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

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Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=t}let s;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(s),()=>(s=n(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(t,n,r){t();try{let s=r();return n(),s}catch(s){throw n(),s}}nextTensorId(){return Rv.nextTensorId++}nextVariableId(){return Rv.nextVariableId++}clone(t){let n=M.runKernel(si,{x:t}),r={x:t},s=o=>({x:()=>{let i="float32",u={x:o},c={dtype:i};return M.runKernel(Bo,u,c)}}),a=[];return this.addTapeNode(this.state.activeScope.name,r,[n],s,a,{}),n}runKernel(t,n,r){if(this.backendName==null&&this.backend,!(wd(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:n,attrs:r})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,n,r){let s=this.backend.numDataIds(),a=0;r.forEach(u=>{a+=u.dtype==="complex64"?3:1});let o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-n-a-o;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${t}'`)}runKernelFunc(t){let n,r=[],s=this.isTapeOn(),a=this.state.numBytes,o=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let u,c=fv(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(fv(t)){let{kernelName:f,inputs:g,attrs:m}=t;this.backendName==null&&this.backend;let b=wd(f,this.backendName);A(b!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();u=b.kernelFunc({inputs:g,attrs:m,backend:this.backend});let v=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,y,v);let x=v.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(s){let k=this.getTensorsForGradient(f,g,x);r=this.saveTensorsForBackwardMode(k)}return x}}else{let{forwardFunc:f}=t,g=m=>{s&&(r=m.map(b=>this.keep(this.clone(b))))};i=()=>{let m=this.backend.numDataIds();u=this.tidy(()=>f(this.backend,g));let b=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,m,b),b}}let{inputs:l,attrs:p}=t,d=fv(t)?null:t.backwardsFunc,h;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?n=i():(h=this.profiler.profileKernel(c,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(h),n=h.outputs)}),s&&this.addTapeNode(c,l,n,d,r,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:c,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-o,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(f=>l[f]!=null?l[f].shape:null),outputShapes:n.map(f=>f.shape),kernelTimeMs:h.timeMs,extraInfo:h.extraInfo}),Array.isArray(u)?n:n[0]}saveTensorsForBackwardMode(t){return t.map(r=>this.keep(this.clone(r)))}getTensorsForGradient(t,n,r){let s=Nv(t);if(s!=null){let a=s.inputsToSave||[],o=s.outputsToSave||[],i;s.saveAllInputs?(A(Array.isArray(n),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(n).map(c=>n[c])):i=a.map(c=>n[c]);let 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this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(t,n){this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++;let r=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(r=t.size*sf(t.dtype)),this.state.numBytes+=r,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:n||this.backend,dtype:t.dtype,shape:t.shape,bytes:r})),t instanceof pa||this.track(t)}incRef(t,n){this.trackTensor(t,n),this.backend.incRef(t.dataId)}removeDataId(t,n){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===n&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let n=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=n.bytes),t.dtype!=="complex64"&&t.dtype!=="string"){let r=t.size*sf(t.dtype);this.state.numBytes-=r}n.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,n.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let n=this.state.registeredVariables[t];this.disposeVariable(n)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let n=this.state.numBytes,r=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-n,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,n,r,s,a,o){let i={id:this.state.nextTapeNodeId++,kernelName:t,inputs:n,outputs:r,saved:a},u=Nv(t);u!=null&&(s=u.gradFunc),s!=null&&(i.gradient=c=>(c=c.map((l,p)=>{if(l==null){let d=r[p],h=Pf(d.size,d.dtype);return this.makeTensor(h,d.shape,d.dtype)}return l}),s(c.length>1?c:c[0],a,o))),this.state.activeTape.push(i)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let n={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(n.name=t),this.state.scopeStack.push(n),this.state.activeScope=n}endScope(t){let n=Ox(t),r=new Set(n.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let o=this.state.activeScope.track[a];!o.kept&&!r.has(o.id)&&o.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],n.forEach(a=>{!a.kept&&a.scopeId===s.id&&this.track(a)})}gradients(t,n,r,s=!1){if(A(n.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));A(a instanceof Ne,()=>"The result y returned by f() must be a tensor.");let o=UP(this.state.activeTape,n,a);if(!s&&o.length===0&&n.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[a.id]=r==null?eO(a.shape):r,GP(i,o,c=>this.tidy(c),tO);let u=n.map(c=>i[c.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(c=>{for(let l of c.saved)l.dispose()}),this.state.activeTape=null),{value:a,grads:u}})}customGrad(t){return A(la(t),()=>"The f passed in customGrad(f) must be a function."),(...n)=>{A(n.every(i=>i instanceof Ne),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let r,s={};n.forEach((i,u)=>{s[u]=i});let a=(i,u)=>(r=t(...n,u),A(r.value instanceof Ne,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),A(la(r.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),r.value),o=(i,u)=>{let c=r.gradFunc(i,u),l=Array.isArray(c)?c:[c];A(l.length===n.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),A(l.every(d=>d instanceof Ne),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let p={};return l.forEach((d,h)=>{p[h]=()=>d}),p};return this.runKernelFunc({forwardFunc:a,backwardsFunc:o,inputs:s})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,n){return this.state.tensorInfo.get(t).backend.readToGPU(t,n)}async time(t){let n=Id(),r=await this.backend.time(t);return r.wallMs=Id()-n,r}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new yS;for(let t in 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|nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function _C(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Bn=G();Bn.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. 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${i.rank}.`),A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&A(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),l!=null&&A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${l.rank}.`),Ea(o,i,u,l,c,a)}var tw=B({batchNorm3d_:M3});function L3(e,t,n,r,s,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),c;s!=null&&(c=_(s,"scale","batchNorm"));let l;return r!=null&&(l=_(r,"offset","batchNorm")),A(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),A(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&A(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 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         ${a.shape}`);let o={data:r,indices:s,segmentIds:a};return M.runKernel(Jd,o)}var TW=B({sparseSegmentSum_:CW});function NW(e,t,n,r,s,a,o,i){let u=_(e,"data","stringNGrams","string");if(u.dtype!=="string")throw new Error("Data must be of datatype string");if(u.shape.length!==1)throw new Error(`Data must be a vector, saw: ${u.shape}`);let c=_(t,"dataSplits","stringNGrams");if(c.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let l={separator:n,nGramWidths:r,leftPad:s,rightPad:a,padWidth:o,preserveShortSequences:i},p={data:u,dataSplits:c},d=M.runKernel(tp,p,l);return{nGrams:d[0],nGramsSplits:d[1]}}var _W=B({stringNGrams_:NW});function EW(e,t,n=!0){let r=_(e,"input","stringSplit","string"),s=_(t,"delimiter","stringSplit","string");if(r.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${r.shape}`);if(s.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${s.shape}`);let a={skipEmpty:n},o={input:r,delimiter:s},i=M.runKernel(np,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var AW=B({stringSplit_:EW});function DW(e,t){let n=_(e,"input","stringToHashBucketFast","string"),r={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let s={input:n};return M.runKernel(rp,s,r)}var $W=B({stringToHashBucketFast_:DW});function FW(e,t,n,r=!0){let s=_(e,"input","staticRegexReplace","string"),a={pattern:t,rewrite:n,replaceGlobal:r};return M.runKernel(ep,{x:s},a)}var RW=B({staticRegexReplace_:FW}),QT={fft:kp,ifft:ec,rfft:Sp,irfft:ym},eN={hammingWindow:uz,hannWindow:jT,frame:qT,stft:pz},er={flipLeftRight:gz,grayscaleToRGB:yz,resizeNearestNeighbor:JT,resizeBilinear:ZT,rgbToGrayscale:xz,rotateWithOffset:Iz,cropAndResize:fz,nonMaxSuppression:Sz,nonMaxSuppressionAsync:$z,nonMaxSuppressionWithScore:Rz,nonMaxSuppressionWithScoreAsync:Oz,nonMaxSuppressionPadded:Lz,nonMaxSuppressionPaddedAsync:zz,threshold:Hz,transform:qz},Xw={bandPart:Xz,gramSchmidt:Zz,qr:Qz},tN={absoluteDifference:nW,computeWeightedLoss:Ps,cosineDistance:sW,hingeLoss:oW,huberLoss:uW,logLoss:lW,meanSquaredError:pW,sigmoidCrossEntropy:mW,softmaxCrossEntropy:yW},nN={sparseFillEmptyRows:xW,sparseReshape:IW,sparseSegmentMean:SW,sparseSegmentSum:TW},rN={stringNGrams:_W,stringSplit:AW,stringToHashBucketFast:$W,staticRegexReplace:RW},re={};Ee(re,{Serializable:()=>sN,SerializationMap:()=>aN,getRegisteredName:()=>OW,registerClass:()=>oN});var PW=new Map,Vv=new Map,sN=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},aN=class Bu{constructor(){this.classNameMap={}}static getMap(){return Bu.instance==null&&(Bu.instance=new Bu),Bu.instance}static register(t){Bu.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function oN(e,t,n){A(e.className!=null,()=>"Class being registered does not have the static className property defined."),A(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),A(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),typeof t=="undefined"&&(t="Custom"),typeof n=="undefined"&&(n=e.className);let r=n,s=t+">"+r;return aN.register(e),PW.set(s,e),Vv.set(e,s),e}function OW(e){return Vv.has(e)?Vv.get(e):e.className}var Os=class extends sN{minimize(e,t=!1,n){let{value:r,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:s[o.name]}));this.applyGradients(a)}else this.applyGradients(s);return _e(s),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return oT(e,t)}dispose(){this.iterations_!=null&&_e(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:xe(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Os,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Yw=class extends Os{static get className(){return"Adadelta"}constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=M.registeredVariables[n],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${n}/accum_grad`,variable:O(()=>je(s).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${n}/accum_var`,variable:O(()=>je(s).variable(a))});let o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[r].variable,u=this.accumulatedUpdates[r].variable;O(()=>{let c=X(z(i,this.rho),z(lt(o),1-this.rho)),l=z(fe(hn(X(u,this.epsilon)),hn(X(i,this.epsilon))),o),p=X(z(u,this.rho),z(lt(l),1-this.rho));i.assign(c),u.assign(p);let d=X(z(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(_e(this.accumulatedGrads.map(e=>e.variable)),_e(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},Zw=class extends Os{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=M.registeredVariables[n];this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${n}/accumulator`,variable:O(()=>vn(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[r].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[r].variable;O(()=>{let i=X(o,lt(a));o.assign(i);let u=X(z(fe(a,hn(X(i,M.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&_e(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)}},Jw=class extends Os{static get className(){return"Adam"}constructor(e,t,n,r=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],O(()=>{this.accBeta1=xe(t).variable(),this.accBeta2=xe(n).variable()}),r==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=le(1,this.accBeta1),r=le(1,this.accBeta2);t.forEach((s,a)=>{let o=M.registeredVariables[s],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:O(()=>je(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:O(()=>je(o).variable(i))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,p=X(z(c,this.beta1),z(u,1-this.beta1)),d=X(z(l,this.beta2),z(lt(u),1-this.beta2)),h=fe(p,n),f=fe(d,r);c.assign(p),l.assign(d);let g=X(z(fe(h,X(hn(f),this.epsilon)),-this.learningRate),o);o.assign(g)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&_e(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),O(()=>{this.accBeta1.assign(Ds(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ds(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.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)}},Qw=class extends Os{static get className(){return"Adamax"}constructor(e,t,n,r=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],O(()=>{this.iteration=xe(0).variable(),this.accBeta1=xe(t).variable()}),r==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=le(1,this.accBeta1),r=fe(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((s,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)}},Em=class extends Os{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=Array.isArray(e)?e[r].tensor:e[n];if(s==null)return;let a=M.registeredVariables[n];O(()=>{let o=X(z(this.c,s),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(xe(-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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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}},tI=class extends Os{static get className(){return"RMSProp"}constructor(e,t=.9,n=0,r=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,r==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${n}/rms`,variable:O(()=>je(s).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${n}/momentum`,variable:O(()=>je(s).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${n}/mg`,variable:O(()=>je(s).variable(a))});let o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[r].variable,u=this.accumulatedMoments[r].variable;O(()=>{let c=X(z(i,this.decay),z(lt(o),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[r].variable,p=X(z(l,this.decay),z(o,1-this.decay)),d=fe(z(o,this.learningRate),hn(le(c,X(lt(p),this.epsilon)))),h=X(z(u,this.momentum),d);i.assign(c),l.assign(p),u.assign(h);let f=le(s,h);s.assign(f)}else{let l=X(z(i,this.decay),z(lt(o),1-this.decay)),p=X(z(u,this.momentum),fe(z(o,this.learningRate),hn(X(l,this.epsilon))));i.assign(l),u.assign(p);let d=le(s,p);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&_e(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&_e(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&_e(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}},MW=[Yw,Zw,Jw,Qw,eI,tI,Em];function LW(){for(let e of MW)oN(e)}var 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let t=[];for(let n of this.layers)t=t.concat(n.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let n of this.layers)t.push(...n.nonTrainableWeights);if(!this.trainable){let n=[];for(let r of this.layers)n.push(...r.trainableWeights);return n.concat(t)}return t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(t,n=!0){let r={},s=0,a=n6(t);a&&this.parseWeights(t);for(let i of this.layers)for(let[u,c]of i.weights.entries()){let l=a?`${c.name.split("/").slice(0,-1).join("/")+"/"}${u}`:c.originalName;if(r[l]!=null)throw new V(`Duplicate weight name: ${l}`);r[l]=c,s++}let o=[];for(let i in t){let u=i;if(r[i]==null){let c=i.split("/");u=c.slice(0,-2).concat([c[c.length-1]]).join("/")}if(r[u]!=null)o.push([r[u],t[i]]);else if(n)throw new V(`Provided weight data has no target variable: ${i}`);delete r[u]}if(n){let i=[];for(let u in r)i.push(u);if(i.length>0)throw new V(`${i.length} of ${s} weights are not set: ${i}`)}bI(o)}parseWeights(t){for(let n in Object.keys(t)){let r=n.split("/"),s=["vars","layer_checkpoint_dependencies"],a=r.map(o=>o.startsWith("_")?o.slice(1):o).filter(o=>!s.includes(o)).join("/");a!==n&&(t[a]=t[n],delete t[n])}}updatedConfig(){let t=this.getConfig(),n={};return n.className=this.getClassName(),n.config=t,n.kerasVersion=`tfjs-layers ${_I}`,n.backend="TensorFlow.js",n}toJSON(t,n=!0){let r=Jv(this.updatedConfig());return n?JSON.stringify(r):r}call(t,n){return O(()=>{t=it(t);let r=new zu;for(let s=0;s<this.inputs.length;++s)r.add(this.inputs[s],t[s]);return id(this.outputs,r,n)})}computeMask(t,n){return O(()=>{t=it(t);let r;return n==null?r=wo(null,t.length):r=it(n),this.runInternalGraph(t,r)[1]})}computeOutputShape(t){let n=hf(t);if(n.length!==this.inputLayers.length)throw new V(`Invalid inputShape argument ${t}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<n.length;i++){let u=this.inputLayers[i],c=n[i],l=u.name+"_0_0";r[l]=c}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Ph);if(s.length>1)for(let i of s){let u=this.nodesByDepth[i];for(let c of u){let l=c.outboundLayer;if(this.inputLayers.map(g=>g.id).indexOf(l.id)!==-1)continue;let p=[];for(let g=0;g<c.inboundLayers.length;g++){let m=c.inboundLayers[g],b=c.nodeIndices[g],y=c.tensorIndices[g],v=`${m.name}_${b}_${y}`,x=r[v];p.push(x)}let d=l.computeOutputShape(Rn(p)),h=hf(d),f=l.inboundNodes.indexOf(c);for(let g=0;g<h.length;g++){let m=`${l.name}_${f}_${g}`;r[m]=h[g]}}}let a=[],o=[];for(let i=0;i<this.outputLayers.length;i++){let u=this.outputLayers[i],c=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],p=`${u.name}_${c}_${l}`;o.push(p)}for(let i=0;i<o.length;i++){let u=o[i];ss(u in r),a.push(r[u])}return Rn(a)}runInternalGraph(t,n){n==null&&(n=wo(null,t.length));let r={};for(let u=0;u<this.inputs.length;++u){let c=this.inputs[u],l=t[u],p=n[u];r[c.id]=[l,p]}let s=Object.keys(this.nodesByDepth).map(u=>parseInt(u,10)).sort(Ph);for(let u of s){let c=this.nodesByDepth[u];for(let l of c){let p=l.outboundLayer,d=l.inputTensors,h=l.outputTensors,f=new Array;for(let g of d)g.id in r&&f.push(r[g.id]);if(f.length===d.length){let g={},m,b,y,v;if(l.callArgs!=null&&(g=l.callArgs),f.length===1){let[x,k]=f[0];g.mask==null&&(g.mask=k),y=it(p.call(x,g)),v=it(p.computeMask(x,k)),m=[x],b=[k]}else m=f.map(x=>x[0]),b=f.map(x=>x[1]),g.mask==null&&(g.mask=b),y=it(p.call(m,g)),v=it(p.computeMask(m,b));if(p.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<h.length;++x){let k=h[x],S=y[x],N=v[x];r[k.id]=[S,N]}}}}let a=[],o=[],i=[];for(let u of this.outputs){ss(u.id in r,`Could not compute output ${u.name} : 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this.layers){let i=o.getClassName(),u=o.getConfig(),c=[];for(let p=0;p<o.inboundNodes.length;p++){let d=o.inboundNodes[p],h=ns.nodeKey(o,p),f={};if(this.containerNodes.has(h)){if(d.callArgs)try{JSON.stringify(d.callArgs),f=d.callArgs}catch(g){console.warn(`Layer ${o.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).`),f={}}if(d.inboundLayers.length>0){let g=[];for(let m=0;m<d.inboundLayers.length;m++){let b=d.inboundLayers[m],y=d.nodeIndices[m],v=d.tensorIndices[m],x=ns.nodeKey(b,y),k=n[x];k==null&&(k=0),g.push([b.name,k,v,f])}c.push(g)}}}let l={};l.name=o.name,l.className=i,l.config=u,l.inboundNodes=c,r.push(l)}t.layers=r;let s=[];for(let o=0;o<this.inputLayers.length;o++){let i=this.inputLayers[o],u=this.inputLayersNodeIndices[o],c=ns.nodeKey(i,u);if(!this.containerNodes.has(c))continue;let l=n[c];l==null&&(l=0);let p=this.inputLayersTensorIndices[o];s.push([i.name,l,p])}t.inputLayers=s;let a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],u=this.outputLayersNodeIndices[o],c=ns.nodeKey(i,u);if(!this.containerNodes.has(c))continue;let l=n[c];l==null&&(l=0);let p=this.outputLayersTensorIndices[o];a.push([i.name,l,p])}return t.outputLayers=a,t}static fromConfig(t,n,r={},s=!1){let a={},o={};function i(m,b){m.name in o?o[m.name].push(b):o[m.name]=[b]}function u(m,b){let y=[],v;for(let x of b){let k=x[0],S=x[1],N=x[2];if(v=x[3]==null?{}:x[3],!(k in a)){i(m,b);return}let E=a[k];if(E.inboundNodes.length<=S){i(m,b);return}let $=E.inboundNodes[S];y.push($.outputTensors[N])}y.length>0&&m.apply(Rn(y),v)}function c(m){let b=m.name,y=Vr(m,n.customObjects!=null?n.customObjects:{});y.setFastWeightInitDuringBuild(s),a[b]=y,m.inboundNodes.forEach(x=>{if(!(x instanceof Array))throw new V(`Corrupted configuration, expected array for nodeData: ${x}`);i(y,x)})}let l=n.name,p=n.layers;for(let m of p)c(m);for(;!_G(o);)for(let m of p){let b=a[m.name];if(b.name in o){let y=o[b.name];delete o[b.name];for(let v of y)u(b,v)}}let d=[],h=[],f=n.inputLayers;for(let m of f){let b=m[0],y=m[1],v=m[2];ss(b in a);let k=a[b].inboundNodes[y].outputTensors;d.push(k[v])}let g=n.outputLayers;for(let m of g){let b=m[0],y=m[1],v=m[2];ss(b in a);let k=a[b].inboundNodes[y].outputTensors;h.push(k[v])}return new t({inputs:d,outputs:h,name:l})}get stateful(){if(this._stateful)throw new V("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 t of this.layers)if(t.stateful)return!0;return!1}resetStates(){O(()=>{this.layers.forEach(t=>{t.stateful&&t.resetStates()})})}};function s6(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let s=[];return t.forEach(a=>{a in e?s.push(e[a]):s.push(null)}),s}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function d2(e,t){return s6(e,t,"classWeight")}async function p2(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let s=O(()=>{if(e.shape.length===1)return us(e);if(e.shape.length===2){if(e.shape[1]>1)return mo(e,1);if(e.shape[1]===1)return W(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await s.data());_e(s);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);for(let u=0;u<o.length;u++)w.assert(o[u].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${o[u].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function XS(e,t,n){if(n instanceof Ne)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let s of t){if(n[s]==null)throw new V(`The feature data generated by the dataset lacks the required ${e} key '${s}'.`);r.push(n[s])}return r}}function i6(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 u6(e,t,n){let r=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let s=n.validationData!=null,a,o;if(s)if(YS(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let m=i6(n.validationData);a=m.xs,o=m.ys}let i=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),c;s?c=u.slice().concat(u.map(m=>"val_"+m)):c=u.slice();let l=r2(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=s2(l,p,n.epochs,null,null,c6(t,n),null,s,c);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,g=await t.iterator();for(;f<n.epochs;){let m={};await d.onEpochBegin(f);let b=0,y=0;for(r||(g=await t.iterator());!r||b<n.batchesPerEpoch;){let v=await g.next();if(r&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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$=S[N][0],F=S[N][1],D=io(k,$,F-$);E.batch=N,E.size=F-$;let R=Qv(t,D),C=e(R);for(let L=0;L<n.length;++L){let U=n[L],H=C[L];E[U]=H,Ht(H)}if(N===S.length-1&&f){let L=this.testLoop(i,u,r);for(let U=0;U<n.length;++U){let H=n[U],K=L[U];Ht(K),x["val_"+H]=K}}}),await b.onBatchEnd(N,E),QN(E),this.stopTraining_)break}k.dispose()}if(await b.onEpochEnd(v,x),this.stopTraining_)break}return await b.onTrainEnd(),await this.history.syncData(),this.history}async fitDataset(e,t){return u6(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],s=n[1],o=this.makeTrainFunction()(r.concat(s)),i=[];for(let u of o){let c=await u.data();i.push(c[0])}return _e(o),Lr(n[0],e),Lr(n[1],t),Rn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,s=this.getWeights(n);for(let a=0;a<r.length;++a)n&&!r[a].trainable||t.push({name:r[a].originalName,tensor:s[a]});return t}set 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=lf().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-lf().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ss(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=>Ss(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=Ss(n[r]);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[Ss(Lh(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ss(Lh(e)));{let e={};for(let t in this.metrics)e[t]=Ss(Lh(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=Ed(e.optimizer_config),n=Vr(t),r;if(typeof e.loss=="string")r=eo(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(a=>eo(a));else if(e.loss!=null){r={};for(let a in e.loss)r[a]=eo(e.loss[a])}let s;if(Array.isArray(e.metrics))s=e.metrics.map(a=>eo(a));else if(e.metrics!=null){s={};for(let a in e.metrics)s[a]=eo(e.metrics[a])}this.compile({loss:r,metrics:s,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=jt.getSaveHandlers(e);if(u.length===0)throw new V(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new V(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new V("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await jt.encodeWeights(this.getNamedWeights(t)),o={modelTopology:this.toJSON(null,!1),format:g6,generatedBy:`TensorFlow.js tfjs-layers v${_I}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:c,specs:l}=await jt.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...l),n.data=jt.concatenateArrayBuffers([n.data,c])}return this.userDefinedMetadata!=null&&(KS(this.userDefinedMetadata,this.name,!0),o.userDefinedMetadata=this.userDefinedMetadata),o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){KS(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Es.className="Model";re.registerClass(Es);var m2=class extends Es{};m2.className="Functional";re.registerClass(m2);async function b6(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Ed(n),s=Vr(r,t);if(e.weightsManifest!=null){let a=await jt.loadWeights(e.weightsManifest,e.pathPrefix,s.weights.map(i=>i.originalName)),o={};for(let i of s.weights)o[i.originalName]=a[i.originalName];s.loadWeights(o),_e(a)}return s}async function y6(e,t){if(t==null&&(t={}),typeof e=="string"){let n=jt.getLoadHandlers(e,t);if(n.length===0)n.push(jt.browserHTTPRequest(e,t));else if(n.length>1)throw new V(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return v6(e,void 0,t)}async function v6(e,t,n){if(n==null&&(n={}),e.load==null)throw new V("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),s=r.modelTopology;s.model_config!=null&&(s=s.model_config);let a=n.strict==null?!0:n.strict,o=r.weightData!=null&&r.weightSpecs!=null&&a,i=Vr(Ed(s),t,o),u=r.trainingConfig;if(u!=null&&i.loadTrainingConfig(u),r.userDefinedMetadata!=null&&i.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new V("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:l}=x6(r.weightData,r.weightSpecs);i.loadWeights(c,a),i.optimizer!=null&&l.length>0&&await i.optimizer.setWeights(l),_e(c),_e(l.map(p=>p.tensor))}return i}function x6(e,t){let n=jt.decodeWeights(e,t),r={},s=[];return t.forEach(a=>{a.group==="optimizer"?s.push({name:a.name,tensor:n[a.name]}):r[a.name]=n[a.name]}),{modelWeights:r,optimizerWeights:s}}var Vm=class tx extends Es{constructor(t){if(super({inputs:[],outputs:[]}),t=t||{},this.trainable=!0,this.built=!1,this.name=t.name!=null?t.name:Am("sequential_"),t.layers!=null)for(let n of t.layers)this.add(n)}checkShape(t){if(t.inboundNodes[0].outputTensors[0].shape.some(r=>r<0))throw new V(`Negative dimension size caused by adding layer ${t.name} with input shape [${t.inboundNodes[0].inputTensors[0].shape}]`)}add(t){let n=t instanceof tx||t instanceof Es,r;if(n){if(r=t,r.outputs.length!==1)throw new V("All layers in a Sequential model should have a single output tensor. 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compiled before being used.");return this.model.evaluate(t,n,r)}async evaluateDataset(t,n){if(!this.built)throw new os("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,n)}predict(t,n={}){return this.model==null&&this.build(),this.model.predict(t,n)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),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(t){this.model.optimizer=t}async fit(t,n,r={}){if(!this.built)throw new os("The model needs to be compiled before being used.");return this.model.fit(t,n,r)}async fitDataset(t,n){if(!this.built)throw new os("The model needs to be compiled before being used.");return this.model.fitDataset(t,n)}async trainOnBatch(t,n){return this.model.trainOnBatch(t,n)}static fromConfig(t,n,r={},s=!1){let a,o={};if(n instanceof Array){if(n[0].className==null||n[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");a=n}else w.assert(n.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=n.layers,delete n.layers,o=n;let i=new t(o);if(!(i instanceof tx))throw new Be(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let u of a){let l=Vr(u,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(t){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};PI.className="ThresholdedReLU";re.registerClass(PI);var OI=class extends ze{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new EI().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){return O(()=>{let n=Te(e),r=t.mask;if(r!=null){let s=z(le(On(n.shape),ae(r,n.dtype)),xe(-1e9));n=X(n,s)}return this.axis instanceof Array?this.axis.length>1?fn(le(n,bp(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};OI.className="Softmax";re.registerClass(OI);function Gu(e,t,n){if(typeof e=="number")return wo(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let s=e[r];if(!BG(s))throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${s}`)}return e}function Ur(e,t,n,r,s=1){if(e==null)return e;let a=t+(t-1)*(s-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+r-1)/r)}function as(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+ba([n-t,0]);else if(r==="same")e=e*t;else throw new V(`Unsupport padding mode: ${r}.`);return e}function MI(e,t){return O(()=>(Pt(t),t==="channelsFirst"?Re(e,[0,2,3,1]):e))}function D2(e,t){return O(()=>(Pt(t),t==="channelsFirst"?Re(e,[0,2,3,4,1]):e))}function T6(e,t,n,r=1,s="valid",a,o=1){return O(()=>{if(a==null&&(a=Hr()),Pt(a),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Re(e,[0,2,1])),s==="causal")throw new Be("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=tm(e,t,r,s==="same"?"same":"valid","NWC",o);return n!=null&&(i=Xr(i,n)),i})}function n1(e,t,n,r=[1,1],s="valid",a,o,i=null){return O(()=>{if(a==null&&(a=Hr()),Pt(a),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=MI(e,a);if(s==="causal")throw new Be("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=tc.conv2d({x:u,filter:t,strides:r,pad:s==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(u=Re(u,[0,3,1,2])),u})}function N6(e,t,n,r=[1,1,1],s="valid",a,o){return O(()=>{if(a==null&&(a=Hr()),Pt(a),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=D2(e,a);if(s==="causal")throw new Be("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=lw(i,t,r,s==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Xr(i,n)),a==="channelsFirst"&&(i=Re(i,[0,4,1,2,3])),i})}var $2=class F2 extends ze{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",F2.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Be(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gu(n.kernelSize,t,"kernelSize"),this.strides=Gu(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,vr(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Pt(this.dataFormat),this.activation=va(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Ct(n.biasRegularizer),this.activityRegularizer=Ct(n.activityRegularizer),this.dilationRate=Gu(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(ss("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!uI(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ya(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},Um=class R2 extends $2{constructor(t,n){super(t,n),this.kernel=null,R2.