Ote}),zte={kernelName:Pa,backendName:"webgl",kernelFunc:Mte},Lte="return (b >= 1.0) ? a : a * (b + 1.0);",Bte=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Wte=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zd(Bte,s.shape,r.shape):new Yu(Lte,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},Vte={kernelName:hh,backendName:"webgl",kernelFunc:Wte},Ute=`
return vec4(equal(a, b));
`,Gte="return float(a == b);",Hte=Tn({opSnippet:Gte,packedOpSnippet:Ute,dtype:"bool",cpuKernelImpl:NY}),jte={kernelName:li,backendName:"webgl",kernelFunc:Hte},qte=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${N.ERF_P};
float a1 = ${N.ERF_A1};
float a2 = ${N.ERF_A2};
float a3 = ${N.ERF_A3};
float a4 = ${N.ERF_A4};
float a5 = ${N.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Xte=ot({opSnippet:qte}),Kte={kernelName:Jl,backendName:"webgl",kernelFunc:Xte},h4="return exp(x);",f4=ot({opSnippet:h4,packedOpSnippet:h4,cpuKernelImpl:EY,dtype:"float32"}),Zte={kernelName:Fa,backendName:"webgl",kernelFunc:f4};function Jy(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var Yte={kernelName:ui,backendName:"webgl",kernelFunc:Jy},m4="return exp(x) - 1.0;",Jte=ot({opSnippet:m4,packedOpSnippet:m4,cpuKernelImpl:RY}),Qte={kernelName:ci,backendName:"webgl",kernelFunc:Jte},g4=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${o}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${s});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${s}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function A4(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new g4("real",l,t),u=new g4("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Oo({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function ene(e){let{inputs:t,backend:n}=e,{input:s}=t;return A4(s,!1,n)}var tne={kernelName:fh,backendName:"webgl",kernelFunc:ene},nne=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function Qd(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 nne(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var sne={kernelName:Ql,backendName:"webgl",kernelFunc:Qd},rne=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},ane={kernelName:di,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new rne(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},y4="return floor(x);",one=ot({opSnippet:y4,packedOpSnippet:y4,cpuKernelImpl:$Y}),ine={kernelName:Oa,backendName:"webgl",kernelFunc:one},lne=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,une=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,cne=Tn({opSnippet:lne,packedOpSnippet:une,dtype:"int32"}),dne={kernelName:Ma,backendName:"webgl",kernelFunc:cne},pne=class{constructor(e){this.variableNames=["A"];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];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},hne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${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);
}
}
${t.output} = result;
}
`}},fne={kernelName:Kc,backendName:"webgl",kernelFunc:mne},ec;function mne(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],d=[c,l,a];(i||o)&&(ec==null&&(ec=document.createElement("canvas").getContext("2d")),ec.canvas.width=l,ec.canvas.height=c,ec.drawImage(r,0,0,l,c),r=ec.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=_s.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=K().getBool("WEBGL_PACK")?new hne(d):new pne(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function gne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(u),g=N.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))A=o4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(K().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=i4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,k=h==="leakyrelu",S=h?Sm(h,!1):null,E=new a4(g,b,S,w,k),$=[r,a];if(o&&$.push(o),i&&$.push(i),k){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));$.push(F),y.push(F)}A=n.runWebGLProgram(E,$,"float32")}let x=ve({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return y.push(A),y.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Ane={kernelName:mo,backendName:"webgl",kernelFunc:gne};function yne(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(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(r.shape,a.shape,l,m,c,d,!0),A=K().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=p?Sm(p,A):null,x=[r,a],b=o!=null,w=i!=null,k=p==="leakyrelu";if(b&&x.push(o),w&&x.push(i),k){let F=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));x.push(F),f.push(F)}let S;A?S=new p4(g,b,y,w,k):S=new d4(g,b,y,w,k);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=n.runWebGLProgram(S,x,"float32",E);return f.forEach(F=>n.disposeIntermediateTensorInfo(F)),$}var xne={kernelName:go,backendName:"webgl",kernelFunc:yne},bne=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=vt(t.length),r=vt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${s} strides = ${s}(${this.strides});
void main() {
${r} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function vne(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]=N.prepareAndValidate(s,r),p=ve({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=ve({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),y=n.bufferSync(s),x=DY(A,y,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,x.values)}let f=new bne(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var wne={kernelName:hi,backendName:"webgl",kernelFunc:vne},kne=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=vt(this.rank),s=Ine(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function Ine(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[r]}`);return s.join()}function x4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=n.readSync(a.dataId),u=r.shape[l];for(let b=0;b<c.length;++b){let w=c[b];v.assert(w<=u-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=ve({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let b=n.bufferSync(m),w=n.bufferSync(f),k=_Y(w,b,g);return h.forEach(S=>n.disposeIntermediateTensorInfo(S)),n.makeTensorInfo(d.outputShape,k.dtype,k.values)}let A=new kne(f.shape,g),y=n.runWebGLProgram(A,[f,m],f.dtype);h.push(y);let x=ve({inputs:{x:y},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Sne={kernelName:pi,backendName:"webgl",kernelFunc:x4},Cne="return float(a > b);",Tne=`
return vec4(greaterThan(a, b));
`,Nne=Tn({opSnippet:Cne,packedOpSnippet:Tne,cpuKernelImpl:PY,dtype:"bool"}),Ene={kernelName:fi,backendName:"webgl",kernelFunc:Nne},Rne="return float(a >= b);",$ne=`
return vec4(greaterThanEqual(a, b));
`,Dne=Tn({opSnippet:Rne,packedOpSnippet:$ne,dtype:"bool",cpuKernelImpl:FY}),_ne={kernelName:La,backendName:"webgl",kernelFunc:Dne};function Pne(e){let{inputs:t,backend:n}=e,{input:s}=t;return A4(s,!0,n)}var Fne={kernelName:mh,backendName:"webgl",kernelFunc:Pne},One="return float(!isnan(x) && !isinf(x));",Mne=ot({opSnippet:One,dtype:"bool"}),zne={kernelName:eu,backendName:"webgl",kernelFunc:Mne},Lne="return float(isinf(x));",Bne=ot({opSnippet:Lne,dtype:"bool"}),Wne={kernelName:tu,backendName:"webgl",kernelFunc:Bne},Vne="return float(isnan(x));",Une=ot({opSnippet:Vne,dtype:"bool"}),Gne={kernelName:nu,backendName:"webgl",kernelFunc:Une},Hne="return float(a < b);",jne=`
return vec4(lessThan(a, b));
`,qne=Tn({opSnippet:Hne,packedOpSnippet:jne,cpuKernelImpl:OY,dtype:"bool"}),Xne={kernelName:gi,backendName:"webgl",kernelFunc:qne},Kne="return float(a <= b);",Zne=`
return vec4(lessThanEqual(a, b));
`,Yne=Tn({opSnippet:Kne,packedOpSnippet:Zne,cpuKernelImpl:MY,dtype:"bool"}),Jne={kernelName:Ai,backendName:"webgl",kernelFunc:Yne};function Qne(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=zY(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var ese={kernelName:gh,backendName:"webgl",kernelFunc:Qne},tse=`if (x < 0.0) return NAN;
return log(x);`,nse=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,sse=ot({opSnippet:tse,packedOpSnippet:nse,cpuKernelImpl:LY}),rse={kernelName:Wa,backendName:"webgl",kernelFunc:sse},ase="return log(1.0 + x);",ose=ot({opSnippet:ase}),ise={kernelName:su,backendName:"webgl",kernelFunc:ose},lse="return float(a >= 1.0 && b >= 1.0);",use=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,cse=Tn({opSnippet:lse,packedOpSnippet:use,dtype:"bool"}),dse={kernelName:yi,backendName:"webgl",kernelFunc:cse},pse="return float(!(x >= 1.0));",hse=ot({opSnippet:pse}),fse={kernelName:ru,backendName:"webgl",kernelFunc:hse},mse="return float(a >= 1.0 || b >= 1.0);",gse=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Ase=Tn({opSnippet:mse,packedOpSnippet:gse,dtype:"bool"}),yse={kernelName:Vc,backendName:"webgl",kernelFunc:Ase},xse=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 * ${i};
setOutput(val);
}
`}},bse=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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);
}
`}},vse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=K().getBool("WEBGL_PACK_NORMALIZATION")?new bse(r.shape,a,o,i,l):new xse(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},wse={kernelName:Uc,backendName:"webgl",kernelFunc:vse},kse=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 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},Ise=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s,d=new kse(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},Sse={kernelName:Ah,backendName:"webgl",kernelFunc:Ise};function Cse(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=yl(i,e.dtype,"max",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function b4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=u!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let x=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S<b.length;S++)b[S]=r.shape[u[S]];let w=qy(x,r.shape,r.dtype,u,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=Cm(r,u,n);c=N.getInnerMostAxes(c.length,i)}N.assertAxesAreInnerMostDims("max",c,i);let[f,m]=N.computeOutAndReduceShapes(h.shape,c),g=f;o&&(g=N.expandShapeToKeepDim(f,l));let A;if(p){let x=n.texData.get(h.dataId).values,b=BY(x,v.sizeFromShape(m),g,r.dtype);A=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(A.dataId);w.values=b}else A=Cse(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),A}var Tse={kernelName:Va,backendName:"webgl",kernelFunc:b4},Nse=zS+`
return max(a, b);
`,Ese=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Im+`
return result;
`,Rse=Tn({opSnippet:Nse,packedOpSnippet:Ese,cpuKernelImpl:WY}),$se={kernelName:Ua,backendName:"webgl",kernelFunc:Rse};function Dse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Hu(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(N.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return bs({inputs:{x:r},backend:n});let d=new Yd(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var _se={kernelName:Ga,backendName:"webgl",kernelFunc:Dse};function Pse(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=N.computePool3DInfo(r.shape,a,o,u,i,c,l),p=new Ky(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Fse={kernelName:Gc,backendName:"webgl",kernelFunc:Pse},Ose=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 * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Mse=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${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 zse(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=N.computePool3DInfo(o.shape,i,l,d,c,u),h=new Ky(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Mse(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Lse={kernelName:xh,backendName:"webgl",kernelFunc:zse};function Bse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Hu([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=N.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new Yd(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Ose(p),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var Wse={kernelName:yh,backendName:"webgl",kernelFunc:Bse};function Vse(e,t,n,s){let r=new Yd(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Yd(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var Use={kernelName:bh,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(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let u=N.computePool2DInfo(s.shape,r,a,c,o),[d,p]=Vse(s,i,u,l);return[d,p]}};function Gse(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=yl(i,"float32","mean",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var Hse={kernelName:Ha,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=N.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 E=0;E<w.length;E++)w[E]=s.shape[u[E]];let k=qy(b,s.shape,s.dtype,u,w);f=o.makeTensorInfo(w,s.dtype);let S=o.texData.get(f.dataId);S.values=k}else f=Cm(s,u,o);h.push(f),c=N.getInnerMostAxes(c.length,i)}N.assertAxesAreInnerMostDims("sum",c,i);let[m,g]=N.computeOutAndReduceShapes(f.shape,c),A=m;r&&(A=N.expandShapeToKeepDim(m,l));let y=Gse(f,g,A,o);for(let x of h)o.disposeIntermediateTensorInfo(x);return y}};function jse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,r.shape.length)),N.assertAxesAreInnerMostDims("min",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=yl(m,m.dtype,"min",n),A;if(o){let y=N.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var qse={kernelName:ja,backendName:"webgl",kernelFunc:jse},Xse=zS+`
return min(a, b);
`,Kse=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Im+`
return result;
`,Zse=Tn({opSnippet:Xse,packedOpSnippet:Kse,cpuKernelImpl:VY}),Yse={kernelName:qa,backendName:"webgl",kernelFunc:Zse},Jse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let s=e.length,r=vt(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}));
}
`}},Qse=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=vt(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);
}
`}},ere=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Qse(s.shape,r,a):new Jse(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},tre={kernelName:Xa,backendName:"webgl",kernelFunc:ere},nre=`if (b == 0.0) return NAN;
return mod(a, b);`,sre=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Im+`
return result;
`,rre=Tn({opSnippet:nre,packedOpSnippet:sre}),are={kernelName:au,backendName:"webgl",kernelFunc:rre},ore=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}));
}
`}},ire=`
if (a == b) {
return 1.0;
};
return a / b;`,lre=`
// 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;
`,v4=Tn({opSnippet:ire,packedOpSnippet:lre,checkOutOfBounds:!0}),ure={kernelName:_a,backendName:"webgl",kernelFunc:v4},w4="return a - b;",k4=Tn({opSnippet:w4,packedOpSnippet:w4,supportsComplex:!0,cpuKernelImpl:rJ}),cre={kernelName:uo,backendName:"webgl",kernelFunc:k4};function I4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=b4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,o),c=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),u=k4({inputs:{a:r,b:c},backend:n}),d=f4({inputs:{x:u},backend:n}),p=Tm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:p},backend:n,attrs:{shape:l}}),f=v4({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 dre={kernelName:io,backendName:"webgl",kernelFunc:I4};function pre(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:I4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new ore(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var hre={kernelName:vh,backendName:"webgl",kernelFunc:pre},S4="return -x;";function fre(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=GY(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zu(s.shape,S4):r=new Fo(s.shape,S4),n.runWebGLProgram(r,[s],s.dtype)}var mre={kernelName:xi,backendName:"webgl",kernelFunc:fre},gre=Xs.nonMaxSuppressionV3Impl;function Are(e){N.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}=gre(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var yre={kernelName:vi,backendName:"webgl",kernelFunc:Are},xre=Xs.nonMaxSuppressionV4Impl;function bre(e){N.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}=xre(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var vre={kernelName:ou,backendName:"webgl",kernelFunc:bre},wre=Xs.nonMaxSuppressionV5Impl;function kre(e){N.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:A}=wre(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var Ire={kernelName:wi,backendName:"webgl",kernelFunc:kre},Sre=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)));
}
`}},Cre=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 Sre(l,a,o,i),u=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=ve({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},Tre={kernelName:Ii,backendName:"webgl",kernelFunc:Cre};function Dm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Jd({inputs:{input:s},backend:n}),a=Dm({inputs:{x:r},backend:n}),o=$m({inputs:{input:s},backend:n}),i=Dm({inputs:{x:o},backend:n}),l=Oo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Qd({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Nre={kernelName:Wi,backendName:"webgl",kernelFunc:Dm};function C4(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Jd({inputs:{input:s},backend:n}),a=C4({inputs:{x:r},backend:n}),o=$m({inputs:{input:s},backend:n}),i=Dm({inputs:{x:o},backend:n}),l=Oo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Qd({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Ere={kernelName:ki,backendName:"webgl",kernelFunc:C4};function Rre(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Jy({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=Jy({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=r4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var $re={kernelName:Si,backendName:"webgl",kernelFunc:Rre},Dre=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=vt(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}));
}
}
`}},_re=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=vt(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<m;f++)h+=`
${d[f]}
if (${p}) {
result[${f}] = float(value);
} else {
${r} source = rc - start;
result[${f}] = getChannel(getX(${l.join()}), ${u});
}
`;h+=s===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${o});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},T4=e=>{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 Qd({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _re(r.shape,a,o):new Dre(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},Pre={kernelName:Za,backendName:"webgl",kernelFunc:T4},Fre=`
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);
`,Ore=`
// 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));
`+Im+`
return result;
`,Mre=Tn({opSnippet:Fre,packedOpSnippet:Ore}),zre={kernelName:Ya,backendName:"webgl",kernelFunc:Mre};function Lre(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=N.getAxesPermutation(u,i),p=r;d!=null&&(p=jn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,i),l.push(p)),N.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:A}=jY(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,A,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),A=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),y=rd(r.dtype),x=yl(A,y,"prod",n);h=ve({inputs:{x},backend:n,attrs:{shape:f}}),l.push(A),l.push(x)}if(o){l.push(h);let f=N.expandShapeToKeepDim(h.shape,c);h=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Bre={kernelName:Ci,backendName:"webgl",kernelFunc:Lre},N4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=qY(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Wre={kernelName:iu,backendName:"webgl",kernelFunc:N4},Vre="return 1.0 / x;",Ure=ot({opSnippet:Vre}),Gre={kernelName:lu,backendName:"webgl",kernelFunc:Ure},Hre=yr+`
return (x < 0.0) ? 0.0 : x;
`,jre=`
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;
`,qre=ot({opSnippet:Hre,packedOpSnippet:jre}),Xre={kernelName:Qa,backendName:"webgl",kernelFunc:qre},Kre=yr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Zre=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,Yre=ot({opSnippet:Kre,packedOpSnippet:Zre}),Jre={kernelName:to,backendName:"webgl",kernelFunc:Yre},Qre=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);
}
`}},eae=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 tae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new eae(r.shape,l,c,a,o):new Qre(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var nae={kernelName:eo,backendName:"webgl",kernelFunc:tae},sae=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 rae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new sae(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var aae={kernelName:kh,backendName:"webgl",kernelFunc:rae},oae=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);
}
`}},iae=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 < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function lae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new iae(r.shape,l,c,a,o):new oae(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var uae={kernelName:uu,backendName:"webgl",kernelFunc:lae},cae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${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),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function dae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new cae(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var pae={kernelName:wh,backendName:"webgl",kernelFunc:dae},hae=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=vt(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},fae=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 < ${this.outputShape[n-2]}`,o=vt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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 = ${u(s.slice())};
}
}
setOutput(result);
}
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((A,y)=>p(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function mae(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 bs({inputs:{x:r},backend:n});let l=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fae(r.shape,i):new hae(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var gae={kernelName:Ni,backendName:"webgl",kernelFunc:mae},Aae=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 < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},yae={kernelName:Vi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Aae(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},xae=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,bae=ot({opSnippet:xae}),vae={kernelName:Ei,backendName:"webgl",kernelFunc:bae},wae="return inversesqrt(x);",kae=ot({opSnippet:wae,cpuKernelImpl:XY}),Iae={kernelName:no,backendName:"webgl",kernelFunc:kae},E4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=vt(r.length),l=vt(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${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 Sae(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}=N.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new E4(l,i,h.shape.length,f.shape.length,u,p),A=n.runWebGLProgram(g,[f,h,m],f.dtype),y=ve({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),y}var Cae={kernelName:Ri,backendName:"webgl",kernelFunc:Sae},Tae=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let c=0;c<t.length;c++)l.push(`${o[c]}`),c<e&&i.push(`${o[c]}`);s=i.join(),r=l.join()}let a=vt(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function Nae(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Tae(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Bn(r.dtype,a.dtype))}var Eae={kernelName:$i,backendName:"webgl",kernelFunc:Nae},Rae=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${N.SELU_SCALEALPHA};
float scale = ${N.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,$ae=ot({opSnippet:Rae}),Dae={kernelName:cu,backendName:"webgl",kernelFunc:$ae},R4="return 1.0 / (1.0 + exp(-1.0 * x));",_ae=ot({opSnippet:R4,packedOpSnippet:R4,cpuKernelImpl:KY}),Pae={kernelName:ro,backendName:"webgl",kernelFunc:_ae},Fae=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Oae=ot({opSnippet:Fae}),Mae={kernelName:du,backendName:"webgl",kernelFunc:Oae},zae=US+`
return sin(x);
`,Lae=ot({opSnippet:zae}),Bae={kernelName:so,backendName:"webgl",kernelFunc:Lae},Wae=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Vae=ot({opSnippet:Wae}),Uae={kernelName:_i,backendName:"webgl",kernelFunc:Vae},Gae=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,Hae=ot({opSnippet:Gae}),jae={kernelName:pu,backendName:"webgl",kernelFunc:Hae},qae=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 WebGL backend not implemented yet");let i=a.reduce((A,y)=>A*y),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let c=[],u=T4({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(u.shape,a,i,!1),f=ve({inputs:{x:u},backend:n,attrs:{shape:d}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),g},Xae={kernelName:Pi,backendName:"webgl",kernelFunc:qae};function Kae(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[d,p,h,f,m]=YY(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Zae={kernelName:Ih,backendName:"webgl",kernelFunc:Kae};function Yae(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[c,u,d]=JY(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Jae={kernelName:Sh,backendName:"webgl",kernelFunc:Yae};function Qae(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=ES(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var eoe={kernelName:Ch,backendName:"webgl",kernelFunc:Qae};function toe(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=ES(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var noe={kernelName:Th,backendName:"webgl",kernelFunc:toe};function soe(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}=N.calculateShapes(a,r,i),p=!1,h=new E4(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var roe={kernelName:jc,backendName:"webgl",kernelFunc:soe};function aoe(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=N.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=Ju({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var ooe={kernelName:Fi,backendName:"webgl",kernelFunc:aoe},$4="return sqrt(x);",ioe=ot({opSnippet:$4,packedOpSnippet:$4,cpuKernelImpl:QY}),loe={kernelName:ao,backendName:"webgl",kernelFunc:ioe},uoe="return x * x;",coe=ot({opSnippet:uoe}),doe={kernelName:hu,backendName:"webgl",kernelFunc:coe},D4="return (a - b) * (a - b);",poe=Tn({opSnippet:D4,packedOpSnippet:D4}),hoe={kernelName:lo,backendName:"webgl",kernelFunc:poe};function foe({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=yr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new Fo(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var moe={kernelName:ho,backendName:"webgl",kernelFunc:foe},goe=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=vt(n.length),a=vt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,c)=>(i++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${i-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${o}));
}
`}};function Aoe(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:A,outShape:y}=An.sliceInfo(r.shape,a,o,i,l,c,u,d,p),x=ve({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(h){let k=Ju({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=ve({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let E=n.texData.get(x.dataId).values,$=Le(x.shape,x.dtype,E),F=eJ(y,$,m,f);b=n.makeTensorInfo(y,x.dtype,F.values)}else{let S=new goe(f,m,y);b=n.runWebGLProgram(S,[x],x.dtype)}let w=ve({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),w}var yoe={kernelName:Oi,backendName:"webgl",kernelFunc:Aoe};function xoe(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]=tJ(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var boe={kernelName:qc,backendName:"webgl",kernelFunc:xoe};function voe(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[c,u,d]=nJ(i,l,r),p=u.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var woe={kernelName:Nh,backendName:"webgl",kernelFunc:voe};function koe(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=sJ(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Ioe={kernelName:Eh,backendName:"webgl",kernelFunc:koe},Soe="return tan(x);",Coe=ot({opSnippet:Soe}),Toe={kernelName:Mi,backendName:"webgl",kernelFunc:Coe},Noe=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Eoe=ot({opSnippet:Noe}),Roe={kernelName:co,backendName:"webgl",kernelFunc:Eoe},$oe=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=vt(this.rank),r=Doe(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function Doe(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function _4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=Le(r.shape,r.dtype,c),d=aJ(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new $oe(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var _oe={kernelName:qr,backendName:"webgl",kernelFunc:_4},Poe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},Foe=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 xl(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function P4(e){let t=1;for(;t<e;)t*=2;return t}function Ooe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=K().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=K().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=r.shape,u=c[c.length-1];if(n.shouldExecuteOnCPU([r])||u<i||a>l){let F=n.readSync(r.dataId),[R,P]=oJ(F,c,r.dtype,a,o);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(P.shape,P.dtype,P.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,Qd({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=ve({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&xl(n,h);let A=P4(a),y=P4(u),x=null,b=()=>x===null?[g,g]:[g,x],w=(F,R,P)=>{let T=b(),M=new Poe(P),H=[[u],[x===null?1:0],[Number.NEGATIVE_INFINITY],[F],[R]],z=x;x=n.runWebGLProgram(M,T,"int32",H),xl(n,z)};for(let F=1;F<A;F*=2){let R=F*2;for(let P=F;P>=1;P/=2)w(R,P,[m,y])}for(let F=y;F>A;F/=2){let R=b(),P=new Foe([m,F/2]),M=[[u],[x===null?1:0],[A]],U=x;x=n.runWebGLProgram(P,R,"int32",M),xl(n,U);let H=A/2,z=H*2;for(let X=H;X>=1;X/=2)w(z,X,x.shape)}let k=x;x=Ju({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),xl(n,k);let S=x4({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});xl(n,g);let E=c.slice(0,-1);E.push(a),k=x,x=ve({inputs:{x},attrs:{shape:E},backend:n}),xl(n,k);let $=S;return S=ve({inputs:{x:S},attrs:{shape:E},backend:n}),xl(n,$),[S,x]}var Moe={kernelName:zi,backendName:"webgl",kernelFunc:Ooe},zoe=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 Loe(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],A=new zoe(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var Boe={kernelName:Li,backendName:"webgl",kernelFunc:Loe};function Woe(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;Hu(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}=iJ(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var Voe={kernelName:Rh,backendName:"webgl",kernelFunc:Woe};function Uoe(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;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=Ju({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=ve({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Goe={kernelName:Bi,backendName:"webgl",kernelFunc:Uoe},Hoe=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 joe(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=N.getAxesPermutation([c],i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=N.getInnerMostAxes(1,i)[0]);let p=N.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=rd(r.dtype),g=(b,w,k,S,E)=>{let $=b.shape[0],F=b.shape[1],R=N.segment_util.segOpComputeOptimalWindowSize(F,E),P={windowSize:R,inSize:F,batchSize:$,numSegments:E},T=new Hoe(P,w),M=n.compileAndRun(T,[b,k],S);if(l.push(M),M.shape[1]===E)return M;let U=N4({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),H=_4({inputs:{x:U},backend:n,attrs:{reps:[F/R]}});return l.push(U),l.push(H),g(M,w,H,S,E)},A=g(f,"unsortedSegmentSum",a,m,o),y=ve({inputs:{x:A},backend:n,attrs:{shape:p}}),x=y;if(u!=null){l.push(y);let b=N.getUndoAxesPermutation(u);x=jn({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var qoe={kernelName:Xc,backendName:"webgl",kernelFunc:joe},Xoe=[wse,Sse,oQ,lQ,dQ,fQ,gQ,xQ,vQ,kQ,TQ,EQ,DQ,FQ,VQ,zQ,HQ,KQ,qQ,QQ,tee,see,iee,fee,gee,yee,Iee,Cee,Ree,_ee,WJ,zee,Xee,Zee,Vee,ete,nte,Jee,ate,lte,dte,hte,mte,yte,Ite,Cte,bte,Ete,Dte,Pte,zte,Vte,jte,Kte,Zte,Yte,Qte,tne,sne,ane,ine,dne,fne,Ane,xne,wne,Sne,Ene,_ne,BJ,Fne,Oee,zne,Wne,Gne,UJ,Xne,Jne,ese,ise,rse,dse,fse,yse,Tse,Fse,_se,Lse,Wse,Use,$se,Hse,qse,Yse,tre,are,hre,XJ,mre,yre,vre,Ire,bee,Tre,Ere,$re,Pre,zre,HJ,Bre,Wre,vee,ure,Gre,Jre,Xre,ZJ,nae,aae,uae,pae,gae,yae,vae,Iae,Cae,Eae,Dae,Pae,Mae,Bae,Uae,pee,dre,jae,Xae,Zae,Jae,eoe,noe,roe,ooe,loe,doe,hoe,moe,yoe,boe,woe,Ioe,cre,sQ,Toe,Roe,_oe,Moe,Boe,rQ,Voe,Goe,qoe,Nre];for(let e of Xoe)Kr(e);var zr=K();zr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zr.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);zr.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);zr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zr.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);zr.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zr.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zr.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Koe(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Qt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function _m(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function We(){return`
let index = getGlobalIndex(globalId, localId);
`}function Fe(){return`
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>, [[builtin(global_invocation_id)]] globalId : vec3<u32>)
`}function Zoe(e,t,n,s=!1){let r=`
let workGroupSizeX = ${n.workGroupSize[0]}u;
let workGroupSizeY = ${n.workGroupSize[1]}u;
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=M4(t.shape),f=`
[[block]] struct Matrix0 {
numbers: array<${_m(t.dtype,n.isVec4)}>;
};
[[block]] struct Uniform {
size : i32;
numChannels : i32;
outShapeStrides : vec2<i32>;
dispatchSize : vec3<u32>;
};
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
[[group(0), binding(2)]] var<uniform> uniforms: Uniform;
`;return[F4,f,r,O4,h,n.getUserCode()].join(`
`)}let a=[],o="[[block]] struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${Qt(e[f].shape.length)}; `}),o+=`outShape : ${Qt(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
outShapeStrides: ${Qt(i)}; `,n.size!=null&&(o+="size : i32; "),o+="dispatchSize : vec3<u32>; ",n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(`
[[block]] struct Matrix0 {
numbers: array<atomic<i32>>;
};
[[group(0), binding(0)]] var<storage, read_write> result : Matrix0;
`):a.push(`
[[block]] struct Matrix0 {
numbers: array<${_m(t.dtype,n.isVec4)}>;
};
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
`),n.variableNames.forEach((h,f)=>{a.push(`
[[block]] struct Matrix${1+f} {
numbers: array<${_m(e[f].dtype,n.isVec4)}>;
};
[[group(0), binding(${1+f})]] var<storage, read> ${h} : Matrix${1+f};
`)}),o!==""&&a.push(`
[[group(0), binding(${1+n.variableNames.length})]] var<uniform> uniforms : Uniforms;
`),a.push(r);let[l,c]=nie(t.shape,n.dispatchLayout),u=M4(t.shape),d=[F4,a.join(`
`),O4,u,l,Yoe(t.shape.length)];if(n.atomic||d.push(Joe(t.shape,t.dtype,n.isVec4)),c===t.shape.length){let h=e.map(f=>Qoe(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
`)}var F4=`
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
var res: i32 = a / b;
let mod: i32 = a % b;
if (sign < 0. && mod != 0) {
res = res - 1;
}
return res;
}
fn isNanCustom(val : f32) -> bool {
if (val > 0.0) {
return false;
}
if (val < 0.0) {
return false;
}
if (val == 0.0) {
return false;
}
return true;
}
fn isNanCustomVec4F32(val : vec4<f32>) -> vec4<f32> {
var res = vec4<f32> (0.0);
for (var i = 0u; i < 4u; i = i + 1u) {
if (isNanCustom(val[i])) {
res[i] = 1.0;
} else {
res[i] = 0.0;
}
}
return res;
}
// Checks whether coordinates lie within the bounds of the shape.
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
return all(coord >= vec4<i32>(0)) &&
all(coord < shape);
}
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
return all(coord >= vec3<i32>(0)) &&
all(coord < shape);
}
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
return all(coord >= vec2<i32>(0)) &&
all(coord < shape);
}
`,O4=`
fn getFlatIndex1D(coord : i32, shape : i32) -> i32 {
return coord;
}
fn getFlatIndex2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(shape.y), 1.0)));
}
fn getFlatIndex3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(shape.y) * f32(shape.z), f32(shape.z), 1.0)));
}
fn getFlatIndex4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return i32(dot(vec4<f32>(coords), vec4<f32>(
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<u32>, localId : vec3<u32>) -> 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<u32>(
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 Yoe(e){let t="";switch(e){case 0:case 1:t+=`
fn getOutputFlatIndex(coords : i32) -> i32 {
return coords;
}
`;break;case 2:t+=`
fn getOutputFlatIndex(coords : vec2<i32>) -> i32 {
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(uniforms.outShapeStrides), 1.0)));
}
`;break;case 3:t+=`
fn getOutputFlatIndex(coords : vec3<i32>) -> i32 {
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), 1.0)));
}
`;break;case 4:t+=`
fn getOutputFlatIndex(coords : vec4<i32>) -> i32 {
return i32(dot(vec4<f32>(coords), vec4<f32>(
f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), f32(uniforms.outShapeStrides.z), 1.0)));
}
`;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Joe(e,t,n){let s=e.length,r=_m(t,n),a;if(n?a=`fn setOutputFlat(flatIndex : i32, value : vec4<f32>) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputFlatI32(flatIndex : i32, value : vec4<i32>) {
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] = ${r}(value);
}`,s>=2){let o=["d0","d1","d2","d3"].slice(0,s),i=Qt(s);n?a+=`
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlat(flatIndex / 4, value);
}
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputFlatIndex(${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}(${o.join(", ")}));
setOutputFlatI32(flatIndex, value);
}
`}return a}function Qoe(e,t,n,s){let r=eie(e,n);return e.shape.length<=t.length&&(r+=tie(e,t,n,s)),r}function eie(e,t){let n=e.name,s=e.shape.length,r=Qt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${u} : i32`).join(", ");if(s<1)return t?`
fn ${a}() -> vec4<f32> {
return vec4<f32>(${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<f32> {
return vec4<f32>(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
${l}) / 4]);
}
`:`
fn ${a}(${i}) -> f32 {
return f32(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
${l})]);
}
`}function tie(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=Qt(l);if(v.arraysEqual(e.shape,t)&&s)return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${r}.numbers[globalIndex]);
}
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
return vec4<f32>(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"} / 4]);
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32 {
return f32(${r}.numbers[globalIndex]);
}
fn ${o}ByCoords(coords : ${c}) -> f32 {
return f32(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"}]);
}
`;let u=N.getBroadcastDims(e.shape,t),d=l-i,p="";if(i===0)return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
return get${a}();
}
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
return get${a}();
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32{
return get${a}();
}
fn ${o}ByCoords(coords : ${c}) -> f32{
return get${a}();
}
`;l<2&&u.length>=1?p="coords = 0;":p=u.map(g=>`coords[${g+d}] = 0;`).join(`
`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=Qt(i),A=e.shape.map((y,x)=>`coords[${x+d}]`).join(", ");h=`${g}(${A})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
var coords = getOutputCoords(globalId, globalIndex);
${p}
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
}
fn ${o}ByCoords(coordsIn : ${c}) -> vec4<f32> {
var coords = coordsIn;
${p}
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, 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 nie(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords(globalId : vec3<u32>, globalIndex : i32) -> ${Qt(a)}{
return getCoordsFromFlatIndex(i32(globalIndex));
}
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=Koe(h,"uniforms.outShape");o+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:o+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let c=[];for(let p=0;p<l;p++)c.push(`d${p}`);let u=Qt(l),d=`fn getOutputCoords(globalId : vec3<u32>, globalIndex : i32) -> ${u} {
${o}
`;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function M4(e){let t=e.length;if(t<=1)return"fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=Qt(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return` fn getCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
return vec2<i32>(d0, d1);
}`;let a="var index2 = index;"+n.map((o,i)=>{let 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 z4={};ze(z4,{ArrayBufferToTypedArray:()=>L4,GPUBytesPerElement:()=>nx,computeDispatch:()=>Me,computeWorkGroupSizeForConv2d:()=>Qy,computeWorkGroupSizeForMatMul:()=>ex,computeWorkPerThreadForConv2d:()=>tx,flatDispatchLayout:()=>Ke,isWebGPUSupported:()=>sx,tilesFitEvenlyIntoShape:()=>ia});var tc=65535,bl=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function ia(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]==0)}function Me(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(bl(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(bl(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(bl(e.z.map(l=>t[l]))/(n[2]*s[2])):1];if(r<=tc&&a<=tc&&o<=tc)return[r,a,o];v.assert(r>tc&&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>tc?(i=Math.ceil(Math.cbrt(r)),v.assert(i<=tc,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function Qy(e,t){let n=bl(e.x.map(r=>t[r])),s=bl(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function ex(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function tx(e,t){let n=bl(e.x.map(r=>t[r])),s=bl(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Ke(e){return{x:e.map((t,n)=>n)}}function nx(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function L4(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<n.length;a++)r[a]=n[a];return r}else throw new Error(`Unknown dtype ${t}`)}function sx(){return!!navigator.gpu}var Vt;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.SUB=2]="SUB",e[e.DIV=3]="DIV",e[e.EQUAL=4]="EQUAL",e[e.GREATER=5]="GREATER",e[e.GREATER_EQUAL=6]="GREATER_EQUAL",e[e.LESS=7]="LESS",e[e.LESS_EQUAL=8]="LESS_EQUAL",e[e.LOGICAL_AND=9]="LOGICAL_AND",e[e.NOT_EQUAL=10]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=11]="SQUARED_DIFFERENCE",e[e.INT_DIV=12]="INT_DIV",e[e.POW=13]="POW",e[e.PRELU=14]="PRELU",e[e.MAX=15]="MAX",e[e.MIN=16]="MIN",e[e.COMPLEX_MULTIPLY_REAL=17]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=18]="COMPLEX_MULTIPLY_IMAG"})(Vt||(Vt={}));var sie="return a + b;",rie="return areal * breal - aimag * bimag;",aie="return areal * bimag + aimag * breal;",oie="return a / b;",iie="return a * b;",lie="return (a - b) * (a - b);",uie="return a - b;",cie="return f32(a == b);",die="return vec4<f32>(a == b);",pie="return f32(a > b);",hie="return vec4<f32>(a > b);",fie="return f32(a >= b);",mie="return vec4<f32>(a >= b);",gie="return f32(a < b);",Aie="return vec4<f32>(a < b);",yie="return f32(a <= b);",xie="return vec4<f32>(a <= b);",bie="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",vie=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,wie=`
if (isNanCustom(a)) { return a; }
if (isNanCustom(b)) { return b; }
`,B4=`
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;
}
`,kie=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,Iie=`
let ia = vec4<i32>(round(a));
let ib = vec4<i32>(round(b));
let cond = ib != vec4<i32>(0);
var resultTemp = vec4<i32>(0);
let s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4<f32>(resultTemp);
`,Sie="return f32(a != b);",Cie="return vec4<f32>(a != b);",Tie=`
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);
`,Nie=`
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
let isModRound1 = vec4<f32>(isModRound1Bool);
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
var resultTemp = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
let isExpZero = b == vec4<f32>(0.0);
if (isExpZero.r) {
resultTemp.r = 1.0;
}
if (isExpZero.g) {
resultTemp.g = 1.0;
}
if (isExpZero.b) {
resultTemp.b = 1.0;
}
if (isExpZero.a) {
resultTemp.a = 1.0;
}
let isNaN = vec4<f32>(a < vec4<f32>(0.0)) * vec4<f32>(floor(b) < b);
${B4}
return resultTemp;
`,Eie="if (a < 0.0) { return b * a; } return a;",Rie=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function W4(e,t){let n=t?B4:wie;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = min(vec4<f32>(isNanCustomVec4F32(a)) + vec4<f32>(isNanCustomVec4F32(b)), vec4<f32>(1.0));
`+n+`
return resultTemp;
`:n+`
return ${e}(a, b);
`}function ep(e,t){switch(e){case 0:return iie;case 1:return sie;case 2:return uie;case 3:return oie;case 4:return t?die:cie;case 5:return t?hie:pie;case 6:return t?mie:fie;case 7:return t?Aie:gie;case 8:return t?xie:yie;case 9:return t?vie:bie;case 10:return t?Cie:Sie;case 11:return lie;case 12:return t?Iie:kie;case 14:return t?Rie:Eie;case 15:return W4("max",t);case 16:return W4("min",t);case 13:return t?Nie:Tie;case 17:return rie;case 18:return aie;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var wt;(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"})(wt||(wt={}));var $ie="return abs(a);",Die="return ceil(a);",_ie="return cos(a);",Pie=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Fie="return exp(a) - 1.0;",Oie="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Mie=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,zie="return exp(a);",Lie="return floor(a);",Bie="return a;",Wie=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,Vie="return f32(!(a >= 1.0));",Uie="return -a;",Gie="return (a < 0.0) ? b * a : a;",Hie="return max(a, 0.0);",jie="return clamp(a, 0.0, 6.0);",qie="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Xie=`
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
let isNaN = isNan(a);
if (isNaN.r) {
resFloat.r = a.r;
}
if (isNaN.g) {
resFloat.g = a.g;
}
if (isNaN.b) {
resFloat.b = a.b;
}
if (isNaN.a) {
resFloat.a = a.a;
}
return resFloat;
`,Kie="return 1.0/sqrt(a);",Zie="return 1.0 / (1.0 + exp(-1.0 * a));",Yie="return sin(a);",Jie=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,Qie="return sqrt(a);",ele="return a * a;",tle=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,nle="return f32(i32((a)));";function nc(e,t){switch(e){case 0:return $ie;case 2:return _ie;case 3:return Pie;case 1:return Die;case 4:return t?Mie:Oie;case 5:return zie;case 6:return Fie;case 7:return Lie;case 8:return Bie;case 9:return Wie;case 10:return Vie;case 11:return Uie;case 12:return Gie;case 13:return t?Xie:Hie;case 14:return t?qie:jie;case 15:return Kie;case 18:return Zie;case 16:return Yie;case 17:return Jie;case 19:return Qie;case 20:return ele;case 21:return tle;case 22:return nle;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Mo(e,t=!1){if(e===null)return null;if(e==="linear")return nc(wt.LINEAR);if(e==="relu")return nc(wt.RELU,t);if(e==="elu")return nc(wt.ELU,t);if(e==="relu6")return nc(wt.RELU6,t);if(e==="prelu")return ep(Vt.PRELU,t);if(e==="sigmoid")return nc(wt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function V4(e,t){let n={RowPerThread:e[1],ColPerThread:e[0],TileAOuter:t[1]*e[1],TileBOuter:t[0]*e[0],TileInner:t[0]*e[0]};return`
var<workgroup> mm_Asub : array<array<vec4<f32>, ${n.TileInner/n.ColPerThread}>, ${n.TileAOuter}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n.TileBOuter/n.ColPerThread}>, ${n.TileInner}>;
let RowPerThread = ${n.RowPerThread};
let ColPerThread = ${n.ColPerThread}; // only support ColPerThread = 4
let TileAOuter = ${n.TileAOuter};
let TileBOuter = ${n.TileBOuter};
let TileInner = ${n.TileInner};
${Fe()} {
let tileRow = i32(localId.y) * RowPerThread;
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y) * RowPerThread;
let globalCol = i32(globalId.x);
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
var acc: array<vec4<f32>, ${n.RowPerThread}>;
var ACached : vec4<f32>;
var BCached : array<vec4<f32>, 4>;
// Loop over shared dimension.