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Ct(n.kernelRegularizer)}build(t){t=Qe(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${t[n]}`);let r=t[n],s=this.kernelSize.concat([r,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:{[n]:r}}],this.built=!0}call(t,n){return O(()=>{t=Te(t);let r,s=this.bias==null?null:this.bias.read(),a=LN(this.activation.getClassName());if(a!=null&&this.rank===2)r=n1(t,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=T6(t,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=n1(t,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=N6(t,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&&(r=this.activation.apply(r))}return r})}computeOutputShape(t){t=Qe(t);let n=[],r=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let a=0;a<r.length;++a){let o=Ur(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);n.push(o)}let s=[t[0]];return this.dataFormat==="channelsLast"?(s=s.concat(n),s.push(this.filters)):(s.push(this.filters),s=s.concat(n)),s}getConfig(){let t={filters:this.filters,kernelInitializer:Et(this.kernelInitializer),kernelRegularizer:mt(this.kernelRegularizer),kernelConstraint:Xt(this.kernelConstraint)},n=super.getConfig();return Object.assign(t,n),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new V(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}},Gm=class P2 extends Um{constructor(t){super(2,t),P2.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!uI(t.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Gm.className="Conv2D";re.registerClass(Gm);var Hm=class O2 extends Um{constructor(t){super(3,t),O2.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hm.className="Conv3D";re.registerClass(Hm);var LI=class extends Gm{constructor(e){if(super(e),this.inputSpec=[new Bt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=r[a],u=r[o],c=this.kernelSize[0],l=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=as(i,p,c,this.padding),f=as(u,d,l,this.padding),g=[s,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Re(n,[0,2,3,1]));let m=nm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(m=Re(m,[0,3,1,2])),this.bias!=null&&(m=Xr(m,this.bias.read(),this.dataFormat)),this.activation!=null&&(m=this.activation.apply(m)),m})}computeOutputShape(e){e=Qe(e);let t=e.slice(),n,r,s;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3):(n=3,r=1,s=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[r]=as(t[r],i,a,this.padding),t[s]=as(t[s],u,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};LI.className="Conv2DTranspose";re.registerClass(LI);var BI=class extends Hm{constructor(e){if(super(e),this.inputSpec=[new Bt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let u=r[i],c=r[a],l=r[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],g=this.strides[1],m=this.strides[2],b=as(u,f,p,this.padding),y=as(c,g,d,this.padding),v=as(l,m,h,this.padding),x=[s,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Re(n,[0,2,3,4,1]));let k=dw(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Re(k,[0,4,1,2,3])),this.bias!==null&&(k=Xr(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=Qe(e);let t=e.slice(),n,r,s,a;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3,a=4):(n=4,r=1,s=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],u=this.kernelSize[2],c=this.strides[0],l=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[r]=as(t[r],c,o,this.padding),t[s]=as(t[s],l,i,this.padding),t[a]=as(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};BI.className="Conv3DTranspose";re.registerClass(BI);var M2=class extends Um{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("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 V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ct(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ct(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Qe(e),e.length<this.rank+2)throw new V(`Inputs to SeparableConv${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 V(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let o=0;o<this.rank;++o)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Bt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{e=Te(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=Re(e,[0,2,3,1])),n=Fa(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Xr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Re(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};M2.className="SeparableConv";var zI=class extends M2{constructor(e){super(2,e)}};zI.className="SeparableConv2D";re.registerClass(zI);var WI=class L2 extends Um{constructor(t){super(1,t),L2.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!uI(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};WI.className="Conv1D";re.registerClass(WI);var VI=class extends ze{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Mh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Mh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Mh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Mh(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}};VI.className="Cropping2D";re.registerClass(VI);var UI=class extends ze{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,OG(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=Te(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Re(n,[0,2,3,1]);let s=this.size[0]*r[2],a=this.size[1]*r[3],o=this.interpolation==="nearest"?er.resizeNearestNeighbor(n,[s,a]):er.resizeBilinear(n,[s,a]);return Re(o,[0,3,1,2])}else{let s=this.size[0]*r[1],a=this.size[1]*r[2];return this.interpolation==="nearest"?er.resizeNearestNeighbor(n,[s,a]):er.resizeBilinear(n,[s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};UI.className="UpSampling2D";re.registerClass(UI);function _6(e,t,n=[1,1],r="valid",s,a){return O(()=>{s==null&&(s=Hr()),Pt(s);let o=MI(e,s);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Aa(o,t,n,r==="same"?"same":"valid","NHWC",a),s==="channelsFirst"&&(o=Re(o,[0,3,1,2])),o})}var GI=class extends $2{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Ct(e.depthwiseRegularizer)}build(e){if(e=Qe(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{e=Te(e);let n=_6(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Xr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=Ur(t,this.kernelSize[0],this.padding,this.strides[0]),a=Ur(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,s,a]:[e[0],s,a,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Et(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};GI.className="DepthwiseConv2D";re.registerClass(GI);function B2(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function s(a){return a==null||Array.isArray(a)?a:[a]}return t=s(t),n=s(n),{inputs:e,initialState:t,constants:n}}function z2(e,t,n,r=!1,s,a,o=!1,i=!1){return O(()=>{let u=t.shape.length;if(u<3)throw new V(`Input should be at least 3D, but is ${u}D.`);let c=[1,0].concat(Gr(2,u));if(t=Re(t,c),a!=null)throw new Be("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=ae(ae(s,"bool"),"float32"),s.rank===u-1&&(s=Gt(s,-1)),s=Re(s,c)),r&&(t=gr(t,0),s!=null&&(s=gr(s,0)));let l=[],p,d=n,h=t.shape[0],f=pt(t),g;s!=null&&(g=pt(s));for(let b=0;b<h;++b){let y=f[b],v=O(()=>e(y,d));if(s==null)p=v[0],d=v[1];else{let x=O(()=>{let k=g[b],S=le(rr(k),k),N=X(z(v[0],k),z(d[0],S)),E=d.map(($,F)=>X(z(v[1][F],k),z($,S)));return{output:N,newStates:E}});p=x.output,d=x.newStates}i&&l.push(p)}let m;return i&&(m=Dt(l,1)),[p,m,d]})}var Ms=class W2 extends ze{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Km({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Bt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Gr(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Xv(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let r=n[0],s;if(this.returnSequences?s=[t[0],t[1],r]:s=[t[0],r],this.returnState){let a=[];for(let o of n)a.push([t[0],o]);return[s].concat(a)}else return s}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let r=this.returnSequences?n:null;if(this.returnState){let s=this.states.map(a=>null);return[r].concat(s)}else return r})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let r=0;r<t;++r)n.push(null);return n}else return this.states_}set states(t){this.states_=t}build(t){if(this.numConstants!=null)throw new Be("Constants support is not implemented in RNN yet.");Xv(t)&&(t=t[0]),t=t;let r=this.stateful?t[0]:null,s=t.slice(2);this.inputSpec[0]=new Bt({shape:[r,null,...s]});let a=[t[0]].concat(t.slice(2));this.cell.build(a);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),o))throw new V(`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=o.map(i=>new Bt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new ea("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new V("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=>kt([r,s])):this.states_=[kt([r,this.cell.stateSize])];else if(t==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>kt([r,s])):this.states_[0]=kt([r,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let a=t[s],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[r,o];if(!w.arraysEqual(a.shape,i))throw new V(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[s]=a}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(t,n){let r=n==null?null:n.initialState,s=n==null?null:n.constants;n==null&&(n={});let a=B2(t,r,s,this.numConstants);t=a.inputs,r=a.initialState,s=a.constants;let o=[],i=[];if(r!=null){n.initialState=r,o=o.concat(r),this.stateSpec=[];for(let c of r)this.stateSpec.push(new Bt({shape:c.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(n.constants=s,o=o.concat(s),this.numConstants=s.length),o[0]instanceof jr){let c=[t].concat(o),l=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=l;let d=super.apply(c,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let r=n==null?null:n.mask,s=n==null?null:n.training,a=n==null?null:n.initialState;t=Te(t),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(t));let o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==o)throw new V(`RNN Layer has ${o} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},c=z2((f,g)=>{let m=this.cell.call([f].concat(g),i);return[m[0],m.slice(1)]},t,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=c[0],p=c[1],d=c[2];this.stateful&&this.resetStates(d,s);let h=this.returnSequences?p:l;return this.returnState?[h].concat(d):h})}getInitialState(t){return O(()=>{let n=kt(t.shape);return n=ge(n,[1,2]),n=Np(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?qv(n,[1,r]):n):this.cell.stateSize>1?[qv(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===W2.className&&(n.cell={className:this.cell.getClassName(),config:r}),Object.assign(Object.assign(Object.assign({},r),t),n)}static fromConfig(t,n,r={}){let s=n.cell,a=Vr(s,r);return new t(Object.assign(n,{cell:a}))}};Ms.className="RNN";re.registerClass(Ms);var $p=class extends ze{},jm=class extends $p{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,tn(this.units,"units"),this.activation=va(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nc([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,ba([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xa({ones:()=>rr(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xa({ones:()=>rr(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));let s,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?s=ls(z(e,a),this.kernel.read()):s=ls(e,this.kernel.read()),this.bias!=null&&(s=Xr(s,this.bias.read())),o!=null&&(n=z(n,o));let i=X(s,ls(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:ya(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};jm.className="SimpleRNNCell";re.registerClass(jm);var HI=class extends Ms{constructor(e){e.cell=new jm(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return new e(t)}};HI.className="SimpleRNN";re.registerClass(HI);var qm=class extends $p{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=va(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=va(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nc([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,ba([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=Qe(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xa({ones:()=>rr(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xa({ones:()=>rr(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,a=this.recurrentDropoutMask,o,i,u;0<this.dropout&&this.dropout<1&&(e=z(e,s[0]));let c=ls(e,this.kernel.read());this.useBias&&(c=Xr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=z(r,a[0]));let l=this.recurrentKernel.read(),[p,d]=Mn(l,[2*this.units,this.units],l.rank-1),h=ls(r,p),[f,g,m]=Mn(c,3,c.rank-1),[b,y]=Mn(h,2,h.rank-1);o=this.recurrentActivation.apply(X(f,b)),i=this.recurrentActivation.apply(X(g,y));let v=ls(z(i,r),d);u=this.activation.apply(X(m,v));let x=X(z(o,r),z(X(1,bt(o)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ya(this.activation),recurrentActivation:ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},e),t)}};qm.className="GRUCell";re.registerClass(qm);var jI=class extends Ms{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 qm(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};jI.className="GRU";re.registerClass(jI);var Fp=class extends $p{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,tn(this.units,"units"),this.activation=va(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=va(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nc([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,ba([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=Qe(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;r=new(t=class extends Pr{apply(i,u){let c=s.apply([a]),l=new $m().apply([a]),p=s.apply([a*2]);return BS(BS(c,l),p)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xa({ones:()=>rr(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xa({ones:()=>rr(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,u,c,l;0<this.dropout&&this.dropout<1&&(e=z(e,a[0]));let p=ls(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=z(r,o[0])),p=X(p,ls(r,this.recurrentKernel.read())),this.useBias&&(p=Xr(p,this.bias.read()));let[d,h,f,g]=Mn(p,4,p.rank-1);i=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),c=X(z(u,s),z(i,this.activation.apply(f))),l=this.recurrentActivation.apply(g);let m=z(l,this.activation.apply(c));return[m,m,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ya(this.activation),recurrentActivation:ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:mt(this.kernelRegularizer),recurrentRegularizer:mt(this.recurrentRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},e),t)}};Fp.className="LSTMCell";re.registerClass(Fp);var qI=class extends Ms{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 Fp(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};qI.className="LSTM";re.registerClass(qI);var Km=class extends $p{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),r=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?r.push(n.splice(0,o.stateSize.length)):r.push(n.splice(0,1));r.reverse();let s=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=r[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),s.push(a.slice(1))}n=[];for(let o of s.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){Xv(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{oo(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),r={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),r)}static fromConfig(e,t,n={}){let r=[];for(let s of t.cells)r.push(Vr(s,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Yv(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,s=e.splice(r);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}bI(t)}};Km.className="StackedRNNCells";re.registerClass(Km);function xa(e){let{ones:t,rate:n,training:r=!1,count:s=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):HN(t(),n),i=()=>Ep(o,t,r);return!s||s<=1?Ht(i().clone()):Array(s).fill(void 0).map(i).map(c=>Ht(c.clone()))}var E6=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,r=Object.getOwnPropertySymbols(e);s<r.length;s++)t.indexOf(r[s])<0&&Object.prototype.propertyIsEnumerable.call(e,r[s])&&(n[r[s]]=e[r[s]]);return n},V2=class extends Ms{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 Bt({ndim:5})]}call(e,t){return O(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return O(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)],a=kt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new ea("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>kt(s)):this.states_=[kt(s)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>kt(s)):this.states_[0]=kt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`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()):_e(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],u=s;if(!w.arraysEqual(i.shape,u))throw new V(`State ${o} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>Ht(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",u=e[i?3:2],c=e[i?4:3],l=Ur(u,r[0],s,a[0],o[0]),p=Ur(c,r[1],s,a[1],o[1]);return[...e.slice(0,2),...i?[n,l,p]:[l,p,n]]}};V2.className="ConvRNN2D";var Xm=class extends Fp{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:a,dilationRate:o}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,tn(this.filters,"filters"),this.kernelSize=Gu(n,2,"kernelSize"),this.kernelSize.forEach(i=>tn(i,"kernelSize")),this.strides=Gu(r||1,2,"strides"),this.strides.forEach(i=>tn(i,"strides")),this.padding=s||"valid",vr(this.padding),this.dataFormat=a||"channelsLast",Pt(this.dataFormat),this.dilationRate=Gu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>tn(i,"dilationRate"))}build(e){var t;e=Qe(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],s=4,a=this.kernelSize.concat([r,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;i=new(t=class extends Pr{apply(p,d){let h=u.apply([c]),f=On([c]),g=u.apply([c*2]);return cI([h,f,g])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],s=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xa({ones:()=>rr(r),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,u=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),c=u(r,i,0),l=u(r,i,1),p=u(r,i,2),d=u(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xa({ones:()=>rr(s),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(s,h,0),g=u(s,h,1),m=u(s,h,2),b=u(s,h,3),y=3,[v,x,k,S]=Mn(this.kernel.read(),o,y),[N,E,$,F]=this.useBias?Mn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,v,N,this.padding),l=this.inputConv(l,x,E,this.padding),p=this.inputConv(p,k,$,this.padding),d=this.inputConv(d,S,F,this.padding);let[D,R,C,L]=Mn(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,D),g=this.recurrentConv(g,R),m=this.recurrentConv(m,C),b=this.recurrentConv(b,L);let U=this.recurrentActivation.apply(X(c,f)),H=this.recurrentActivation.apply(X(l,g)),K=X(z(H,a),z(U,this.activation.apply(X(p,m)))),q=z(this.recurrentActivation.apply(X(d,b)),this.activation.apply(K));return[q,q,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=E6(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),r)}inputConv(e,t,n,r){let s=Ft(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Xr(s,n,this.dataFormat):s}recurrentConv(e,t){return Ft(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xm.className="ConvLSTM2DCell";re.registerClass(Xm);var KI=class extends V2{constructor(e){let t=new Xm(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};KI.className="ConvLSTM2D";re.registerClass(KI);var Ym=class extends ze{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,s=this.getNoiseShape(n);return Ep(()=>HN(n,this.rate,s,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Ym.className="Dropout";re.registerClass(Ym);var XI=class extends Ym{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};XI.className="SpatialDropout1D";re.registerClass(XI);var YI=class extends ze{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,tn(this.units,"units"),this.activation=va(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Ct(e.kernelRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.activityRegularizer=Ct(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Qe(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=Qe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),r=LN(this.activation.getClassName()),s;return r!=null?s=ls(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=ls(n,this.kernel.read()),this.bias!=null&&(s=Xr(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:ya(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:mt(this.kernelRegularizer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};YI.className="Dense";re.registerClass(YI);var ZI=class extends ze{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Qe(e);for(let t of e.slice(1))if(t==null)throw new V(`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],ca(e,1)]}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let s=2;s<n.rank;++s)r.push(s);r.push(1),n=Re(n,r)}return VG(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};ZI.className="Flatten";re.registerClass(ZI);var JI=class extends ze{constructor(e){super(e),this.supportsMasking=!0,this.activation=va(e.activation)}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e);return this.activation.apply(n)})}getConfig(){let e={activation:ya(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};JI.className="Activation";re.registerClass(JI);var QI=class extends ze{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 O(()=>(e=Te(e),zG(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};QI.className="RepeatVector";re.registerClass(QI);var ek=class extends ze{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),s=1,a=null;for(let i=0;i<r.length;++i){let u=r[i];if(this.isUnknown(u))if(a===null)a=i;else throw new V("Can only specifiy one unknown dimension.");else s*=u}let o=ca(e);if(a!==null){if(s===0||o%s!==0)throw new V(n);r[a]=o/s}else if(o!==s)throw new V(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),r=n.shape,s=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return W(n,s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};ek.className="Reshape";re.registerClass(ek);var tk=class extends ze{constructor(e){if(super(e),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=Gr(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Bt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return Re(Te(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};tk.className="Permute";re.registerClass(tk);var nk=class extends ze{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=Te(e);return Sd(vo(n,this.maskValue),-1)}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=Sd(vo(n,this.maskValue),-1,!0);return z(n,ae(a,n.dtype))})}};nk.className="Masking";re.registerClass(nk);var rk=class extends ze{constructor(e){if(super(e),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(it(e.inputLength))}this.inputDim=e.inputDim,tn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,tn(this.outputDim,"outputDim"),this.embeddingsInitializer=St(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ct(e.embeddingsRegularizer),this.activityRegularizer=Ct(e.activityRegularizer),this.embeddingsConstraint=Yt(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 O(()=>this.maskZero?(e=Te(e),vo(e,je(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=it(this.inputLength);if(t.length!==e.length-1)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let s=t[r],a=e[r+1];if(s!=null&&a!=null&&s!==a)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e);n.dtype!=="int32"&&(n=cs(n,"int32"));let r=GN(this.embeddings.read(),W(n,[n.size]));return W(r,Qe(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Et(this.embeddingsInitializer),embeddingsRegularizer:mt(this.embeddingsRegularizer),activityRegularizer:mt(this.activityRegularizer),embeddingsConstraint:Xt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};rk.className="Embedding";re.registerClass(rk);var Qi=class extends ze{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 r=0;r<t.length;++r){let s=e[e.length-t.length+r],a=t[r];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new V("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Qe(e)]),e=e,e.length<2)throw new V(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=ua(t),t.length>1)throw new V(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let r=e.map(s=>s.length);e.indexOf(null)===-1&&ua(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return O(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(s=>s.rank);if(r.indexOf(null)===-1){let s=ba(r);for(let a of e){let o=a.rank;for(let i=0;i<s-o;++i)a=Np(a,1);n.push(a)}return this.mergeFunction(n)}else{let s=!1;for(let i of e){let u=i.rank;if(u==null){let c=i.shape,l=c[0],p=c.slice(1).concat([l]),d=W(i,[l].concat(ca(c.slice(1))));d=Re(d,[1,0]),d=W(d,p),n.push(d),s=!0}else if(u>1){let c=Gr(1,u).concat([0]);n.push(Re(i,c)),s=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(s){if(o==null){let i=a.shape,u=i.length,c=i[u-1],l=[c].concat(i.slice(0,i.length-1));a=W(Re(W(a,[-1,c]),[1,0]),l)}else if(o>1){let i=[o-1].concat(Gr(0,o-1));a=Re(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=ua(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return O(()=>{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Gt(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=$r(n,t[r]);return n})}},sk=class extends Qi{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}};sk.className="Add";re.registerClass(sk);var ak=class extends Qi{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=z(t,e[n]);return t})}};ak.className="Multiply";re.registerClass(ak);var ok=class extends Qi{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return z(1/e.length,t)})}};ok.className="Average";re.registerClass(ok);var ik=class extends Qi{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ms(t,e[n]);return t})}};ik.className="Maximum";re.registerClass(ik);var uk=class extends Qi{constructor(e){super(e)}mergeFunction(e){return O(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ga(t,e[n]);return t})}};uk.className="Minimum";re.registerClass(uk);var ck=class extends Qi{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 V("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let s=e[r].slice();s.splice(this.axis,1);let a=!1;for(let o of n)if(w.arraysEqual(o,s)){a=!0;break}a||n.push(s)}if(n.length>1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ck.className="Concatenate";re.registerClass(ck);function nd(e,t){for(;e<0;)e+=t;return e}function A6(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(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.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 r=e.shape.length,s=t.shape.length;n==null&&(n=[r-1,s-2]);let a=n;return O(()=>{let o;if(r>s){o=r-s;let u=[];for(let c=0;c<o;++c)u.push(1);t=W(t,t.shape.concat(u))}else if(s>r){o=s-r;let u=[];for(let c=0;c<o;++c)u.push(1);e=W(e,e.shape.concat(u))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ge(z(e,t),a[0]):i=ge(z(Re(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,c=a[1]===t.shape.length-1;i=Fe(e,t,u,c)}if(o>0){let u;r>s?u=r+s-3:u=r-1;let c=[];for(let l=u;l<u+o;++l)c.push(l);i=Ra(i,c)}return i.shape.length===1&&(i=Gt(i,1)),i})}var lk=class extends Qi{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Be("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new V(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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ze{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e);return Ep(()=>X(Dm(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};dk.className="GaussianNoise";re.registerClass(dk);var pk=class extends ze{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e);return this.rate>0&&this.rate<1?Ep(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return z(n,Dm(n.shape,1,s))},()=>n,t.training||!1):n})}};pk.className="GaussianDropout";re.registerClass(pk);var hk=class extends ze{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Te(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return O(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Ep(()=>{let s=Te(e),i=-1.6732632423543772*1.0507009873554805,u=Rs($a(n),this.rate);u=cs(u,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,l=-c*i*this.rate,p=X(z(s,u),z(X(u,-1),i));return X(z(p,c),l)},()=>Te(e),t.training||!1)}return e})}};hk.className="AlphaDropout";re.registerClass(hk);function Ad(e,t,n,r,s,a=.001){let o;if(e.rank===2)o=ew(e,t,n,r,s,a);else if(e.rank===3)o=tw(e,t,n,r,s,a);else if(e.rank===4)o=nw(e,t,n,r,s,a);else throw new Be(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function D6(e,t,n,r,s=.001){return O(()=>{let a=vp(e,r),o=a.mean,i=a.variance;return[Ad(e,o,i,n,t,s),o,i]})}function $6(e,t,n,r,s=.001){return O(()=>{let a=vp(e,r),o=a.mean,i=a.variance,u=[];for(let f of 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Bt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training,r=Te(e),s=r.shape,a=s.length,o=Gr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let u=wo(1,a);u[i]=s[i];let c=o.slice();c.sort();let l=!w.arraysEqual(c,Gr(0,a).slice(0,a-1)),p=()=>{if(l){let b=W(this.movingMean.read(),u),y=W(this.movingVariance.read(),u),v=this.center?W(this.beta.read(),u):null,x=this.scale?W(this.gamma.read(),u):null;return Ad(r,b,y,v,x,this.epsilon)}else return Ad(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=F6(r,this.gamma.read(),this.beta.read(),o,this.epsilon),g=(b,y,v)=>{O(()=>{let x=1-v,k=b.read(),S=z(le(k,y),x);b.write(le(k,S))})};return(()=>{g(this.movingMean,h,this.momentum),g(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),movingMeanInitializer:Et(this.movingMeanInitializer),movingVarianceInitializer:Et(this.movingVarianceInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer),betaConstraint:Xt(this.betaConstraint),gammaConstraint:Xt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};fk.className="BatchNormalization";re.registerClass(fk);var mk=class extends ze{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=St(e.betaInitializer||"zeros"),this.gammaInitializer=St(e.gammaInitializer||"ones"),this.betaRegularizer=Ct(e.betaRegularizer),this.gammaRegularizer=Ct(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Qe(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==ua(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Te(e),r=n.shape,s=r.length;return O(()=>{let{mean:o,variance:i}=vp(n,this.axis,!0),u=wo(1,s);for(let f of this.axis)u[f]=r[f];let c=f=>f!=null&&f.shape.length!==s?W(f,u):f,l=this.scale?c(this.gamma.read()):null,p=this.center?c(this.beta.read()):null,d=[],h=[];for(let f=0;f<s;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),h.push(1)):(d.push(1),h.push(r[f]));return o=Pn(o,d),i=Pn(i,d),l!=null&&(l=Pn(l,h)),p!=null&&(p=Pn(p,h)),Ad(n,o,i,p,l,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),betaRegularizer:mt(this.betaRegularizer),gammaRegularizer:mt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};mk.className="LayerNormalization";re.registerClass(mk);function R6(e,t,n){return O(()=>{if(e.rank!