var globalColA = tileCol;
let RowPerThreadB = TileInner / ${t[1]};
let tileRowB = i32(localId.y) * RowPerThreadB;
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileCol;
mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
}
globalColA = globalColA + TileInner / ColPerThread;
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol;
mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];
for (var i = 0; i < RowPerThread; i = i + 1) {
ACached = mm_Asub[tileRow + i][k];
acc[i] = BCached[0] * ACached.x + acc[i];
acc[i] = BCached[1] * ACached.y + acc[i];
acc[i] = BCached[2] * ACached.z + acc[i];
acc[i] = BCached[3] * ACached.w + acc[i];
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
mm_write(globalRow + innerRow,
globalCol,
acc[innerRow], globalId);
}
}`}function sle(e){return`
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
let tileSize = ${e[0]*4};
${Fe()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
// Without this initialization strange values show up in acc.
var acc = vec4<f32>(0.0);
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * tileSize / 4 + tileCol;
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < tileSize / 4; k = k + 1) {
let rowB = t * tileSize + k * 4;
let BCached0 = mm_readB(rowB, globalCol, globalId);
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
let ACached = mm_Asub[k];
acc = acc + BCached0 * ACached.x;
acc = acc + BCached1 * ACached.y;
acc = acc + BCached2 * ACached.z;
acc = acc + BCached3 * ACached.w;
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var rle=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=ex(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${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[ia(o,this.aShape.slice(1)),ia(i,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
}
return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0)`,n="",s="";if(this.activation){let o=Mo(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / ${this.vecSize};
let batch = i32(globalId.z);
${e};
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / ${this.vecSize};
let batch = i32(globalId.z);
${t};
}
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
{
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col * 4);
${r}
${s}
setOutput(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${this.outputShape[1]>1?V4([this.vecSize,this.workPerThread,1],this.workGroupSize):sle(this.workGroupSize)}
`}};function rx(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
${Fe()} {
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<array<f32, ${e[0]}>, ${e[1]}>;
var ACached : f32;
var BCached : array<f32, ${e[0]}>;
// Without this initialization strange values show up in acc.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = 0.0;
}
}
let ColPerThreadA = ${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 ale(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${Fe()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
// Without this initialization strange values show up in acc.
var acc = 0.0;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * TileSize + tileCol * 4;
mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
mm_readA(globalRow, colA + 1, globalId),
mm_readA(globalRow, colA + 2, globalId),
mm_readA(globalRow, colA + 3, globalId));
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileSize / 4; k = k + 1) {
let rowB = t * TileSize + k * 4;
let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
mm_readB(rowB + 1, globalCol, globalId),
mm_readB(rowB + 2, globalCol, globalId),
mm_readB(rowB + 3, globalCol, globalId));
let ACached = mm_Asub[k];
acc = acc + dot(ACached, BCached);
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var U4=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=ex(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Me(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[ia(r,this.aShape.slice(1)),ia(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
}
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
}
return 0.0;`;let n="",s="";if(this.activation){let o=Mo(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutput(batch, row, col, value);
}
${this.outputShape[1]>1?rx([this.workPerThread,this.workPerThread,1],this.workGroupSize):ale(this.workGroupSize)}
`}};function ole(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${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.
${Fe()} {
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 >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var ile=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`,n="",s="";if(this.activation){let o=Mo(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
var value = valueIn;
${r}
${s}
setOutput(batch, row, col, value);
}
}
${ole(this.workGroupSize)}
`}};function je(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var lle={kernelName:Ti,backendName:"webgpu",kernelFunc:je};function ax({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),y=v.sizeFromShape(g),x=A===y||A===1||y===1;v.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let w=(A>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[A,d,h]:[A,h,d],S=s?[y,f,p]:[y,p,f],E=je({inputs:{x:e},backend:r,attrs:{shape:k}}),$=je({inputs:{x:t},backend:r,attrs:{shape:S}}),F=[E,$],R=Math.max(A,y),P=d%4==0&&f%4==0&&!n&&!s&&f>=32,T;!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?T=new ile(k,S,[R,h,f],a,l,o):P?T=new rle(k,[R,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):T=new U4(k,[R,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let M=[E,$];a&&M.push(a),o&&M.push(o);let U=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],H=r.runWebGPUProgram(T,M,e.dtype,U),z=je({inputs:{x:H},backend:r,attrs:{shape:w}});F.push(H);for(let X of F)r.disposeData(X.dataId);return z}function ule(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 ax({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var cle={kernelName:fo,backendName:"webgpu",kernelFunc:ule},G4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(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 {
${ep(this.op,!1)}
}
${Fe()} {
${We()}
if(index < uniforms.size) {
let areal = getARealAtOutCoordsByGlobalId(globalId, index);
let aimag = getAImagAtOutCoordsByGlobalId(globalId, index);
let breal = getBRealAtOutCoordsByGlobalId(globalId, index);
let bimag = getBImagAtOutCoordsByGlobalId(globalId, index);
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},dle=class{constructor(e,t,n,s){this.variableNames=["A","B"];let r=256;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ke(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%(this.workGroupSize[0]*this.workPerThread)==0,this.shaderKey=`binaryShared_${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 {
${ep(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${Fe()} {
${We()}
// 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;
${n}
}
}
`}},ple=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.fitShape=this.size%this.workGroupSize[0]==0,this.shaderKey=`binaryVec4_${e}_${this.fitShape}`,this.size=v.sizeFromShape(this.outputShape)/this.workPerThread}getUserCode(){let e,n=`fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${ep(this.op,this.isVec4)}
}`;return this.fitShape?e=`
${n}
${Fe()} {
${We()}
let a = vec4<f32>(A.numbers[index]);
let b = vec4<f32>(B.numbers[index]);
setOutputFlat(index, binaryOperation(a, b));
}
`:e=`
${n}
${Fe()} {
${We()}
if (index < uniforms.size) {
let a = vec4<f32>(A.numbers[index]);
let b = vec4<f32>(B.numbers[index]);
setOutputFlat(index, binaryOperation(a, b));
}
}
`,e}},H4=class{constructor(e,t,n){this.variableNames=["A","B"];let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ke(this.outputShape),this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%s==0,this.shapesFit=v.arraysEqual(t,n)&&this.sizeFit,this.workPerThread=this.sizeFit||this.shapesFit?1:2,this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=`binary_${e}_${this.sizeFit}_${this.shapesFit}`,this.op=e}getUserCode(){let e,n=` fn binaryOperation(a : f32, b : f32) -> f32 {
${ep(this.op,!1)}
}`;return this.shapesFit?e=`
${n}
${Fe()} {
${We()}
let a = f32(A[index]);
let b = f32(B[index]);
setOutputFlat(index, binaryOperation(a, b));
}
`:this.sizeFit?e=`
${n}
${Fe()} {
${We()}
let coords = getCoordsFromFlatIndex(index);
let a = getAAtOutCoordsByCoords(coords);
let b = getBAtOutCoordsByCoords(coords);
setOutputFlat(index, binaryOperation(a, b));
}
`:e=`
${n}
${Fe()} {
${We()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1 ) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
let a = getAAtOutCoordsByCoords(coords);
let b = getBAtOutCoordsByCoords(coords);
setOutputFlat(flatIndex, binaryOperation(a, b));
}
}
}
`,e}};function j4(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new ple(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new dle(e,t,n,a):new H4(e,t,n)}function Js(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var hle={kernelName:Ba,backendName:"webgpu",kernelFunc:Js};function sc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=Js({inputs:{x:s},backend:n}),l=Js({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var fle={kernelName:Oc,backendName:"webgpu",kernelFunc:sc},Pm=class{constructor(e,t){this.variableNames=["A"];let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.size=v.sizeFromShape(this.outputShape),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${nc(this.op,!1)}
}
${Fe()} {
${We()}
if (index < uniforms.size) {
let a = getAAtOutCoordsByGlobalId(globalId, index);
setOutputFlat(index, unaryOperation(a));
}
}
`}};function Nn({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 Pm(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function qn({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!==Vt.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[A,y]=g,x={dataId:A.dataId,dtype:A.dtype,shape:o.shape},b={dataId:y.dataId,dtype:y.dtype,shape:i.shape},w=j4(e,o.shape,i.shape);return l.runWebGPUProgram(w,[x,b],Bn(A.dtype,y.dtype))});else{let g=new G4(Vt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),A=new G4(Vt.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),y=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,y,"float32"),f=l.runWebGPUProgram(A,y,"float32")}let m=sc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||Bn(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"?N.fromUint8ToStringArray(d):d,f=o.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,c);return l.makeTensorInfo(g,c,m)}let u=j4(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:mle,ceilImpl:gle,concatImpl:Ale,equalImpl:yle,expImpl:xle,expm1Impl:ble,floorImpl:vle,gatherNdImpl:wle,gatherV2Impl:kle,greaterEqualImpl:Ile,greaterImpl:Sle,lessEqualImpl:Cle,lessImpl:Tle,logImpl:Nle,maxImpl:Ele,maximumImpl:Rle,minimumImpl:$le,multiplyImpl:Dle,negImpl:_le,notEqualImpl:Ple,prodImpl:Fle,rangeImpl:Ole,rsqrtImpl:Mle,simpleAbsImpl:zle,sliceImpl:Lle,stridedSliceImpl:Ble,stringNGramsImpl:Wle,subImpl:Vle,tileImpl:Ule,topKImpl:Gle,transposeImpl:Hle,uniqueImpl:Jge}=om,jle=Nn({opType:wt.ABS,cpuKernelImpl:zle}),qle={kernelName:ni,backendName:"webgpu",kernelFunc:jle},Xle=qn({opSnippet:Vt.ADD,cpuKernelImpl:mle,supportsComplex:!0}),Kle={kernelName:Hr,backendName:"webgpu",kernelFunc:Xle},Zle=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}AtOutCoordsByCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
${Fe()} {
${We()}
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, ${t});
}
}
}
`}};function Yle(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Js({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Bn(i,l)),a=s.map(i=>i.shape),o=new Zle(a);return n.runWebGPUProgram(o,s,r)}var Jle={kernelName:wa,backendName:"webgpu",kernelFunc:Yle},q4=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="axis : i32;";let s=[t];N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r,a]=N.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r;let o=v.sizeFromShape(a);this.reductionFactor=2;let i=256,l=Math.min(Math.ceil(o/this.reductionFactor),i);this.workGroupSize=[l,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((c,u)=>u)},this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=this.workGroupSize[0]>1,t=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,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 ${this.op} bestValue && !isNanCustom(candidate)) {
bestValue = candidate;
bestIndex = candidateIndex;
}
}
}
xBestIndices[localId.x] = bestIndex;
xBestValues[localId.x] = bestValue;
}
if (localId.x == 0u) {
setOutputFlatI32(flatOutputIndex, i32(bestIndex));
}
`,s=Qt(this.outputShape.length),r=(i,l)=>this.outputShape.length===1?i:`${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<u32>, globalIndex : i32) -> vec2<i32>{
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<i32>(offset, inputStride);
}
fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
return coordInfo[0] + coordInfo[1] * index;
}
${Fe()} {
${We()}
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;
bestIndex = i;
}
}
}
let flatOutputIndex = i32(globalId.y);
${e?n:"setOutputFlatI32(flatOutputIndex, bestIndex);"}
}
`}},Qle=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
let TILE_DIM = ${this.workGroupSize[0]};
var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
${Fe()} {
${We()}
let workGroupID = (globalId - localId)/vec3<u32>(${this.workGroupSize[0]}u, ${this.workGroupSize[1]}u, ${this.workGroupSize[2]}u);
var x = i32(workGroupID.x) * TILE_DIM + i32(localId.x);
var y = i32(workGroupID.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] =
A.numbers[y * width + x];
}
workgroupBarrier();
x = i32(workGroupID.y) * TILE_DIM + i32(localId.x);
y = i32(workGroupID.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputFlat((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},eue=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=Qt(this.outputShape.length),t=tue(this.newDim);return`
${Fe()} {
${We()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromFlatIndex(flatIndex);
setOutputFlat(flatIndex, A.numbers[getFlatIndex${this.outputShape.length}D(
${e}(${t}), uniforms.aShape)]);
}
}
}
`}};function tue(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function vl(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];if(n.shouldExecuteOnCPU([r])){let d=o.tensorMap.get(r.dataId).values,p=Hle(d,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,p)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let u=new Qle(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new eue(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var nue={kernelName:po,backendName:"webgpu",kernelFunc:vl};function sue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=vl({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=new q4(l.shape,o[0],"max"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var rue={kernelName:ka,backendName:"webgpu",kernelFunc:sue};function aue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=vl({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=new q4(l.shape,o[0],"min"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var oue={kernelName:jl,backendName:"webgpu",kernelFunc:aue},X4=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(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"),`
${Fe()} {
${We()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
var count = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
let xR = xRCorner + wR;
if (xR < 0 || xR >= uniforms.convDims.x) {
continue;
}
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
let xC = xCCorner + wC;
if (xC < 0 || xC >= uniforms.convDims.y) {
continue;
}
let value = getX(batch, xR, xC, coords[3]);
${e}
}
}
setOutput(batch, coords[1], coords[2], coords[3], ${t});
}
}
`}},K4=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${Fe()} {
${We()}
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 iue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Js({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new K4(u):(d=new X4(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 lue={kernelName:Ia,backendName:"webgpu",kernelFunc:iue};function uue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return ax({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var cue={kernelName:Sa,backendName:"webgpu",kernelFunc:uue},due=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=t,this.rank=t.length,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Qt(e.length)}; `,this.shaderKey="slice",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=Qt(this.rank),t=pue(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${ox[a]} = uniforms.start[${a}] + coords.${ox[a]};`),`
${Fe()} {
${We()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getOutputCoords(globalId, index);
${n.join(`
`)}
setOutputFlat(index, getSource(${t}));
}
}
`}},ox=["x","y","z","w","u","v"];function pue(e){if(e===1)return"sourceLoc";if(e<=6)return ox.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function rc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=An.parseSliceParams(r,a,o);if(An.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=Lle(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 due(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var hue={kernelName:Di,backendName:"webgpu",kernelFunc:rc},fue=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((y,x)=>y*x),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=[],f=je({inputs:{x:r},backend:n,attrs:{shape:l}}),m=vl({inputs:{x:f},backend:n,attrs:{perm:c}}),g=je({inputs:{x:m},backend:n,attrs:{shape:u}}),A=rc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),A},mue={kernelName:si,backendName:"webgpu",kernelFunc:fue},Z4=qn({opSnippet:Vt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Ple}),gue={kernelName:bi,backendName:"webgpu",kernelFunc:Z4};function tp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Js({inputs:{x:r.complexTensorInfos.real},backend:n})}var Aue={kernelName:Hc,backendName:"webgpu",kernelFunc:tp};function yue(e,t){let n=new Pm(e.shape,wt.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function ix(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Js({inputs:{x:r},backend:n});let o=Ht(r.shape),i=ix({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=sc({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=tp({inputs:{input:r},backend:n}),i=ix({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Js({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return yue(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=Z4({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 xue={kernelName:Ca,backendName:"webgpu",kernelFunc:ix},bue=Nn({opType:wt.CEIL,cpuKernelImpl:gle}),vue={kernelName:Ta,backendName:"webgpu",kernelFunc:bue},wue=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=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4",this.size=v.sizeFromShape(this.outputShape)/4}getUserCode(){return`
${Fe()} {
${We()}
if(index < uniforms.size) {
let value = getAAtOutCoordsByGlobalId(globalId, index);
var clampedValue : vec4<f32>;
for (var i = 0; i < 4; i = i + 1) {
if (isNanCustom(value[i])) {
clampedValue[i] = value[i];
} else {
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
}
}
setOutputFlat(index, clampedValue);
}
}
`}},kue=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${We()}
if(index < uniforms.size) {
let value = getAAtOutCoordsByGlobalId(globalId, index);
if (isNanCustom(value)) {
setOutputFlat(index, value);
return;
}
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function Iue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new wue(r.shape):i=new kue(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var Sue={kernelName:jr,backendName:"webgpu",kernelFunc:Iue},Cue=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(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;a<e.length;a++)e[a]=e[a-1]+this.shapes[a][1];t.push(`if (yC < ${e[0]}){ setOutput(coords.x, coords.y, getT0(yR, yC)); }`);for(let a=1;a<e.length;a++){let o=e[a-1];t.push(`elseif (yC < ${e[a]}){ setOutput(coords.x, coords.y, getT${a}(yR, yC - ${o})); }`)}let s=e.length,r=e[e.length-1];t.push(`else { setOutput(coords.x, coords.y, getT${s}(yR, yC - ${r})); }`)}else t.push("setOutput(coords.x, coords.y, getT0(yR, yC));");return`
${Fe()} {
${We()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
let yR = coords.x;
let yC = coords.y;
${t.join(`
`)}
}
}
}
`}};function Fm(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Js({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Tue={kernelName:Wc,backendName:"webgpu",kernelFunc:Fm};function lx(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>tp({inputs:{input:m},backend:n})),d=e.map(m=>Fm({inputs:{input:m},backend:n})),p=lx(u,t,n),h=lx(d,t,n),f=sc({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(A=>{let y=v.sizeFromShape(A.shape.slice(t));return je({inputs:{x:A},backend:n,attrs:{shape:[-1,y]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=N.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=Ale(d,p,s,h),m=N.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeData(A.dataId)),g}let{tensors2D:a,outShape:o}=Nue(e,t,n),i=new Cue(a.map(u=>u.shape)),l=n.runWebGPUProgram(i,a,a[0].dtype);a.forEach(u=>n.disposeData(u.dataId));let c=je({inputs:{x:l},backend:n,attrs:{shape:o}});return n.disposeData(l.dataId),c}function Nue(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>je({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function Y4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=N.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 Js({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return N.assertParamsConsistent(l,a),lx(i,a,n)}var Eue={kernelName:ri,backendName:"webgpu",kernelFunc:Y4},Rue=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(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`
${Fe()} {
${We()}
for(var i = 0; i<${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 J4({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=je({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=je({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=ax({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=je({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function $ue({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:A,dataFormat:y}=n,x=y==="channelsLast",b=l*c*u,w=m*f,k=[w,b],S=!1,E=!1,$=[],F=je({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=je({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});$.push(F),$.push(R);let P=new Rue(k,x),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,A]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],M=s.runWebGPUProgram(P,[F],F.dtype,T),U=je({inputs:{x:M},backend:s,attrs:{shape:[1,k[0],k[1]]}});$.push(M),$.push(U);let H=[1,k[0],k[1]],z=new U4(H,[1,w,n.outChannels],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,E),X=H[1],ee=H[2],Y=n.outChannels,ne=[{type:"int32",data:[X]},{type:"int32",data:[Y]},{type:"int32",data:[ee]}],se=s.runWebGPUProgram(z,[U,R],U.dtype,ne),J=x?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],te=je({inputs:{x:se},backend:s,attrs:{shape:J}});$.push(se);for(let ue of $)s.disposeData(ue.dataId);return te}var Q4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=[8,8,1];let a=[4,4,1];this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,a),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),[this.fitA,this.fitB]=this.getShapeFit(a),this.shaderKey=`conv2DMMVec4_${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[ia(r,[o,l]),ia(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<f32>(curData${e}.yzw, nextData${e}.x);
} elseif (divBy4Remainder${e} == 2) {
temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
} elseif (divBy4Remainder${e} == 3) {
temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
}
}
`}getUserCode(){let t=V4([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<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
inChCoord);
var resData = vec4<f32>(0.0);
${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
// the 'same' padding type.
if (coordsInBounds4D(coord, uniforms.xShape)) {
resData = x.numbers[getFlatIndex4D(coord, uniforms.xShape) / 4];
} else {
resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
${this.getSampleAWithRemainder(1)}
resData = temp;
if (WCol == (uniforms.filterDims[1] - 1)) {
coord = vec4<i32>(
coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
${this.getSampleAWithRemainder(2)}
if (inChCoord == 0) {
resData = vec4<f32>(resData.xyz, temp.x);
} elseif (inChCoord == 1) {
resData = vec4<f32>(resData.xy, temp.xy);
} else {
resData = vec4<f32>(resData.x, temp.xyz);
}
}
`}
return resData;`,a=this.fitA?`${r}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
${r}
}
return vec4<f32>(0.0);
`,o=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0);
`,i="",l="";if(this.activation){let d=Mo(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${d}
}`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(a: vec4<f32>) -> vec4<f32> {
let b = getLeakyreluAlphaAtOutCoords();
${d}
}`,new Error("Leakyrelu is not supported.");i=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${d}
}`}l="value = activation(value, outCoord);"}let c=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${i}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let r = row;
let c = col * 4;
var batch = i32(globalId.z);
${a}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${o}
}
fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
{
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col * 4);
${c}
${l}
setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
value);
}
}
${t}
`}},eC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Qy(this.dispatchLayout,this.outputShape),this.elementsPerThread=tx(this.dispatchLayout,this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ia(s,[a,i]),ia(r,[i,o])]}getUserCode(){let e=rx(this.elementsPerThread,this.workGroupSize),t=`
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
let coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
col % uniforms.xShape[3]);
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
}
return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${t}
}
return 0.0;
`,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter + col];
}
return 0.0;
`,r="",a="";if(this.activation){let l=Mo(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${l}
}`:r=`
fn activation(a : f32, outCoord : vec4<i32>) -> 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<u32>) -> f32 {
var batch = i32(globalId.z);
${n}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${s}
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
${o}
${a}
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
}
${e}
`}},tC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Mo(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${r}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> 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<i32>(batch, row, col, chan);
if(coordsInBounds4D(coord, uniforms.xShape)) {
return getX(batch, row, col, chan);
}
return 0.0;
}
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
let coord = vec4<i32>(row, col, xChannel, outChannel);
if(coordsInBounds4D(coord, uniforms.wShape)) {
return getW(row, col, xChannel, outChannel);
}
return 0.0;
}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
${n}
${t}
setOutput(batch, row, col, chan, value);
}
}
${Fe()} {
${We()}
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 Due(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=N.convertConv2DDataFormat(l),p=N.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 J4({x:r,filter:a,convInfo:p,backend:s});if(K().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return $ue({x:r,filter:a,convInfo:p,backend:s});let h,f=[p.padInfo.top,p.padInfo.left],m=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]}],g=K().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new tC(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new Q4(p):h=new eC(p),!g){let A=p.outShape[1]*p.outShape[2],y=p.outShape[3],x=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[A]},{type:"int32",data:[y]},{type:"int32",data:[x]})}return s.runWebGPUProgram(h,[r,a],r.dtype,m)}var _ue={kernelName:Na,backendName:"webgpu",kernelFunc:Due},Pue=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Qy(this.dispatchLayout,this.outputShape),this.elementsPerThread=tx(this.dispatchLayout,this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
return 0.0;
}
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
return 0.0;
}
let coord = vec4<i32>(
batch,
i32(xR),
i32(xC),
col % uniforms.outBackprop[3]);
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
}
return 0.0;
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let coordX = uniforms.filterDims.x - 1 -
row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let coordY = uniforms.filterDims.y - 1 -
(row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
coordX >= 0 && coordY >= 0) {
let coord = vec4<i32>(coordX, coordY, col,
row % uniforms.outBackprop[3]);
return W.numbers[getFlatIndex4D(coord, uniforms.wShape)];
}
return 0.0;
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
}
${rx(this.elementsPerThread,this.workGroupSize)}
`}},Fue=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.outputShape=e.inShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
${Fe()} {
${We()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
let batch = coords[0];
let d1 = coords[${n}];
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
let dyRCorner = dyCorner.x;
let dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR = dyR;
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC = dyC;
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
if (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd);
}
}
`}};function Oue(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(K().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Fue(p);else{f=new Pue(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],A=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[A]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Mue={kernelName:Ea,backendName:"webgpu",kernelFunc:Oue},zue=Nn({opType:wt.COS}),Lue={kernelName:Ra,backendName:"webgpu",kernelFunc:zue},Bue=Nn({opType:wt.COSH}),Wue={kernelName:$a,backendName:"webgpu",kernelFunc:Bue},Vue=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=Ke(this.outputShape),this.dispatch=Me(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<i32>, value : f32) {
if (coordsInBounds4D(coords, uniforms.outShape)) {
setOutput(coords[0], coords[1], coords[2], coords[3], value);
}
}
${Fe()} {
${We()}
let height_ratio = f32(${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 > ${t} ) {
writeResult(coords, uniforms.extrapolationValue);
return;
}
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
if(${this.methodId} == 1) {
// Compute the four integer indices.
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
let top = topLeft + (topRight - topLeft) * fracCR.x;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
let newValue = top + (bottom - top) * fracCR.y;
writeResult(coords, newValue);
} else {
// Compute the coordinators of nearest neighbor point.
let sourceNearestCR = vec2<i32>(floor(
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
let newValue = getImage(
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
writeResult(coords,newValue);
}
}
`}},Uue=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 Vue(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},Gue={kernelName:oi,backendName:"webgpu",kernelFunc:Uue},Hue=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.size=v.sizeFromShape(this.outputShape),this.dataFormat=t}getUserCode(){return`
${Fe()} {
${We()}
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 = ${this.getInputSamplingString()};
setOutputFlat(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function jue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new Hue(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var que={kernelName:ii,backendName:"webgpu",kernelFunc:jue},nC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Mo(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, globalIndex);
${r}
}`:e=`
fn activation(a : vec4<f32>, globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
${r}
}
`,t="dotProd[i] = activation(dotProd[i], globalId, index);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);":"";return`
${e}
${Fe()} {
${We()}
let batch = 0;
let r = i32(globalId.x);
let c = i32(globalId.y) * 4;
let d2 = i32(globalId.z) * 4;
let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
let d1 = d2;
let q = 0;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var wVals : array<vec4<f32>, 9>;
wVals[0] = getW(0, 0, d1, q);
wVals[1] = getW(0, 1, d1, q);
wVals[2] = getW(0, 2, d1, q);
wVals[3] = getW(1, 0, d1, q);
wVals[4] = getW(1, 1, d1, q);
wVals[5] = getW(1, 2, d1, q);
wVals[6] = getW(2, 0, d1, q);
wVals[7] = getW(2, 1, d1, q);
wVals[8] = getW(2, 2, d1, q);
var xVals : array<array<vec4<f32>, 6>, 3>;
for (var wR = 0; wR < 3; wR = wR + 1) {
let xR = xRCorner + wR * uniforms.dilation[0];
for (var wC = 0; wC < 6; wC = wC + 1) {
let xC = xCCorner + wC * uniforms.dilation[1];
if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
xVals[wR][wC] = vec4<f32>(0.0);
} else {
xVals[wR][wC] = getX(batch, xR, xC, d1);
}
}
}
var dotProd : array<vec4<f32>, 4>;
dotProd[0] = vec4<f32>(0.0);
dotProd[1] = vec4<f32>(0.0);
dotProd[2] = vec4<f32>(0.0);
dotProd[3] = vec4<f32>(0.0);
for (var wR = 0; wR < 3; wR = wR + 1) {
for (var wC = 0; wC < 3; wC = wC + 1) {
let indexW = wR * 3 + wC;
dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
}
}
for (var i = 0; i < 4; i = i + 1) {
let coords = vec4<i32>(batch, r, c + i, d2);
if (coordsInBounds4D(coords, uniforms.outShape)) {
${n}
${t}
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},sC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${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=Mo(this.activation,!1);this.hasPreluActivation?t=`fn activation(a : f32, globalId : vec3<u32>, index : i32) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, index);
${a}
}`:t=`
fn activation(a : f32, globalId : vec3<u32>, 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<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
setOutput(batch, row, col, chan, value);
}
}
${Fe()} {
${We()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let xRCCorner = vec2<i32>(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}
${n}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function Xue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=N.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?p=new nC(d):p=new sC(d);let h=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}];return n.runWebGPUProgram(p,[r,a],r.dtype,h)}var Kue={kernelName:Da,backendName:"webgpu",kernelFunc:Xue},rC=qn({opSnippet:Vt.MUL,cpuKernelImpl:Dle,supportsComplex:!0}),Zue={kernelName:Ka,backendName:"webgpu",kernelFunc:rC},Yue=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.inputShape=[e.batchSize,e.inSize];let[s]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=s.length===0?[1]:s,this.reductionFactor=2;let r=256,a=Math.min(Math.ceil(e.inSize/this.reductionFactor),r);this.workGroupSize=[a,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((o,i)=>i)},this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.reduceType=t,this.shaderKey=`reduce_${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<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,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<u32>, index : i32) -> i32 {
let outputCoords = getOutputCoords(globalId, index);
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
return offset;
}
${Fe()} {
${We()}
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);
${e?a:s}
}
`}};function np(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=N.getAxesPermutation(l,a),u=e;c!=null&&(u=vl({inputs:{x:e},attrs:{perm:c},backend:r}),l=N.getInnerMostAxes(l.length,a),o.push(u)),N.assertAxesAreInnerMostDims(s,l,a);let[d,p]=N.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=Ele(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:A,outShape:y,outDtype:x}=Fle(u.shape,u.dtype,m,l);f=r.makeTensorInfo(y,x,A);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),A=v.sizeFromShape(u.shape)/m,y={windowSize:m,inSize:m,batchSize:A,outSize:1},x=s==="mean"?"float32":rd(e.dtype),b=[{type:"int32",data:[m]}],w=new Yue(y,s,x),k=r.runWebGPUProgram(w,[u],x,b);o.push(k),f=je({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function ux(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return np(r,a,o,"sum",n)}var Jue={kernelName:oo,backendName:"webgpu",kernelFunc:ux};function Que(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=N.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:A,expandDims:y}=N.getEinsumPermutation(h,l[g]),x;N.isIdentityPermutation(A)?x=a[g]:(x=vl({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(x));let b=x.shape.slice();for(let w=0;w<y.length;++w)b.splice(y[w],0,1);v.arraysEqual(x.shape,b)||(x=je({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=rC({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=ux({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 ece={kernelName:Bc,backendName:"webgpu",kernelFunc:Que},tce=Nn({opType:wt.ELU}),nce={kernelName:Pa,backendName:"webgpu",kernelFunc:tce},sce=qn({opSnippet:Vt.EQUAL,dtype:"bool",cpuKernelImpl:yle}),rce={kernelName:li,backendName:"webgpu",kernelFunc:sce},aC=Nn({opType:wt.EXP,cpuKernelImpl:xle,dtype:"float32"}),ace={kernelName:Fa,backendName:"webgpu",kernelFunc:aC};function cx(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),je({inputs:{x:a},backend:s,attrs:{shape:i}})}var oce={kernelName:ui,backendName:"webgpu",kernelFunc:cx},ice=Nn({opType:wt.EXPM1,cpuKernelImpl:ble}),lce={kernelName:ci,backendName:"webgpu",kernelFunc:ice},uce=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${We()}
if (index < uniforms.size) {
setOutputFlat(index, uniforms.value);
}
}
`}};function ac(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 uce(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var cce={kernelName:Ql,backendName:"webgpu",kernelFunc:ac},dce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${We()}
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);
}
}
`}},pce={kernelName:di,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new dce(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},hce=Nn({opType:wt.FLOOR,cpuKernelImpl:vle}),fce={kernelName:Oa,backendName:"webgpu",kernelFunc:hce},mce=qn({opSnippet:Vt.INT_DIV,dtype:"int32"}),gce={kernelName:Ma,backendName:"webgpu",kernelFunc:mce},Ace=(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}))})},oC=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=Zoe(s,o,t,a),l=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"}})};function iC(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 lC(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=iC(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>oC(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 A=[i,o,...l,...u.dispatch];u.setUniform(n.device,A);let y;if(a){let x={source:t};y=n.device.importExternalTexture(x)}else y=u.inputTexture.createView();return n.runFromPixelsProgram(u,g.bufferInfo.buffer,f,y,c.dataId),c}var yce={kernelName:Kc,backendName:"webgpu",kernelFunc:xce},oc;function xce(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||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,c=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[u,d]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[d,u,a];if(K().getBool("WEBGPU_USE_IMPORT")&&o)return lC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((o||i)&&(oc==null&&(oc=document.createElement("canvas").getContext("2d")),oc.canvas.width=u,oc.canvas.height=d,oc.drawImage(r,0,0,u,d),r=oc.canvas),c||l||o||i)return lC({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 A=h.length,y=0;for(let x=0;x<A;x++)x%4<a&&(f[y++]=h[x])}let m=n.makeTensorInfo(p,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var bce=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetAtOutCoordsByGlobalId(globalId, index)");let t="1.0";this.scaleShape!=null&&(t="getScaleAtOutCoordsByGlobalId(globalId, index)");let n=this.outputShape.length,s=Qt(n),r="setOutput(coords[0], coords[1], coords[2], coords[3], value);";return n===2&&(r="setOutput(coords[0], coords[1], value);"),n===3&&(r="setOutput(coords[0], coords[1], coords[2], value);"),`
fn writeResult(coords : ${s}, value : f32) {
if (coordsInBounds${n}D(coords, uniforms.outShape)) {
${r}
}
}
${Fe()} {
${We()}
let coords = getOutputCoords(globalId, index);
let xValue = getXAtOutCoordsByGlobalId(globalId, index);
let meanValue = getMeanAtOutCoordsByGlobalId(globalId, index);
let varianValue = getVarianceAtOutCoordsByGlobalId(globalId, index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
writeResult(coords,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
`}},vce={kernelName:za,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 bce(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function wce(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=N.convertConv2DDataFormat(u),g=N.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A=o!=null,y=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 J4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=K().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 tC(g,A,h,y);else{w?x=new Q4(g,A,h,y):x=new eC(g,A,h,y);let $=g.outShape[1]*g.outShape[2],F=g.outShape[3],R=g.filterHeight*g.filterWidth*g.inShape[3];S.push({type:"int32",data:[$]},{type:"int32",data:[F]},{type:"int32",data:[R]})}let E=[r,a];return A&&E.push(o),y&&E.push(i),n.runWebGPUProgram(x,E,r.dtype,S)}var kce={kernelName:mo,backendName:"webgpu",kernelFunc:wce};function Ice(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(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let f=N.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),m=[r,a],g=o!=null,A=i!=null;g&&m.push(o),A&&m.push(i);let y;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?y=new nC(f,g,p,A):y=new sC(f,g,p,A);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(y,m,"float32",x)}var Sce={kernelName:go,backendName:"webgpu",kernelFunc:Ice},Cce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.size=v.sizeFromShape(this.outputShape),this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${Qt(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${Fe()} {
${We()}
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 Tce(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]=N.prepareAndValidate(s,r),p=je({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=je({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),x=n.bufferSync(s),b=wle(y,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new Cce(o,[c,u]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),A=je({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),A}var Nce={kernelName:hi,backendName:"webgpu",kernelFunc:Tce},Ece=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=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=Rce(this.aShape,"i32");return`
${Fe()} {
${We()}
let resRC = getOutputCoords(globalId, index);
if (index < uniforms.size) {
setOutputFlat(index, getA(${e}));
}
}
`}};function Rce(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function uC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=N.segment_util.collectGatherOpShapeInfo(r,a,l,i),u=v.sizeFromShape(a.shape),d=[],p=je({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=je({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});d.push(p),d.push(h);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let x=n.tensorMap.get(h.dataId).values,b=Le(h.shape,h.dtype,x),k=n.tensorMap.get(p.dataId).values,S=Le(p.shape,p.dtype,k),E=kle(S,b,f);return d.forEach($=>n.disposeData($.dataId)),n.makeTensorInfo(c.outputShape,E.dtype,E.values)}let m=new Ece(p.shape,f),g=n.runWebGPUProgram(m,[p,h],p.dtype);d.push(g);let A=je({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(y=>n.disposeData(y.dataId)),A}var $ce={kernelName:pi,backendName:"webgpu",kernelFunc:uC},Dce=qn({opSnippet:Vt.GREATER,cpuKernelImpl:Sle,dtype:"bool"}),_ce={kernelName:fi,backendName:"webgpu",kernelFunc:Dce},Pce=qn({opSnippet:Vt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Ile}),Fce={kernelName:La,backendName:"webgpu",kernelFunc:Pce},Oce=qn({opSnippet:Vt.LESS,dtype:"bool",cpuKernelImpl:Tle}),Mce={kernelName:gi,backendName:"webgpu",kernelFunc:Oce},zce=qn({opSnippet:Vt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Cle}),Lce={kernelName:Ai,backendName:"webgpu",kernelFunc:zce},Bce=Nn({opType:wt.LOG,cpuKernelImpl:Nle}),Wce={kernelName:Wa,backendName:"webgpu",kernelFunc:Bce},Vce=qn({opSnippet:Vt.LOGICAL_AND,dtype:"bool"}),Uce={kernelName:yi,backendName:"webgpu",kernelFunc:Vce},Gce=Nn({opType:wt.LOGICAL_NOT}),Hce={kernelName:ru,backendName:"webgpu",kernelFunc:Gce};function cC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return np(r,a,o,"max",n)}var jce={kernelName:Va,backendName:"webgpu",kernelFunc:cC},qce=qn({opSnippet:Vt.MAX,cpuKernelImpl:Rle}),Xce={kernelName:Ua,backendName:"webgpu",kernelFunc:qce};function Kce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=N.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 Js({inputs:{x:r},backend:n});d=new K4(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new X4(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 Zce={kernelName:Ga,backendName:"webgpu",kernelFunc:Kce};function Yce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return np(r,o,a,"mean",n)}var Jce={kernelName:Ha,backendName:"webgpu",kernelFunc:Yce};function Qce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return np(r,a,o,"min",n)}var ede={kernelName:ja,backendName:"webgpu",kernelFunc:Qce},tde=qn({opSnippet:Vt.MIN,cpuKernelImpl:$le}),nde={kernelName:qa,backendName:"webgpu",kernelFunc:tde},sde=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=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${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=Qt(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Fe()} {