==4)throw new V(`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 V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Hr()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],yr(e,r)})}var gk=class extends ze{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Hr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 V(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new V(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){e=Qe(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return O(()=>R6(Te(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};gk.className="ZeroPadding2D";re.registerClass(gk);function Zm(e,t,n,r,s,a){return O(()=>{Pt(s),zN(a),vr(r),n==null&&(n=[1,1]),r==null&&(r="valid"),s==null&&(s=Hr()),a==null&&(a="max"),e=MI(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=Rt(e,t,n,i):o=br(e,t,n,i),s==="channelsFirst"&&(o=Re(o,[0,3,1,2])),o})}function U2(e,t,n,r,s,a){return O(()=>{Pt(s),zN(a),vr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),s==null&&(s=Hr()),a==null&&(a="max"),e=D2(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=Nw(e,t,n,i):o=Qx(e,t,n,i),s==="channelsFirst"&&(o=Re(o,[0,4,1,2,3])),o})}var G2=class extends ze{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(tn(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 V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,vr(this.padding),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=Ur(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return O(()=>{this.invokeCallHook(e,t),e=Np(Te(e),2);let n=this.poolingFunction(Te(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ra(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},bk=class extends G2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),Zm(e,t,n,r,s,"max")}};bk.className="MaxPooling1D";re.registerClass(bk);var yk=class extends G2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),Zm(e,t,n,r,s,"avg")}};yk.className="AveragePooling1D";re.registerClass(yk);var H2=class extends ze{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`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];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),vr(this.padding),this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ur(t,this.poolSize[0],this.padding,this.strides[0]),n=Ur(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},vk=class extends H2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),Zm(e,t,n,r,s,"max")}};vk.className="MaxPooling2D";re.registerClass(vk);var xk=class extends H2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),Zm(e,t,n,r,s,"avg")}};xk.className="AveragePooling2D";re.registerClass(xk);var j2=class extends ze{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`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];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),vr(this.padding),this.inputSpec=[new Bt({ndim:5})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ur(t,this.poolSize[0],this.padding,this.strides[0]),n=Ur(n,this.poolSize[1],this.padding,this.strides[1]),r=Ur(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},wk=class extends j2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),U2(e,t,n,r,s,"max")}};wk.className="MaxPooling3D";re.registerClass(wk);var Ik=class extends j2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),U2(e,t,n,r,s,"avg")}};Ik.className="AveragePooling3D";re.registerClass(Ik);var q2=class extends ze{constructor(e){super(e),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Be}},kk=class extends q2{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=Te(e);return _t(n,1)})}};kk.className="GlobalAveragePooling1D";re.registerClass(kk);var Sk=class extends q2{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=Te(e);return hr(n,1)})}};Sk.className="GlobalMaxPooling1D";re.registerClass(Sk);var K2=class extends ze{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),this.inputSpec=[new Bt({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}},Ck=class extends K2{call(e,t){return O(()=>{let n=Te(e);return this.dataFormat==="channelsLast"?_t(n,[1,2]):_t(n,[2,3])})}};Ck.className="GlobalAveragePooling2D";re.registerClass(Ck);var Tk=class extends K2{call(e,t){return O(()=>{let n=Te(e);return this.dataFormat==="channelsLast"?hr(n,[1,2]):hr(n,[2,3])})}};Tk.className="GlobalMaxPooling2D";re.registerClass(Tk);var X2=class extends ze{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,s=Vr(r,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},Nk=class extends X2{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=Qe(e),e.length<3)throw new V(`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=Qe(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return O(()=>(e=Te(e),z2((a,o)=>[Te(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Nk.className="TimeDistributed";re.registerClass(Nk);function P6(e){Zi(PG,"BidirectionalMergeMode",e)}var O6="concat",_k=class extends X2{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Vr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Vr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?O6:e.mergeMode,P6(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,r,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Rn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=B2(e,n,r,this.numConstants);if(e=s.inputs,n=s.initialState,r=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let u=n.length;if(u%2>0)throw new V("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(l=>new Bt({shape:l.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),o.push(...c)}if(r!=null)throw new Be("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof jr;for(let u of a)if(u instanceof jr!==i)throw new V("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let u=[e].concat(a),c=this.inputSpec.concat(o),l=this.inputSpec;this.inputSpec=c;let p=super.apply(u,t);return this.inputSpec=l,p}else return super.apply(e,t)}call(e,t){return O(()=>{let n=t.initialState,r,s;if(n==null)r=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),u=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(r)&&(a=r.slice(1).concat(s.slice(1))),r=r[0],s=s[0]),this.returnSequences&&(s=gr(s,1));let 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r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=I(a,e,t,n),i=e.op==="TensorListReserve"?-1:o,u=Z5(r,s,o,i);return n.addTensorList(u),[u.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),s=I("indices",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(s,o,a)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),s=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(s,a,o)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),s=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=Y5(r,s,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let r=I("tensorListId",e,t,n),s=n.getTensorList(r.id),a=I("dtype",e,t,n),o=I("elementShape",e,t,n);return[s.concat(a,o)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),s=I("tensor",e,t,n),a=n.getTensorList(r.id);return 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s=I("strides",e,t,n),a=I("pad",e,t,n),o=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:u,indexes:c}=r.maxPoolWithArgmax(I("x",e,t,n),[o[1],o[2]],[s[1],s[2]],a,i);return[u,c]}case"AvgPool3D":{let s=I("strides",e,t,n),a=I("pad",e,t,n),o=I("kernelSize",e,t,n);return[r.avgPool3d(I("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],a)]}case"MaxPool3D":{let s=I("strides",e,t,n),a=I("pad",e,t,n),o=I("kernelSize",e,t,n);return[r.maxPool3d(I("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],a)]}case"Dilation2D":{let s=I("strides",e,t,n),a=I("pad",e,t,n),o=I("dilations",e,t,n),i=s[1],u=s[2],c=o[1],l=o[2];return[r.dilation2d(I("x",e,t,n),I("filter",e,t,n),[i,u],a,[c,l],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},nq=(e,t,n,r=an)=>{switch(e.op){case"Fill":{let s=I("shape",e,t,n),a=I("dtype",e,t,n),o=I("value",e,t,n);return[r.fill(s,o,a)]}case"LinSpace":{let s=I("start",e,t,n),a=I("stop",e,t,n),o=I("num",e,t,n);return[r.linspace(s,a,o)]}case"Multinomial":{let s=I("logits",e,t,n),a=I("numSamples",e,t,n),o=I("seed",e,t,n);return[r.multinomial(s,a,o)]}case"OneHot":{let s=I("indices",e,t,n),a=I("depth",e,t,n),o=I("onValue",e,t,n),i=I("offValue",e,t,n),u=I("dtype",e,t,n);return[r.oneHot(s,a,o,i,u)]}case"Ones":return[r.ones(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[r.onesLike(I("x",e,t,n))];case"RandomStandardNormal":return[r.randomStandardNormal(I("shape",e,t,n),I("dtype",e,t,n),I("seed",e,t,n))];case"RandomUniform":return[r.randomUniform(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"RandomUniformInt":return[r.randomUniformInt(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("seed",e,t,n))];case"Range":{let s=I("start",e,t,n),a=I("stop",e,t,n),o=I("step",e,t,n);return[r.range(s,a,o,I("dtype",e,t,n))]}case"TruncatedNormal":{let 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r=[];for(let s=0;s<n.length;s++){let a=n[s],o=this.findWithDefault(a,t);r.push(o)}return Dt(r)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},iq=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let s=r.getHashTableHandleByName(e.name);if(s!=null)return[s];{let a=I("keyDType",e,t,n),o=I("valueDType",e,t,n),i=new oq(a,o);return r.addHashTable(e.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let s=I("tableHandle",e,t,n,r),a=I("keys",e,t,n),o=I("values",e,t,n);return[await r.getHashTableById(s.id).import(a,o)]}case"LookupTableFind":case"LookupTableFindV2":{let s=I("tableHandle",e,t,n,r),a=I("keys",e,t,n),o=I("defaultValue",e,t,n);return[await 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implemented`)}},pq=(e,t,n,r=an)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:s,outputDenseValues:a}=r.raggedGather(I("paramsNestedSplits",e,t,n),I("paramsDenseValues",e,t,n),I("indices",e,t,n),I("outputRaggedRank",e,t,n));return s.concat(a)}case"RaggedRange":{let{rtNestedSplits:s,rtDenseValues:a}=r.raggedRange(I("starts",e,t,n),I("limits",e,t,n),I("splits",e,t,n));return[s,a]}case"RaggedTensorToTensor":return[r.raggedTensorToTensor(I("shape",e,t,n),I("values",e,t,n),I("defaultValue",e,t,n),I("rowPartitionTensors",e,t,n),I("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hq=(e,t,n,r=an)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.max(I("x",e,t,n),i,u)]}case"Mean":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.mean(I("x",e,t,n),i,u)]}case"Min":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.min(I("x",e,t,n),i,u)]}case"Sum":{let 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implemented`)}},fq=(e,t,n,r=an)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),a=I("axis",e,t,n),o=I("tensors",e,t,n);return o=o.slice(0,s),[r.concat(o,a)]}case"Gather":{let s=I("x",e,t,n),a=I("indices",e,t,n);return[r.gather(s,r.cast(a,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),a=I("batchDims",e,t,n),o=I("x",e,t,n),i=I("indices",e,t,n);return[r.gather(o,r.cast(i,"int32"),s,a)]}case"Reverse":{let s=I("dims",e,t,n),a=[];for(let i=0;i<s.length;i++)s[i]&&a.push(i);let o=I("x",e,t,n);return[r.reverse(o,a)]}case"ReverseV2":{let s=I("axis",e,t,n),a=I("x",e,t,n);return[r.reverse(a,s)]}case"Slice":{let s=I("begin",e,t,n),a=I("size",e,t,n);return[r.slice(I("x",e,t,n),s,a)]}case"StridedSlice":{let s=I("begin",e,t,n),a=I("end",e,t,n),o=I("strides",e,t,n),i=I("beginMask",e,t,n),u=I("endMask",e,t,n),c=I("ellipsisMask",e,t,n),l=I("newAxisMask",e,t,n),p=I("shrinkAxisMask",e,t,n),d=I("x",e,t,n);return[r.stridedSlice(d,s,a,o,i,u,c,l,p)]}case"Pack":return O(()=>{let s=I("axis",e,t,n),a=I("tensors",e,t,n),o=a[0].shape,i=r.squeeze(a[0]).shape,u=a.map(c=>{let l=w.arraysEqual(c.shape,o);if(!l&&!w.arraysEqual(r.squeeze(c).shape,i))throw new Error("the input tensors shape does not match");return l?c:r.reshape(c,o)});return[r.stack(u,s)]});case"Unpack":{let s=I("axis",e,t,n),a=I("tensor",e,t,n);return r.unstack(a,s)}case"Tile":{let s=I("reps",e,t,n);return[r.tile(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),a=I("numOrSizeSplits",e,t,n),o=I("x",e,t,n);return r.split(o,a,s)}case"ScatterNd":{let s=I("indices",e,t,n),a=I("values",e,t,n),o=I("shape",e,t,n);return[r.scatterND(s,a,o)]}case"GatherNd":{let s=I("x",e,t,n),a=I("indices",e,t,n);return[r.gatherND(s,a)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),a=I("outputShape",e,t,n),o=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[r.sparseToDense(s,o,a,o.dtype===i.dtype?i:r.cast(i,o.dtype))]}case"TensorScatterUpdate":{let s=I("indices",e,t,n),a=I("values",e,t,n),o=I("tensor",e,t,n);return[r.tensorScatterUpdate(o,s,a)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},mq=(e,t,n,r=an)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:a,emptyRowIndicator:o,reverseIndexMap:i}=r.sparse.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,a,o,i]}case"SparseReshape":{let{outputIndices:s,outputShape:a}=r.sparse.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,a]}case"SparseSegmentMean":return[r.sparse.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[r.sparse.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},gq=(e,t,n,r=an)=>{switch(e.op){case"FFT":return[r.fft(I("x",e,t,n))];case"IFFT":return[r.ifft(I("x",e,t,n))];case"RFFT":return[r.rfft(I("x",e,t,n))];case"IRFFT":return[r.irfft(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bq=(e,t,n,r=an)=>{switch(e.op){case"StaticRegexReplace":return[r.string.staticRegexReplace(I("input",e,t,n),I("pattern",e,t,n),I("rewrite",e,t,n),I("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:s,nGramsSplits:a}=r.string.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,a]}case"StringSplit":{let{indices:s,values:a,shape:o}=r.string.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,a,o]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},yq=(e,t,n,r=an)=>{switch(e.op){case"Cast":return[r.cast(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[r.expandDims(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[r.squeeze(I("x",e,t,n),s)]}case"Reshape":return[r.reshape(I("x",e,t,n),I("shape",e,t,n))];case"EnsureShape":return[r.ensureShape(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[r.mirrorPad(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[r.pad(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),a=I("paddings",e,t,n);return[r.spaceToBatchND(I("x",e,t,n),s,a)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),a=I("crops",e,t,n);return[r.batchToSpaceND(I("x",e,t,n),s,a)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),a=I("dataFormat",e,t,n).toUpperCase();return[r.depthToSpace(I("x",e,t,n),s,a)]}case"BroadcastTo":return[r.broadcastTo(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[r.broadcastArgs(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function p1(e,t,n,r,s=O){let a=((o,i,u)=>{switch(o.category){case"arithmetic":return s(()=>q5(o,i,u));case"basic_math":return s(()=>K5(o,i,u));case"control":return eq(o,i,u);case"convolution":return s(()=>tq(o,i,u));case"creation":return s(()=>nq(o,i,u));case"dynamic":return rq(o,i,u);case"evaluation":return s(()=>sq(o,i,u));case"image":return s(()=>uq(o,i,u));case"graph":return s(()=>aq(o,i,u));case"logical":return s(()=>cq(o,i,u));case"matrices":return s(()=>lq(o,i,u));case"normalization":return s(()=>dq(o,i,u));case"ragged":return s(()=>pq(o,i,u));case"reduction":return s(()=>hq(o,i,u));case"slice_join":return s(()=>fq(o,i,u));case"sparse":return s(()=>mq(o,i,u));case"spectral":return s(()=>gq(o,i,u));case"string":return s(()=>bq(o,i,u));case"transformation":return s(()=>yq(o,i,u));case"hash_table":return iq(o,i,u,r);case"custom":let c=i_(o.op);if(c&&c.customExecutor)return c.customExecutor(new j5(o,i,u));throw TypeError(`Custom op ${o.op} is not registered.`);default:throw TypeError(`Unknown op '${o.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(a)?a.then(o=>[].concat(o)):[].concat(a)}var h1=class{constructor(e={},t={},n={},r={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.parseNodeNameCache=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 f1(e,t,n,r){let s=new Set,a=[],o=null,i=null,u=new Set,c=new Set(Object.keys(e).map(d=>Jn(d)[0]));r=r||[];let l=new Set(r.map(d=>Jn(d.name)[0])),p=[...t];for(;p.length>0;){let d=p.pop();if((to(d)||Tq(d)||Nq(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>s.has(h))),s.add(d.name),n[d.name]==null&&!c.has(d.name)&&!l.has(d.name)){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}function vq(e,t){let{usedNodes:n,inputs:r}=t,s=Object.keys(r).map(m=>Jn(m)[0]).map(m=>e.nodes[m]),a=e.initNodes||[],o=m=>n.has(typeof m=="string"?m:m.name);function i(m){return[...new Map(m.map(b=>[b.name,b])).values()]}let u=i([...s,...e.weights,...a]).filter(o),c=i([...u,...Object.values(e.nodes)]).filter(o),l=new Map(c.map(m=>[m.name,m])),p={};for(let m of c){p[m.name]=p[m.name]||0;for(let b of m.children)o(b)||(p[b.name]=Number.POSITIVE_INFINITY),p[b.name]=(p[b.name]||0)+1}let d=Object.entries(p).filter(([,m])=>m===0).map(([m])=>m),h=[...d];for(;d.length>0;){let m=d.pop(),b=l.get(m);for(let y of b.children.filter(o))--p[y.name]===0&&(h.push(y.name),d.push(y.name))}let f=h.map(m=>l.get(m)),g=xq(f,u);return wq(g,u),g}function xq(e,t){let n=new Map(e.map(o=>[o.name,o])),r=t.map(o=>o.name),s=new Set(r);for(;r.length>0;){let o=r.pop(),i=n.get(o);for(let u of i.children)!n.has(u.name)||s.has(u.name)||(s.add(u.name),r.push(u.name))}return e.filter(o=>s.has(o.name))}var zh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function wq(e,t){let n=new Map(e.map((i,u)=>[i.name,u])),r=new Set(t.map(i=>i.name)),s=i=>r.has(typeof i=="string"?i:i.name),a=new Set(e.map(i=>i.name)),o=i=>a.has(typeof i=="string"?i:i.name);for(let i of e){for(let u of i.children.filter(o)){if(!n.has(u.name))throw new zh(`Child ${u.name} of node ${i.name} is unreachable.`);if(n.get(i.name)>n.get(u.name))throw new zh(`Node ${i.name} is scheduled to run after its child ${u.name}.`)}if(!s(i))for(let u of i.inputs){if(!n.has(u.name))throw new zh(`Input ${u.name} of node ${i.name} is unreachable.`);if(n.get(u.name)>n.get(i.name))throw new zh(`Node ${i.name} is scheduled to run before its input ${u.name}.`)}}}function Iq(e){let t=new Map(e.map((i,u)=>[i.name,u])),n=Number.MAX_SAFE_INTEGER,r=e.map((i,u)=>to(i)?n:u),s=i=>{let u=r[t.get(i.name)];return u==null?-1:u},a=e.map((i,u)=>i.children.map(s).reduce((c,l)=>Math.max(c,l),r[u])),o=new Map;for(let i=0;i<e.length;++i){let u=a[i];if(u===n)continue;let c=e[i],l=e[u];o.has(l.name)||o.set(l.name,[]),o.get(l.name).push(c)}return o}var kq=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),Sq=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),Cq=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function to(e){return kq.has(e.op)}function Tq(e){return Sq.has(e.op)}function Nq(e){return Cq.has(e.op)}var m1=class E_{get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let n=Object.keys(t).map(r=>t[r].map(s=>s.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(r=>{this._functionExecutorMap[r]=new E_(t.functions[r],this)})}getCompilationKey(t,n){let r=t.map(a=>a.name).sort(),s=n.map(a=>a.name).sort();return r.join(this.SEPARATOR)+"--"+s.join(this.SEPARATOR)}compile(t,n){let r=f1(t,n,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:a,syncInputs:o}=r;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){let c=n.map(p=>p.name),l=Object.keys(t);throw new Error(`Cannot compute the outputs [${c}] from the provided inputs [${l}]. Missing the following inputs: [${s}]`)}let i=vq(this.graph,r),u=Iq(i);return{orderedNodes:i,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(r=>this.cloneAndKeepTensor(r)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,r])=>[n,this.cloneTensorList(r)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let r=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let s=r.map(d=>this.graph.nodes[Jn(d)[0]]),a=n.map(d=>Jn(d)[0]),o=new Set(a),i=a.map(d=>this.graph.nodes[d]);i.length===0&&(i=this._outputs);let u=this.getCompilationKey(s,i),c=this.compiledMap.get(u);c==null&&(c=this.compile(t,i),this.compiledMap.set(u,c));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let l={},p={};return O(()=>{let d=new h1(this.weightMap,l,p,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,v]=Jn(b,d),x=[];x[v]=t[b],h[y]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(x))});let f=this.getFrozenTensorIds(h),{orderedNodes:g,nodeLiveUntilMap:m}=c;for(let b of g){if(h[b.name])continue;let y=p1(b,h,d,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. Please use model.executeAsync() instead.`);h[b.name]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[b.name]=this.cloneTensorList(y)),this.checkTensorForDisposalWithNodeLiveUntilInfo(b,h,d,f,o,m.get(b.name))}return this.parent==null&&d.dispose(f),n.map(b=>dn(b,h,d))})}getFrozenTensorIds(t){let n=[].concat.apply([],Object.keys(t).map(r=>t[r]).map(r=>r.map(s=>s.id)));return new Set(n)}checkTensorForDisposal(t,n,r,s,a,o,i){if(!(to(n)||o.has(t))){for(let u of r[t])u!=null&&(i[u.id]=(i[u.id]||0)+n.children.length);for(let u of n.inputs){if(to(u))continue;let c=i1(u.name,r,s);if(c!=null)for(let l of c){if(!l||l.kept||a.has(l.id))continue;let p=i[l.id];p===1?(l.dispose(),delete i[l.id]):p!=null&&i[l.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,n,r,s,a,o){function i(u){return to(u)||a.has(u.name)}if(!(to(t)||o==null))for(let u of o){if(i(u))continue;let c=i1(u.name,n,r);for(let l of c)!l||l.kept||s.has(l.id)||l.dispose()}}async executeAsync(t,n){return this._executeAsync(t,n)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let n of t)n&&!n.isDisposed&&n.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,n,r=!1,s={},a={}){this.disposeIntermediateTensors(),r||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let o=new h1(this.weightMap,s,a,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(t,o,n,r),u=n.map(d=>dn(d,i,o)),c=u.map(d=>d.id),l=Object.keys(t).map(d=>t[d].id),p=new Set([...c,...l,...this.weightIds]);return Object.values(i).forEach(d=>{d.forEach(h=>{h&&!h.isDisposed&&!p.has(h.id)&&h.dispose()})}),this.parent==null&&o.dispose(p),u}async executeFunctionAsync(t,n,r){let s=t.reduce((a,o,i)=>(a[this.inputs[i].name]=o,a),{});return this._executeAsync(s,this.outputNodes,!0,n,r)}async executeWithControlFlow(t,n,r,s){let a=Object.keys(t),o=a.map(x=>this.graph.nodes[Jn(x)[0]]),i=r.map(x=>Jn(x)[0]),u=new Set(i),c=i.map(x=>this.graph.nodes[x]);c.length===0&&(c=this._outputs);let{usedNodes:l,missingInputs:p,dynamicNode:d,syncInputs:h}=f1(t,c,this.weightMap,this._initNodes),f=[...o,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:n.currentContext})),g=Object.assign({},this.weightMap);Object.keys(t).forEach(x=>{let[k,S]=Jn(x),N=[];N[S]=t[x],g[k]=N});let m={},b=this.getFrozenTensorIds(g),y={};for(;f.length>0;){let x=this.processStack(o,f,n,g,y,b,u,m,l);await Promise.all(x)}d==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 v=c.filter(x=>!to(x)&&!dn(x.name,g,n)).map(x=>x.name);if(v.length>0){let x="";throw d!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${v}] from the provided inputs [${a}]. Consider providing the following inputs: [${p}]. ${x}`)}return g}processStack(t,n,r,s,a,o,i,u,c){let l=[];for(;n.length>0;){let p=n.pop();r.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&I("isConstant",p.node,s,r)&&([d]=Cs(p.node.name,r)),s[p.node.name]==null){let h=p1(p.node,s,r,this._resourceManager);d||([d]=Cs(p.node.name,r));let f=r.currentContext;w.isPromise(h)?l.push(h.then(g=>(s[d]=g,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(g)),r.currentContext=f,this.checkTensorForDisposal(d,p.node,s,r,o,i,u),this.processChildNodes(p.node,n,r,s,a,c),g))):(s[d]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(h)),this.checkTensorForDisposal(d,p.node,s,r,o,i,u),this.processChildNodes(p.node,n,r,s,a,c))}else this.processChildNodes(p.node,n,r,s,a,c)}return l}processChildNodes(t,n,r,s,a,o){t.children.forEach(i=>{let[u]=Cs(i.name,r);a[u]||!o.has(i.name)||(i.op==="Merge"?i.inputNames.some(c=>!!dn(c,s,r))&&(a[u]=!0,n.push({contexts:r.currentContext,node:i})):i.inputNames.every(c=>!!dn(c,s,r))&&(a[u]=!0,n.push({contexts:r.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let r=t[n],[s]=Jn(n),a=this.graph.nodes[s];if(a.attrParams.shape&&a.attrParams.shape.value){let o=a.attrParams.shape.value,i=o.length===r.shape.length&&r.shape.every((u,c)=>o[c]===-1||o[c]===u);w.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${o}], but was [${r.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&w.assert(r.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(t){var n,r;let s={};for(let a in t){let o=(r=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||r===void 0?void 0:r[a];o!=null?s[o.name]=t[a]:s[a]=t[a]}return s}checkInputs(t){let n=Object.keys(t).filter(r=>{let[s]=Jn(r);return this.graph.nodes[s]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var r,s;let a=(s=(r=this._signature)===null||r===void 0?void 0:r.outputs)===null||s===void 0?void 0:s[n];return a!=null?a.name:n},{})}checkOutputs(t){t.forEach(n=>{let[r]=Jn(n);if(!this.graph.nodes[r])throw new Error(`The output '${n}' is not found in the graph`)})}},_q=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Eq="?tfjs-format=file",Aq="model.json",Bk=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=jt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new _q}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return w.isPromise(e)?e.then(t=>t.getWeightStream==null?this.loadSync(t):this.loadStreaming(t)):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(e.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let t=await RC(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let n=this.artifacts.modelTopology,r=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(r=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=r,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new m1(u1.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=u1.Instance.transformGraph(e.modelInitializer);this.initializer=new m1(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ne?[e]:e,n={};return t.forEach((r,s)=>n[this.structuredOutputKeys[s]]=r),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ne)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let o=s[a];o.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[o.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let r=0;return this.inputNodes.reduce((s,a)=>{var o,i,u;let c=(u=(i=(o=this.signature)===null||o===void 0?void 0:o.inputs)===null||i===void 0?void 0:i[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[r++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let r=0;r<n.length;r++){let s=n[r],a=t[s];this.resourceIdToCapturedInput[a.resourceId]=e[r]}}}execute(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),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){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&_e(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function Dq(e,t={},n=jt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=Fq(e));let r=new Bk(e,t,n);return await r.load(),r}function $q(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. 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this.trav++,{value:Wq(e),done:!1}}},jq=class extends sn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},qq=class extends sn{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Kq=class extends sn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;_e(e.value)}return this.upstream.next()}},Xq=class extends sn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Yq=class extends sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Zq=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},Jq=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wr.getTensorsInContainer(e.value),n=this.transform(e.value),r=Wr.getTensorsInContainer(n);for(let s of t)Wr.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},Qq=class extends sn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},g1=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=Wr.getTensorsInContainer(n);for(let s of t)Wr.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},Wk=class extends sn{constructor(){super(),this.outputQueue=new P_,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},e8=class extends Wk{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Wr.getTensorsInContainer(e.value),n=this.transform(e.value),r=Wr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of t)Wr.isTensorInList(s,r)||s.dispose();return!0}},L_=class extends sn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},aa;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(aa||(aa={}));var t8=class extends sn{constructor(e,t=aa.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(a){return a instanceof sn?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let s=await F_(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case aa.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case aa.SHORTEST:return{value:null,done:!0};case aa.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},B_=class extends sn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new R_(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},n8=class extends B_{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Oq.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},vl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},i8=class V_ extends sn{constructor(t){super(),this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let n=Math.log2(this.fftSize);if(this.fftSize<0||n<4||n>14||!Number.isInteger(n))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.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(t={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new V_(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.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 t,n,r=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(r.freqDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(r.timeDataQueue);n=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],r=0;return new Promise(s=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),s({freqDataQueue:t,timeDataQueue:n}))},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(t){let n=t[0].length,r=new Float32Array(t.length*n);return t.forEach((s,a)=>r.set(s,a*n)),r}getTensorFromAudioDataArray(t,n){let r=new Float32Array(w.sizeFromShape(n));return r.set(t,r.length-t.length),yn(r,n)}},u8=class U_ extends sn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=He([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,o=(1-s)/2,i=a+r,u=s+o;this.cropBox=Dr([o,a,u,i],[1,4])}else this.cropBox=Dr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let r=new U_(t,n);return await r.start(),r}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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r=0;for(let s=0;s<n-1;++s){let a=t[s+1]-t[s];a>r&&(r=a)}return r}static getMaxWidthValueRowID(t){let n=t.length;if(n===0)return 0;let r=0,s=t[0],a=0;for(let o=1;o<n;++o){let i=t[o];i!==s&&(s=i,a=Math.max(o-r,a),r=o)}return Math.max(n-r,a)}tensorShapeFromTensor(t,n,r=!0){if(n.length===0){if(t[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return I1(t,r)}calculateOutputSize(t){let n=this.valuesShape,r=this.defaultValueShape;T.validateDefaultValueShape(r,n);let s=this.tensorShapeFromTensor(this.shape,this.shapeShape),o=T.combineRaggedTensorToTensorShapes(this.raggedRank,s,n);o[0]<0&&(o[0]=t);for(let i=1;i<=this.raggedRank;++i)o[i]<0&&(o[i]=this.getMaxWidth(i));return o}calculateFirstParentOutputIndex(t,n,r){let s=Math.min(t,r),a=[],o=0;for(let i=0;i<s;++i,o+=n)a.push(o);for(let i=s;i<t;++i)a.push(-1);return w.assert(a.length===t,()=>"Final length of result must be equal to firstDimension."),a}calculateOutputIndexRowSplit(t,n,r,s){let a=t.length,o=[];for(let i=0;i<a-1;++i){let u=t[i+1]-t[i],c=Math.min(s,u),l=n[i];l===-1&&(c=0);for(let p=0;p<c;++p)o.push(l),l+=r;for(let p=0;p<u-c;++p)o.push(-1)}if(a>0&&o.length!==t[a-1])throw new Error("Invalid row split size.");return o}calculateOutputIndexValueRowID(t,n,r,s){let a=t.length,o=[];if(a===0)return[];let i=0,u=t[0];if(u>=n.length)throw new Error(`Got currentValueRowId=${u}, which is not less than ${n.length}`);let c=n[u];o.push(c);for(let l=1;l<a;++l){let p=t[l];if(p===u)c>=0&&(++i,i<s?c+=r:c=-1);else{if(i=0,u=p,p>=n.length)throw new Error(`Got nextValueRowId=${p} which is not less than ${n.length}`);c=n[p]}o.push(c)}if(o.length!==t.length)throw new Error("Invalid row ids.");return o}calculateOutputIndex(t,n,r,s){let a=this.getRowPartitionTensor(t),o=this.getRowPartitionTypeByDimension(t);switch(o){case _r.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(a,n,r,s);case 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      }
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  }
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  const float FLOAT_MIN = 1.17549435e-38;