${We()}
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}));
}
}
`}},rde={kernelName:Xa,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 sde(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function ade(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=_le(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new Pm(s.shape,wt.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var ode={kernelName:xi,backendName:"webgpu",kernelFunc:ade};function ide(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}=Xs.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var lde={kernelName:vi,backendName:"webgpu",kernelFunc:ide};function ude(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:A}=Xs.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var cde={kernelName:wi,backendName:"webgpu",kernelFunc:ude};function Om(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=tp({inputs:{input:s},backend:n}),a=Om({inputs:{x:r},backend:n}),o=Fm({inputs:{input:s},backend:n}),i=Om({inputs:{x:o},backend:n}),l=sc({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 ac({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var dde={kernelName:Wi,backendName:"webgpu",kernelFunc:Om};function dC(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=tp({inputs:{input:s},backend:n}),a=dC({inputs:{x:r},backend:n}),o=Fm({inputs:{input:s},backend:n}),i=Om({inputs:{x:o},backend:n}),l=sc({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 ac({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var pde={kernelName:ki,backendName:"webgpu",kernelFunc:dC};function hde(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return cx({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=cx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=Y4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var fde={kernelName:Si,backendName:"webgpu",kernelFunc:hde},mde=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=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.xShape.length,t=Qt(e),n=this.xShape.map((u,d)=>`uniforms.pad${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`
${Fe()} {
${We()}
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}));
}
}
}
`}},pC=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 Js({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 ac({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 mde(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},gde={kernelName:Za,backendName:"webgpu",kernelFunc:pC},Ade=qn({opSnippet:Vt.POW}),yde={kernelName:Ya,backendName:"webgpu",kernelFunc:Ade};function xde(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new H4(Vt.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var bde={kernelName:Ja,backendName:"webgpu",kernelFunc:xde};function vde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return np(r,a,o,"prod",n)}var wde={kernelName:Ci,backendName:"webgpu",kernelFunc:vde},kde=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Ole(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Ide={kernelName:iu,backendName:"webgpu",kernelFunc:kde},hC=qn({opSnippet:Vt.DIV}),Sde={kernelName:_a,backendName:"webgpu",kernelFunc:hC},Cde=Nn({opType:wt.RELU}),Tde={kernelName:Qa,backendName:"webgpu",kernelFunc:Cde},Nde=Nn({opType:wt.RELU6}),Ede={kernelName:to,backendName:"webgpu",kernelFunc:Nde},Rde=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=Ke(this.outputShape),this.dispatch=Me(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`
${Fe()} {
${We()}
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<f32>(
${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<f32>(
${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<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC - vec2<f32>(0.5)":"vec2<f32>(rc) * effectiveInputOverOutputRatioRC"};
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutput(b, coords[1], coords[2], d, newValue);
}
}
`}};function $de(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=new Rde(r.shape,l,c,a,i);return n.runWebGPUProgram(u,[r],"float32")}var Dde={kernelName:eo,backendName:"webgpu",kernelFunc:$de},_de=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=Ke(this.outputShape),this.dispatch=Me(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<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":t="vec2<f32>(rc) * effectiveInputOverOutputRatioRC";let n=this.alignCorners&&this.outputShape[1]>1,s=this.alignCorners&&this.outputShape[2]>1;return`
${Fe()} {
${We()}
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<f32>(
${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<f32>(
${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>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
let sourceNearestRC = vec2<i32>(
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 Pde(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=new _de(r.shape,l,c,a,o);return n.runWebGPUProgram(u,[r],r.dtype)}var Fde={kernelName:uu,backendName:"webgpu",kernelFunc:Pde},Ode=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(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<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
${Fe()} {
${We()}
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));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputFlat(index, outputValue);
}
}
`}},Mde={kernelName:Vi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Ode(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},zde=Nn({opType:wt.RSQRT,cpuKernelImpl:Mle}),Lde={kernelName:no,backendName:"webgpu",kernelFunc:zde},Bde=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=Ke(e),this.dispatch=Me(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`,this.size=v.sizeFromShape(e);let i=Qt(r.length);this.uniforms=`sliceDim : i32; strides: ${i};`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
return index;
}
`):this.updatesRank===2&&(s="coords[0], coords[1]",r="vec2<i32>(flattenedIndex, coords[1])",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.updatesShape[1];
let d1 = index - d0 * uniforms.updatesShape[1];
return vec2<i32>(d0, d1);
}
`);let o=`getUpdates(${s})`,i=this.type==="int32"?"ignore(atomicAdd(&(result.numbers[flatIndex]), i32(updateValue)));":`
var oldI32 = atomicLoad(&(result.numbers[flatIndex]));
var assumed = oldI32 - 1;
for (; assumed != oldI32;) {
assumed = oldI32;
let new = bitcast<f32>(assumed) + updateValue;
let newI32 = bitcast<i32>(new);
oldI32 = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32)[0];
}
`;return`
${a}
${Fe()} {
${We()}
if (index < uniforms.size) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t}));
flattenedIndex = flattenedIndex + indexInside * ${n};
}
let updateValue = ${o};
let flatIndex = getOutputFlatIndex(${r});
${i}
}
}`}};function Wde(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}=N.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=je({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=je({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=f.dtype,g=ac({backend:n,attrs:{shape:p,value:0,dtype:m}}),A=[{type:"int32",data:[i]},{type:"int32",data:u}],y=new Bde(f.shape,i,h.shape.length,f.shape.length,u,p,m),x=n.runWebGPUProgram(y,[f,h],m,A,g),b=je({inputs:{x},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),b}var Vde={kernelName:Ri,backendName:"webgpu",kernelFunc:Wde},Ude=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(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 ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
${Fe()} {
${We()}
if (index < uniforms.size) {
let resRC = getOutputCoords(globalId, index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputFlat(index, getA(${t}));
} else {
setOutputFlat(index, getB(${t}));
}
}
}
`}};function Gde(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Ude(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Bn(r.dtype,a.dtype))}var Hde={kernelName:$i,backendName:"webgpu",kernelFunc:Gde},jde=Nn({opType:wt.SIGMOID}),qde={kernelName:ro,backendName:"webgpu",kernelFunc:jde},Xde=Nn({opType:wt.SIN}),Kde={kernelName:so,backendName:"webgpu",kernelFunc:Xde},Zde=Nn({opType:wt.SINH}),Yde={kernelName:_i,backendName:"webgpu",kernelFunc:Zde},fC=qn({opSnippet:Vt.SUB,cpuKernelImpl:Vle,supportsComplex:!0}),Jde={kernelName:uo,backendName:"webgpu",kernelFunc:fC};function Qde(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=cC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,o),c=je({inputs:{x:i},backend:n,attrs:{shape:l}}),u=fC({inputs:{a:r,b:c},backend:n}),d=aC({inputs:{x:u},backend:n}),p=ux({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=je({inputs:{x:p},backend:n,attrs:{shape:l}}),f=hC({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 epe={kernelName:io,backendName:"webgpu",kernelFunc:Qde},tpe=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((A,y)=>A*y),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let c=[],u=pC({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(u.shape,a,i,!1),f=je({inputs:{x:u},backend:n,attrs:{shape:d}}),m=vl({inputs:{x:f},backend:n,attrs:{perm:p}}),g=je({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeData(A.dataId)),g},npe={kernelName:Pi,backendName:"webgpu",kernelFunc:tpe},spe=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=Ke(this.outputShape),this.dispatch=Me(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=Qt(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`
${Fe()} {
${We()}
let globalIndex = index * ${this.workPerThread};
if (globalIndex < uniforms.size) {
var sum = vec4<f32>(0.0);
var found = vec4<bool>(false);
for (var i = 0; i < uniforms.updateSize; i = i + 1) {
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${this.indicesSnippet}));
flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
let coords = getCoordsFromFlatIndex(curIndex);
if (flattenedIndex == coords[0]) {
sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
found[innerIndex] = true;
}
}
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
if (curIndex < uniforms.size)
{
setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function rpe(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}=N.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new spe(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=je({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var ape={kernelName:jc,backendName:"webgpu",kernelFunc:rpe};function ope(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=N.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=rc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var ipe={kernelName:Fi,backendName:"webgpu",kernelFunc:ope},lpe=Nn({opType:wt.SQRT}),upe={kernelName:ao,backendName:"webgpu",kernelFunc:lpe},cpe={kernelName:hu,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new Pm(n.shape,wt.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},dpe=qn({opSnippet:Vt.SQUARED_DIFFERENCE}),ppe={kernelName:lo,backendName:"webgpu",kernelFunc:dpe},hpe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Qt(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`
${Fe()} {
${We()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
setOutputFlat(index, getX(${t}));
}
}
`}};function fpe(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:A,outShape:y}=An.sliceInfo(r.shape,a,o,i,l,c,u,d,p),x=je({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(h){let k=rc({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=je({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeData(k.dataId)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let E=n.tensorMap.get(x.dataId).values,$=Le(x.shape,x.dtype,E),F=Ble(y,$,m,f);b=n.makeTensorInfo(y,x.dtype,F.values)}else{let S=new hpe(y),E=[{type:"int32",data:f},{type:"int32",data:m}];b=n.runWebGPUProgram(S,[x],x.dtype,E)}let w=je({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeData(x.dataId),n.disposeData(b.dataId),w}var mpe={kernelName:Oi,backendName:"webgpu",kernelFunc:fpe};function gpe(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]=Wle(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Ape={kernelName:qc,backendName:"webgpu",kernelFunc:gpe},ype=Nn({opType:wt.TANH}),xpe={kernelName:co,backendName:"webgpu",kernelFunc:ype},bpe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.size=v.sizeFromShape(this.outputShape),this.shaderKey="tile"}getUserCode(){let e=vpe(this.rank,"uniforms.");return`
${Fe()} {
${We()}
if (index < uniforms.size) {
let resRC = getOutputCoords(globalId, index);
setOutputFlat(index, getA(${e}));
}
}
`}};function vpe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function wpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=Le(r.shape,r.dtype,c),d=Ule(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new bpe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var kpe={kernelName:qr,backendName:"webgpu",kernelFunc:wpe},Ipe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
dir : i32; inc : i32;`,this.shaderKey="swap",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${We()}
if (index < uniforms.size) {
let outC = getOutputCoords(globalId, index);
let batch = outC[0];
let elemIdx = outC[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced
// above, Figure5(a) shows that element[1] is in the second half of
// the group when group size is 2, but it is in the first half of
// the group when group size is 4.
let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
var i = 0;
if (isFirstInPair) {
i = elemIdx;
} else {
i = elemIdx - uniforms.inc;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.inc;
} else {
i1 = i32(getIndices(batch, i + uniforms.inc));
}
var x0 = f32(0.0);
var x1 = f32(0.0);
if (i0 < uniforms.inputSize) {
x0 = getX(batch, i0);
} else {
x0 = uniforms.negativeInf;
}
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = uniforms.negativeInf;
}
let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) {
// Elements in opposite order of direction
let iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutputFlat(index, f32(i0));
} else {
setOutputFlat(index, f32(i1));
}
}
}
`}},Spe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${We()}
if (index < uniforms.size) {
let outC = getOutputCoords(globalId, index);
let batch = outC[0];
let elemIdx = outC[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _
// (k=4), we only need to output the indices at positions |, the
// indices at positions _ can be thrown away, see Figure5(b) After
// Phase 2 (Merge phase) in the Bitonic Top K paper referenced
// above.
// For example, the paper shows we only need to output the orange
// bars. The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back to
// the previous sequence to find the corresponding value, we need
// to double the index. When we double the index, we basically
// interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
// position of each 2k positions by - elemIdx % k. E.g. for output
// at index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
var i = 0;
if (elemIdx < uniforms.k) {
i = elemIdx;
} else {
i = elemIdx * 2 - elemIdx % uniforms.k;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.k;
} else {
i1 = i32(getIndices(batch, i + uniforms.k));
}
let x0 = getX(batch, i0);
var x1 = f32(0.0);
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = x0;
}
if (x0 >= x1) {
setOutputFlat(index, f32(i0));
} else {
setOutputFlat(index, f32(i1));
}
}
}
`}};function ic(e,t){t!==null&&e.disposeData(t.dataId)}function mC(e){let t=1;for(;t<e;)t*=2;return t}function Cpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,l=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let w=n.readSync(r.dataId),[k,S]=Gle(w,i,r.dtype,a,o);return[n.makeTensorInfo(k.shape,k.dtype,k.values),n.makeTensorInfo(S.shape,S.dtype,S.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(l===1)return[r,ac({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let u=v.sizeFromShape(i)/l,d=je({inputs:{x:r},attrs:{shape:[u,l]},backend:n}),p=mC(a),h=mC(l),f=null,m=()=>f===null?[d,d]:[d,f],g=(w,k,S)=>{let E=m(),$=new Ipe(S),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[w]},{type:"int32",data:[k]}],P=f;f=n.runWebGPUProgram($,E,"int32",R),ic(n,P)};for(let w=1;w<p;w*=2){let k=w*2;for(let S=w;S>=1;S/=2)g(k,S,[u,h])}for(let w=h;w>p;w/=2){let k=m(),S=new Spe([u,w/2]),$=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[p]}],F=f;f=n.runWebGPUProgram(S,k,"int32",$),ic(n,F);let R=p/2,P=R*2;for(let T=R;T>=1;T/=2)g(P,T,f.shape)}let A=f;f=rc({inputs:{x:f},backend:n,attrs:{begin:0,size:[u,a]}}),ic(n,A);let y=uC({inputs:{x:d,indices:f},backend:n,attrs:{axis:1,batchDims:1}});ic(n,d);let x=i.slice(0,-1);x.push(a),A=f,f=je({inputs:{x:f},attrs:{shape:x},backend:n}),ic(n,A);let b=y;return y=je({inputs:{x:y},attrs:{shape:x},backend:n}),ic(n,b),[y,f]}var Tpe={kernelName:zi,backendName:"webgpu",kernelFunc:Cpe},Npe=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=Ke(this.outputShape),this.dispatch=Me(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;
}
${Fe()} {
${We()}
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 Epe(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],A=new Npe(g),y=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:[y]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return n.runWebGPUProgram(A,[r,a],"float32",b)}var Rpe={kernelName:Li,backendName:"webgpu",kernelFunc:Epe};function $pe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=rc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=je({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeData(m.dataId)),f}var Dpe={kernelName:Bi,backendName:"webgpu",kernelFunc:$pe},_pe=[cle,qle,Kle,Jle,rue,oue,lue,cue,mue,xue,vue,Sue,fle,Eue,_ue,Mue,Lue,Wue,Gue,que,Kue,ece,nce,rce,oce,ace,lce,cce,pce,yce,fce,gce,vce,kce,Sce,Nce,$ce,_ce,Fce,hle,Tue,Mce,Lce,Wce,Uce,Hce,jce,Xce,Zce,Jce,ede,nde,rde,Zue,ode,lde,cde,gue,pde,fde,gde,bde,wde,yde,Ide,Aue,Sde,Tde,Ede,lle,Dde,Fde,Mde,Lde,Vde,Hde,qde,Kde,Yde,hue,mpe,Ape,epe,npe,ipe,ape,upe,cpe,ppe,Jde,Jue,xpe,kpe,Tpe,Rpe,nue,Dpe,dde];for(let e of _pe)Kr(e);var Ppe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=gC(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=gC(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function gC(e,t){return`${e}_${t}`}var AC=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=Ke(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
[[binding(1), group(0)]] var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
${Fe()} {
${We()}
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}}},Fpe=class extends AC{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}}},Ope=K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),yC=class extends Ll{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,!sx())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 Ppe(this.device),this.tensorMap=new Dc(this,ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),K().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 yC.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)*nx(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)*nx(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 AC),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Fpe),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),K().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 ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=N.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=L4(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${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 ${a.data.length}D shape`)}n=Math.ceil(t/i)*i-t;for(let l=0;l<n;++l)r.push({type:a.type,data:[0]}),s++;r.push({type:a.type,data:a.data}),s=s+a.data.length,t+=a.data.length+n}),this.arrayToDataView(r,s)}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),v.sizeFromShape(r.shape)===0){let E=this.tensorMap.get(r.dataId);return E.values=v.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}let a=[{type:"float32",data:[NaN]}],o=t.concat(r).map(E=>E.shape),i="int32";o.map(E=>{a.push({type:i,data:E})});let l=v.computeStrides(r.shape);a.push({type:i,data:l}),e.size!=null&&a.push({type:i,data:[e.size]}),a.push({type:"uint32",data:e.dispatch}),s&&(a=[...a,...s]);let c=null,u=this.computePadding(a),d=u.byteLength;c=this.makeUniformsDataView(u);let p=t.map((E,$)=>{if(E.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(E.dataId),{dtype:this.tensorMap.get(E.dataId).dtype,shape:E.shape,name:e.variableNames[$]}}),h=p.map(E=>E.dtype).concat(r.dtype),f=p.map(E=>N.getBroadcastDims(E.shape,r.shape)),m=p.map(E=>v.arraysEqual(E.shape,r.shape)).join("_"),g=f.map(E=>E.join("_")).join(";"),A=iC(e,o,h,g,m),{bindGroupLayout:y,pipelineLayout:x}=this.getCachedOrCreateLayout(e.variableNames.length),b=this.getAndSavePipeline(A,()=>oC(this.device,e,x,p,r)),w=this.activeTimers!=null,k=Ace(this.device,y,t.map(E=>this.tensorToBinding(E)),this.tensorToBinding(r),c);this.ensureCommandEncoderReady();let S=this.getComputePass();if(w&&this.supportTimeQuery&&S.writeTimestamp(this.querySet,0),S.setPipeline(b),S.setBindGroup(0,k),S.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),w&&this.supportTimeQuery&&S.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(E=>{this.commandQueueOwnedIds.add(E.dataId)}),this.commandQueueOwnedIds.add(r.dataId),c){let E={byteSize:d,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:c.buffer};this.uniformDisposalQueue.push(E)}return K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),w&&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=Ope){return K().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&v.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},dx=yC;dx.nextDataId=0;var xC={};ze(xC,{WebGPUBackend:()=>dx,webgpu_util:()=>z4});gu.isBrowser()&&sx()&&Ki("webgpu",async()=>{K().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:K().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. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let r=await t.requestDevice(n);return new dx(r,s)},3);var en;(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"})(en||(en={}));var sp;(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"})(sp||(sp={}));var bC;function Mpe(e){bC=e.wasm.cwrap(fo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function zpe(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 E=n.dataIdMap.get(o.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=sp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],y=c?a.shape[1]:a.shape[2],x=r.shape[0],b=n.makeOutput([x,A,y],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 bC(p,k,r.shape.length,h,S,a.shape.length,l,c,g,f,m,d||0,w),b}var Lpe={kernelName:fo,backendName:"wasm",setupFunc:Mpe,kernelFunc:zpe};function En(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:o,inputs:{x:i}}=a,l=o.dataIdMap.get(i.dataId).id,c=o.makeOutput(i.shape,t||i.dtype),u=o.dataIdMap.get(c.dataId).id;return v.sizeFromShape(c.shape)===0||n(l,en[i.dtype],u),c}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var Bpe=En(ni);function Xn(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=N.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),A=new Uint8Array(new Int32Array(u.shape).buffer),y=i.dataIdMap.get(m.dataId).id,x=()=>s(d,g,c.shape.length,p,A,u.shape.length,en[c.dtype],y);if(t&&c.dtype==="float32")return x(),m;let b=N.getBroadcastDims(c.shape,f),w=N.getBroadcastDims(u.shape,f),k=b.every((E,$)=>E===$),S=w.every((E,$)=>E===$);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 Wpe=!0,Vpe=Xn(Hr,Wpe),vC;function Upe(e){vC=e.wasm.cwrap(wa,null,["array","number","number","number"])}function Gpe(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 vC(a,r.length,en[s.dtype],o),s}var Hpe={kernelName:wa,backendName:"wasm",setupFunc:Upe,kernelFunc:Gpe};function Mm(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 jpe={kernelName:Ba,backendName:"wasm",kernelFunc:Mm},wC;function qpe(e){wC=e.wasm.cwrap(po,null,["number","array","number","number","number","array","number"])}function lc(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Kpe(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=Xpe(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=Mm({inputs:t,backend:n});return f.shape=i,f}let c=n.makeOutput(i,l.dtype),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(c.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return wC(u,h,l.shape.length,en[l.dtype],d,p,a.length),c}function Xpe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function Kpe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var Zpe={kernelName:po,backendName:"wasm",kernelFunc:lc,setupFunc:qpe};function zo(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=N.getAxesPermutation(o,r),l=null,c=!1;if(i!=null){let u=new Array(r);for(let h=0;h<u.length;h++)u[h]=s[i[h]];o=N.getInnerMostAxes(o.length,r),l=lc({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(c=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:c}}var kC;function Ype(e){kC=e.wasm.cwrap(Gl,null,["number, number, number"])}function Jpe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=zo(o,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;c=u,l=x}let f=c.shape.length;N.assertAxesAreInnerMostDims("all",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;kC(l,A,x)}if(h&&t.disposeData(u.dataId),a){let x=N.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Qpe={kernelName:Gl,backendName:"wasm",setupFunc:Ype,kernelFunc:Jpe},IC;function ehe(e){IC=e.wasm.cwrap(Hl,null,["number, number, number"])}function the(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=zo(o,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;c=u,l=x}let f=c.shape.length;N.assertAxesAreInnerMostDims("any",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;IC(l,A,x)}if(h&&t.disposeData(u.dataId),a){let x=N.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var nhe={kernelName:Hl,backendName:"wasm",setupFunc:ehe,kernelFunc:the},SC;function she(e){SC=e.wasm.cwrap(ka,null,["number","number","number","number","number"])}function rhe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:c,axes:u,inputWasTransposed:d}=zo(a,r,t);if(d){let A=t.dataIdMap.get(c.dataId).id;A!==o&&(l=c,i=A)}let p=l.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=v.sizeFromShape(h.shape),g=l.shape[u[0]];return SC(i,en[l.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var ahe={kernelName:ka,backendName:"wasm",kernelFunc:rhe,setupFunc:she},CC;function ohe(e){CC=e.wasm.cwrap(Ia,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ihe(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=N.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,A=u.strideHeight,y=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let b=s.makeOutput(u.outShape,"float32"),w=s.dataIdMap.get(b.dataId).id;return CC(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,y,x,w),b}var lhe={kernelName:Ia,backendName:"wasm",setupFunc:ohe,kernelFunc:ihe};function us(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a);return v.assert(a===v.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var uhe={kernelName:Ti,backendName:"wasm",kernelFunc:us},TC;function che(e){TC=e.wasm.cwrap(Sa,null,["number","array","number","number","array","number","number","number","number"])}function dhe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),A=v.sizeFromShape(m),y=g===A||g===1||A===1;v.assert(l>=2&&c>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(g>A?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let w=o?[g,u,p]:[g,p,u],k=i?[A,h,d]:[A,d,h],S=us({inputs:{x:r},backend:n,attrs:{shape:w}}),E=us({inputs:{x:a},backend:n,attrs:{shape:k}}),$=n.dataIdMap.get(S.dataId).id,F=n.dataIdMap.get(E.dataId).id,R=o?S.shape[2]:S.shape[1],P=i?E.shape[1]:E.shape[2],T=Math.max(g,A),M=n.makeOutput([T,R,P],S.dtype),U=n.dataIdMap.get(M.dataId).id,H=new Uint8Array(new Int32Array(S.shape).buffer),z=new Uint8Array(new Int32Array(E.shape).buffer);return TC($,H,S.shape.length,F,z,E.shape.length,o,i,U),n.disposeData(S.dataId),n.disposeData(E.dataId),M.shape=b,M}var phe={kernelName:Sa,backendName:"wasm",setupFunc:che,kernelFunc:dhe};function rp(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=An.parseSliceParams(t,n,s),i=An.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=An.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=um(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)hhe(l,u[0],p,a,o);else if(h===3)fhe(l,u[0],u[1],p,a,o);else if(h===4)mhe(l,u[0],u[1],u[2],p,a,o);else{let f=um(l,a,o,t.shape,t.dtype);p.set(f)}return c}function hhe(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let c=o;c<l;c++){let u=c*t+i;n.set(e.subarray(u,u+r[1]),a),a+=r[1]}}function fhe(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],c=r[2],u=i+a[0],d=l+a[1];for(let p=i;p<u;p++)for(let h=l;h<d;h++){let f=p*t+h*n+c;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function mhe(e,t,n,s,r,a,o){let i=0,l=a[0],c=a[1],u=a[2],d=l+o[0],p=c+o[1],h=u+o[2],f=a[3];for(let m=l;m<d;m++)for(let g=c;g<p;g++)for(let A=u;A<h;A++){let y=m*t+g*n+A*s+f;r.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var ghe={kernelName:Di,backendName:"wasm",kernelFunc:rp};function Ahe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((A,y)=>A*y),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=us({inputs:{x:r},backend:n,attrs:{shape:l}}),f=lc({inputs:{x:h},backend:n,attrs:{perm:c}}),m=us({inputs:{x:f},backend:n,attrs:{shape:u}}),g=rp({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 yhe={kernelName:si,backendName:"wasm",kernelFunc:Ahe};function ap(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 xhe={kernelName:Ca,backendName:"wasm",kernelFunc:ap},bhe=En(Ta),NC;function vhe(e){NC=e.wasm.cwrap(jr,null,["number","number","number","number"])}function whe(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 NC(i,a,o,c),l}var khe={kernelName:jr,backendName:"wasm",setupFunc:vhe,kernelFunc:whe};function EC(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=N.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return Mm({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(N.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=N.computeOutShape(h.map(x=>x.shape),1);let m=h[0].shape[0]===1,g=vy(f,r,t[0].dtype,m),A=N.computeOutShape(a.map(x=>x.shape),s);o.shape=A;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=N.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<l;h++){let f=h*c;for(let m=0;m<d.length;m++){let g=u[m],A=h*g,y=d[m].subarray(A,A+g);p.set(y,f),f+=g}}return o}var Ihe={kernelName:ri,backendName:"wasm",kernelFunc:EC},RC;function She(e){RC=e.wasm.cwrap(Na,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Che(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,dataFormat:p}=n,h=N.convertConv2DDataFormat(p),f=N.computeConv2DInfo(r.shape,a.shape,l,c,u,d,!1,h),m=f.filterHeight,g=f.filterWidth,A=f.padInfo.top,y=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,k=f.dilationWidth,S=f.strideHeight,E=f.strideWidth,$=f.inChannels,F=f.outChannels,R=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let P=s.makeOutput(f.outShape,"float32"),T=s.dataIdMap.get(P.dataId).id;return RC(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,A,y,x,b,R,w,k,S,E,$,F,T),P}var The={kernelName:Na,backendName:"wasm",setupFunc:She,kernelFunc:Che},$C;function Nhe(e){$C=e.wasm.cwrap(Ea,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ehe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,inputShape:u}=s,d=1,p=N.convertConv2DDataFormat(l),h=N.computeConv2DInfo(u,a.shape,o,d,i,c,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:A,inHeight:y,inWidth:x,outChannels:b,outHeight:w,outWidth:k,strideHeight:S,strideWidth:E}=h,$=m-1-h.padInfo.top,F=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",P=v.computeStrides(h.inShape),T=v.computeStrides(r.shape),[M,U,H]=v.computeStrides(a.shape),z=P[0],X=R?P[1]:P[2],ee=R?P[2]:1,Y=R?1:P[1],ne=T[0],se=R?T[1]:T[2],J=R?T[2]:1,te=R?1:T[1],ue=t.makeOutput(h.inShape,"float32"),ce=t.dataIdMap.get(ue.dataId).id,be=t.dataIdMap.get(r.dataId).id,we=t.dataIdMap.get(a.dataId).id;return $C(be,we,f,m,g,y,x,A,w,k,b,S,E,$,F,M,U,H,z,X,ee,Y,ne,se,J,te,ce),ue}var Rhe={kernelName:Ea,backendName:"wasm",setupFunc:Nhe,kernelFunc:Ehe},$he=En(Ra),Dhe=En($a),px;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(px||(px={}));var DC;function _he(e){DC=e.wasm.cwrap(oi,null,["number","number","number","number","array","number","number","number","number","number"])}function Phe(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:c}=n,u=l.shape[0],[d,p]=o,h=[u,d,p,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=ap({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,A=t.dataIdMap.get(l.dataId).id,y=t.dataIdMap.get(c.dataId).id,x=t.makeOutput(h,"float32"),b=t.dataIdMap.get(x.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return DC(g,A,y,u,w,d,p,px[r],a,b),m!=null&&t.disposeData(m.dataId),x}var Fhe={kernelName:oi,backendName:"wasm",setupFunc:_he,kernelFunc:Phe},_C;function Ohe(e){_C=e.wasm.cwrap(ai,null,["number","number","number","number","number","number"])}function Mhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=N.getAxesPermutation([a],l),u=r;c!==null&&(u=lc({inputs:{x:r},attrs:{perm:c},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.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;_C(f,o?1:0,i?1:0,h,m,en[r.dtype]);let g=p;if(c!==null){let A=N.getUndoAxesPermutation(c);g=lc({inputs:{x:p},attrs:{perm:A},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var zhe={kernelName:ai,backendName:"wasm",setupFunc:Ohe,kernelFunc:Mhe},PC;function Lhe(e){PC=e.wasm.cwrap(ii,null,["number","number","number","array","number","array","array","number","number"])}function Bhe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{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=t.makeOutput(f,"float32"),A=t.dataIdMap.get(r.dataId).id,y=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 PC(A,a,o==="NHWC"?1:0,y,r.shape.length-1,x,b,f.length,w),m}var Whe={kernelName:ii,backendName:"wasm",setupFunc:Lhe,kernelFunc:Bhe},FC;function Vhe(e){FC=e.wasm.cwrap(Da,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Uhe(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=N.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,A=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,S=h.strideWidth,E=h.inChannels,$=h.outChannels,F=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(h.outShape,"float32"),P=s.dataIdMap.get(R.dataId).id;return FC(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,A,y,x,F,b,w,k,S,E,$,P),R}var Ghe={kernelName:Da,backendName:"wasm",setupFunc:Vhe,kernelFunc:Uhe},Hhe=En(Pa),jhe=!1,qhe=Xn(li,jhe,"bool"),Xhe=En(Fa,"float32");function hx(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)}, ${o}]`),l=o+a+1),i.splice(l,0,1),us({inputs:{x:r},backend:s,attrs:{shape:i}})}var Khe={kernelName:ui,backendName:"wasm",kernelFunc:hx};function OC(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var Zhe={kernelName:Ql,backendName:"wasm",kernelFunc:OC},MC;function Yhe(e){MC=e.wasm.cwrap(di,null,["number","number","number","number","number","number"])}function Jhe(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return MC(a,i,l,c,u,o),r}var Qhe={kernelName:di,backendName:"wasm",kernelFunc:Jhe,setupFunc:Yhe},efe=En(Oa),tfe=!1,nfe=Xn(Ma,tfe),zC;function sfe(e){zC=e.wasm.cwrap(za,null,["number","number","number","number","number","number","number"])}function rfe(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:c}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return zC(u,d,p,h,f,r,g),m}var afe={kernelName:za,backendName:"wasm",setupFunc:sfe,kernelFunc:rfe},LC;function ofe(e){LC=e.wasm.cwrap(mo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ife(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=sp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let J=s.dataIdMap.get(o.dataId);if(J.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let w=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,$=m.padInfo.bottom,F=m.padInfo.left,R=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,U=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:'${d}'. Please use 'NHWC'.`);let Y=s.makeOutput(m.outShape,"float32"),ne=s.dataIdMap.get(Y.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return LC(A,z,X,ee,y,w,k,b,S,E,$,F,H,R,P,T,M,U,x,g,se,f||0,ne),Y}var lfe={kernelName:mo,backendName:"wasm",setupFunc:ofe,kernelFunc:ife},BC;function ufe(e){BC=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 cfe(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=N.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!0),g=sp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let J=s.dataIdMap.get(o.dataId);if(J.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let w=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,$=m.padInfo.bottom,F=m.padInfo.left,R=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,U=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:'${d}'. Please use 'NHWC'.`);let Y=s.makeOutput(m.outShape,"float32"),ne=s.dataIdMap.get(Y.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return BC(A,z,X,ee,y,w,k,b,S,E,$,F,H,R,P,T,M,U,x,g,se,f||0,ne),Y}var dfe={kernelName:go,backendName:"wasm",setupFunc:ufe,kernelFunc:cfe},WC;function pfe(e){WC=e.wasm.cwrap(hi,null,["number","number","number","number","number","number","array","number"])}function hfe(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=g2.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),A=t.dataIdMap.get(c.dataId).id;return WC(h,en[s.dtype],m,o,d,i,g,A),c}var ffe={kernelName:hi,backendName:"wasm",setupFunc:pfe,kernelFunc:hfe},VC;function mfe(e){VC=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gfe(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=t.readSync(a.dataId),u=r.shape[l];for(let $=0;$<c.length;++$){let F=c[$];v.assert(F<=u-1&&F>=0,()=>`GatherV2: the index value ${F} is not in [0, ${u-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=us({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(a.shape),f=us({inputs:{x:a},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),m=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(m,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let A=p.shape.length-1,x=t.dataIdMap.get(p.dataId).id,w=t.dataIdMap.get(f.dataId).id,k=t.dataIdMap.get(g.dataId).id,S=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),E=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer);return VC(x,en[r.dtype],S,A,w,d.batchSize,E,k),t.disposeData(p.dataId),t.disposeData(f.dataId),g.shape=d.outputShape,g}var Afe={kernelName:pi,backendName:"wasm",setupFunc:mfe,kernelFunc:gfe},yfe=!1,xfe=Xn(fi,yfe,"bool"),bfe=!1,vfe=Xn(La,bfe,"bool"),UC;function wfe(e){UC=e.wasm.cwrap(mi,null,["number","number","number","number"])}function kfe(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;UC(r,en[t.dtype],n,o)}return a}var Ife={kernelName:mi,backendName:"wasm",setupFunc:wfe,kernelFunc:kfe},Sfe=!1,Cfe=Xn(gi,Sfe,"bool"),Tfe=!1,Nfe=Xn(Ai,Tfe,"bool"),Efe=En(Wa),Rfe=!1,$fe=Xn(yi,Rfe,"bool"),GC;function Dfe(e){GC=e.wasm.cwrap(Va,null,["number","number","number","number"])}function _fe(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}=zo(o,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;c=u,l=x}let f=c.shape.length;N.assertAxesAreInnerMostDims("max",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;GC(l,en[o.dtype],A,x)}if(h&&t.disposeData(u.dataId),a){let x=N.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Pfe={kernelName:Va,backendName:"wasm",setupFunc:Dfe,kernelFunc:_fe},Ffe=!1,Ofe=Xn(Ua,Ffe),HC;function Mfe(e){HC=e.wasm.cwrap(Ga,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zfe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=N.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,A=u.dilationHeight,y=u.dilationWidth,x=u.strideHeight,b=u.strideWidth,w=u.inChannels,k=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let S=s.makeOutput(u.outShape,"float32"),E=s.dataIdMap.get(S.dataId).id;return HC(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,y,x,b,w,k,E),S}var Lfe={kernelName:Ga,backendName:"wasm",setupFunc:Mfe,kernelFunc:zfe},jC;function Bfe(e){jC=e.wasm.cwrap(Ha,null,["number, number, number"])}function Wfe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=zo(o,r,t),f=d;if(h){let b=t.dataIdMap.get(u.dataId).id;b!==i&&(c=u,l=b,f=N.getInnerMostAxes(f.length,c.shape.length))}N.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,g]=N.computeOutAndReduceShapes(c.shape,f),A=v.sizeFromShape(g),y=c;c.dtype!=="float32"&&(y=ap({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(m,"float32");if(v.sizeFromShape(c.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;jC(l,A,b)}if(h&&t.disposeData(u.dataId),a){let b=N.expandShapeToKeepDim(x.shape,p);x.shape=b}return c.dtype!=="float32"&&t.disposeData(y.dataId),x}var Vfe={kernelName:Ha,backendName:"wasm",setupFunc:Bfe,kernelFunc:Wfe},qC;function Ufe(e){qC=e.wasm.cwrap(ja,null,["number","number","number","number"])}function Gfe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=zo(o,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;x!==i&&(c=u,l=x)}let f=c.shape.length;N.assertAxesAreInnerMostDims("min",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),y=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;qC(l,en[o.dtype],A,x)}if(h&&t.disposeData(u.dataId),a){let x=N.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Hfe={kernelName:ja,backendName:"wasm",setupFunc:Ufe,kernelFunc:Gfe},jfe=!1,qfe=Xn(qa,jfe),fx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(fx||(fx={}));var XC;function Xfe(e){XC=e.wasm.cwrap(Xa,null,["number","array","number","number","array","array","number","number"])}function Kfe(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(d).buffer);return XC(o,c,t.shape.length,en[t.dtype],p,h,fx[r],l),i}var Zfe={kernelName:Xa,backendName:"wasm",kernelFunc:Kfe,setupFunc:Xfe},Yfe=!0,Jfe=Xn(Ka,Yfe),Qfe=En(xi);function mx(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var KC;function eme(e){KC=e.wasm.cwrap(vi,"number",["number","number","number","number","number"])}function tme(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,c=t.dataIdMap.get(i.dataId).id,u=t.dataIdMap.get(l.dataId).id,d=KC(c,u,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=mx(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var nme={kernelName:vi,backendName:"wasm",setupFunc:eme,kernelFunc:tme},ZC;function sme(e){ZC=e.wasm.cwrap(ou,"number",["number","number","number","number","number","bool"])}function rme(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=ZC(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=mx(t,p);t.wasm._free(m);let A=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[A,y]}var ame={kernelName:ou,backendName:"wasm",setupFunc:sme,kernelFunc:rme},YC;function ome(e){YC=e.wasm.cwrap(wi,"number",["number","number","number","number","number","number"])}function ime(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=YC(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=mx(t,p);t.wasm._free(g);let A=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[A,y]}var lme={kernelName:wi,backendName:"wasm",setupFunc:ome,kernelFunc:ime},ume=!1,cme=Xn(bi,ume,"bool"),JC;function dme(e){JC=e.wasm.cwrap(Ii,null,["number","number","number","number","number"])}function pme(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=n.makeOutput([...r.shape,a],"int32"),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return JC(d,a,o,i,c),l}var hme={kernelName:Ii,backendName:"wasm",setupFunc:dme,kernelFunc:pme};function fme(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var mme={kernelName:ki,backendName:"wasm",kernelFunc:fme};function gme(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return hx({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=hx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=EC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var Ame={kernelName:Si,backendName:"wasm",kernelFunc:gme},QC;function yme(e){QC=e.wasm.cwrap(Za,null,["number","array","number","number","array","array","number","number"])}function xme(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(v.sizeFromShape(t.shape)===0)return OC({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),c=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=s.map(m=>m[0]),p=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(d).buffer),f=new Uint8Array(new Int32Array(p).buffer);return QC(o,u,t.shape.length,en[t.dtype],h,f,r,c),i}var e6={kernelName:Za,backendName:"wasm",kernelFunc:xme,setupFunc:yme},bme=!1,vme=Xn(Ya,bme),t6;function 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precision highp float;