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    }

    highp float av = abs(v);

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

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

    highp float e = floor(log2(av));
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    c[1] = floor(32768.0 * m);
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    c[0] = floor(8388608.0 * m);

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

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

    return c / 255.0;
  }
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    }
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    }
  `}function wJ(e){return`
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    }
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    precision highp int;
    precision highp sampler2D;
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    ${e.defineOutput}
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    {
      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;
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    ${e.defineSpecialInf}
    ${e.defineRound}

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    }

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      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);
    }

    ${kJ}
    ${SJ}
    ${CJ}
  `}var kJ=`
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);
}
`,SJ=`
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);
}
`,CJ=`
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);
}
`,TJ=`
  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 EA(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function NJ(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return r[0]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${r[1]}.0);
      }
    `:r[1]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${r[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${r[0]}, ${r[1]}));
      return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);
    }
  `}function _J(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 EJ(e,t,n){if(n)return`
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

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

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

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

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

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

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

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

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

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

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

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

      ${i}

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

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

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

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

      ${n}

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

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

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

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

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

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

        return ${u.texture2D}(${r}, uv);
      }
    `;if(t)return`
    vec4 ${s}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${r}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${u.texture2D}(${r}, uv);
    }
  `;let c=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=Math.ceil(n[1]/2);return`
    vec4 ${s}(int row, int col) {
      vec2 uv = packedUVfrom2D(${l}, ${c[0]}, ${c[1]}, row, col);
      return ${u.texture2D}(${r}, uv);
    }
  `}function VJ(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
      float ${s}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `;let d=a[0],h=a[1];return`
    float ${s}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
      return sampleTexture(${r}, uv);
    }
  `}let{newShape:o,keptDims:i}=w.squeezeShape(n),u=o;if(u.length<n.length){let d=Sl(e,u),h=["row","col"];return`
      ${Il(d,t)}
      float ${s}(int row, int col) {
        return ${s}(${Cl(h,i)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${kl(e)}
      }
    `;let c=a[0],l=a[1],p=tu(r);return l===1?t?`
      float ${s}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${r}TexShape[0]));
        return sampleTexture(${r}, uv);
      }
    `:`
    float ${s}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
      return sampleTexture(${r}, uv);
    }
  `:c===1?t?`
      float ${s}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${r}TexShape[1]), 0.5);
        return sampleTexture(${r}, uv);
      }
    `:`
    float ${s}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${l}.0, 0.5);
      return sampleTexture(${r}, uv);
    }
  `:t?`
      float ${s}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${r}Shape[1] + col + ${p};
        vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
        return sampleTexture(${r}, uv);
      }
    `:`
  float ${s}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${n[1]} + col + ${p};
    vec2 uv = uvFromFlat(${c}, ${l}, index);
    return sampleTexture(${r}, uv);
  }
`}function UJ(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=Sl(e,d),g=["b","row","col"];return`
        ${_A(f,t)}
        vec4 ${s}(int b, int row, int col) {
          return ${s}(${Cl(g,h)});
        }
      `}let i=An();if(t)return`
    vec4 ${s}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${r}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${i.texture2D}(${r}, uv);
    }
  `;let u=o[0],c=o[1],l=Math.ceil(n[2]/2),p=l*Math.ceil(n[1]/2);return`
    vec4 ${s}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${u}, ${c}, ${p}, ${l}, b, row, col);
      return ${i.texture2D}(${r}, uv);
    }
  `}function GJ(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:u}=w.squeezeShape(n),c=i;if(c.length<n.length){let g=Sl(e,c),m=["row","col","depth"];return`
        ${Il(g,t)}
        float ${s}(int row, int col, int depth) {
          return ${s}(${Cl(m,u)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${a}, ${o}, 1)));
        ${kl(e)}
      }
    `;let l=e.shapeInfo.texShape,p=l[0],d=l[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
      float ${s}(int row, int col, int depth) {
        int stride1 = ${r}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `:`
        float ${s}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${o}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${d}.0, ${p}.0);
          return sampleTexture(${r}, uv);
        }
      `;if(d===o&&h==null)return t?`
      float ${s}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${r}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `:`
    float ${s}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);
      return sampleTexture(${r}, uv);
    }
  `;let f=tu(r);return t?`
    float ${s}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${r}Shape[1] * ${r}Shape[2];
      int stride1 = ${r}Shape[2];
      int index = row * stride0 + col * stride1 + depth + ${f};
      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
      return sampleTexture(${r}, uv);
    }
    `:`
      float ${s}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${a} + col * ${o} + depth + ${f};
        vec2 uv = uvFromFlat(${p}, ${d}, index);
        return sampleTexture(${r}, uv);
      }
  `}function HJ(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=An();if(t)return`
    vec4 ${r}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${n}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${s.texture2D}(${n}, uv);
    }
  `;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=u[0],l=u[1],p=Math.ceil(a[o-1]/2),d=p*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let g=2;g<o-1;g++)h=`int b${g}, `+h,d*=a[o-g-1],f=`b${g} * ${d} + `+f;return`
    vec4 ${r}(${h}) {
      int index = ${f};
      int texR = index / ${l};
      int texC = index - texR * ${l};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${c});
      return ${s.texture2D}(${n}, uv);
    }
  `}function jJ(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:u,keptDims:c}=w.squeezeShape(n);if(u.length<n.length){let y=Sl(e,u),v=["row","col","depth","depth2"];return`
      ${Il(y,t)}
      float ${s}(int row, int col, int depth, int depth2) {
        return ${s}(${Cl(v,c)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${i}, ${o}, ${a}, 1)));
        ${kl(e)}
      }
    `;let l=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${r}Shape[3];`,g=`int stride1 = ${r}Shape[2] * stride2;`,m=`int stride0 = ${r}Shape[1] * stride1;`;if(h===i&&l==null)return t?`
      float ${s}(int row, int col, int depth, int depth2) {
        ${f}
        ${g}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `:`
      float ${s}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${o}, ${a}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${d}.0);
        return sampleTexture(${r}, uv);
      }
    `;if(h===a&&l==null)return t?`
      float ${s}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${r}Shape[1] * ${r}Shape[2], ${r}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `:`
      float ${s}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${n[1]*n[2]}, ${n[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${h}.0, ${d}.0);
        return sampleTexture(${r}, uv);
      }
    `;let b=tu(r);return t?`
    float ${s}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${f}
      ${g}
      ${m}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${b});
      return sampleTexture(${r}, uv);
    }
  `:`
    float ${s}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${o} +
          depth * ${a} + depth2;
      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});
      return sampleTexture(${r}, uv);
    }
  `}function qJ(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[4],a=t[3]*s,o=t[2]*a,i=t[1]*o,{newShape:u,keptDims:c}=w.squeezeShape(t);if(u.length<t.length){let g=Sl(e,u),m=["row","col","depth","depth2","depth3"];return`
      ${Il(g)}
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        return ${r}(${Cl(m,c)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${i}, ${o}, ${a}, ${s})) +
          depth3;
        ${kl(e)}
      }
    `;let l=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===i&&l==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${o}, ${a}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(h===s&&l==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${h}.0, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;let f=tu(n);return`
    float ${r}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${o} + depth * ${a} +
          depth2 * ${s} + depth3 + ${f};
      vec2 uv = uvFromFlat(${d}, ${h}, index);
      return sampleTexture(${n}, uv);
    }
  `}function KJ(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:s,keptDims:a}=w.squeezeShape(t);if(s.length<t.length){let m=Sl(e,s),b=["row","col","depth","depth2","depth3","depth4"];return`
      ${Il(m)}
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${r}(${Cl(b,a)});
      }
    `}let o=t[5],i=t[4]*o,u=t[3]*i,c=t[2]*u,l=t[1]*c;if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${l}, ${c}, ${u}, ${i})) +
          dot(
            vec2(depth3, depth4),
            vec2(${o}, 1)));
        ${kl(e)}
      }
    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===l&&p==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${c}, ${u}, ${i}, ${o})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(f===o&&p==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;let g=tu(n);return`
    float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${l} + col * ${c} + depth * ${u} +
          depth2 * ${i} + depth3 * ${o} + depth4 + ${g};
      vec2 uv = uvFromFlat(${h}, ${f}, index);
      return sampleTexture(${n}, uv);
    }
  `}function kl(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
    for (int i = 0; i < ${n}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function XJ(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=NA(e.shapeInfo.logicalShape,t.logicalShape),u=ht(o),c=o-a,l,p=["x","y","z","w","u","v"];a===0?l="":o<2&&i.length>=1?l="coords = 0;":l=i.map(y=>`coords.${p[y+c]} = 0;`).join(`
`);let d="";o<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+c]}`).join(", ");let h="return outputValue;",g=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!g&&!b)h=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(g&&!b)o===1?h=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:h=`
        return vec4(outputValue.x);
      `;else if(i.length){let y=a-2,v=a-1;i.indexOf(y)>-1&&i.indexOf(v)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${s}() {
      ${u} coords = getOutputCoords();
      ${l}
      vec4 outputValue = get${r}(${d});
      ${h}
    }
  `}function YJ(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`
      float ${s}() {
        return sampleTexture(${n}, resultUV);
      }
    `;let c=ht(u),l=NA(e.shapeInfo.logicalShape,t.logicalShape),p=u-i,d,h=["x","y","z","w","u","v"];i===0?d="":u<2&&l.length>=1?d="coords = 0;":d=l.map(g=>`coords.${h[g+p]} = 0;`).join(`
`);let f="";return u<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((g,m)=>`coords.${h[m+p]}`).join(", "),`
    float ${s}() {
      ${c} coords = getOutputCoords();
      ${d}
      return get${r}(${f});
    }
  `}function ht(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 ${e} is not yet supported`)}function c0(e,t,n){let{newShape:r,keptDims:s}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):r,u=!e&&a>1&&!w.arraysEqual(t,n)&&r.length<a||o;return{useSqueezeShape:u,uniformShape:u?i:t,keptDims:s}}function Sl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Cl(e,t){return t.map(n=>e[n]).join(", ")}function ZJ(e,t,n,r){let s=n.map((l,p)=>{let d={logicalShape:l.shape,texShape:l.isUniform?null:l.texData.texShape,isUniform:l.isUniform,isPacked:l.isUniform?!1:l.texData.isPacked,flatOffset:null};return l.texData!=null&&l.texData.slice!=null&&l.texData.slice.flatOffset>0&&(d.flatOffset=l.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=s.map(l=>l.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},i=mJ(s,o,t),u=aA(e.gl,i),c=e.createProgram(u);return G().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:i,webGLProgram:c,inShapeInfos:a,outShapeInfo:o,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(c),Object.assign({program:t,fragmentShader:u,source:i,webGLProgram:c,inShapeInfos:a,outShapeInfo:o},AA(e,t,c)))}function AA(e,t,n){let r=[],s=[],a,o,i,u=null,c=null;c=e.getUniformLocation(n,"NAN",!1),G().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(n,"INFINITY",!1));let l=!1;for(let p of t.variableNames){let d={name:p,uniform:e.getUniformLocation(n,p,l),offset:e.getUniformLocation(n,`offset${p}`,l)};t.enableShapeUniforms&&(d.shape=e.getUniformLocation(n,`${p}Shape`,l),d.texShape=e.getUniformLocation(n,`${p}TexShape`,l)),r.push(d)}if(t.enableShapeUniforms&&(a=e.getUniformLocation(n,"outShape",l),i=e.getUniformLocation(n,"outShapeStrides",l),o=e.getUniformLocation(n,"outTexShape",l)),t.customUniforms)for(let p of t.customUniforms)s.push(e.getUniformLocation(n,p.name,l));return{variablesLocations:r,customUniformLocations:s,infLoc:u,nanLoc:c,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:o}}function C1(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let s=n.logicalShape,a=t[r],o=a.shape;if(!w.arraysEqual(s,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${s} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,u))throw Error(`Binary was compiled with different texture shapes than the current args. 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      void main() {
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        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
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        ${t.output} = result;
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    `}},tQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Dd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=An();this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?eg(["r","c","d"],e):eu(["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);
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        }

        ${t.output} = result;
      }
    `}},nQ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=lr.DOWNLOAD;let t=An();this.outputShape=e,this.userCode=`
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        ${t.output} = encode_float(x);
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      ${TA}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},sQ={R:0,G:1,B:2,A:3},T1=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=An();this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)");let a="";for(let o=0;o<n.length;o++){let i=n[o];a+=`
          if(offset == ${o}) {
            result = values[${sQ[i]}];
          }`}this.userCode=`
      ${this.enableShapeUniforms?u0():i0(e)}

      void main() {
        ivec3 coords = getOutputCoords();
        int flatIndex = getFlatIndex(coords);
        float result = 0.;
        int offset = imod(flatIndex, ${n.length});

        flatIndex = idiv(flatIndex, ${n.length}, 1.);

        int r = flatIndex / texShape[1];
        if (r < texShape[0]) {
          int c = imod(flatIndex, texShape[1]);
          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
          vec4 values = ${r.texture2D}(A, uv);
          ${a}
        }
        ${r.output} = vec4(${s}, 0., 0., 0.);
      }
    `}},aQ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=An();this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let r="",s="result";t&&(s="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;r+=`
          localCoords = coords;
          if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
          localCoords[2] += ${o};
          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
            localCoords[1] += ${a};

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

            int r = flatIndex / texShape[1];
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            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=`
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        void main() {
          ivec3 coords = getOutputCoords();

          vec4 result = vec4(0.);
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          vec4 values;

          ${r}

          ${n.output} = ${s};
        }
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this.throwIfDisposed(),BA(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),LA(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(yx(this.gl,this.framebuffer),this.outputTexture=null),he(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>HA(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,s,a){return jA(this.gl,e,t,n,r,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return GA(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=UA(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(G().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,s=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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t=this.gl;he(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),zA(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(he(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Zh(this.gl,this.program),he(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?fA(this.gl,e,t):mA(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),he(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),gA(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,s]=xl(t,n);this.setOutputMatrixTextureDriver(e,r,s)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Zh(this.gl,this.program),dd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}he(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),he(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ld(this.gl,G().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(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=oQ(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){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in G().platform&&(n=G().platform.setTimeoutCustom.bind(G().platform)),w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Jh(this.gl,e,this.framebuffer),this.debug&&dd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Jh(this.gl,this.outputTexture,this.framebuffer),this.debug&&dd(this.gl)):yx(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;Jh(r,e,this.framebuffer),this.debug&&dd(r),this.outputTexture=e,he(r,()=>r.viewport(0,0,t,n)),he(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),he(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function oQ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:iQ,bincountImpl:KA,bincountReduceImpl:uQ,bitwiseAndImpl:cQ,castImpl:lQ,ceilImpl:dQ,concatImpl:pQ,equalImpl:hQ,expImpl:fQ,expm1Impl:mQ,floorImpl:gQ,gatherNdImpl:bQ,gatherV2Impl:yQ,greaterImpl:vQ,greaterEqualImpl:xQ,lessImpl:wQ,lessEqualImpl:IQ,linSpaceImpl:kQ,logImpl:SQ,maxImpl:CQ,maximumImpl:TQ,minimumImpl:NQ,multiplyImpl:_Q,negImpl:EQ,notEqualImpl:AQ,prodImpl:DQ,raggedGatherImpl:$Q,raggedRangeImpl:FQ,raggedTensorToTensorImpl:RQ,rangeImpl:PQ,rsqrtImpl:OQ,scatterImpl:MQ,sigmoidImpl:LQ,simpleAbsImpl:XA,sliceImpl:BQ,sparseFillEmptyRowsImpl:zQ,sparseReshapeImpl:WQ,sparseSegmentReductionImpl:YA,sqrtImpl:VQ,staticRegexReplaceImpl:UQ,stridedSliceImpl:GQ,stringNGramsImpl:HQ,stringSplitImpl:jQ,stringToHashBucketFastImpl:qQ,subImpl:KQ,tileImpl:XQ,topKImpl:YQ,transposeImpl:m0,uniqueImpl:ZQ}=Uk;function ZA(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Cn(e,t){return t===1?[e]:ZA(e,t)}function JQ(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var QQ=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=xn(this.outputShape.length),this.rank===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let t=Cn("rc",this.rank),n=ht(this.rank),r=this.getOutOfBoundsCondition(t),s=this.getSetup(t),a=this.getOutput(t);this.userCode=`
        void main() {
          ${n} rc = getOutputCoords();

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

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

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

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

          result[${r}] =
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      `}this.userCode=`
      ${eee(t,this.enableShapeUniforms)}
      ${this.enableShapeUniforms?u0():i0(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 eee(e,t){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t?fJ(["r","c","d"],"inputShape"):eu(["r","c","d"],e)}
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  `}var tee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let r=_1(t,n),s=E1(e,r,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=N1(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[s].pop();return this.usedTextures[s].push(i),i}let o;return r===pn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===pn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===pn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===pn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===pn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let s=_1(n,r),a=E1(t,s,r);a in this.freeTextures||(this.freeTextures[a]=[]);let o=N1(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,r),i=G().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function nee(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function N1(e,t,n,r,s){let a=ree(t,r),o;if(s){let[u,c]=xl(e[0],e[1]);o=u*c}else{let[u,c]=Pp(e[0],e[1]);o=u*c}let i=nee(n,a);return o*i}function ree(e,t){switch(e){case pn.PACKED_2X2_FLOAT32:return h0(t);case pn.PACKED_2X2_FLOAT16:return f0(t);case pn.UNPACKED_FLOAT32:return l0(t);case pn.UNPACKED_FLOAT16:return d0(t);case pn.PACKED_4X1_UNSIGNED_BYTE:return p0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function see(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?pn.PACKED_2X2_FLOAT32:pn.UNPACKED_FLOAT32:e?pn.PACKED_2X2_FLOAT16:pn.UNPACKED_FLOAT16}function _1(e,t){if(e===lr.UPLOAD)return pn.PACKED_2X2_FLOAT32;if(e===lr.RENDER||e==null)return see(t);if(e===lr.DOWNLOAD||e===lr.PIXELS)return pn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function E1(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var is=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=`
      float unaryOperation(float x) {
        ${t}
      }

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

        setOutput(y);
      }
    `}},Or="if (isnan(x)) return x;",aee="return x;",A1="return abs(x);",oee="return (x >= 0.0) ? x : (exp(x) - 1.0);",iee=Or+`
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`,uee=Or+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
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  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;
`,pee=`
  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;
`,hee=`
  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;
`,fee="return 1.0 / (1.0 + exp(-1.0 * x));",oa=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