attribute vec2 pos;
attribute vec2 uv;
varying vec2 vUv;
uniform float flipY;
void main(void) {
vUv = uv;
gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
}
`;var k6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
}
`,I6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
gl_FragColor.a = c.a;
}
`,S6=`
precision highp float;
varying vec2 vUv;
uniform vec2 size;
uniform sampler2D texture;
vec2 pixelate(vec2 coord, vec2 size) {
return floor( coord / size ) * size;
}
void main(void) {
gl_FragColor = vec4(0.0);
vec2 coord = pixelate(vUv, size);
gl_FragColor += texture2D(texture, coord);
}
`,C6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
void main(void) {
gl_FragColor = vec4(0.0);
gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv )*0.159576912161;
gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;
}
`,T6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
uniform float m[9];
void main(void) {
vec4 c11 = texture2D(texture, vUv - px); // top left
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
vec4 c22 = texture2D(texture, vUv); // mid center
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
gl_FragColor =
c11 * m[0] + c12 * m[1] + c22 * m[2] +
c21 * m[3] + c22 * m[4] + c23 * m[5] +
c31 * m[6] + c32 * m[7] + c33 * m[8];
gl_FragColor.a = c22.a;
}
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s=e.landmarks.length>=Tx.count?Tx.symmetryLine:cp.symmetryLine,r=nge(e.landmarks[s[0]],e.landmarks[s[1]]),a=Wm({startPoint:e.startPoint,endPoint:e.endPoint}),o=[a[0]/t.shape[2],a[1]/t.shape[1]],i=$e.rotateWithOffset(t,r,0,o),l=B6(-r,a),c=$x({startPoint:e.startPoint,endPoint:e.endPoint},i,[n,n]),u=fe(c,255);return Q(c),Q(i),[r,l,u]}var U6=6,Os,_x=[],G6=null,Ms=0,mp=()=>Ms;async function H6(e){var t,n;return ye.initial&&(Os=null),Os?e.debug&&ae("cached model:",Os.modelUrl):(Os=await st(at(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Os||!Os.modelUrl?ae("load model failed:",(n=e.face.detector)==null?void 0:n.modelPath):e.debug&&ae("load model:",Os.modelUrl)),Ms=Os.inputs[0].shape?Os.inputs[0].shape[2]:0,Ms===-1&&(Ms=64),_x=W6(Ms),G6=ur(_x),Os}function oge(e){let t=_e(e,[0,1],[-1,2]),n=ie(t,G6),s=_e(e,[0,3],[-1,2]),r=fe(s,Ms),a=fe(n,Ms),o=fe(r,2),i=xe(a,o),l=ie(a,o),c=W(i,Ms),u=W(l,Ms);return ku([c,u],1)}async function j6(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=$e.resizeBilinear(e,[Ms,Ms]),f=xe(fe(h,127.5),.5),m=Os==null?void 0:Os.execute(f),g;if(Array.isArray(m)){let b=m.sort((E,$)=>E.size-$.size),w=It([b[0],b[2]],2),k=It([b[1],b[3]],2),S=It([k,w],1);g=pt(S,0)}else g=pt(m);let A=oge(g),y=_e(g,[0,0],[-1,1]),x=pt(hs(y));return[g,A,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();Q(a);let i=[],l=await r.data();for(let h=0;h<o.length;h++){let f=l[o[h]];if(f>(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let m=_e(s,[o[h],0],[1,-1]),g=j(()=>G(pt(_e(n,[o[h],U6-1],[1,-1])),[U6,-1]));i.push({box:O6(m),landmarks:g,anchor:_x[o[h]],confidence:f}),Q(m)}}return Q(n),Q(s),Q(r),{boxes:i,scaleFactor:[e.shape[2]/Ms,e.shape[1]/Ms]}}var Ox={};qp(Ox,{connected:()=>Fx,kpt:()=>Px});var Px=["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"],Fx={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 q6={initial:!0},dn=[null,null],Vo=[[0,0],[0,0]],Mx=Number.MAX_SAFE_INTEGER,zx,Gm=null,Uo=[[0,0],[0,0],[0,0],[0,0]],X6=0;async function K6(e){var t,n,s;if(q6.initial&&(dn[0]=null),!dn[0]&&((t=e.body.detector)==null?void 0:t.modelPath)){dn[0]=await st(at(e.modelBasePath,((n=e.body.detector)==null?void 0:n.modelPath)||""));let r=Object.values(dn[0].modelSignature.inputs);Vo[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Vo[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0,!dn[0]||!dn[0].modelUrl?ae("load model failed:",(s=e.body.detector)==null?void 0:s.modelPath):e.debug&&ae("load model:",dn[0].modelUrl)}else e.debug&&dn[0]&&ae("cached model:",dn[0].modelUrl);return dn[0]}async function Z6(e){var t;if(q6.initial&&(dn[1]=null),dn[1])e.debug&&ae("cached model:",dn[1].modelUrl);else{dn[1]=await st(at(e.modelBasePath,e.body.modelPath||""));let 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m=Y6(o[l*f+3]),g=Y6(o[l*f+4]),A=Math.trunc(100*m*g*a)/100,y=[o[l*f+0]/Vo[1][0],o[l*f+1]/Vo[1][1],o[l*f+2]+0],x=[Math.trunc(n[0]*y[0]),Math.trunc(n[1]*y[1]),y[2]];i.push({part:Px[f],positionRaw:y,position:x,score:A})}if(a<(t.body.minConfidence||0))return null;let c=uge(i,n),u=ige(c,[n[0],n[1]]);Object.keys(s).forEach(f=>Q(s[f]));let d={};for(let[f,m]of Object.entries(Fx)){let g=[];for(let A=0;A<m.length-1;A++){let y=c.find(b=>b.part===m[A]),x=c.find(b=>b.part===m[A+1]);y&&x&&y.score>(t.body.minConfidence||0)&&x.score>(t.body.minConfidence||0)&&g.push([y.position,x.position])}d[f]=g}return{id:0,score:Math.trunc(100*a)/100,box:u.keypointsBox,boxRaw:u.keypointsBoxRaw,keypoints:c,annotations:d}}async function Lx(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>pe()-X6,r=Mx<(t.body.skipFrames||0);return t.skipAllowed&&s&&r&&Gm!==null?Mx++:(Gm=await cge(e,t,n),X6=pe(),Mx=0),Gm?[Gm]:[]}var 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Vx=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Ux={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,e8=0,Zn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Hx=Number.MAX_SAFE_INTEGER;async function jx(e){return ye.initial&&(pn=null),pn?e.debug&&ae("cached model:",pn.modelUrl):(pn=await st(at(e.modelBasePath,e.body.modelPath||"")),!pn||!pn.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",pn.modelUrl)),pn}function pge(e,t){let[n,s]=e.shape;return j(()=>{let r=(i,l)=>xe(i,W(fe(i,Ee(l,"int32")),Ee(l,"int32"))),a=G(e,[s*n]),o=rs(a,0).dataSync()[0];if(o>t){let 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g=W(h,Kx[0]),A=W(f,Kx[1]),y=W(m,Kx[2]);Q(h),Q(f),Q(m);let x=Vh([g,A,y]);Q(g),Q(A),Q(y);let b=j(()=>W(xe(x,.5),2));Q(x);let w=await(hn==null?void 0:hn.predict(b));n8=pe();let k=await w.data();Q(w);for(let S=0;S<k.length;S++)k[S]>(((d=t.face.emotion)==null?void 0:d.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*k[S])/100),emotion:hge[S]});c.sort((S,E)=>E.score-S.score),Q(b)}jm[n]=c,t8=s,l(c)}))}var er,Go=0,fge=2.3,Yx=Lr.leftEyeLower0,Jx=Lr.rightEyeLower0,pc={leftBounds:[Yx[0],Yx[Yx.length-1]],rightBounds:[Jx[0],Jx[Jx.length-1]]},hc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function r8(e){var t,n;return ye.initial&&(er=null),er?e.debug&&ae("cached model:",er.modelUrl):(er=await st(at(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!er||!er.modelUrl?ae("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&ae("load model:",er.modelUrl)),Go=er.inputs[0].shape?er.inputs[0].shape[2]:0,Go===-1&&(Go=64),er}function 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S.data():[];d.descriptor=Array.from(E),m.forEach($=>Q($))}Xm[n]=d,g8=s,u(d)}))}function Km(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function gp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function y8(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return $e.cropAndResize(t,a,[0],n)}function x8(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]],r=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:s,palmLandmarks:r,confidence:e.confidence}}function Zm(e,t=1.5){let n=gp(e),s=Km(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function Ym(e){let t=gp(e),n=Km(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function gge(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function b8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return gge(n)}var v8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ho(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function Age(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function w8(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Ho(e[r],Age(t,a)))}return n}function sb(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=v8(t[0],t[1]),o=w8(a,r),i=v8(-t[0],-t[1]);return w8(o,i)}function k8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Ho(t[0],n),-Ho(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function rb(e,t){return[Ho(e,t[0]),Ho(e,t[1])]}var 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s={};s.batched=this.model.predict(t),s.predictions=pt(s.batched),s.scores=j(()=>pt(hs(_e(s.predictions,[0,0],[-1,1]))));let r=await s.scores.data();s.boxes=_e(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await $e.nonMaxSuppressionAsync(s.norm,s.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l=_e(s.norm,[i,0],[1,-1]),c=j(()=>G(this.normalizeLandmarks(_e(s.predictions,[i,5],[1,14]),i),[-1,2]));o.push({box:l,palmLandmarks:c,confidence:r[i]})}for(let i of Object.keys(s))Q(s[i]);return o}async estimateHandBounds(t,n){let s=t.shape[1],r=t.shape[2],a=j(()=>xe(fe($e.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),o=await this.getBoxes(a,n);Q(a);let i=[];if(!o||o.length===0)return i;for(let l of o){let c=await l.box.data(),u=c.slice(0,2),d=c.slice(2,4),p=await l.palmLandmarks.array();Q(l.box),Q(l.palmLandmarks),i.push(x8({startPoint:u,endPoint:d,palmLandmarks:p,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};var yge=5,S8=1.65,C8=[0,5,9,13,17,1,2],xge=0,bge=2,T8=0,ob=class{constructor(t,n){de(this,"handDetector");de(this,"handPoseModel");de(this,"inputSize");de(this,"storedBoxes");de(this,"skipped");de(this,"detectedHands");this.handDetector=t,this.handPoseModel=n,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[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=>rb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return Zm(Ym(r),yge)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=Zm(Ym(n),S8);s.palmLandmarks=[];for(let r=0;r<C8.length;r++)s.palmLandmarks.push(t[C8[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=Km(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(h=>[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=sb(s,[0,0]),c=i.map(h=>[...rb(h,l),h[2]]),u=k8(r),d=[...gp(n),1],p=[Ho(d,u[0]),Ho(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,a=(n.hand.skipTime||0)>pe()-T8,o=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&a&&o&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipAllowed&&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 i=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(!!c)if(n.hand.landmarks){let u=n.hand.rotation?b8(c.palmLandmarks[xge],c.palmLandmarks[bge]):0,d=gp(c),p=[d[0]/t.shape[2],d[1]/t.shape[1]],h=n.hand.rotation&&ye.kernels.includes("rotatewithoffset")?$e.rotateWithOffset(t,u,0,p):t.clone(),f=sb(-u,d),m=s?this.getBoxForPalmLandmarks(c.palmLandmarks,f):c,g=y8(m,h,[this.inputSize,this.inputSize]),A=fe(g,255);Q(g),Q(h);let[y,x]=await this.handPoseModel.predict(A);T8=pe(),Q(A);let b=(await y.data())[0];if(Q(y),b>=n.hand.minConfidence/4){let w=G(x,[-1,3]),k=await w.array();Q(x),Q(w);let S=this.transformRawCoords(k,m,u,f),E=this.getBoxForHandLandmarks(S);this.storedBoxes[l]={...E,confidence:b};let $={landmarks:S,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};i.push($)}else this.storedBoxes[l]=null;Q(x)}else{let u=Zm(Ym(c),S8),d={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:u.startPoint,bottomRight:u.endPoint},landmarks:[]};i.push(d)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var Ze={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Ze.nameMapping[e],getPoints:e=>Ze.pointsMapping[e]},cs={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>cs.nameMapping[e]},qe={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>qe.nameMapping[e]},Jm=class{constructor(t){de(this,"name");de(this,"curls");de(this,"directions");de(this,"weights");de(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}addCurl(t,n,s){typeof 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of[Ze.index,Ze.middle,Ze.ring,Ze.pinky])jo.addCurl(e,cs.full,1),jo.addDirection(e,qe.horizontalLeft,1),jo.addDirection(e,qe.horizontalRight,1);var tn=new Jm("victory");tn.addCurl(Ze.thumb,cs.half,.5);tn.addCurl(Ze.thumb,cs.none,.5);tn.addDirection(Ze.thumb,qe.verticalUp,1);tn.addDirection(Ze.thumb,qe.diagonalUpLeft,1);tn.addCurl(Ze.index,cs.none,1);tn.addDirection(Ze.index,qe.verticalUp,.75);tn.addDirection(Ze.index,qe.diagonalUpLeft,1);tn.addCurl(Ze.middle,cs.none,1);tn.addDirection(Ze.middle,qe.verticalUp,1);tn.addDirection(Ze.middle,qe.diagonalUpLeft,.75);tn.addCurl(Ze.ring,cs.full,1);tn.addDirection(Ze.ring,qe.verticalUp,.2);tn.addDirection(Ze.ring,qe.diagonalUpLeft,1);tn.addDirection(Ze.ring,qe.horizontalLeft,.2);tn.addCurl(Ze.pinky,cs.full,1);tn.addDirection(Ze.pinky,qe.verticalUp,.2);tn.addDirection(Ze.pinky,qe.diagonalUpLeft,1);tn.addDirection(Ze.pinky,qe.horizontalLeft,.2);tn.setWeight(Ze.index,2);tn.setWeight(Ze.middle,2);var N8=[jo,tn];var vge=.7,Cl={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 E8(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 R8(e,t){if(!e||!t)return[0,0];let n=E8(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=E8(e[1],e[2],t[1],t[2]);return[n,s]}function $8(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 wge(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 A;return g>Cl.NO_CURL_START_LIMIT?A=cs.none:g>Cl.HALF_CURL_START_LIMIT?A=cs.half:A=cs.full,A}function D8(e,t,n,s){let r;return 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g=Math.sqrt(r*r+i*i),A=Math.sqrt(a*a+l*l),y=Math.sqrt(o*o+c*c),x=Math.max(g,A,y),b=e[0],w=e[1],k=n[0],S=n[1];x===g?(k=n[0],S=n[1]):x===y&&(b=t[0],w=t[1]);let F=R8([b,w],[k,S]),R=$8(F,Cl.TOTAL_ANGLE_VOTE_POWER);p+=R[0],h+=R[1],f+=R[2];for(let T of s){let M=$8(T,Cl.SINGLE_ANGLE_VOTE_POWER);p+=M[0],h+=M[1],f+=M[2]}let P;return p===Math.max(p,h,f)?P=_8(l,i,c,d):f===Math.max(h,f)?P=D8(a,r,o,u):P=kge(l,i,c,d,a,r,o,u),P}function P8(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Ze.all){let o=Ze.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=R8(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of Ze.all){let o=a===Ze.thumb?1:0,i=Ze.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=wge(l,c,u),p=Ige(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function Qm(e){if(!e||e.length===0)return null;let t=P8(e),n={};for(let s of 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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=Qm(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 lb(e){var n,s,r,a,o,i;ye.initial&&(la=null,ua=null),!la||!ua?([la,ua]=await Promise.all([e.hand.enabled?st(at(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?st(at(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&&(!la||!la.modelUrl?ae("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&ae("load model:",la.modelUrl),!ua||!ua.modelUrl?ae("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&ae("load model:",ua.modelUrl))):(e.debug&&ae("cached model:",la.modelUrl),e.debug&&ae("cached model:",ua.modelUrl));let t=new ab(la);return M8=new ob(t,ua),[la,ua]}function Tl(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 z8(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 e0(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 ub(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 Nt=[null,null],Sge=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qo=[[0,0],[0,0]],Cge=["hand","fist","pinch","point","face","tip","pinchtip"],L8=4,B8=1.6,Tge=512,Nge=1.4,t0=Number.MAX_SAFE_INTEGER,cb=0,ca=[0,0],Xt={boxes:[],hands:[]},W8={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 V8(e){var t,n;if(ye.initial&&(Nt[0]=null),Nt[0])e.debug&&ae("cached model:",Nt[0].modelUrl);else{dc(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Nt[0]=await st(at(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let s=Object.values(Nt[0].modelSignature.inputs);qo[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,qo[0][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!Nt[0]||!Nt[0].modelUrl?ae("load model failed:",(n=e.hand.detector)==null?void 0:n.modelPath):e.debug&&ae("load model:",Nt[0].modelUrl)}return Nt[0]}async function U8(e){var t,n;if(ye.initial&&(Nt[1]=null),Nt[1])e.debug&&ae("cached model:",Nt[1].modelUrl);else{Nt[1]=await st(at(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let s=Object.values(Nt[1].modelSignature.inputs);qo[1][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,qo[1][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!Nt[1]||!Nt[1].modelUrl?ae("load model failed:",(n=e.hand.skeleton)==null?void 0:n.modelPath):e.debug&&ae("load model:",Nt[1].modelUrl)}return Nt[1]}async function Ege(e,t){let n=[];if(!e||!Nt[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,Tge),o=Math.round(a*r/8)*8;s.resize=$e.resizeBilinear(e,[a,o]),s.cast=he(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Nt[0].executeAsync(s.cast,Sge),s.boxes=pt(s.rawBoxes,[0,2]),s.scores=pt(s.rawScores,[0]);let i=as(s.scores,1);Q(i[L8]),i.splice(L8,1),s.filtered=Pn(i,1),Q(i),s.max=rs(s.filtered,1),s.argmax=Hs(s.filtered,1);let l=0;s.nms=await $e.nonMaxSuppressionAsync(s.boxes,s.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data(),u=await s.max.data(),d=await s.argmax.data();for(let p of Array.from(c)){let h=_e(s.boxes,p,1),f=await h.data();Q(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=e0(m,Nge),A=ub(g),y=[Math.trunc(m[0]*ca[0]),Math.trunc(m[1]*ca[1]),Math.trunc(m[2]*ca[0]),Math.trunc(m[3]*ca[1])],x=u[p],b=Cge[d[p]],w={id:l++,score:x,box:y,boxRaw:g,boxCrop:A,label:b};n.push(w)}return Object.keys(s).forEach(p=>Q(s[p])),n.sort((p,h)=>h.score-p.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function db(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Nt[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=$e.cropAndResize(e,[t.boxCrop],[0],[qo[1][0],qo[1][1]],"bilinear"),r.cast=he(r.crop,"float32"),r.div=fe(r.cast,255),[r.score,r.keypoints]=Nt[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]/qo[1][1],u[1]/qo[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);s.keypoints=c.map(u=>[ca[0]*(u[0]+t.boxRaw[0]),ca[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=Qm(s.keypoints);for(let u of Object.keys(W8))s.annotations[u]=W8[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>Q(r[i]))}return s}async function pb(e,t){var r,a;if(!Nt[0]||!Nt[1]||!((r=Nt[0])==null?void 0:r.inputs[0].shape)||!((a=Nt[1])==null?void 0:a.inputs[0].shape))return[];ca=[e.shape[2]||0,e.shape[1]||0],t0++;let n=(t.hand.skipTime||0)>pe()-cb,s=t0<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?Xt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>pe()-cb,l=t0<3*(t.hand.skipFrames||0);t.skipAllowed&&Xt.hands.length===t.hand.maxDetected?Xt.hands=await Promise.all(Xt.boxes.map(u=>db(e,u,t))):t.skipAllowed&&i&&l&&Xt.hands.length>0?Xt.hands=await Promise.all(Xt.boxes.map(u=>db(e,u,t))):(Xt.boxes=await Ege(e,t),cb=pe(),Xt.hands=await Promise.all(Xt.boxes.map(u=>db(e,u,t))),t0=0);let c=[...Xt.boxes];if(Xt.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u<Xt.hands.length;u++){let d=z8(Xt.hands[u].keypoints,ca);if(d.box[2]/(e.shape[2]||1)>.05&&d.box[3]/(e.shape[1]||1)>.05&&Xt.hands[u].fingerScore&&Xt.hands[u].fingerScore>(t.hand.minConfidence||0)){let p=e0(d.box,B8),h=e0(d.boxRaw,B8),f=ub(h);Xt.boxes.push({...c[u],box:p,boxRaw:h,boxCrop:f})}}for(let u=0;u<Xt.hands.length;u++){let d=Tl(Xt.hands[u].keypoints,ca);Xt.hands[u].box=d.box,Xt.hands[u].boxRaw=d.boxRaw}o(Xt.hands)})}var gb={};qp(gb,{connected:()=>s0,horizontal:()=>hb,kpt:()=>n0,relative:()=>mb,vertical:()=>fb});var n0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],hb=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],fb=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],mb=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],s0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var G8=.005,vs={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Ab(e){for(let t of hb){let n=e.keypoints.findIndex(r=>r.part===t[0]),s=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[0]<e.keypoints[s].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[s],e.keypoints[s]=r}}for(let t of fb){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),s=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[1]<e.keypoints[s].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of mb){let s=e.keypoints.findIndex(c=>c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),a=e.keypoints.findIndex(c=>c&&c.part===n[0]),o=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[a]||!e.keypoints[o])continue;let i=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[o].position[0]-e.keypoints[s].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[o].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let c=e.keypoints[s];e.keypoints[s]=e.keypoints[r],e.keypoints[r]=c}}}function H8(e){for(let t=0;t<e.length;t++)if(e[t]&&vs.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-vs.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-vs.keypoints[t].positionRaw[1])];n[0]<G8&&n[1]<G8?e[t]=vs.keypoints[t]:vs.keypoints[t]=e[t]}else vs.keypoints[t]=e[t];return e}function j8(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;vs.padding=[[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]],n.pad=qs(e,vs.padding),n.resize=$e.resizeBilinear(n.pad,[t,t]);let s=he(n.resize,"int32");return Object.keys(n).forEach(r=>Q(n[r])),s}function q8(e,t){e.keypoints=e.keypoints.filter(s=>s&&s.position);for(let s of e.keypoints)s.position=[s.position[0]*(t[0]+vs.padding[2][0]+vs.padding[2][1])/t[0]-vs.padding[2][0],s.position[1]*(t[1]+vs.padding[1][0]+vs.padding[1][1])/t[1]-vs.padding[1][0]],s.positionRaw=[s.position[0]/t[0],s.position[1]/t[1]];let n=Tl(e.keypoints.map(s=>s.