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

        setOutput(y);
      }
    `}},mee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let t=e.length,n=Cn("rc",t),r=ht(t),s=JQ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
      void main() {
        ${r} rc = getOutputCoords();
        vec4 packedInput = getA(${s});

        setOutput(getChannel(packedInput, ${o}));
      }
    `}},gee=gs.whereImpl,bee=1e-7,yee=1e-4,jh={};function vee(e){return e in jh||(jh[e]={}),jh[e]}var xee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),wee=600;function Iee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*wee/1024/1024}var g0=class QA extends Md{nextDataId(){return QA.nextDataId++}constructor(t){if(super(),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,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof tf)n=t;else{let r=qr(G().getNumber("WEBGL_VERSION"),t);n=new tf(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=qr(G().getNumber("WEBGL_VERSION"));n=new tf(r),this.binaryCache=vee(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new tee(this.gpgpu),this.numMBBeforeWarning=Iee(),this.texData=new Rf(this,Er())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,r,s,a,o){let i=this.makeTensorInfo(n,r),u=this.texData.get(i.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[s,a]},u.texShape=[s,a];let c=pd(n),l=new T1(c,!1,o),p=this.runWebGLProgram(l,[i],r,[[s,a]]);return p.shape=n,u.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(t,n,r){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),r==="complex64"&&t!=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:n,dtype:r,values:t,usage:lr.UPLOAD,refCount:1}),s}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,r,s,a){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:r,dtype:s,values:n,usage:lr.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:r,dtype:s,complexTensorInfos:a,slice:o,shape:i,isPacked:u}=n;if(o!=null){let d;u?d=new oa(i,ta):d=new is(i,ta);let h=this.runWebGLProgram(d,[{dataId:t,shape:i,dtype:s}],s),f=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),f}if(r!=null)return this.convertAndCacheOnCPU(t);if(s==="string")return r;let c=this.activeTimers!=null,l;c&&(l=w.now());let p;if(s==="complex64"){let d=this.readSync(a.real.dataId),h=this.readSync(a.imag.dataId);p=T.mergeRealAndImagArrays(d,h)}else p=this.getValuesFromTexture(t);return c&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let f=this.pendingRead.get(t);return new Promise(g=>f.push(g))}let n=this.texData.get(t),{values:r,shape:s,slice:a,dtype:o,complexTensorInfos:i,isPacked:u}=n;if(a!=null){let f;u?f=new oa(s,ta):f=new is(s,ta);let g=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:o}],o),m=this.read(g.dataId);return this.disposeIntermediateTensorInfo(g),m}if(r!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let c=null,l;if(o!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(t);let f=this.texData.get(l.dataId);c=this.gpgpu.createBufferFromTexture(f.texture.texture,...Gh(s))}this.pendingRead.set(t,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(o==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),g=f[0],m=f[1];p=T.mergeRealAndImagArrays(g,m)}else if(c==null)p=this.getValuesFromTexture(t);else{let f=w.sizeFromShape(s);p=this.gpgpu.downloadFloat32MatrixFromBuffer(c,f)}if(l!=null&&this.disposeIntermediateTensorInfo(l),c!=null){let f=this.gpgpu.gl;he(f,()=>f.deleteBuffer(c))}let d=this.convertAndCacheOnCPU(t,p),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(f=>f(d)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Er().removeDataId(t,this),this.pendingDeletes--),d}readToGPU(t,n={}){let r=this.texData.get(t),{values:s,shape:a,slice:o,dtype:i,isPacked:u,texture:c}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(o!=null){let h;u?h=new oa(a,ta):h=new is(a,ta);let f=this.runWebGLProgram(h,[{dataId:t,shape:a,dtype:i}],i),g=this.readToGPU(f,n);return this.disposeIntermediateTensorInfo(f),g}if(c==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(t,n.customTexShape),p=Er().makeTensorFromTensorInfo(l),d=this.texData.get(l.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let r=n.map(s=>w.decodeString(s));return Me(t.shape,t.dtype,r)}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Me(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n<t.length;n++){let r=t[n];if(!nA(r))throw G().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:n,dtype:r,isPacked:s}=this.texData.get(t),a=w.sizeFromShape(n);if(G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(t),h=this.texData.get(d.dataId),f=this.gpgpu.downloadMatrixFromPackedTexture(h.texture.texture,...Gh(n)).subarray(0,a);return this.disposeIntermediateTensorInfo(d),f}let o=G().getBool("WEBGL_PACK")&&s===!0,i=o?pd(n):n,u=o?new rQ(i):new nQ(i),c=this.runWebGLProgram(u,[{shape:i,dtype:r,dataId:t}],"float32"),l=this.texData.get(c.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(c),p}timerAvailable(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(t){let n=this.activeTimers,r=[],s=!1;this.programTimersStack==null?(this.programTimersStack=r,s=!0):this.activeTimers.push(r),this.activeTimers=r,t();let a=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),o=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=n,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(a);i.kernelMs=w.sum(u),i.getExtraProfileInfo=()=>u.map((c,l)=>({name:o[l],ms:c})).map(c=>`${c.name}: ${c.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:r}=this.texData.get(t);return r!=null&&(this.disposeData(r.real.dataId,n),this.disposeData(r.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:r,texShape:s,usage:a,isPacked:o,slice:i}=this.texData.get(t),u=i&&i.origDataId||t,c=this.dataRefCount.get(u);c>1?this.dataRefCount.set(u,c-1):(this.dataRefCount.delete(u),n!=null&&(this.numBytesInGPU-=this.computeBytes(s,r),this.textureManager.releaseTexture(n,s,a,o)));let l=this.texData.get(t);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=xee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)<n)}getGPGPUContext(){return this.gpgpu}where(t){T.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let n=t.dataSync();return gee(t.shape,n)}packedUnaryOp(t,n,r){let s=new oa(t.shape,n),a=this.compileAndRun(s,[t],r);return Er().makeTensorFromTensorInfo(a)}abs(t){if(this.shouldExecuteOnCPU([t])&&t.dtype!=="complex64"){let s=XA(this.texData.get(t.dataId).values);return this.makeOutput(t.shape,t.dtype,s)}if(G().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,A1,t.dtype);let n=new is(t.shape,A1),r=this.compileAndRun(n,[t]);return Er().makeTensorFromTensorInfo(r)}makeTensorInfo(t,n,r){let s;if(n==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(o=>w.encodeString(o));s=this.write(a,t,n)}else s=this.write(r,t,n);return this.texData.get(s).usage=null,{dataId:s,shape:t,dtype:n}}makeOutput(t,n,r){return Er().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,r),this)}unpackTensor(t){let n=new mee(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new QQ(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let r=[So(t.shape),...Co(t.shape)],s={dtype:t.dtype,shape:r,dataId:t.dataId},a=[So(n),...Co(n)],o=new JA(a,r),i=!0,u=[r],c=this.runWebGLProgram(o,[s],t.dtype,u,i);return{dataId:c.dataId,shape:n,dtype:c.dtype}}decode(t,n){let r=this.texData.get(t),{isPacked:s,shape:a,dtype:o}=r;if(n!=null){let d=w.sizeFromShape(a),h=n[0]*n[1]*4;w.assert(d<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=pd(a),u;s?u=new tQ(i):u=new eQ(i);let c=!0,l=[n!=null?n:Gh(i)],p=this.runWebGLProgram(u,[{shape:i,dtype:o,dataId:t}],o,l,c,n);return{dtype:o,shape:a,dataId:p.dataId}}runWebGLProgram(t,n,r,s,a=!1,o){let i=this.makeTensorInfo(t.outputShape,r),u=this.texData.get(i.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Dd.DENSE){let b=o!=null?o:Gh(t.outputShape);u.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),w.sizeFromShape(i.shape)===0)return u.values=w.getTypedArrayFromDType(i.dtype,0),i;let c=[],l=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),c.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!$d(y.shape,b.shape)){let v=b,x=b.shape;b.shape=y.shape,b=this.packedReshape(b,x),c.push(b),y=this.texData.get(b.dataId),v.shape=x}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:u,isUniform:!1},d=QJ(t,l,p),h=this.getAndSaveBinary(d,()=>ZJ(this.gpgpu,t,l,p)),f=this.activeTimers!=null,g;f&&(g=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||JJ(this.gpgpu,h,l,p,s),c.forEach(b=>this.disposeIntermediateTensorInfo(b)),f&&(g=this.endTimer(g),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(g)}));let m=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(m>0){let b=w.now();b-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&a===!1){let b=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),b}return i}compileAndRun(t,n,r,s,a=!1){return r=r||n[0].dtype,this.runWebGLProgram(t,n,r,s,a)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(n=>{this.gpgpu.deleteProgram(this.binaryCache[n].webGLProgram),delete this.binaryCache[n]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?bee:yee}uploadToGPU(t){let n=this.texData.get(t),{shape:r,dtype:s,values:a,texture:o,usage:i,isPacked:u}=n;if(o!=null)return;let c=this.activeTimers!=null,l;c&&(l=w.now());let p=n.texShape;if(p==null&&(p=vA(r,u),n.texShape=p),a!=null){let d=pd(r),h,f=p[1],g=p[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(u||!m)&&([f,g]=xl(p[0],p[1])),u?h=new aQ(d,m):h=new T1(d,m);let b=m?[g,f]:p,y=this.makeTensorInfo(b,s),v=this.texData.get(y.dataId);m?v.usage=lr.PIXELS:v.usage=lr.UPLOAD,v.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),f,g,a);let x=[[g,f]],S=this.runWebGLProgram(h,[y],s,x,!0),N=this.texData.get(S.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(S.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(S.dataId)),this.disposeIntermediateTensorInfo(y),c&&(this.uploadWaitMs+=w.now()-l)}else{let d=this.acquireTexture(p,i,s,u);n.texture=d}}convertAndCacheOnCPU(t,n){let r=this.texData.get(t),{dtype:s}=r;return n!=null&&(r.values=kee(n,s)),r.values}acquireTexture(t,n,r,s){if(this.numBytesInGPU+=this.computeBytes(t,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,s)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let r=new Promise(s=>{try{this.checkCompletion_(n),s(!0)}catch(a){throw a}});t.push(r)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await aI(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(o0(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:r,infLoc:s,nanLoc:a,outShapeLocation:o,outShapeStridesLocation:i,outTexShapeLocation:u}=AA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=r,t.infLoc=s,t.nanLoc=a,t.outShapeLocation=o,t.outShapeStridesLocation=i,t.outTexShapeLocation=u}}createTensorFromGPUData(t,n,r){t.channels=t.channels||"RGBA";let{texture:s,height:a,width:o,channels:i}=t,u=Er().backend;if(!u.gpgpu.gl.isTexture(s))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let c=u.writeTexture(s,n,r,a,o,i);return Er().makeTensorFromDataId(c,n,r,u)}};g0.nextDataId=0;function kee(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var See="4.16.0";function eD(){G().set("WEBGL_FORCE_F16_TEXTURES",!0)}cp.isBrowser()&&Jf("webgl",()=>new g0,2);var Cee={forceHalfFloat:eD},b0=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,To=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

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

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

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

        setOutput(result);
      }
    `}};function sr(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Tee={kernelName:si,backendName:"webgl",kernelFunc:sr};function Oa(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(a.dataId),i=sr({inputs:{x:r},backend:n}),u=sr({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var Nee={kernelName:Mf,backendName:"webgl",kernelFunc:Oa},tD="return (a < 0.) ? b * a : a;",nD=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function _ee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tl(nD,s.shape,o.shape):new To(tD,s.shape,o.shape),u=n.runWebGLProgram(i,[s,o],"float32");return n.disposeIntermediateTensorInfo(o),u}var Eee={kernelName:ui,backendName:"webgl",kernelFunc:_ee},rD="return (a < 0.) ? b * a : a;",sD=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Aee(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tl(sD,r.shape,s.shape):new To(rD,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],"float32")}var Dee={kernelName:Si,backendName:"webgl",kernelFunc:Aee},Nl="if (isnan(x)) return x;";function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:s,backend:a})=>{let{x:o}=s,i=a,u=r||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,u);return i.makeTensorInfo(o.shape,u,d)}let c=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,l;return c?l=new oa(o.shape,t):l=new is(o.shape,e),i.runWebGLProgram(l,[o],u)}}function mn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:a}){return({inputs:o,backend:i})=>{let{a:u,b:c}=o,l=i;if(r&&u.dtype==="complex64"){let f=l.texData.get(u.dataId),g=l.texData.get(c.dataId),[m,b]=[[f.complexTensorInfos.real,g.complexTensorInfos.real],[f.complexTensorInfos.imag,g.complexTensorInfos.imag]].map(v=>{let[x,k]=v,S={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:c.shape},E=new To(e,u.shape,c.shape);return l.runWebGLProgram(E,[S,N],fr(x.dtype,k.dtype))}),y=Oa({inputs:{real:m,imag:b},backend:l});return l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(b),y}let p=a||fr(u.dtype,c.dtype);if((u.dtype==="string"||c.dtype==="string"||l.shouldExecuteOnCPU([u,c]))&&s!=null){let f=l.texData.get(u.dataId).values,g=l.texData.get(c.dataId).values,m=u.dtype==="string"?T.fromUint8ToStringArray(f):f,b=u.dtype==="string"?T.fromUint8ToStringArray(g):g,[y,v]=s(u.shape,c.shape,m,b,p),x=l.makeTensorInfo(v,p),k=l.texData.get(x.dataId);return k.values=y,x}let d=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Tl(t,u.shape,c.shape,n):h=new To(e,u.shape,c.shape),l.runWebGLProgram(h,[u,c],p)}}function Fd(e,t=!1){if(e==="linear")return t?lee:aee;if(e==="relu")return t?pee:iee;if(e==="elu")return t?dee:oee;if(e==="relu6")return t?hee:uee;if(e==="prelu")return t?sD:rD;if(e==="leakyrelu")return t?nD:tD;if(e==="sigmoid")return t?fee:cee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var aD=class{constructor(e,t,n,r=!1,s=!1,a=!1,o=null,i=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=xn(this.outputShape.length);let c=r?e[1]:e[2],l=Math.ceil(c/2),p=r?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",m="";o&&(i?g=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }`:u?g=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }`:g=`vec4 activation(vec4 x) {
          ${o}
        }`,m="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let y="rc.x",v="rc.x";e[0]<t[0]?y=`imod(rc.x, ${e[0]})`:t[0]<e[0]&&(v=`imod(rc.x, ${t[0]})`),this.userCode=`
      ${g}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${l}.0;

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

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

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

        ${b}

        ${m}

        setOutput(result);
      }
    `}},D1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},$1=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=T.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));
      }
    `}},F1="return a * b;";function y0(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=T.upcastType(r.dtype,s.dtype);if(r.dtype==="complex64"){let i=n.texData.get(r.dataId),u=n.texData.get(s.dataId),c=new $1(D1.REAL,r.shape,s.shape),l=new $1(D1.IMAG,r.shape,s.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:r.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:s.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:s.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(l,p,"float32"),f=Oa({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([r,s])){let i=n.texData.get(r.dataId),u=n.texData.get(s.dataId),[c,l]=_Q(r.shape,s.shape,i.values,u.values,a),p=n.makeTensorInfo(l,a),d=n.texData.get(p.dataId);return d.values=c,p}let o;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Tl(F1,r.shape,s.shape):o=new To(F1,r.shape,s.shape),n.runWebGLProgram(o,[r,s],a)}var $ee={kernelName:xi,backendName:"webgl",kernelFunc:y0};function Fee(e,t,n){let r=[So(e.shape),...Co(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[So(t),...Co(t)],o=new JA(a,r),i=!0,u=[r],c=n.runWebGLProgram(o,[s],e.dtype,u,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function pe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=n,i=w.sizeFromShape(s.shape),u=w.inferFromImplicitShape(a,i),c=w.sizeFromShape(u);w.assert(i===c,()=>`The new shape (${u}) has ${c} elements and the old shape (${s.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let l=o.texData.get(s.dataId);return l.isPacked&&!$d(s.shape,u)&&!(l.texture!==null&&$d(l.shape,u))?Fee(s,u,o):(o.incRef(s.dataId),{dataId:s.dataId,shape:u,dtype:s.dtype})}var Ree={kernelName:qc,backendName:"webgl",kernelFunc:pe},R1=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o=Math.floor(n/4)*4,i=n%4,u="sumValue += dot(values, ones);";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${w.isInt(l)?l.toPrecision(2):l}, ones);`}let c="";s%n>0&&(c=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${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)
          );

          ${u}
        }

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

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

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

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

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

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

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

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

          ${p}
        }

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

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

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

          ${p}
        }
        setOutput(${u});
      }
    `}};function Oee(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=T.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function ru(e,t,n,r){let s=Oee(e.shape),a=e;for(let o=0;o<s.length;o++){let{inSize:i,windowSize:u,outSize:c}=s[o],l,p;n==="mean"?l=o===0?new R1({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:c},i):new R1({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:c}):l=new Pee({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:c},n),p=a,a=r.runWebGLProgram(l,[a],t),p.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(p)}return a}var Mee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let r=ht(this.rank),s=Lee(t);this.userCode=`
    void main() {
      ${r} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `}};function Lee(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let s=0;s<e.length;s++)r[e[s]]=n[s];return r.join()}var Bee=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 ${this.rank} is not yet supported.`);let r=ht(this.rank),s=ZA("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let o=`vec2(${a.slice(-2).join()})`,i=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
    void main() {
      ${r} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${u};
      if(${i}) {
        result[1] = ${u};
      }
      --${s[this.rank-1]};
      if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${u};
        if(${i}) {
          result[3] = ${u};
        }
      }
      setOutput(result);
    }
    `}};function tg(e,t,n){let r=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Bee(e.shape,t):new Mee(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function zee(e,t,n,r){let s=t,a=e.shape.length,o=w.parseAxisParam(s,e.shape),i=o,u=T.getAxesPermutation(i,a),c=u!=null,l=e;c&&(l=tg(e,u,r),i=T.getInnerMostAxes(i.length,a)),T.assertAxesAreInnerMostDims("sum",i,a);let[p,d]=T.computeOutAndReduceShapes(l.shape,i),h=p;n&&(h=T.expandShapeToKeepDim(p,o));let f=w.sizeFromShape(d),m=w.sizeFromShape(e.shape)/f,b=pe({inputs:{x:l},attrs:{shape:[m,f]},backend:r}),y=Zf(e.dtype),v=ru(b,y,"sum",r),x=pe({inputs:{x:v},attrs:{shape:h},backend:r});return r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(v),c&&r.disposeIntermediateTensorInfo(l),x}function ng(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return zee(s,a,o,n)}var Wee={kernelName:Wi,backendName:"webgl",kernelFunc:ng};function Tn(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,u=new Array(i);for(let l=0;l<u.length;l++)u[l]=s.shape[a[l]];let c;if(o.shouldExecuteOnCPU([s])){let p=o.texData.get(s.dataId).values,d=m0(p,s.shape,s.dtype,a,u);c=o.makeTensorInfo(u,s.dtype);let h=o.texData.get(c.dataId);h.values=d}else c=tg(s,a,o);return c}var Vee={kernelName:_s,backendName:"webgl",kernelFunc:Tn},oD=1e3;function Ef({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let c=e.shape.length,l=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[l-1]:t.shape[l-2],h=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[l-2]:t.shape[l-1],g=e.shape.slice(0,-2),m=t.shape.slice(0,-2),b=w.sizeFromShape(g),y=w.sizeFromShape(m),x=dl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let k=n?[b,p,h]:[b,h,p],S=r?[y,f,d]:[y,d,f],N=pe({inputs:{x:e},backend:s,attrs:{shape:k}}),E=pe({inputs:{x:t},backend:s,attrs:{shape:S}}),$=[N,E],F=Math.max(b,y),D=n?N.shape[1]:N.shape[2],R=a!=null,C=o!=null,L=u==="leakyrelu",U=u!=null?Fd(u,!0):null,H=R||C||L||U!=null,K;if((h===1||f===1)&&D>oD&&H===!1){let Z=N,J=E;n&&(Z=Tn({inputs:{x:N},backend:s,attrs:{perm:[0,2,1]}}),$.push(Z)),r&&(J=Tn({inputs:{x:E},backend:s,attrs:{perm:[0,2,1]}}),$.push(J));let ee=f!==1,se=f===1,te=Z;ee&&(te=pe({inputs:{x:Z},backend:s,attrs:{shape:[F,D,1]}}),$.push(te));let oe=f===1?2:1,ne=J;se&&(ne=pe({inputs:{x:J},backend:s,attrs:{shape:[F,1,D]}}),$.push(ne));let de=y0({inputs:{a:te,b:ne},backend:s});K=ng({inputs:{x:de},backend:s,attrs:{axis:oe,keepDims:!0}}),$.push(de)}else{let Z=fr(e.dtype,t.dtype),J=new aD(k,S,[F,h,f],n,r,R,U,C,L),ee=[N,E];if(a!=null&&ee.push(a),C&&ee.push(o),L){let se=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));ee.push(se),$.push(se)}K=s.runWebGLProgram(J,ee,Z)}let q=pe({inputs:{x:K},backend:s,attrs:{shape:x}});$.push(K);for(let Z of $)s.disposeIntermediateTensorInfo(Z);return q}function Uee(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:c,activation:l,leakyreluAlpha:p}=r;return Ef({a:s,b:a,transposeA:u,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:l})}var Gee={kernelName:uo,backendName:"webgl",kernelFunc:Uee},P1="return abs(x);";function Hee(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let a=n.texData.get(r.dataId),o=XA(a.values);return n.makeTensorInfo(r.shape,r.dtype,o)}let s;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new oa(r.shape,P1):s=new is(r.shape,P1),n.runWebGLProgram(s,[r],r.dtype)}var jee={kernelName:cc,backendName:"webgl",kernelFunc:Hee},qee=Or+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,Kee=Ze({opSnippet:qee}),Xee={kernelName:Eo,backendName:"webgl",kernelFunc:Kee},Yee=Or+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,Zee=Ze({opSnippet:Yee}),Jee={kernelName:Ao,backendName:"webgl",kernelFunc:Zee},O1="return a + b;",Qee=mn({opSnippet:O1,packedOpSnippet:O1,supportsComplex:!0,cpuKernelImpl:iQ}),ete={kernelName:Sa,backendName:"webgl",kernelFunc:Qee},tte=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let r=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

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

        vec4 result = ${r};
        setOutput(result);
      }
    `}};function nf(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return sr({inputs:{x:r[0]},backend:n});if(r.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=nf({inputs:r.slice(0,u),backend:n}),l=nf({inputs:r.slice(u),backend:n});return nf({inputs:[c,l],backend:n})}let s=r.map(u=>u.dtype).reduce((u,c)=>fr(u,c)),a=r.map(u=>u.shape),i=G().getBool("WEBGL_PACK")?new nte(r[0].shape,a):new tte(r[0].shape,a);return n.runWebGLProgram(i,r,s)}var rte={kernelName:Do,backendName:"webgl",kernelFunc:nf};function ste(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),c=u,l=T.getAxesPermutation(c,i),p=s;l!=null&&(p=Tn({inputs:{x:s},backend:n,attrs:{perm:l}}),c=T.getInnerMostAxes(c.length,i)),T.assertAxesAreInnerMostDims("all",c,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,c),f=w.sizeFromShape(h),g=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),m=ru(g,g.dtype,"all",n),b;if(o){let y=T.expandShapeToKeepDim(d,u);b=pe({inputs:{x:m},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:m},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(m),l!=null&&n.disposeIntermediateTensorInfo(p),b}var ate={kernelName:lc,backendName:"webgl",kernelFunc:ste};function ote(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),c=u,l=T.getAxesPermutation(c,i),p=s;l!=null&&(p=Tn({inputs:{x:s},backend:n,attrs:{perm:l}}),c=T.getInnerMostAxes(c.length,i)),T.assertAxesAreInnerMostDims("any",c,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,c),f=w.sizeFromShape(h),g=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),m=ru(g,g.dtype,"any",n),b;if(o){let y=T.expandShapeToKeepDim(d,u);b=pe({inputs:{x:m},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:m},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(m),l!=null&&n.disposeIntermediateTensorInfo(p),b}var ite={kernelName:dc,backendName:"webgl",kernelFunc:ote},ute=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${r};

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

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

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

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function iD(e,t,n,r=null){let s=t.shape[0],a=t.shape[1];r!=null&&(s=r.shape[0],a=r.shape[1]);let o=T.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:s,outSize:Math.ceil(a/o)},u=new ute(i,n,r==null),c=[t];r!=null&&c.push(r);let l=e.runWebGLProgram(u,c,"int32");if(l.shape[1]===1)return l;let p=iD(e,t,n,l);return e.disposeIntermediateTensorInfo(l),p}function uD(e,t,n,r=null){let s=r!=null?r.shape:t.shape,a=s[s.length-1],o=T.computeOptimalWindowSize(a),i=new cte(s,o,n,r==null),u=r==null?[t]:[t,r],c=e.runWebGLProgram(i,u,"int32");if(c.shape.length===t.shape.length){let l=uD(e,t,n,c);return e.disposeIntermediateTensorInfo(c),l}return c}function cD(e,t,n,r){let s=[n];if(T.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),s,t.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,u=t;i&&(u=e.unpackTensor(t),a.push(u));let[c,l]=T.computeOutAndReduceShapes(u.shape,s),p=w.sizeFromShape(l),d=pe({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=iD(e,d,r);a.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(g=>e.disposeIntermediateTensorInfo(g)),f}return uD(e,t,r)}function lte(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=T.getAxesPermutation(o,s.shape.length),u=s,c=[];i!=null&&(u=Tn({inputs:{x:s},backend:n,attrs:{perm:i}}),c.push(u),o=T.getInnerMostAxes(o.length,u.shape.length)),T.assertAxesAreInnerMostDims("argMax",[o[0]],u.shape.length);let l=cD(n,u,o[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),l}var dte={kernelName:pc,backendName:"webgl",kernelFunc:lte};function pte(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=T.getAxesPermutation(o,s.shape.length),u=s,c=[];i!=null&&(u=Tn({inputs:{x:s},backend:n,attrs:{perm:i}}),c.push(u),o=T.getInnerMostAxes(o.length,u.shape.length)),T.assertAxesAreInnerMostDims("argMin",[o[0]],u.shape.length);let l=cD(n,u,o[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),l}var hte={kernelName:hc,backendName:"webgl",kernelFunc:pte},fte=Or+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,mte=Ze({opSnippet:fte}),gte={kernelName:$o,backendName:"webgl",kernelFunc:mte},bte=Or+"return log(x + sqrt(x * x + 1.0));",yte=Ze({opSnippet:bte}),vte={kernelName:Fo,backendName:"webgl",kernelFunc:yte},xte=Or+`
  return atan(x);
`,wte=Ze({opSnippet:xte}),Ite={kernelName:Ro,backendName:"webgl",kernelFunc:wte},kte=b0+`
  return atan(a, b);
`,Ste=`
  vec4 result = atan(a, b);
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+nu+`
  return result;
`,Cte=mn({opSnippet:kte,packedOpSnippet:Ste}),Tte={kernelName:Oo,backendName:"webgl",kernelFunc:Cte},Nte=Or+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_te=Ze({opSnippet:Nte}),Ete={kernelName:Po,backendName:"webgl",kernelFunc:_te},Rd=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",g=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,m=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
        const ivec2 strides = ivec2(${o}, ${i});
        const ivec2 pads = ivec2(${d}, ${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 < ${l};
              wR += ${u}) {
            int xR = xRCorner + wR;

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

            for (int wC = 0; wC < ${p};
                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 ${N} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${r?s?g:m:`wR * ${p} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let y="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / max(count, 1.0)");let x=Math.floor(a/4)*4,k=a%4,S=`
      if (${f}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${y}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${o}, ${i});
      const ivec2 pads = ivec2(${d}, ${h});
      const float initializationValue = ${b};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

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

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

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

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

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

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

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

            ${S}
          }

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

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

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

            ${S}
          }
        }
        setOutput(${v});
      }
    `}},v0=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,g=e.padInfo.front,m=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${o}, ${i}, ${u});
        const ivec3 pads = ivec3(${g}, ${m}, ${b});

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

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

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

          for (int wD = 0; wD < ${d};
              wD += ${c}) {
            int xD = xDCorner + wD;

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

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

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

              for (int wC = 0; wC < ${f};
                  wC += ${p}) {
                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 = ${r?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
                      wR * ${f} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let x="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / max(count, 1.0)");let S=Math.floor(a/4)*4,N=a%4,E=`
      if (${y}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${x}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${o}, ${i}, ${u});
      const ivec3 pads = ivec3(${g}, ${m}, ${b});
      const float initializationValue = ${v};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

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

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

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

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

        for (int wD = 0; wD < ${d};
            wD += ${c}) {
          int xD = xDCorner + wD;

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

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

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

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

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

              ${E}
            }

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

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

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

              ${E}
            }
          }
        }
        setOutput(${k});
      }
    `}};function Ate(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;wl(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=r,c=1;w.assert(T.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let l=T.computePool2DInfo(s.shape,a,o,c,i,u);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return sr({inputs:{x:s},backend:n});let p=new Rd(l,"avg",!1);return n.runWebGLProgram(p,[s],"float32")}var Dte={kernelName:Mo,backendName:"webgl",kernelFunc:Ate};function $te(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u,dataFormat:c}=r,l=[1,1,1],p=T.computePool3DInfo(s.shape,a,o,l,i,u,c),d=new v0(p,"avg",!1);return n.runWebGLProgram(d,[s],"float32")}var Fte={kernelName:fc,backendName:"webgl",kernelFunc:$te},Rte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.top,l=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
      const ivec2 pads = ivec2(${c}, ${l});
      const float avgMultiplier = float(${p});

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

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

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

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

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

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

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

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

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

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

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

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

            for (int wC = 0; wC < ${d};
                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 Ote(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:u,pad:c,dimRoundingMode:l}=r,p=[1,1,1],d=T.computePool3DInfo(o.shape,i,u,p,c,l),h=new Pte(d);return n.runWebGLProgram(h,[s],o.dtype)}var Mte={kernelName:zd,backendName:"webgl",kernelFunc:Ote};function Lte(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;wl([s,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:c}=r,l=T.computePool2DInfo(o.shape,i,u,1,c),p=new Rte(l);return n.runWebGLProgram(p,[s],o.dtype)}var Bte={kernelName:Bd,backendName:"webgl",kernelFunc:Lte};function zte(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return Ef({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var Wte={kernelName:Lo,backendName:"webgl",kernelFunc:zte},Vte=class{constructor(e,t,n,r,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="0.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${o};
        float scale = ${i};
        float inv = scale * inversesqrt(variance + float(${a}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}},Ute=class{constructor(e,t,n,r,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${o};
        vec4 scale = ${i};

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

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

        setOutput((x - mean) * inv + offset);
      }
    `}},Gte=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:s,variance:a,offset:o,scale:i}=e;w.assert(s.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||s.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||s.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let c=[r,s,a],l=null;o!=null&&(l=o.shape,c.push(o));let p=null;i!=null&&(p=i.shape,c.push(i));let d=G().getBool("WEBGL_PACK_NORMALIZATION")?new Ute(r.shape,s.shape,a.shape,l,p,u):new Vte(r.shape,s.shape,a.shape,l,p,u);return t.runWebGLProgram(d,c,c[0].dtype)},Hte={kernelName:ni,backendName:"webgl",kernelFunc:Gte},jte=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=qte(this.rank),r,s=e.map((a,o)=>`sourceLoc.${wx[o]} = start[${o}] + coords.${wx[o]};`);r=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${s.join(`
`)}
      `,this.userCode=`
      void main() {
        ${r}
        setOutput(getSource(${n}));
      }
    `}},wx=["x","y","z","w","u","v"];function qte(e){if(e===1)return"sourceLoc";if(e<=6)return wx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Kte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ht(this.rank),n=Cn("coords",this.rank),r=Cn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,a=`getChannel(getSource(${r.join()}), ${s})`,o=`
      result.x = ${a};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${r[this.rank-1]};
        result.y = ${a};
        --${r[this.rank-1]};
      }
    `,i=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${r[this.rank-2]};
        result.z = ${a};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${r[this.rank-1]};
          result.w = ${a};
        }
      }
    `,u=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${r[l]} = ${n[l]} + start[${l}];`).join(`
`);this.userCode=`
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${u}
        vec4 result = vec4(0.);
        ${o}
        ${i}
        setOutput(result);
      }
    `}};function Xte(e,t,n,r){let s=r.texData.get(e.dataId),a=r.makeTensorInfo(n,e.dtype),o=r.texData.get(a.dataId);Object.assign(o,s),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Kt.computeFlatOffset(t,w.computeStrides(e.shape));s.slice&&(i+=s.slice.flatOffset),o.slice={flatOffset:i,origDataId:s.slice&&s.slice.origDataId||e.dataId};let u=r.dataRefCount.get(o.slice.origDataId)||1;return r.dataRefCount.set(o.slice.origDataId,u+1),a}function _l(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,u]=Kt.parseSliceParams(s,a,o);if(Kt.assertParamsValid(s,i,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,s.dtype,[]);if(n.shouldExecuteOnCPU([s])||s.dtype==="string"){let p=n.texData.get(s.dataId),d=BQ(p.values,i,u,s.shape,s.dtype);return n.makeTensorInfo(u,s.dtype,d)}let{isPacked:c}=n.texData.get(s.dataId),l=Kt.isSliceContinous(s.shape,i,u);if(c||!l){let p=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kte(u):new jte(u),d=[i];return n.runWebGLProgram(p,[s],s.dtype,d)}return n.uploadToGPU(s.dataId),Xte(s,i,u,n)}var Yte={kernelName:el,backendName:"webgl",kernelFunc:_l},Zte=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;w.assert(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,v)=>y*v),u=T.getReshaped(s.shape,a,i),c=T.getPermuted(u.length,a.length),l=T.getReshapedPermuted(s.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(l,o,a.length),h=[],f=pe({inputs:{x:s},backend:n,attrs:{shape:u}}),g=Tn({inputs:{x:f},backend:n,attrs:{perm:c}}),m=pe({inputs:{x:g},backend:n,attrs:{shape:l}}),b=_l({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(g),h.push(m),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},Jte={kernelName:mc,backendName:"webgl",kernelFunc:Zte};function Qte(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.readSync(s.dataId),u=n.readSync(a.dataId),c=KA(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var ene={kernelName:gc,backendName:"webgl",kernelFunc:Qte},tne=`
  int r = int(a.r) & int(b.r);
  int g = int(a.g) & int(b.g);
  int rb = int(a.b) & int(b.b);
  int ra = int(a.a) & int(b.a);
  return vec4(r, g, rb, ra);
`,nne=`
  return float(int(a.r) & int(b.r));
`;function rne(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=G().getBool("WEBGL_PACK_BINARY_OPERATIONS"),o=G().getNumber("WEBGL_VERSION");if(n.shouldExecuteOnCPU([r,s])||o===1){let u=n.texData.get(r.dataId).values,c=n.texData.get(s.dataId).values,[l,p]=cQ(r.shape,s.shape,u,c,r.dtype),d=n.makeTensorInfo(p,r.dtype),h=n.texData.get(d.dataId);return h.values=l,d}let i;return a?i=new Tl(tne,r.shape,s.shape,!1):i=new To(nne,r.shape,s.shape),n.runWebGLProgram(i,[r,s],r.dtype)}var sne={kernelName:bc,backendName:"webgl",kernelFunc:rne};function ane(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.readSync(r.dataId),o=n.readSync(s.dataId),i=T.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var one={kernelName:Wd,backendName:"webgl",kernelFunc:ane},ine="return float(a != b);",lD=mn({opSnippet:ine,cpuKernelImpl:AQ,dtype:"bool"}),une={kernelName:Wc,backendName:"webgl",kernelFunc:lD};function Mp(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return sr({inputs:{x:s.complexTensorInfos.real},backend:n})}var cne={kernelName:Xf,backendName:"webgl",kernelFunc:Mp},lne="return float(int(x));";function dne(e,t){let n=new is(e.shape,lne),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Ix(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return sr({inputs:{x:s},backend:n});let o=kt(s.shape),i=Ix({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),u=Oa({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),u}if(s.dtype==="complex64"){let o=Mp({inputs:{input:s},backend:n}),i=Ix({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(s.dtype,a)){let o=sr({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(n.shouldExecuteOnCPU([s])){let o=n.texData.get(s.dataId).values,[i,u,c]=lQ(o,s.shape,s.dtype,a);return n.makeTensorInfo(i,u,c)}if(a==="int32")return dne(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=lD({inputs:{a:s,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),u}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var pne={kernelName:Bo,backendName:"webgl",kernelFunc:Ix},M1="return ceil(x);",hne=Ze({opSnippet:M1,packedOpSnippet:M1,cpuKernelImpl:dQ}),fne={kernelName:zo,backendName:"webgl",kernelFunc:hne},mne=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));
      }
    `}},gne=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 bne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i;G().getBool("WEBGL_PACK_CLIP")?i=new gne(s.shape):i=new mne(s.shape);let u=[[a],[o]];return n.runWebGLProgram(i,[s],s.dtype,u)}var yne={kernelName:Ca,backendName:"webgl",kernelFunc:bne},vne=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 L1(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function xne(e){let{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),a=new vne(r.shape),o=[L1(r,s.complexTensorInfos.real),L1(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var wne={kernelName:Vd,backendName:"webgl",kernelFunc:xne},Ine=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let r=t.length,s=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${s}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

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

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

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

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

        ${a[r-1]} = ${a[r-1]} - 1;
        if (${a[r-2]} < ${n[r-2]} &&
            ${a[r-1]} < ${n[r-1]}) {
          result.b = getValue(${a});
        }
        setOutput(result);
      }
    `}};function qh(e,t,n){let r=e.indexOf(t);return e.map((a,o)=>o===r?`${a} - ${n}`:a).join()}function rg(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return sr({inputs:{x:s.complexTensorInfos.imag},backend:n})}var Sne={kernelName:Hf,backendName:"webgl",kernelFunc:rg};function hd(e,t,n){let r=e[0].dtype;if(r==="complex64"){let h=e.map(y=>Mp({inputs:{input:y},backend:n})),f=e.map(y=>rg({inputs:{input:y},backend:n})),g=hd(h,t,n),m=hd(f,t,n),b=Oa({inputs:{real:g,imag:m},backend:n});return h.forEach(y=>n.disposeIntermediateTensorInfo(y)),f.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(m),b}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let h=e.map(x=>{let S=[-1,w.sizeFromShape(x.shape.slice(t))];return pe({inputs:{x},backend:n,attrs:{shape:S}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),g=T.computeOutShape(h.map(x=>x.shape),1),m=h[0].shape[0]===1,b=pQ(f,g,r,m),y=T.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,r,b);return h.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}let a=e.filter(h=>w.sizeFromShape(h.shape)>0),o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&a[0].shape.length>1;if(a.length===1){let h=o?new is(e[0].shape,ta):new oa(e[0].shape,ta);return n.runWebGLProgram(h,e,r)}let i=G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(a.length>i){let h=[];for(let g=0;g<a.length;g+=i){let m=a.slice(g,g+i);h.push(hd(m,t,n))}let f=hd(h,t,n);for(let g of h)n.disposeIntermediateTensorInfo(g);return f}if(o){let h=new kne(a.map(f=>f.shape),t);return n.runWebGLProgram(h,a,r)}let{tensors2D:u,outShape:c}=Cne(a,t,n),l=new Ine(u.map(h=>h.shape)),p=n.runWebGLProgram(l,u,r);u.forEach(h=>n.disposeIntermediateTensorInfo(h));let d=pe({inputs:{x:p},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(p),d}function Cne(e,t,n){let r=T.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>pe({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function dD(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=t.map(c=>c.shape);T.assertParamsConsistent(o,a);let i=T.computeOutShape(t.map(c=>c.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let u=t.filter(c=>w.sizeFromShape(c.shape)>0);return u.length===1?sr({inputs:{x:u[0]},backend:n}):hd(u,a,n)}var Tne={kernelName:yc,backendName:"webgl",kernelFunc:dD},pD=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,g=e.dataFormat==="channelsLast",m=g?1:2,b=g?2:3,y=g?3:1,v="",x="";n&&(r?v=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?v=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:v=`
          float activation(float x) {
            ${n}
          }
        `,x="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${v}

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

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

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

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

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

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

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

            }
          }
        }

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

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

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

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

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

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

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

            for (int wC = 0; wC < ${d}; 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, ${h} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},hD=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let a=e.padInfo.left,o=e.strideWidth,i=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,p=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)p+=`
           vec4 xTexelC${g*2};
           int xTexelC${g*2}Ready;
           vec4 xTexelC${g*2+1};
           int xTexelC${g*2+1}Ready;
           vec4 xC${g};`;p+=`
     for (int r = 0; r < ${u}; r++) {
      for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {
       `;for(let g=0;g<c;g++)p+=`
           xTexelC${g*2} = vec4(0.0);
           xTexelC${g*2}Ready = 0;
           xTexelC${g*2+1} = vec4(0.0);
           xTexelC${g*2+1}Ready = 0;
           xC${g} = vec4(0.0);`;p+=`
         xR = xRCorner + r * dilations[0];
         if (xR >=0 && xR < inDims[0]) {
       `;for(let g=0;g<(l+1)/2;g++){let m=g*2;if(p+=`
           xC = xCCorner + ${m*i};
           `,o===1){if(m<c&&(a%2===1?(p+=`
                 xCOffset = xC + 1;
                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${m}Ready == 0) {
                   xTexelC${m} = 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${m}.zw = vec2(0.0);
                   }
                   xTexelC${m}Ready = 1;
                 }
               `,i===1&&m>0?p+=`
                 xC${m} = vec4(xTexelC${m-2}.zw, xTexelC${m}.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${m} = vec4(previous.zw, xTexelC${m}.xy);
                   } else {
                     xC${m} = vec4(0.0, 0.0, xTexelC${m}.xy);
                   }
                   `):p+=`
                 if (xC >= 0 && xC < inDims[1] && xTexelC${m}Ready == 0) {
                   xTexelC${m} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${m}.zw = vec2(0.0);
                   }
                   xTexelC${m}Ready = 1;
                 }

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

                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${m+1}Ready == 0) {
                     xTexelC${m+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${m+1}.zw = vec2(0.0);
                     }
                     xTexelC${m+1}Ready = 1;
                   }
                   `,i>1?p+=`
                     xCOffset -= 2;
                     if (xCOffset >= 0 && xCOffset < inDims[1]) {
                      previous = getX(batch, xR, xCOffset, d1);
                      xC${m+1} = vec4(previous.zw, xTexelC${m+1}.xy);
                     } else {
                      xC${m+1} = vec4(0.0, 0.0, xTexelC${m+1}.xy);
                     }
                     `:p+=`
                     xC${m+1} = vec4(xTexelC${m}.zw, xTexelC${m+1}.xy);
                     `):b===1?p+=`
                     xC${m+1} = xTexelC${m};
                     `:p+=`
                     xCOffset = xC + ${b};

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

                     xC${m+1} = xTexelC${m+1};
                     `}}else m<c&&(a%2===1?(p+=`
                 xCOffset = xC + 1 - strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${m}Ready == 0) {
                   xTexelC${m} = 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${m}.zw = vec2(0.0);
                   }
                   xTexelC${m}Ready = 1;
                 }

                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${m+1}Ready == 0) {
                   xTexelC${m+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${m+1}.zw = vec2(0.0);
                   }
                   xTexelC${m+1}Ready = 1;
                 }

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

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

                 xC${m} = vec4(
                   xTexelC${m}.xy, xTexelC${m+1}.xy);
               `,m+1<c&&(p+=`
                   xC${m+1} = vec4(xTexelC${m}.zw, xTexelC${m+1}.zw);
                 `)));m<c&&(p+=`
             wTexel = getW(r, ${m}, d1, d2);
             dotProd += xC${m}.xxzz * vec4(wTexel.xy, wTexel.xy);
             if(d1 + 1 < ${e.inChannels}) {
               dotProd += xC${m}.yyww * vec4(wTexel.zw, wTexel.zw);
             }
           `,m+1<c&&(p+=`
               wTexel = getW(r, ${m+1}, d1, d2);
               dotProd += xC${m+1}.xxzz * vec4(wTexel.xy, wTexel.xy);
               if(d1 + 1 < ${e.inChannels}) {
                 dotProd += xC${m+1}.yyww * vec4(wTexel.zw, wTexel.zw);
               }
             `))}p+=`
     }
   `,p+=`
     }
   `,p+=`
     }
   `;let d="",h="";n&&(r?d=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:s?d=`vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
           ${n}
         }`:d=`vec4 activation(vec4 x) {
           ${n}
         }`,h="result = activation(result);");let f=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${d}

       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
         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);
         ${f}
         ${h}
         setOutput(result);
       }
     `}},_ne=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let{dataFormat:n}=t,r=An(),s=n==="channelsLast",a=s?1:2,o=s?2:3,i=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,u="";for(let c=0;c<=1;c++)for(let l=0;l<=1;l++)u+=`
          blockIndex = rc.z + ${l};
          pos = rc.y + ${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 (${s}) {
                  innerDims = vec2(d1, ch);
                  result[${c*2+l}] = getChannel(
                    getA(rc.x, d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${c*2+l}] = getChannel(
                    getA(rc.x, ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;this.userCode=`
      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0);

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

        ${u}

        ${r.output} = result;
      }
    `}};function Af(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function fD({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=e.shape,c=r.texData.get(e.dataId),l=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,g=!1,m,b=[];if(a!=null){let x=Af(a.shape,h);x!=null&&(a=pe({inputs:{x:a},backend:r,attrs:{shape:x}}),b.push(a))}if(s!=null){let x=Af(s.shape,h);x!=null&&(s=pe({inputs:{x:s},backend:r,attrs:{shape:x}}),b.push(s))}if(!((p===1||d===1)&&l>oD)&&c.isPacked&&h&&c.texture!=null&&u[2]%2!==0&&w.arraysEqual(c.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},S=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,w.assert($d(c.shape,k.shape),()=>`packed reshape ${c.shape} to ${k.shape} isn't free`);let N=pe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=Ef({a:k,b:N,backend:r,transposeA:f,transposeB:g,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),$=r.texData.get(E.dataId);w.assert($.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=S,$.shape=n.outShape,m=sr({inputs:{x:E},backend:r}),m.shape=n.outShape,b.push(E)}else{let x=n.outHeight*n.outWidth,k=pe({inputs:{x:e},backend:r,attrs:{shape:h?[n.batchSize,x,n.inChannels]:[n.batchSize,n.inChannels,x]}}),S=pe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Ef({a:h?k:S,b:h?S:k,transposeA:!h,transposeB:g,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});m=pe({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),b.push(k),b.push(S),b.push(N)}for(let x of b)r.disposeIntermediateTensorInfo(x);return m}function mD({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:u,filterHeight:c,inChannels:l,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",g=u*c*l,m=d*p,b=[n.batchSize,g,m],y=!0,v=!1,x=[];if(a!=null){let q=Af(a.shape,f);q!=null&&(a=pe({inputs:{x:a},backend:r,attrs:{shape:q}}),x.push(a))}if(s!=null){let q=Af(s.shape,f);q!=null&&(s=pe({inputs:{x:s},backend:r,attrs:{shape:q}}),x.push(s))}let k=pe({inputs:{x:t},backend:r,attrs:{shape:[1,g,w.sizeFromShape(t.shape)/g]}});x.push(k);let S=new _ne(b,n),N=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=r.runWebGLProgram(S,[e],"float32",N),$=pe({inputs:{x:E},backend:r,attrs:{shape:b}});x.push(E),x.push($);let F=s!=null,D=a!=null,R=i==="leakyrelu",C=i?Fd(i,!0):null,L=new aD(f?$.shape:k.shape,f?k.shape:$.shape,f?[n.batchSize,m,n.outChannels]:[n.batchSize,n.outChannels,m],y,v,F,C,D,R),U=f?[$,k]:[k,$];if(s&&U.push(s),D&&U.push(a),R){let q=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));U.push(q),x.push(q)}let H=r.runWebGLProgram(L,U,"float32"),K=pe({inputs:{x:H},backend:r,attrs:{shape:n.outShape}});x.push(H);for(let q of x)r.disposeIntermediateTensorInfo(q);return K}function Ene(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:c,dimRoundingMode:l}=r,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(s.shape,a.shape,o,c,i,l,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=fD({x:s,filter:a,convInfo:d,backend:n});else if(d.strideWidth<=2&&p==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let g=new hD(d),m=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=n.runWebGLProgram(g,[s,a],"float32",m)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=mD({x:s,filter:a,convInfo:d,backend:n});else{let g=new pD(d);h=n.runWebGLProgram(g,[s,a],"float32")}let f=pe({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var Ane={kernelName:Wo,backendName:"webgl",kernelFunc:Ene},Dne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

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

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

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

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

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

              ${a?`float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);
              float xValue = getX(b, d1, xR, xC);
              dotProd += (xValue * dyValue);`}
            }
          }
        }
        setOutput(dotProd);
      }
    `}},$ne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${i});

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

        ivec2 dyCorner = ivec2(coords[${u}], coords[${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) / ${r}.0;

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

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

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

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

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

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

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

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

        float dotProd = 0.0;

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

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

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

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

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

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

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},Rne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${i}, ${u}, ${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) / ${s}.0;

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

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

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

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

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

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

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

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

              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function Pne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:c,filterShape:l}=r,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(s.shape,l,o,1,i,c,!1,p),h=new Dne(d);return n.runWebGLProgram(h,[s,a],"float32")}var One={kernelName:Lf,backendName:"webgl",kernelFunc:Pne},Mne=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=xn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,r=t-1-e.padInfo.top,s=n-1-e.padInfo.left;this.userCode=`
      const ivec2 pads = ivec2(${r}, ${s});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];

        ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        vec4 result = vec4(0.);
        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / strides[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++) {
            int wCPerm = ${n} - 1 - wC;

            float dyC = float(dyCCorner + wC) / strides[1];
            bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)
              && (fract(dyC) == 0.0);
            int idyC = int(dyC);

            float dyC2 = float(dyCCorner + wC + 1) / strides[1];
            bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)
              && (fract(dyC2) == 0.0);
            int idyC2 = int(dyC2);

            if (idyCVal && idyCVal2) {
              for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC, d2);
                vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?
                  dySample : getDy(batch, idyR, idyC2, d2);

                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.xy += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));

                dyValue = mod(float(idyC2), 2.) == 0. ?
                  dySample2.xy : dySample2.zw;
                result.zw += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            } else if (idyCVal) {
              for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC, d2);
                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.xy += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            } else if (idyCVal2) {
              for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC2, d2);
                vec2 dyValue = mod(float(idyC2), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.zw += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            }
          }
        }
        setOutput(result);
      }
    `}};function Lne(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:c,dimRoundingMode:l}=r,p=T.convertConv2DDataFormat(c),d=T.computeConv2DInfo(o,a.shape,i,1,u,l,!1,p);if(G().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&p==="channelsLast"){let h=[[d.strideHeight,d.strideWidth]],f=new Mne(d);return n.runWebGLProgram(f,[s,a],"float32",h)}else{let h=new $ne(d);return n.runWebGLProgram(h,[s,a],"float32")}}var Bne={kernelName:Vo,backendName:"webgl",kernelFunc:Lne};function zne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u}=r,c=T.computeConv3DInfo(s.shape,a.shape,o,u,i),l=new Nne(c);return n.runWebGLProgram(l,[s,a],"float32")}var Wne={kernelName:Uo,backendName:"webgl",kernelFunc:zne};function Vne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:u}=r,c=T.computeConv3DInfo(s.shape,u,o,1,i),l=new Fne(c);return n.runWebGLProgram(l,[s,a],"float32")}var Une={kernelName:vc,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:u}=r,c=T.computeConv3DInfo(u,a.shape,i,1,o),l=new Rne(c);return n.runWebGLProgram(l,[s,a],"float32")}var Hne={kernelName:xc,backendName:"webgl",kernelFunc:Gne},jne=Nl+`
  return cos(x);
`,qne=`
  vec4 result = cos(x);
  bvec4 isNaN = isnan(x);
  ${nu}
  return result;
`,Kne=Ze({opSnippet:jne,packedOpSnippet:qne}),Xne={kernelName:Go,backendName:"webgl",kernelFunc:Kne},Yne=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,Zne=Ze({opSnippet:Yne}),Jne={kernelName:Ho,backendName:"webgl",kernelFunc:Zne},Qne=class{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,u]=e,[c]=t,[l,p]=n;this.outputShape=[c,l,p,u];let d=r==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[g,m,b]=l>1?[`${(o-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(i-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
      const float height_ratio = float(${g});
      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 = ${m};
        float width_scale = ${v};

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

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${d} == 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);
        }
      }
    `}},ere=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:c}=r,l=new Qne(s.shape,a.shape,i,u,c);return n.runWebGLProgram(l,[s,a,o],"float32")},tre={kernelName:Ic,backendName:"webgl",kernelFunc:ere},Pd;(function(e){e.Prod="*",e.Sum="+"})(Pd||(Pd={}));var B1=class{constructor(e,t,n,r){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===Pd.Prod?"1.0":"0.0",o=n?a:`getX(${z1(s,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1],u="",c="";n?(u=r?`end != ${i-1}`:"end != 0",c=r?"end + 1":"end - 1"):(u=r?`end + pow2 < ${i}`:"end >= pow2",c=r?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${ht(s)} coords = getOutputCoords();
        int end = ${W1(s,"coords",this.op)};
        float val = ${o};
        int pow2 = int(pow(2.0, index));
        if (${u}) {
          int idx = ${c};
          ${W1(s,"coords",this.op)} = idx;
          val ${this.op}= getX(${z1(s,"coords",this.op)});
        }
        setOutput(val);
      }
    `}};function z1(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function W1(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function gD(e,t,n,r,s,a){let o=t.shape.length,i=T.getAxesPermutation([r],o),u=t;i!=null&&(u=Tn({inputs:{x:t},backend:n,attrs:{perm:i}}));let c=T.getInnerMostAxes(1,o)[0];if(c!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${r}`);let l=u.shape[c],p=sr({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(l))-1;d++){let h=new B1(e,u.shape,!1,a),f=[[d]],g=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(g)}if(s){let d=new B1(e,u.shape,s,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(i!=null){let d=T.getUndoAxesPermutation(i),h=Tn({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),h}return p}function nre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return gD(Pd.Prod,s,n,a,o,i)}var rre={kernelName:wc,backendName:"webgl",kernelFunc:nre};function sre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return gD(Pd.Sum,s,n,a,o,i)}var are={kernelName:jo,backendName:"webgl",kernelFunc:sre};function ore(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let u=n.readSync(s.dataId),c=n.readSync(a.dataId),l=KA(u,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}else if(s.shape.length===2){let u=n.bufferSync(s),c=n.bufferSync(a),l=uQ(u,c,o,i);return n.makeTensorInfo(l.shape,a.dtype,l.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var ire={kernelName:Ud,backendName:"webgl",kernelFunc:ore},ure=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 cre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],u=o==="NHWC"?s.shape[1]:s.shape[2],c=o==="NHWC"?s.shape[2]:s.shape[3],l=o==="NHWC"?s.shape[3]:s.shape[1],p=u*a,d=c*a,h=l/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],g=new ure(f,a,o);return n.runWebGLProgram(g,[s],s.dtype)}var lre={kernelName:kc,backendName:"webgl",kernelFunc:cre},bD=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,u="",c="";n&&(r?u=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?u=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:u=`
          float activation(float x) {
            ${n}
          }
        `,c="result = activation(result);");let l=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${u}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        ${d}