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var Fn,r0=0,yb=Number.MAX_SAFE_INTEGER,Nl={boxes:[],bodies:[],last:0};async function X8(e){return ye.initial&&(Fn=null),Fn?e.debug&&ae("cached model:",Fn.modelUrl):(dc(["size"],e),Fn=await st(at(e.modelBasePath,e.body.modelPath||"")),!Fn||!Fn.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",Fn.modelUrl)),r0=Fn.inputs[0].shape?Fn.inputs[0].shape[2]:0,r0===-1&&(r0=256),Fn}async function Rge(e,t,n,s){let r=e[0][0],a=[],o=0;for(let d=0;d<r.length;d++)if(o=r[d][2],o>t.body.minConfidence){let p=[(s[3]-s[1])*r[d][1]+s[1],(s[2]-s[0])*r[d][0]+s[0]];a.push({score:Math.round(100*o)/100,part:n0[d],positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}o=a.reduce((d,p)=>p.score>d?p.score:d,0);let i=[],l=Tl(a.map(d=>d.position),[n.shape[2],n.shape[1]]),c={};for(let[d,p]of Object.entries(s0)){let h=[];for(let f=0;f<p.length-1;f++){let m=a.find(A=>A.part===p[f]),g=a.find(A=>A.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}c[d]=h}let u={id:0,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:a,annotations:c};return Ab(u),i.push(u),i}async function $ge(e,t,n,s){let r=[];for(let a=0;a<e[0].length;a++){let o=e[0][a],i=Math.round(100*o[51+4])/100;if(i>t.body.minConfidence){let l=[];for(let p=0;p<17;p++){let h=o[3*p+2];if(h>t.body.minConfidence){let f=[(s[3]-s[1])*o[3*p+1]+s[1],(s[2]-s[0])*o[3*p+0]+s[0]];l.push({part:n0[p],score:Math.round(100*h)/100,positionRaw:f,position:[Math.round((n.shape[2]||0)*f[0]),Math.round((n.shape[1]||0)*f[1])]})}}let c=Tl(l.map(p=>p.position),[n.shape[2],n.shape[1]]),u={};for(let[p,h]of Object.entries(s0)){let f=[];for(let m=0;m<h.length-1;m++){let g=l.find(y=>y.part===h[m]),A=l.find(y=>y.part===h[m+1]);g&&A&&g.score>(t.body.minConfidence||0)&&A.score>(t.body.minConfidence||0)&&f.push([g.position,A.position])}u[p]=f}let d={id:a,score:i,box:c.box,boxRaw:c.boxRaw,keypoints:[...l],annotations:u};Ab(d),r.push(d)}}return r.sort((a,o)=>o.score-a.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function xb(e,t){if(!Fn||!(Fn==null?void 0:Fn.inputs[0].shape))return[];t.skipAllowed||(Nl.boxes.length=0),yb++;let n=(t.body.skipTime||0)>pe()-Nl.last,s=yb<(t.body.skipFrames||0);return t.skipAllowed&&n&&s?Nl.bodies:new Promise(async r=>{let a={};yb=0,a.input=j8(e,r0),a.res=await(Fn==null?void 0:Fn.predict(a.input)),Nl.last=pe();let o=await a.res.array();Nl.bodies=a.res.shape[2]===17?await Rge(o,t,e,[0,0,1,1]):await $ge(o,t,e,[0,0,1,1]);for(let i of Nl.bodies)q8(i,[e.shape[2]||1,e.shape[1]||1]),H8(i.keypoints);Object.keys(a).forEach(i=>Q(a[i])),r(Nl.bodies)})}var ws,a0=[],K8=0,bb=Number.MAX_SAFE_INTEGER,o0=2.5;async function Z8(e){if(!ws||ye.initial){ws=await st(at(e.modelBasePath,e.object.modelPath||""));let t=Object.values(ws.modelSignature.inputs);if(ws.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!ws.inputSize)throw new Error(`cannot determine model inputSize: ${e.object.modelPath}`);!ws||!ws.modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",ws.modelUrl)}else e.debug&&ae("cached model:",ws.modelUrl);return ws}async function Dge(e,t,n,s){let r=0,a=[];for(let c of[1,2,4])j(async()=>{var g,A;let u=c*13,d=(g=e.find(y=>y.shape[1]===u**2&&y.shape[2]===cc.length))==null?void 0:g.squeeze(),p=(A=e.find(y=>y.shape[1]===u**2&&y.shape[2]<cc.length))==null?void 0:A.squeeze(),f=await p.reshape([-1,4,p.shape[1]/4]).argMax(2).array(),m=await d.array();for(let y=0;y<d.shape[0];y++)for(let x=0;x<d.shape[1];x++){let b=m[y][x];if(b>s.object.minConfidence&&x!==61){let w=(.5+Math.trunc(y%u))/u,k=(.5+Math.trunc(y/u))/u,S=f[y].map(U=>U*(u/c/t)),[E,$]=[w-o0/c*S[0],k-o0/c*S[1]],[F,R]=[w+o0/c*S[2]-E,k+o0/c*S[3]-$],P=[E,$,F,R];P=P.map(U=>Math.max(0,Math.min(U,1)));let T=[P[0]*n[0],P[1]*n[1],P[2]*n[0],P[3]*n[1]],M={id:r++,score:Math.round(100*b)/100,class:x+1,label:cc[x].label,box:T.map(U=>Math.trunc(U)),boxRaw:P};a.push(M)}}});e.forEach(c=>Q(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 $e.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await c.data(),Q(c)}return a=a.filter((c,u)=>l.includes(u)).sort((c,u)=>u.score-c.score),a}async function vb(e,t){let n=(t.object.skipTime||0)>pe()-K8,s=bb<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&a0.length>0?(bb++,a0):(bb=0,!ye.kernels.includes("mod")||!ye.kernels.includes("sparsetodense")?a0:new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=$e.resizeBilinear(e,[ws.inputSize,ws.inputSize],!1),i=fe(o,255),l=i.transpose([0,3,1,2]);Q(i),Q(o);let c;t.object.enabled&&(c=await ws.predict(l)),K8=pe(),Q(l);let u=await Dge(c,ws.inputSize,a,t);a0=u,r(u)}))}var Ap=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_ge=Ap.length,yp=Ap.reduce((e,t,n)=>(e[t]=n,e),{}),Pge=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],hAe=Pge.map(([e,t])=>[yp[e],yp[t]]),Y8=[["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 J8(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 Q8(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 wb=class{constructor(t,n){de(this,"priorityQueue");de(this,"numberOfElements");de(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(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let s=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=s}};function 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pitch:${mc(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&d.push(`gaze: ${mc(u.rotation.gaze.bearing)}\xB0`)),d.length===0&&d.push("face"),r.fillStyle=s.color;for(let p=d.length-1;p>=0;p--){let h=Math.max(u.box[0],0),f=p*s.lineHeight+u.box[1];s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(d[p],h+5,f+16)),r.fillStyle=s.labelColor,r.fillText(d[p],h+4,f+15)}}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(s.drawPoints)for(let d of u.mesh)Db(r,d[0],d[1],d[2],s);if(s.drawPolygons){if(r.lineWidth=1,u.mesh.length>450)for(let d=0;d<kl.length/3;d++){let p=[kl[d*3+0],kl[d*3+1],kl[d*3+2]].map(h=>u.mesh[h]);lT(r,p,s)}if(u.annotations&&u.annotations.leftEyeIris&&u.annotations.leftEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,p=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris&&u.annotations.rightEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,p=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(s.drawGaze&&((a=u.rotation)==null?void 0:a.angle)){r.strokeStyle="pink";let d=u.box[0]+u.box[2]/2-u.box[3]*mc(u.rotation.angle.yaw)/90,p=u.box[1]+u.box[3]/2+u.box[2]*mc(u.rotation.angle.pitch)/90,h=new Path2D(`
M ${u.box[0]+u.box[2]/2} ${u.box[1]}
C
${d} ${u.box[1]},
${d} ${u.box[1]+u.box[3]},
${u.box[0]+u.box[2]/2} ${u.box[1]+u.box[3]}
`),f=new Path2D(`
M ${u.box[0]} ${u.box[1]+u.box[3]/2}
C
${u.box[0]} ${p},
${u.box[0]+u.box[2]} ${p},
${u.box[0]+u.box[2]} ${u.box[1]+u.box[3]/2}
`);r.stroke(f),r.stroke(h)}if(s.drawGaze&&((i=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:i.strength)&&((c=(l=u.rotation)==null?void 0:l.gaze)==null?void 0:c.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.fillStyle="pink";let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];uT(r,[u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]],[d[0],d[1]],4);let p=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];uT(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function Fb(e,t,n){var a;let s=$n(da,n);if(!t||!e)return;let r=El(e);r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(xp(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+s.lineHeight,t[o].box[2])),r.fillStyle=s.labelColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints&&t[o].keypoints)for(let i=0;i<t[o].keypoints.length;i++)r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,Db(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s);if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4)}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)Uge(r,l,s)}}async function Ob(e,t,n){let s=$n(da,n);if(!t||!e)return;let r=El(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,xp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*(o[2]||0)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,Db(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 0.5)`:s.color,r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4))};r.font=s.font,o(a.annotations.index,"index"),o(a.annotations.middle,"middle"),o(a.annotations.ring,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palm,"palm")}if(s.drawPolygons&&a.annotations){let o=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l<i.length;l++)r.beginPath(),r.strokeStyle=s.useDepth?`rgba(${127.5+2*i[l][2]}, ${127.5-2*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}async function Mb(e,t,n){let s=$n(da,n);if(!t||!e)return;let r=El(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,xp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}async function cT(e,t,n){let s=$n(da,n);if(!t||!e)return;let r=El(e);r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,xp(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}async function dT(e,t){if(!e||!t)return;El(t).drawImage(e,0,0)}async function pT(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=pe(),r=$n(da,n),a=Promise.all([Pb(e,t.face,r),Fb(e,t.body,r),Ob(e,t.hand,r),Mb(e,t.object,r),_b(e,t.gesture,r)]);return t.performance.draw=ye.perfadd?(t.performance.draw||0)+Math.trunc(pe()-s):Math.trunc(pe()-s),a}var Gge=e=>{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}},hT=(e,t)=>{let n=g=>{let A=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=A,g[1]/=A,g[2]/=A,g},s=(g,A)=>{let y=g[0]-A[0],x=g[1]-A[1],b=g[2]-A[2];return[y,x,b]},r=(g,A)=>{let y=g[1]*A[2]-g[2]*A[1],x=g[2]*A[0]-g[0]*A[2],b=g[0]*A[1]-g[1]*A[0];return[y,x,b]},a=g=>{let[A,y,x,b,w,k,S,E,$]=g,F,R,P;return b<1?b>-1?(P=Math.asin(b),R=Math.atan2(-S,A),F=Math.atan2(-k,w)):(P=-Math.PI/2,R=-Math.atan2(E,$),F=0):(P=Math.PI/2,R=Math.atan2(E,$),F=0),isNaN(F)&&(F=0),isNaN(R)&&(R=0),isNaN(P)&&(P=0),{pitch:2*-F,yaw:2*-R,roll:2*-P}},o=g=>{let A=(x,b,w,k)=>Math.atan2(k-b,w-x);return{pitch:A(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:A(g[33][0],g[33][2],g[263][0],g[263][2]),roll:A(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?Gge(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var zb=async(e,t)=>{var p,h,f,m;let n,s,r,a,o,i,l,c,u=[];e.state="run:face",n=pe();let d=await d8(t,e.config);if(e.performance.face=ye.perfadd?(e.performance.face||0)+Math.trunc(pe()-n):Math.trunc(pe()-n),!t.shape||t.shape.length!==4)return[];if(!d)return[];for(let g=0;g<d.length;g++){if(e.analyze("Get Face"),!d[g].tensor||d[g].tensor.isDisposedInternal){ae("Face object is disposed:",d[g].tensor);continue}let A=hT(d[g],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?o=e.config.face.emotion.enabled?Zx(d[g].tensor||Gt([]),e.config,g,d.length):null:(e.state="run:emotion",n=pe(),o=e.config.face.emotion.enabled?await Zx(d[g].tensor||Gt([]),e.config,g,d.length):null,e.performance.emotion=ye.perfadd?(e.performance.emotion||0)+Math.trunc(pe()-n):Math.trunc(pe()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=e.config.face.antispoof.enabled?Cx(d[g].tensor||Gt([]),e.config,g,d.length):null:(e.state="run:antispoof",n=pe(),l=e.config.face.antispoof.enabled?await Cx(d[g].tensor||Gt([]),e.config,g,d.length):null,e.performance.antispoof=ye.perfadd?(e.performance.antispoof||0)+Math.trunc(pe()-n):Math.trunc(pe()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Description:"),e.config.async?c=e.config.face.description.enabled?nb(d[g].tensor||Gt([]),e.config,g,d.length):null:(e.state="run:description",n=pe(),c=e.config.face.description.enabled?await nb(d[g].tensor||Gt([]),e.config,g,d.length):null,e.performance.embedding=ye.perfadd?(e.performance.embedding||0)+Math.trunc(pe()-n):Math.trunc(pe()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,c,r,l]=await Promise.all([s,a,o,i,c,r,l])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((h=(p=d[g])==null?void 0:p.annotations)==null?void 0:h.leftEyeIris)&&((m=(f=d[g])==null?void 0:f.annotations)==null?void 0:m.rightEyeIris)&&(delete d[g].annotations.leftEyeIris,delete d[g].annotations.rightEyeIris);let y=d[g].annotations&&d[g].annotations.leftEyeIris&&d[g].annotations.leftEyeIris[0]&&d[g].annotations.rightEyeIris&&d[g].annotations.rightEyeIris[0]&&d[g].annotations.leftEyeIris.length>0&&d[g].annotations.rightEyeIris.length>0&&d[g].annotations.leftEyeIris[0]!==null&&d[g].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(d[g].annotations.leftEyeIris[3][0]-d[g].annotations.leftEyeIris[1][0]),Math.abs(d[g].annotations.rightEyeIris[4][1]-d[g].annotations.rightEyeIris[2][1]))/t.shape[2]:0,x=e.config.face.detector.return?pt(d[g].tensor):null;Q(d[g].tensor),d[g].tensor&&delete d[g].tensor,u.push({...d[g],id:g,age:c==null?void 0:c.age,gender:c==null?void 0:c.gender,genderScore:c==null?void 0:c.genderScore,embedding:c==null?void 0:c.descriptor,emotion:o,real:l,iris:y!==0?Math.trunc(500/y/11.7)/100:0,rotation:A,tensor:x}),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),u};var fT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.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]<a.position[1]&&r.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&s&&s.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>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},mT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){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 i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},gT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),c=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(p>.06||d>.06)&&(c=!1),p>.06&&t.push({iris:n,gesture:"looking right"}),d>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},AT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=F8(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var Pe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function 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2Q==`;async function Hge(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(c0);break;case"body":case"full":n=await t(d0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function jge(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+c0;break;case"full":case"body":n="data:image/jpeg;base64,"+d0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:ye.Image&&(s=new ye.Image),s.onload=async()=>{let r=Kn(s.naturalWidth,s.naturalHeight);if(!r)ae("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function qge(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(c0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(d0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&ae("Warmup tfjs-node not loaded");return s}async function wT(e,t){let n=pe();if(e.state="warmup",t&&(e.config=$n(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 Hge(e):typeof Image!="undefined"||ye.Canvas!==void 0?s=await jge(e):s=await qge(e);let a=pe();e.config.debug&&ae("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var gc,bp,vp,p0,Xge=class{constructor(t){de(this,"version");de(this,"config");de(this,"result");de(this,"state");de(this,"process");de(this,"tf");de(this,"env");de(this,"draw");de(this,"models");de(this,"events");de(this,"faceTriangulation");de(this,"faceUVMap");de(this,"performance");Rc(this,gc,void 0);Rc(this,bp,void 0);Rc(this,vp,void 0);de(this,"gl");de(this,"analyze",(...t)=>{if(!Ec(this,bp))return;let n=this.tf.engine().state.numTensors,s=Ec(this,gc);$c(this,gc,n);let r=n-s;r!==0&&ae(...t,r)});Rc(this,p0,t=>{if(!Ec(this,vp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof Ye))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});de(this,"similarity",xT);de(this,"distance",u0);de(this,"match",bT);de(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});this.env=ye,Jo.wasmPath=up.includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${up}/dist/`,Jo.modelBasePath=ye.browser?"../models/":"file://models/",Jo.backend=ye.browser?"humangl":"tensorflow",this.version=Ix,Object.defineProperty(this,"version",{value:Ix}),this.config=JSON.parse(JSON.stringify(Jo)),Object.seal(this.config),t&&(this.config=$n(this.config,t)),this.tf=wl,this.state="idle",$c(this,gc,0),$c(this,bp,!1),$c(this,vp,!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 Rb,this.draw={options:da,canvas:(n,s)=>dT(n,s),face:(n,s,r)=>Pb(n,s,r),body:(n,s,r)=>Fb(n,s,r),hand:(n,s,r)=>Ob(n,s,r),gesture:(n,s,r)=>_b(n,s,r),object:(n,s,r)=>Mb(n,s,r),person:(n,s,r)=>cT(n,s,r),all:(n,s,r)=>pT(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=h8,this.faceUVMap=f8,this.gl=zt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Jo)),this.config.backend=t}validate(t){return Og(Jo,t||this.config)}now(){return pe()}image(t,n=!0){return uc(t,this.config,n)}async segmentation(t,n){return rT(t,n,this.config)}enhance(t){return tb(t)}async init(){await l0(this,!0),await this.tf.ready()}async load(t){this.state="load";let n=pe(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=$n(this.config,t)),this.env.initial&&(this.config.debug&&ae(`version: ${this.version}`),this.config.debug&&ae(`tfjs version: ${this.tf.version_core}`),await l0(this)||ae("error: backend check failed"),await Wh(),this.env.browser&&(this.config.debug&&ae("configuration:",this.config),this.config.debug&&ae("tf flags:",this.tf.ENV.flags))),await aT(this),this.env.initial&&this.config.debug&&ae("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await oT(this),this.emit("load"));let a=Math.trunc(pe()-n);a>(this.performance.load||0)&&(this.performance.load=this.env.perfadd?(this.performance.load||0)+a:a)}next(t=this.result){return yT(t,this.config)}async warmup(t){return wT(this,t)}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,A,y,x,b,w,k,S,E,$,F,R,P,T,M,U,H,z,X,ee,Y,ne;this.state="config";let r;this.config=$n(this.config,n),this.state="check";let a=Ec(this,p0).call(this,t);a&&(ae(a,t),s({error:a}));let o=pe();await l0(this),await this.load(),r=pe(),this.state="image";let i=uc(t,this.config);if(this.process=i,this.performance.image=this.env.perfadd?(this.performance.image||0)+Math.trunc(pe()-r):Math.trunc(pe()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&ae("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=pe(),this.config.skipAllowed=await R6(this.config,i.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipAllowed&&this.performance.cached++,this.performance.changed=this.env.perfadd?(this.performance.changed||0)+Math.trunc(pe()-r):Math.trunc(pe()-r),this.analyze("Check Changed:");let l=[],c=[],u=[],d=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?zb(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=pe(),l=this.config.face.enabled?await zb(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(pe()-r):Math.trunc(pe()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?$n(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?c=this.config.body.enabled?Tb(i.tensor,p):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("blazepose"))?c=this.config.body.enabled?Lx(i.tensor,p):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("efficientpose"))?c=this.config.body.enabled?qx(i.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("movenet"))&&(c=this.config.body.enabled?xb(i.tensor,p):[]),this.performance.body&&delete this.performance.body):(r=pe(),((b=this.config.body.modelPath)==null?void 0:b.includes("posenet"))?c=this.config.body.enabled?await Tb(i.tensor,p):[]:((w=this.config.body.modelPath)==null?void 0:w.includes("blazepose"))?c=this.config.body.enabled?await Lx(i.tensor,p):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("efficientpose"))?c=this.config.body.enabled?await qx(i.tensor,p):[]:((S=this.config.body.modelPath)==null?void 0:S.includes("movenet"))&&(c=this.config.body.enabled?await xb(i.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(pe()-r):Math.trunc(pe()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?$n(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((($=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:$.includes("handdetect"))?u=this.config.hand.enabled?ib(i.tensor,h):[]:((R=(F=this.config.hand.detector)==null?void 0:F.modelPath)==null?void 0:R.includes("handtrack"))&&(u=this.config.hand.enabled?pb(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=pe(),((T=(P=this.config.hand.detector)==null?void 0:P.modelPath)==null?void 0:T.includes("handdetect"))?u=this.config.hand.enabled?await ib(i.tensor,h):[]:((U=(M=this.config.hand.detector)==null?void 0:M.modelPath)==null?void 0:U.includes("handtrack"))&&(u=this.config.hand.enabled?await pb(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(pe()-r):Math.trunc(pe()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((H=this.config.object.modelPath)==null?void 0:H.includes("nanodet"))?d=this.config.object.enabled?vb(i.tensor,this.config):[]:((z=this.config.object.modelPath)==null?void 0:z.includes("centernet"))&&(d=this.config.object.enabled?Wx(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=pe(),((X=this.config.object.modelPath)==null?void 0:X.includes("nanodet"))?d=this.config.object.enabled?await vb(i.tensor,this.config):[]:((ee=this.config.object.modelPath)==null?void 0:ee.includes("centernet"))&&(d=this.config.object.enabled?await Wx(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(pe()-r):Math.trunc(pe()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,u,d]=await Promise.all([l,c,u,d])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=pe(),f=[...mT(l),...fT(c),...AT(u),...gT(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(pe()-r):Math.trunc(pe()-r)),this.performance.total=Math.trunc(pe()-o);let m=((ne=(Y=this.process)==null?void 0:Y.tensor)==null?void 0:ne.shape)||[];this.result={face:l,body:c,hand:u,gesture:f,object:d,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return vT(l,c,u,f,m)}},Q(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};gc=new WeakMap,bp=new WeakMap,vp=new WeakMap,p0=new WeakMap;export{Xge as Human,Xge as default,Jo as defaults,ye as env};
/**
* @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. */
//# sourceMappingURL=human.custom.esm.js.map