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

        float dotProd = 0.0;

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

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

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

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

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

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

        float dotProd = 0.0;

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

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

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

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

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

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

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

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

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

          if (hIn >= 0 && hIn < ${t}) {
            for (int w = 0; w < ${i}; w++) {
              int wIn = wBeg + w * ${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 kre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u}=r,c=T.computeDilation2DInfo(s.shape,a.shape,o,i,"NHWC",u),l,p=new Ire(c);l=n.runWebGLProgram(p,[s,a],"float32");let d=pe({inputs:{x:l},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(l),d}var Sre={kernelName:Ko,backendName:"webgl",kernelFunc:kre};function Cre(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:u}=T.decodeEinsumEquation(s,a.length);T.checkEinsumDimSizes(o.length,u,a);let{path:c,steps:l}=T.getEinsumComputePath(i,u),p=l.length,d=null,h=o.length,f=[];for(let g=0;g<p;++g){for(let m of l[g]){let{permutationIndices:b,expandDims:y}=T.getEinsumPermutation(h,u[m]),v;T.isIdentityPermutation(b)?v=a[m]:(v=Tn({inputs:{x:a[m]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=pe({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=y0({inputs:{a:v,b:d},backend:n}),f.push(d))}g<p-1&&(c[g]>=0&&(d=ng({inputs:{x:d},backend:n,attrs:{axis:c[g]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let g of f)g!==d&&n.disposeIntermediateTensorInfo(g);return d}var Tre={kernelName:Vf,backendName:"webgl",kernelFunc:Cre},Nre="return (x >= 0.0) ? x : (exp(x) - 1.0);",_re=`
  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;
`,Ere=Ze({opSnippet:Nre,packedOpSnippet:_re}),Are={kernelName:Yo,backendName:"webgl",kernelFunc:Ere},Dre="return (b >= 0.0) ? a : a * (b + 1.0);",$re=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Fre=e=>{let{inputs:t,backend:n}=e,{dy:r,y:s}=t,a=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tl($re,r.shape,s.shape):new To(Dre,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)},Rre={kernelName:Sc,backendName:"webgl",kernelFunc:Fre},Pre=`
  return vec4(equal(a, b));
`,Ore="return float(a == b);",Mre=mn({opSnippet:Ore,packedOpSnippet:Pre,dtype:"bool",cpuKernelImpl:hQ}),Lre={kernelName:Cc,backendName:"webgl",kernelFunc:Mre},Bre=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${T.ERF_P};
  float a1 = ${T.ERF_A1};
  float a2 = ${T.ERF_A2};
  float a3 = ${T.ERF_A3};
  float a4 = ${T.ERF_A4};
  float a5 = ${T.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));
`,zre=Ze({opSnippet:Bre}),Wre={kernelName:Zo,backendName:"webgl",kernelFunc:zre},Vre=Nl+`
  return exp(x);
`,Ure=`
  vec4 result = exp(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`,vD=Ze({opSnippet:Vre,packedOpSnippet:Ure,cpuKernelImpl:fQ,dtype:"float32"}),Gre={kernelName:Jo,backendName:"webgl",kernelFunc:vD};function kx(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=s;return s<0&&(w.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+s+1),i.splice(u,0,1),pe({inputs:{x:a},backend:r,attrs:{shape:i}})}var Hre={kernelName:Tc,backendName:"webgl",kernelFunc:kx},V1="return exp(x) - 1.0;",jre=Ze({opSnippet:V1,packedOpSnippet:V1,cpuKernelImpl:mQ}),qre={kernelName:Qo,backendName:"webgl",kernelFunc:jre},U1=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${r}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
      const float exponentMultiplier = ${s};

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

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

        float result = 0.0;

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

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

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function xD(e,t,n){let r=n.texData.get(e.dataId),s=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=s/a,i=pe({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),u=i.shape,c=new U1("real",u,t),l=new U1("imag",u,t),p=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:u},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(l,p,"float32"),f=Oa({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let g=pe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),g}function Kre(e){let{inputs:t,backend:n}=e,{input:r}=t;return xD(r,!1,n)}var Xre={kernelName:Uf,backendName:"webgl",kernelFunc:Kre},Yre=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 Lp(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||w.inferDtype(s),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new Yre(r,s),i=[[s]];return t.runWebGLProgram(o,[],a,i)}}var Zre={kernelName:Hd,backendName:"webgl",kernelFunc:Lp},Jre=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);
        }
    `}},Qre={kernelName:Nc,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new Jre(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},G1="return floor(x);",ese=Ze({opSnippet:G1,packedOpSnippet:G1,cpuKernelImpl:gQ}),tse={kernelName:ei,backendName:"webgl",kernelFunc:ese},nse=`
  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;
  }
`,rse=`
  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);
`,sse=mn({opSnippet:nse,packedOpSnippet:rse,dtype:"int32"}),ase={kernelName:ti,backendName:"webgl",kernelFunc:sse},ose=class{constructor(e){this.variableNames=["A"];let t=An(),[n,r]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);

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

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

        vec4 result = vec4(0.);

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

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

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

        ${t.output} = result;
      }
    `}},use={kernelName:of,backendName:"webgl",kernelFunc:cse},Ou,Tv=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function cse(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r,o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,[u,c]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],l=[c,u],p=[c,u,a];if(i||o){let g=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ou==null||g!==Tv)&&(Tv=g,Ou=document.createElement("canvas").getContext("2d",{willReadFrequently:Tv})),Ou.canvas.width=u,Ou.canvas.height=c,Ou.drawImage(s,0,0,u,c),s=Ou.canvas}let d=n.makeTensorInfo(l,"int32");n.texData.get(d.dataId).usage=lr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),s);let h=G().getBool("WEBGL_PACK")?new ise(p):new ose(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function lse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:c,dataFormat:l,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=r,g=T.convertConv2DDataFormat(l),m=T.computeConv2DInfo(s.shape,a.shape,u,p,c,d,!1,g),b,y=[],v=o!=null,x=i!=null,k=h==="leakyrelu",S=()=>{let E=[s,a],$=(F,D)=>{if(D==="NCHW"&&F.shape.length===1&&F.shape[0]!==1){let R=pe({inputs:{x:F},backend:n,attrs:{shape:[F.shape[0],1,1]}});return y.push(R),R}return F};if(v&&E.push($(o,l)),x&&E.push($(i,l)),k){let F=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));E.push(F),y.push(F)}return E};if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))b=fD({x:s,filter:a,convInfo:m,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(m.strideWidth<=2&&g==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let E=h?Fd(h,!0):null,$=new hD(m,v,E,x,k),F=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]],D=S();b=n.runWebGLProgram($,D,"float32",F)}else if(G().getBool("WEBGL_CONV_IM2COL"))b=mD({x:s,filter:a,convInfo:m,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let E=h?Fd(h,!1):null,$=new pD(m,v,E,x,k),F=S();b=n.runWebGLProgram($,F,"float32")}let N=pe({inputs:{x:b},backend:n,attrs:{shape:m.outShape}});return y.push(b),y.forEach(E=>n.disposeIntermediateTensorInfo(E)),N}var dse={kernelName:co,backendName:"webgl",kernelFunc:lse};function pse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:c,dilations:l,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=r,f=[],g=l;g==null&&(g=[1,1]),w.assert(T.eitherStridesOrDilationsAreOne(u,g),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${g}'`);let m=T.computeConv2DInfo(s.shape,a.shape,u,g,c,p,!0),b=G().getBool("WEBGL_PACK_DEPTHWISECONV")&&m.strideWidth<=2&&m.outChannels/m.inChannels===1,y=d?Fd(d,b):null,v=[s,a],x=o!=null,k=i!=null,S=d==="leakyrelu";if(x&&v.push(o),k&&v.push(i),S){let F=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(F),f.push(F)}let N;b?N=new yD(m,x,y,k,S):N=new bD(m,x,y,k,S);let E=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]],$=n.runWebGLProgram(N,v,"float32",E);return f.forEach(F=>n.disposeIntermediateTensorInfo(F)),$}var hse={kernelName:lo,backendName:"webgl",kernelFunc:pse},fse=class{constructor(e,t,n,r){this.sliceDim=e,this.strides=t,this.paramsShape=r,this.variableNames=["x","indices"],this.outputShape=n;let s=ht(n.length),a=`
    int index;`;for(let o=0;o<this.sliceDim;o++)a+=`
          index = round(getIndices(coords[0], ${o}));
          out_of_bounds = out_of_bounds || index < 0;
          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[o]};
          flattenIndex += index * ${this.strides[o]};`;this.userCode=`
         void main() {
          ${s} coords = getOutputCoords();
          int flattenIndex = 0;
          bool out_of_bounds = false;

          ${a}

          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
        }
      `}};function mse(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=w.sizeFromShape(r.shape),[u,c,l,p]=T.prepareAndValidate(r,s),d=pe({inputs:{x:s},backend:n,attrs:{shape:[c,o]}}),h=pe({inputs:{x:r},backend:n,attrs:{shape:[w.sizeFromShape(r.shape)/l,l]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.readSync(s.dataId),y=n.bufferSync(r),v=bQ(b,y,r.dtype,c,o,l,p,r.shape,i);return n.makeTensorInfo(u,r.dtype,v.values)}let f=new fse(o,p,[c,l],r.shape),g=n.runWebGLProgram(f,[h,d],h.dtype),m=pe({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(g),m}var gse={kernelName:Ec,backendName:"webgl",kernelFunc:mse},bse=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ht(this.rank),r=yse(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${r}));
      }
    `}};function yse(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e.length;s++)s===2?r.push("index"):r.push(`${n[s]}`);return r.join()}function wD(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r,u=w.parseAxisParam(o,s.shape)[0];if(G().get("DEBUG")){let y=n.readSync(a.dataId),v=s.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let c=T.segment_util.collectGatherOpShapeInfo(s,a,u,i),l=w.sizeFromShape(a.shape),p=[],d=pe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=pe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,l/c.batchSize]}});p.push(d),p.push(h);let f=[c.batchSize,c.outerSize,l/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([s,a])||s.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=yQ(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let g=new bse(d.shape,f),m=n.runWebGLProgram(g,[d,h],d.dtype);p.push(m);let b=pe({inputs:{x:m},backend:n,attrs:{shape:c.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var vse={kernelName:_c,backendName:"webgl",kernelFunc:wD},xse="return float(a > b);",wse=`
  return vec4(greaterThan(a, b));
`,Ise=mn({opSnippet:xse,packedOpSnippet:wse,cpuKernelImpl:vQ,dtype:"bool"}),kse={kernelName:Ac,backendName:"webgl",kernelFunc:Ise},Sse="return float(a >= b);",Cse=`
  return vec4(greaterThanEqual(a, b));
`,Tse=mn({opSnippet:Sse,packedOpSnippet:Cse,dtype:"bool",cpuKernelImpl:xQ}),Nse={kernelName:ri,backendName:"webgl",kernelFunc:Tse};function _se(e){let{inputs:t,backend:n}=e,{input:r}=t;return xD(r,!0,n)}var Ese={kernelName:Gf,backendName:"webgl",kernelFunc:_se},Ase="return float(!isnan(x) && !isinf(x));",Dse=Ze({opSnippet:Ase,dtype:"bool"}),$se={kernelName:ai,backendName:"webgl",kernelFunc:Dse},Fse="return float(isinf(x));",Rse=Ze({opSnippet:Fse,dtype:"bool"}),Pse={kernelName:oi,backendName:"webgl",kernelFunc:Rse},Ose="return float(isnan(x));",Mse=Ze({opSnippet:Ose,dtype:"bool"}),Lse={kernelName:ii,backendName:"webgl",kernelFunc:Mse},Bse="return float(a < b);",zse=`
  return vec4(lessThan(a, b));
`,Wse=mn({opSnippet:Bse,packedOpSnippet:zse,cpuKernelImpl:wQ,dtype:"bool"}),Vse={kernelName:Dc,backendName:"webgl",kernelFunc:Wse},Use="return float(a <= b);",Gse=`
  return vec4(lessThanEqual(a, b));
`,Hse=mn({opSnippet:Use,packedOpSnippet:Gse,cpuKernelImpl:IQ,dtype:"bool"}),jse={kernelName:$c,backendName:"webgl",kernelFunc:Hse};function qse(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=kQ(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var Kse={kernelName:Fc,backendName:"webgl",kernelFunc:qse},Xse=Nl+`
  return x < 0.0 ? 0./0. : log(x);
`,Yse=`
  vec4 result = log(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
  return result;
`,Zse=Ze({opSnippet:Xse,packedOpSnippet:Yse,cpuKernelImpl:SQ}),Jse={kernelName:ci,backendName:"webgl",kernelFunc:Zse},Qse=Nl+`
  return log(1.0 + x);
`,eae=Ze({opSnippet:Qse}),tae={kernelName:li,backendName:"webgl",kernelFunc:eae},nae="return float(a >= 1.0 && b >= 1.0);",rae=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,sae=mn({opSnippet:nae,packedOpSnippet:rae,dtype:"bool"}),aae={kernelName:Rc,backendName:"webgl",kernelFunc:sae},oae="return float(!(x >= 1.0));",iae=Ze({opSnippet:oae}),uae={kernelName:Pc,backendName:"webgl",kernelFunc:iae},cae="return float(a >= 1.0 || b >= 1.0);",lae=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,dae=mn({opSnippet:cae,packedOpSnippet:lae,dtype:"bool"}),pae={kernelName:Oc,backendName:"webgl",kernelFunc:dae},hae=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${u})`:s===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${a}; j <= ${a}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${o}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${i};
        setOutput(val);
      }
    `}},fae=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${u})`:s===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

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

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

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

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

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

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

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

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

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

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

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

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${r})
                * float(${s})
                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${s});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},yae=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:u,alpha:c,beta:l}=r,p=new bae(s.shape,i,u,c,l);return n.runWebGLProgram(p,[s,a,o],s.dtype)},vae={kernelName:Mc,backendName:"webgl",kernelFunc:yae};function xae(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=pe({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),u=ru(i,e.dtype,"max",r),c=pe({inputs:{x:u},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(u),c}function ID(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),c=u,l=T.getAxesPermutation(c,i),p=l!=null,d=n.shouldExecuteOnCPU([s]),h=s;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let N=0;N<x.length;N++)x[N]=s.shape[l[N]];let k=m0(v,s.shape,s.dtype,l,x);h=n.makeTensorInfo(x,s.dtype);let S=n.texData.get(h.dataId);S.values=k}else h=tg(s,l,n);c=T.getInnerMostAxes(c.length,i)}T.assertAxesAreInnerMostDims("max",c,i);let[f,g]=T.computeOutAndReduceShapes(h.shape,c),m=f;o&&(m=T.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=CQ(v,w.sizeFromShape(g),m,s.dtype);b=n.makeTensorInfo(m,s.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=xae(h,g,m,n);return p&&n.disposeIntermediateTensorInfo(h),b}var wae={kernelName:pi,backendName:"webgl",kernelFunc:ID},Iae=b0+`
  return max(a, b);
`,kae=`
  vec4 result = vec4(max(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+nu+`
  return result;
`,Sae=mn({opSnippet:Iae,packedOpSnippet:kae,cpuKernelImpl:TQ}),Cae={kernelName:hi,backendName:"webgl",kernelFunc:Sae};function Tae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;wl(s,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=r,c=1;w.assert(T.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let l=T.computePool2DInfo(s.shape,a,o,c,i,u);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return sr({inputs:{x:s},backend:n});let p=new Rd(l,"max",!1);return n.runWebGLProgram(p,[s],s.dtype)}var Nae={kernelName:fi,backendName:"webgl",kernelFunc:Tae};function _ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dataFormat:u,dimRoundingMode:c}=r,l=[1,1,1],p=T.computePool3DInfo(s.shape,a,o,l,i,c,u),d=new v0(p,"max",!1);return n.runWebGLProgram(d,[s],s.dtype)}var Eae={kernelName:Lc,backendName:"webgl",kernelFunc:_ae},Aae=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=s-1-e.padInfo.top,i=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${i});

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function $ae(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:u,pad:c,dimRoundingMode:l}=r,p=[1,1,1],d=T.computePool3DInfo(o.shape,i,u,p,c,l),h=new v0(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),g=new Dae(d),m=n.runWebGLProgram(g,[s,f],o.dtype);return n.disposeIntermediateTensorInfo(f),m}var Fae={kernelName:qd,backendName:"webgl",kernelFunc:$ae};function Rae(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;wl([a,o],"maxPoolGrad");let{filterSize:u,strides:c,pad:l,dimRoundingMode:p}=r,d=T.computePool2DInfo(i.shape,u,c,1,l,p),h=!0,f=new Rd(d,"max",h),g=n.runWebGLProgram(f,[i],i.dtype),m=new Aae(d),b=n.runWebGLProgram(m,[s,g],i.dtype);return n.disposeIntermediateTensorInfo(g),b}var Pae={kernelName:jd,backendName:"webgl",kernelFunc:Rae};function Oae(e,t,n,r){let s=new Rd(n,"max",!1),a=r.runWebGLProgram(s,[e],"float32");s=new Rd(n,"max",!0,!0,t);let o=r.runWebGLProgram(s,[e],"float32");return[a,o]}var Mae={kernelName:Kd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:s,strides:a,pad:o,includeBatchInIndex:i}=t,u=n;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=T.computePool2DInfo(r.shape,s,a,c,o),[p,d]=Oae(r,i,l,u);return[p,d]}};function Lae(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=pe({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),u=ru(i,"float32","mean",r),c=pe({inputs:{x:u},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(u),c}var Bae={kernelName:mi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:s,axis:a}=t,o=n,i=r.shape.length,u=w.parseAxisParam(a,r.shape),c=u,l=T.getAxesPermutation(c,i),p=l!=null,d=o.shouldExecuteOnCPU([r]),h=[],f=r;if(p){if(d){let x=o.texData.get(f.dataId).values,k=new Array(i);for(let E=0;E<k.length;E++)k[E]=r.shape[l[E]];let S=m0(x,r.shape,r.dtype,l,k);f=o.makeTensorInfo(k,r.dtype);let N=o.texData.get(f.dataId);N.values=S}else f=tg(r,l,o);h.push(f),c=T.getInnerMostAxes(c.length,i)}T.assertAxesAreInnerMostDims("sum",c,i);let[g,m]=T.computeOutAndReduceShapes(f.shape,c),b=g;s&&(b=T.expandShapeToKeepDim(g,u));let y=Lae(f,m,b,o);for(let v of h)o.disposeIntermediateTensorInfo(v);return y}};function zae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),c=u,l=T.getAxesPermutation(c,i),p=s;l!=null&&(p=Tn({inputs:{x:s},backend:n,attrs:{perm:l}}),c=T.getInnerMostAxes(c.length,s.shape.length)),T.assertAxesAreInnerMostDims("min",c,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,c),f=w.sizeFromShape(h),g=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),m=ru(g,g.dtype,"min",n),b;if(o){let y=T.expandShapeToKeepDim(d,u);b=pe({inputs:{x:m},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:m},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(m),l!=null&&n.disposeIntermediateTensorInfo(p),b}var Wae={kernelName:gi,backendName:"webgl",kernelFunc:zae},Vae=b0+`
  return min(a, b);
`,Uae=`
  vec4 result = vec4(min(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+nu+`
  return result;
`,Gae=mn({opSnippet:Vae,packedOpSnippet:Uae,cpuKernelImpl:NQ}),Hae={kernelName:bi,backendName:"webgl",kernelFunc:Gae},jae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,l)=>c[0]+e[l]+c[1]);let r=e.length,s=ht(r),a=t.map(c=>c[0]).join(","),o=t.map((c,l)=>c[0]+e[l]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),u=n==="reflect"?0:1;if(r===1){this.userCode=`
        int start = ${a};
        int end = ${o};

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

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

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `}},Kae=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:s,mode:a}=n,o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qae(r.shape,s,a):new jae(r.shape,s,a);return t.runWebGLProgram(o,[r],r.dtype)},Xae={kernelName:yi,backendName:"webgl",kernelFunc:Kae},Yae=`if (b == 0.0) return NAN;
  return mod(a, b);`,Zae=`
  vec4 result = mod(a, b);
  bvec4 isNaN = equal(b, vec4(0.0));
  `+nu+`
  return result;
`,Jae=mn({opSnippet:Yae,packedOpSnippet:Zae}),Qae={kernelName:vi,backendName:"webgl",kernelFunc:Jae},eoe=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}));
      }
    `}},toe=`
if (a == b) {
  return 1.0;
};
return a / b;`,noe=`
  // 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;
`,kD=mn({opSnippet:toe,packedOpSnippet:noe,checkOutOfBounds:!0}),roe={kernelName:Xo,backendName:"webgl",kernelFunc:kD},H1="return a - b;",SD=mn({opSnippet:H1,packedOpSnippet:H1,supportsComplex:!0,cpuKernelImpl:KQ}),soe={kernelName:Gi,backendName:"webgl",kernelFunc:SD};function CD(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=ID({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=T.expandShapeToKeepDim(i.shape,o),c=pe({inputs:{x:i},backend:n,attrs:{shape:u}}),l=SD({inputs:{a:s,b:c},backend:n}),p=vD({inputs:{x:l},backend:n}),d=ng({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=pe({inputs:{x:d},backend:n,attrs:{shape:u}}),f=kD({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var aoe={kernelName:Vi,backendName:"webgl",kernelFunc:CD};function ooe(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,u=i?s:CD({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),c=u.shape[0],l=u.shape[1],p=new eoe(c,l,a),d=[[o]],h=n.runWebGLProgram(p,[u],"int32",d);return i||n.disposeIntermediateTensorInfo(u),h}var ioe={kernelName:Bc,backendName:"webgl",kernelFunc:ooe},uoe=Or+`
  return -x;
`,coe=`
  vec4 result = -x;
  bvec4 isNaN = isnan(x);

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

  return result;
`;function loe(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.texData.get(r.dataId),[o,i]=EQ(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new oa(r.shape,coe):s=new is(r.shape,uoe),n.runWebGLProgram(s,[r],r.dtype)}var doe={kernelName:zc,backendName:"webgl",kernelFunc:loe},poe=gs.nonMaxSuppressionV3Impl;function hoe(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=r,c=n.readSync(s.dataId),l=n.readSync(a.dataId),{selectedIndices:p}=poe(c,l,o,i,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var foe={kernelName:Vc,backendName:"webgl",kernelFunc:hoe},moe=gs.nonMaxSuppressionV4Impl;function goe(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,padToMaxOutputSize:c}=r,l=n.readSync(s.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=moe(l,p,o,i,u,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var boe={kernelName:Uc,backendName:"webgl",kernelFunc:goe},yoe=gs.nonMaxSuppressionV5Impl;function voe(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:c}=r,l=n.readSync(s.dataId),p=n.readSync(a.dataId),d=o,h=i,f=u,g=c,{selectedIndices:m,selectedScores:b}=yoe(l,p,d,h,f,g);return[n.makeTensorInfo([m.length],"int32",new Int32Array(m)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var xoe={kernelName:Gc,backendName:"webgl",kernelFunc:voe},woe=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${r}), float(${n}),
                      float(index == coords.y)));
      }
    `}},Ioe=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{dtype:a,depth:o,onValue:i,offValue:u}=r,c=w.sizeFromShape(s.shape),l=new woe(c,o,i,u),p=pe({inputs:{x:s},backend:n,attrs:{shape:[c]}}),d=n.runWebGLProgram(l,[p],a);n.disposeIntermediateTensorInfo(p);let h=[...s.shape,o],f=pe({inputs:{x:d},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(d),f},koe={kernelName:wi,backendName:"webgl",kernelFunc:Ioe};function Df(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=Mp({inputs:{input:r},backend:n}),a=Df({inputs:{x:s},backend:n}),o=rg({inputs:{input:r},backend:n}),i=Df({inputs:{x:o},backend:n}),u=Oa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return Lp({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Soe={kernelName:cl,backendName:"webgl",kernelFunc:Df};function TD(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=Mp({inputs:{input:r},backend:n}),a=TD({inputs:{x:s},backend:n}),o=rg({inputs:{input:r},backend:n}),i=Df({inputs:{x:o},backend:n}),u=Oa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return Lp({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Coe={kernelName:Hc,backendName:"webgl",kernelFunc:TD};function Toe(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return kx({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(l=>{w.assertShapesMatch(a,l.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(l=>{let p=kx({inputs:{input:l},backend:n,attrs:{dim:s}});return i.push(p),p}),c=dD({inputs:u,backend:n,attrs:{axis:s}});return i.forEach(l=>n.disposeIntermediateTensorInfo(l)),c}var Noe={kernelName:jc,backendName:"webgl",kernelFunc:Toe},_oe=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,s=ht(r),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
        int start = ${a};
        int end = ${o};

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

      void main() {
        ${s} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${s} coords = outC - start;
          setOutput(getX(${i}));
        }
      }
    `}},Eoe=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,g)=>f[0]+e[g]+f[1]);let r=e.length,s=ht(r),a=t.map(f=>f[0]).join(","),o=t.map((f,g)=>f[0]+e[g]).join(","),i=Cn("rc",r),u=Cn("source",r),c=`${i[r-1]} < ${this.outputShape[r-1]}`,l=r===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${s} rc = outputLoc;`,`${i[r-1]} += 1;
       if(${c}) {
      `,r===1?"":`}
       rc = outputLoc;
       ${i[r-2]} += 1;
       if(${i[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":`  ${i[r-1]} += 1;
         if(${c}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,g=r===1?2:4;f<g;f++)h+=`
        ${p[f]}
        if (${d}) {
          result[${f}] = float(value);
        } else {
          ${s} source = rc - start;
          result[${f}] = getChannel(getX(${u.join()}), ${l});
        }
      `;h+=r===1?"} ":"}}",this.userCode=`
      const ${s} start = ${s}(${a});
      const ${s} end = ${s}(${o});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `}},ND=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;if(w.sizeFromShape(s.shape)===0){let c=a.map((l,p)=>l[0]+s.shape[p]+l[1]);return Lp({backend:n,attrs:{shape:c,value:o,dtype:s.dtype}})}let i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Eoe(s.shape,a,o):new _oe(s.shape,a,o),u=[[o]];return n.runWebGLProgram(i,[s],s.dtype,u)},Aoe={kernelName:Ii,backendName:"webgl",kernelFunc:ND},Doe=`
  if(a < 0.0 && floor(b) < b){
    return NAN;
  }
  if (b == 0.0) {
    return 1.0;
  }
  return (round(mod(b, 2.0)) != 1) ?
      pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,$oe=`
  // 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;

  bvec4 isNaN1 = lessThan(a, vec4(0.0));
  bvec4 isNaN2 = lessThan(floor(b), b);
  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
  `+nu+`
  return result;
`,Foe=mn({opSnippet:Doe,packedOpSnippet:$oe}),Roe={kernelName:ki,backendName:"webgl",kernelFunc:Foe};function Poe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=[],c=w.parseAxisParam(a,s.shape),l=c,p=T.getAxesPermutation(l,i),d=s;p!=null&&(d=Tn({inputs:{x:s},backend:n,attrs:{perm:p}}),l=T.getInnerMostAxes(l.length,i),u.push(d)),T.assertAxesAreInnerMostDims("prod",l,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:g,outShape:m,outDtype:b}=DQ(d.shape,d.dtype,f,l);h=n.makeTensorInfo(m,b,g)}else{let[f,g]=T.computeOutAndReduceShapes(d.shape,l),m=w.sizeFromShape(g),b=pe({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),y=Zf(s.dtype),v=ru(b,y,"prod",n);h=pe({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(o){u.push(h);let f=T.expandShapeToKeepDim(h.shape,c);h=pe({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Ooe={kernelName:Ci,backendName:"webgl",kernelFunc:Poe};function Moe(e){let{inputs:t,backend:n,attrs:r}=e,{paramsNestedSplits:s,paramsDenseValues:a,indices:o}=t,{outputRaggedRank:i}=r,u=s.map(b=>n.readSync(b.dataId)),c=s.map(b=>b.shape),l=n.readSync(a.dataId),p=n.readSync(o.dataId),[d,h,f]=$Q(u,c,l,a.shape,a.dtype,p,o.shape,i),g=d.map(b=>n.makeTensorInfo([b.length],"int32",b)),m=n.makeTensorInfo(f,a.dtype,h);return g.concat([m])}var Loe={kernelName:jf,backendName:"webgl",kernelFunc:Moe};function Boe(e){let{inputs:t,backend:n}=e,{starts:r,limits:s,deltas:a}=t,o=n.readSync(r.dataId),i=n.readSync(s.dataId),u=n.readSync(a.dataId),[c,l]=FQ(o,r.shape,r.dtype,i,s.shape,u,a.shape),p=n.makeTensorInfo([c.length],"int32",c),d=n.makeTensorInfo([l.length],r.dtype,l);return[p,d]}var zoe={kernelName:qf,backendName:"webgl",kernelFunc:Boe};function Woe(e){let{inputs:t,backend:n,attrs:r}=e,{shape:s,values:a,defaultValue:o,rowPartitionTensors:i}=t,{rowPartitionTypes:u}=r,c=n.readSync(s.dataId),l=n.readSync(a.dataId),p=n.readSync(o.dataId),d=i.map(m=>n.readSync(m.dataId)),h=i.map(m=>m.shape),[f,g]=RQ(c,s.shape,l,a.shape,a.dtype,p,o.shape,d,h,u);return n.makeTensorInfo(f,a.dtype,g)}var Voe={kernelName:Kf,backendName:"webgl",kernelFunc:Woe},_D=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=PQ(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},Uoe={kernelName:Xd,backendName:"webgl",kernelFunc:_D},Goe="return 1.0 / x;",Hoe=Ze({opSnippet:Goe}),joe={kernelName:Ti,backendName:"webgl",kernelFunc:Hoe},qoe=Or+`
  return (x < 0.0) ? 0.0 : x;
`,Koe=`
  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;
`,Xoe=Ze({opSnippet:qoe,packedOpSnippet:Koe}),Yoe={kernelName:Ni,backendName:"webgl",kernelFunc:Xoe},Zoe=Or+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Joe=`
  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;
`,Qoe=Ze({opSnippet:Zoe,packedOpSnippet:Joe}),eie={kernelName:Ai,backendName:"webgl",kernelFunc:Qoe},tie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let c=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p;s?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0]/l[0]},
          ${c[1]/l[1]});
      const vec2 inputShapeRC = vec2(${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 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);
      }
    `}},nie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let c=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p;s?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${c[0]/l[0]},
          ${c[1]/l[1]},
          ${c[1]/l[1]});
      const vec3 inputShapeRC = vec3(${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 four integer indices.
        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

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

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

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

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

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

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

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

        setOutput(newValue);
      }
    `}};function rie(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[u,c]=i,l=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new nie(s.shape,u,c,a,o):new tie(s.shape,u,c,a,o);return n.runWebGLProgram(l,[s],"float32")}var sie={kernelName:Ei,backendName:"webgl",kernelFunc:rie},aie=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/u[0],l=i[1]/u[1],p=1/c,d=1/l,h=Math.ceil(p)*2+2,f=Math.ceil(d)*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(${c});
        const float widthScale = float(${l});

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        setOutput(accumulator);
      }
    `}};function oie(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new aie(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var iie={kernelName:Xc,backendName:"webgl",kernelFunc:oie},uie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let c=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0]/l[0]},
          ${c[1]/l[1]});
      const vec2 inputShapeRC = vec2(${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 coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}},cie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let c=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${c[0]/l[0]},
          ${c[1]/l[1]},
          ${c[1]/l[1]});
      const vec3 inputShapeRC = vec3(${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 coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));

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

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

        setOutput(newValue);
      }
    `}};function lie(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[u,c]=i,l=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new cie(s.shape,u,c,a,o):new uie(s.shape,u,c,a,o);return n.runWebGLProgram(l,[s],s.dtype)}var die={kernelName:_i,backendName:"webgl",kernelFunc:lie},pie=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/u[0],l=i[1]/u[1],p=1/c,d=1/l,h=Math.ceil(p)*2+2,f=Math.ceil(d)*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(${c});
        const float widthScale = float(${l});

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

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

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

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

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

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

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

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

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

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

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

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

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

        setOutput(accumulator);
      }
    `}};function hie(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new pie(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var fie={kernelName:Kc,backendName:"webgl",kernelFunc:hie},mie=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let r=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,s=e.map((o,i)=>r(i)).join(","),a=ht(n);this.userCode=`
      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `}},gie=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=Cn("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=ht(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${s}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${o} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${i(r.slice())};
          if(${s}){
            result.g = ${u(r.slice())};
          }
          if(${a}) {
            result.b = ${c(r.slice())};
            if(${s}) {
              result.a = ${l(r.slice())};
            }
          }
          setOutput(result);
        }
    `;function i(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),g=f.join(","),m=f.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${m}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function bie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r,o=s.shape.length,i=w.parseAxisParam(a,s.shape);if(o===0)return sr({inputs:{x:s},backend:n});let u=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gie(s.shape,i):new mie(s.shape,i);return n.runWebGLProgram(u,[s],s.dtype)}var yie={kernelName:Di,backendName:"webgl",kernelFunc:bie},vie=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],r=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
        vec3 fill = vec3(${t.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${s}
          if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}},xie={kernelName:ll,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,u=new vie(r.shape,a),[c,l]=T.getImageCenter(o,r.shape[1],r.shape[2]),p=[[c,l,Math.sin(s),Math.cos(s)]];return i.runWebGLProgram(u,[r],r.dtype,p)}},wie=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,Iie=Ze({opSnippet:wie}),kie={kernelName:$i,backendName:"webgl",kernelFunc:Iie},Sie="return inversesqrt(x);",Cie=Ze({opSnippet:Sie,cpuKernelImpl:OQ}),Tie={kernelName:Fi,backendName:"webgl",kernelFunc:Cie},x0=class{constructor(e,t,n,r,s,a,o=!0,i=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let u=ht(s.length),c=ht(a.length),l="";n===1?l="i":n===2&&(l="i, j");let p=`getIndices(${l})`,d="";r===1?d="i":r===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,f="";i&&(f="coords[0], coords[1]");let g=`getDefaultValue(${f})`,m=t>1?"strides[j]":"strides";this.userCode=`
        ${u} strides = ${u}(${s});

        void main() {
          ${c} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${e}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${t}; j++) {
              int index = round(${p});
              flattenedIndex += index * ${m};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${h};
              found = true;
            }
          }
          setOutput(mix(${g}, sum, float(found)));
        }
      `}},Nie=class{constructor(e,t,n,r,s,a,o=!0,i=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=ht(s.length),c=ht(a.length),l="";n===1?l="i":n===2&&(l="i, j");let p=`getIndices(${l})`,d="";r===1?d="i":r===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,f="";i&&(f="coords[0], coords[1]");let g=`getDefaultValue(${f})`,m=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=`
        ${u} strides = ${u}(${s});

        void main() {
          ${c} coords = getOutputCoords();
          vec4 sum = vec4(0.);
          vec4 found = vec4(0.);
          for (int i = 0; i < ${e}; i+=2) {
            ivec2 flattenedIndex = ivec2(0);
            for (int j = 0; j < ${t}; j+=2) {
              ivec4 index = round(${p});
              flattenedIndex += index.xz * ${m};
              if (j + 1 < ${t}) {
                flattenedIndex += index.yw * ${b};
              }
            }
            if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||
                flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {
              vec4 updVals = ${h};
              if (flattenedIndex[0] == coords[0]) {
                sum.xy += updVals.xy;
                found.xy = vec2(1.);
              } else if (flattenedIndex[0] == coords[0] + 1) {
                sum.zw += updVals.xy;
                found.zw = vec2(1.);
              }
              if (flattenedIndex[1] == coords[0]) {
                sum.xy += updVals.zw;
                found.xy = vec2(1.);
              } else if (flattenedIndex[1] == coords[0] + 1) {
                sum.zw += updVals.zw;
                found.zw = vec2(1.);
              }
            }
          }
          setOutput(mix(${g}, sum, found));
        }
      `}};function _ie(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:u,sliceSize:c,strides:l,outputSize:p}=T.calculateShapes(a,s,o),d=[p/c,c];if(p===0)return n.makeTensorInfo(o,s.dtype);let h=pe({inputs:{x:s},backend:n,attrs:{shape:[u,i]}}),f=pe({inputs:{x:a},backend:n,attrs:{shape:[u,c]}}),g=n.makeTensorInfo([],"float32",new Float32Array([0])),m;G().getBool("WEBGL_PACK")?m=new Nie(u,i,h.shape.length,f.shape.length,l,d):m=new x0(u,i,h.shape.length,f.shape.length,l,d);let b=n.runWebGLProgram(m,[f,h,g],f.dtype),y=pe({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(g),y}var Eie={kernelName:Yc,backendName:"webgl",kernelFunc:_ie},Aie=class{constructor(e,t,n,r){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=G().getNumber("WEBGL_VERSION")===2?s:a,i=r==="left"?"<":"<=";this.userCode=`
       int findBound(int batch, float value) {
         int left = 0;
         int right = numInputs;
         int mid;
         ${o}
           mid = (left + right) / 2;
           if (getSortedSequence(batch, mid) ${i} value) {
             left = mid + 1;
           } else {
             right = mid;
           }
         }
         return right;
       }

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

         float value = getValues(batch, valueIndex);

         setOutput(float(findBound(batch, value)));
       }
     `}};function Die(e){let{inputs:t,backend:n,attrs:r}=e,{sortedSequence:s,values:a}=t,{side:o}=r,i=new Aie(s.shape[0],s.shape[1],a.shape[1],o),u=[[s.shape[1]]];return n.runWebGLProgram(i,[s,a],"int32",u)}var $ie={kernelName:Jc,backendName:"webgl",kernelFunc:Die},Fie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",r="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],u=[];for(let c=0;c<t.length;c++)u.push(`${o[c]}`),c<e&&i.push(`${o[c]}`);r=i.join(),s=u.join()}let a=ht(n);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        float cVal = getC(${r});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `}};function Rie(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new Fie(r.shape.length,s.shape,s.shape.length);return n.runWebGLProgram(o,[r,s,a],fr(s.dtype,a.dtype))}var Pie={kernelName:Qc,backendName:"webgl",kernelFunc:Rie},Oie=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${T.SELU_SCALEALPHA};
  float scale = ${T.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Mie=Ze({opSnippet:Oie}),Lie={kernelName:Ri,backendName:"webgl",kernelFunc:Mie},Bie=Nl+`
  return 1.0 / (1.0 + exp(-1.0 * x));
`,zie=`
  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
  bvec4 isNaN = isnan(x);

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

  return result;
`,Wie=Ze({opSnippet:Bie,packedOpSnippet:zie,cpuKernelImpl:LQ}),Vie={kernelName:Li,backendName:"webgl",kernelFunc:Wie},Uie=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,Gie=Ze({opSnippet:Uie}),Hie={kernelName:Mi,backendName:"webgl",kernelFunc:Gie},jie=Nl+`
  return sin(x);
`,qie=`
  vec4 result = sin(x);
  bvec4 isNaN = isnan(x);
  ${nu}
  return result;
`,Kie=Ze({opSnippet:jie,packedOpSnippet:qie}),Xie={kernelName:Pi,backendName:"webgl",kernelFunc:Kie},Yie=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,Zie=Ze({opSnippet:Yie}),Jie={kernelName:Oi,backendName:"webgl",kernelFunc:Zie},Qie=`
  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;
`,eue=Ze({opSnippet:Qie}),tue={kernelName:Bi,backendName:"webgl",kernelFunc:eue},nue=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r;w.assert(s.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...o);for(let b=1+a.length;b<s.shape.length;++b)u.push([0,0]);let c=[],l=ND({inputs:{x:s},backend:n,attrs:{paddings:u,constantValue:0}}),p=T.getReshaped(l.shape,a,i,!1),d=T.getPermuted(p.length,a.length,!1),h=T.getReshapedPermuted(l.shape,a,i,!1),f=pe({inputs:{x:l},backend:n,attrs:{shape:p}}),g=Tn({inputs:{x:f},backend:n,attrs:{perm:d}}),m=pe({inputs:{x:g},backend:n,attrs:{shape:h}});return c.push(l),c.push(f),c.push(g),c.forEach(b=>n.disposeIntermediateTensorInfo(b)),m},rue={kernelName:tl,backendName:"webgl",kernelFunc:nue};function sue(e){let{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${a.shape}`);if(r.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${r.shape}`);if(s.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${s.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${o.shape}`);let i=n.readSync(r.dataId),u=n.readSync(s.dataId),c=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[p,d,h,f,g]=zQ(i,r.shape,r.dtype,u,s.dtype,c,l);return[n.makeTensorInfo(d,r.dtype,p),n.makeTensorInfo([d[0]],s.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(m=>Number(m)))),n.makeTensorInfo([g.length],r.dtype,new Int32Array(g))]}var aue={kernelName:Yd,backendName:"webgl",kernelFunc:sue};function oue(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:s,newShape:a}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${s.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(s.dataId)),i=n.readSync(r.dataId),u=Array.from(n.readSync(a.dataId)),[c,l,p]=WQ(i,r.shape,r.dtype,o,u);return[n.makeTensorInfo(l,r.dtype,c),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var iue={kernelName:rl,backendName:"webgl",kernelFunc:oue};function uue(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),u=n.readSync(a.dataId),[c,l]=YA(o,r.shape,r.dtype,i,u,!0);return n.makeTensorInfo(l,r.dtype,c)}var cue={kernelName:Zd,backendName:"webgl",kernelFunc:uue};function lue(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),u=n.readSync(a.dataId),[c,l]=YA(o,r.shape,r.dtype,i,u);return n.makeTensorInfo(l,r.dtype,c)}var due={kernelName:Jd,backendName:"webgl",kernelFunc:lue};function pue(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:u,numUpdates:c,sliceSize:l,strides:p,outputSize:d}=T.calculateShapes(a,s,i),h=!1;if(a.dtype==="string"){let b=n.bufferSync(s),y=n.bufferSync(a),v=w.decodeString(n.readSync(o.dataId)[0]),x=MQ(b,y,i,d,l,c,u,p,v,h);return n.makeTensorInfo(i,x.dtype,x.values)}let f=new x0(c,u,s.shape.length,a.shape.length,p,[d,1],h),g=n.runWebGLProgram(f,[a,s,o],a.dtype),m=pe({inputs:{x:g},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(g),m}var hue={kernelName:sl,backendName:"webgl",kernelFunc:pue};function fue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=w.parseAxisParam(o,s.shape)[0],u=T.prepareSplitSize(s,a,i),c=s.shape.length,l=new Array(c).fill(0),p=s.shape.slice();return u.map(d=>{let h=[...p];h[i]=d;let f=_l({inputs:{x:s},backend:n,attrs:{begin:l,size:h}});return l[i]+=d,f})}var mue={kernelName:nl,backendName:"webgl",kernelFunc:fue},j1="return sqrt(x);",gue=Ze({opSnippet:j1,packedOpSnippet:j1,cpuKernelImpl:VQ}),bue={kernelName:zi,backendName:"webgl",kernelFunc:gue},yue="return x * x;",vue=Ze({opSnippet:yue}),xue={kernelName:Qd,backendName:"webgl",kernelFunc:vue},q1="return (a - b) * (a - b);",wue=mn({opSnippet:q1,packedOpSnippet:q1}),Iue={kernelName:Ui,backendName:"webgl",kernelFunc:wue};function kue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");let a=n.readSync(s.dataId),o=T.fromUint8ToStringArray(a),i=UQ(o,"string",r);return n.makeTensorInfo(s.shape,"string",i)}var Sue={kernelName:ep,backendName:"webgl",kernelFunc:kue};function Cue({inputs:e,attrs:t,backend:n}){let{x:r}=e,s=Or+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,a=new is(r.shape,s);return n.runWebGLProgram(a,[r],r.dtype)}var Tue={kernelName:Na,backendName:"webgl",kernelFunc:Cue},Nue=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,s=ht(n.length),a=ht(n.length),o="";if(r===1)o="coords * strides + begin";else{let i=0;o=n.map((u,c)=>(i++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${i-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
      ${s} begin = ${s}(${e});
      ${s} strides = ${s}(${t});

      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${o}));
      }
    `}};function _ue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:u,endMask:c,ellipsisMask:l,newAxisMask:p,shrinkAxisMask:d}=r,{finalShapeSparse:h,finalShape:f,isIdentity:g,sliceDim0:m,isSimpleSlice:b,begin:y,end:v,strides:x}=Kt.sliceInfo(s.shape,a,o,i,u,c,l,p,d),k;if(g)k=pe({inputs:{x:s},backend:n,attrs:{shape:f}});else if(m||b){w.assert(s.shape.length>=1,()=>`Input must have rank at least 1, got: ${s.shape.length}`);let N=Kt.computeOutShape(y,v,x),E=_l({inputs:{x:s},backend:n,attrs:{begin:y,size:N}});k=pe({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([s])){let E=n.readSync(s.dataId),$=Me(s.shape,s.dtype,E),F=GQ(h,$,x,y);k=n.makeTensorInfo(f,s.dtype,F.values)}else{let E=new Nue(y,x,h);k=n.runWebGLProgram(E,[s],s.dtype)}let S=pe({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),S}var Eue={kernelName:al,backendName:"webgl",kernelFunc:_ue};function Aue(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:c}=r,{data:l,dataSplits:p}=t,d=n.readSync(l.dataId),h=n.readSync(p.dataId),[f,g]=HQ(d,h,s,a,o,i,u,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",g)]}var Due={kernelName:tp,backendName:"webgl",kernelFunc:Aue};function $ue(e){let{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[c,l,p]=jQ(i,u,s),d=l.length;return[n.makeTensorInfo([d,2],"int32",c),n.makeTensorInfo([d],"string",l),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var Fue={kernelName:np,backendName:"webgl",kernelFunc:$ue};function Rue(e){let{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=qQ(o,s);return n.makeTensorInfo(a.shape,"int32",i)}var Pue={kernelName:rp,backendName:"webgl",kernelFunc:Rue},Oue="return tan(x);",Mue=Ze({opSnippet:Oue}),Lue={kernelName:Hi,backendName:"webgl",kernelFunc:Mue},Bue=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,zue=Ze({opSnippet:Bue}),Wue={kernelName:ji,backendName:"webgl",kernelFunc:zue};function Vue(e){let{inputs:t,backend:n,attrs:r}=e,{tensor:s,indices:a,updates:o}=t,{}=r,{sliceRank:i,numUpdates:u,sliceSize:c,strides:l,outputSize:p}=T.calculateShapes(o,a,s.shape),d=[p/c,c];if(p===0)return n.makeTensorInfo(s.shape,a.dtype);let h=pe({inputs:{x:a},backend:n,attrs:{shape:[u,i]}}),f=pe({inputs:{x:o},backend:n,attrs:{shape:[u,c]}}),g=pe({inputs:{x:s},backend:n,attrs:{shape:d}}),m=new x0(u,i,h.shape.length,f.shape.length,l,d,!1,!0),b=n.runWebGLProgram(m,[f,h,g],g.dtype),y=pe({inputs:{x:b},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(b),y}var Uue={kernelName:Zc,backendName:"webgl",kernelFunc:Vue},Gue=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let r=ht(this.rank),s=Hue(e);this.userCode=`
      void main() {
        ${r} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}};function Hue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<e.length;s++)r.push(`imod(${n[s]}, ${e[s]})`);return r.join()}function ED(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(s.dtype==="string"||s.shape.length>5){let u=n.readSync(s.dataId),c=s.dtype==="string"?u.map(d=>w.decodeString(d)):u,l=Me(s.shape,s.dtype,c),p=XQ(l,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Gue(s.shape,a);return n.runWebGLProgram(o,[s],s.dtype)}var jue={kernelName:Ta,backendName:"webgl",kernelFunc:ED},que=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));
         }
       }
     `}},Kue=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 Xa(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function K1(e){let t=1;for(;t<e;)t*=2;return t}function Xue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r,i=G().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=G().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=s.shape,l=c[c.length-1];if(n.shouldExecuteOnCPU([s])||l<i||a>u){let F=n.readSync(s.dataId),[D,R]=YQ(F,c,s.dtype,a,o);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,s.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(l===1)return[s,Lp({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let p=n.texData.get(s.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(s):s,g=w.sizeFromShape(c)/l,m=pe({inputs:{x:h},attrs:{shape:[g,l]},backend:n});d&&Xa(n,h);let b=K1(a),y=K1(l),v=null,x=()=>v===null?[m,m]:[m,v],k=(F,D,R)=>{let C=x(),L=new que(R),H=[[l],[v===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],K=v;v=n.runWebGLProgram(L,C,"int32",H),Xa(n,K)};for(let F=1;F<b;F*=2){let D=F*2;for(let R=F;R>=1;R/=2)k(D,R,[g,y])}for(let F=y;F>b;F/=2){let D=x(),R=new Kue([g,F/2]),L=[[l],[v===null?1:0],[b]],U=v;v=n.runWebGLProgram(R,D,"int32",L),Xa(n,U);let H=b/2,K=H*2;for(let q=H;q>=1;q/=2)k(K,q,v.shape)}let S=v;v=_l({inputs:{x:v},backend:n,attrs:{begin:0,size:[g,a]}}),Xa(n,S);let N=wD({inputs:{x:m,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Xa(n,m);let E=c.slice(0,-1);E.push(a),S=v,v=pe({inputs:{x:v},attrs:{shape:E},backend:n}),Xa(n,S);let $=N;return N=pe({inputs:{x:N},attrs:{shape:E},backend:n}),Xa(n,$),[N,v]}var Yue={kernelName:ol,backendName:"webgl",kernelFunc:Xue},Zue=class{constructor(e,t,n,r,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(r){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${i} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

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

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

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

      float getValue(int batch, int inIdx) {
        ${d}
        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
          );

          ${p}
        }

        int inIdx = inOffset + ${c};
        if (${l===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
          );

          ${p}
        } else if (${l===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
          );

          ${p}
        } else if (${l===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
          );

          ${p}
        }
        setOutput(${u});
      }
    `}};function ace(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:a}=t,{numSegments:o}=r,i=s.shape.length,u=[],c=0,l=T.getAxesPermutation([c],i),p=s;l!=null&&(p=Tn({inputs:{x:s},backend:n,attrs:{perm:l}}),u.push(p),c=T.getInnerMostAxes(1,i)[0]);let d=T.segment_util.computeOutShape(p.shape,c,o),h=w.sizeFromShape([p.shape[c]]),f=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let g=Zf(s.dtype),m=(x,k,S,N,E)=>{let $=x.shape[0],F=x.shape[1],D=T.segment_util.segOpComputeOptimalWindowSize(F,E),R={windowSize:D,inSize:F,batchSize:$,numSegments:E},C=new sce(R,k),L=n.compileAndRun(C,[x,S],N);if(u.push(L),L.shape[1]===E)return L;let U=_D({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),H=ED({inputs:{x:U},backend:n,attrs:{reps:[F/D]}});return u.push(U),u.push(H),m(L,k,H,N,E)},b=m(f,"unsortedSegmentSum",a,g,o),y=pe({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(l!=null){u.push(y);let x=T.getUndoAxesPermutation(l);v=Tn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var oce={kernelName:ap,backendName:"webgl",kernelFunc:ace},ice=[Gee,jee,Xee,Jee,ete,rte,ate,ite,dte,hte,gte,vte,Ite,Tte,Ete,Dte,Fte,Mte,Bte,Wte,Hte,Jte,ene,sne,one,pne,fne,yne,Nee,wne,Tne,Ane,One,Bne,Wne,Une,Hne,Xne,Jne,tre,rre,are,ire,lre,pre,gre,yre,wre,Sre,Tre,Are,Rre,Lre,Wre,Gre,Hre,qre,Xre,Zre,Qre,tse,ase,use,dse,hse,gse,vse,kse,Nse,Tee,Ese,Sne,$se,Pse,Lse,Eee,Vse,jse,Kse,Jse,tae,aae,uae,pae,gae,vae,wae,Cae,Nae,Eae,Fae,Pae,Mae,Bae,Wae,Hae,Xae,Qae,ioe,$ee,doe,foe,boe,xoe,une,koe,Coe,Noe,Aoe,Roe,Dee,Ooe,Loe,zoe,Voe,Uoe,cne,roe,joe,Yoe,eie,Ree,sie,iie,die,fie,yie,xie,kie,Tie,Eie,$ie,Pie,Lie,Vie,Hie,Xie,Jie,Yte,aoe,tue,rue,aue,iue,cue,due,hue,mue,bue,xue,Iue,Sue,Tue,Eue,Due,Fue,Pue,soe,Wee,Lue,Wue,Uue,jue,Yue,Que,Vee,tce,rce,oce,Soe];for(let e of ice)op(e);var et;(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"})(et||(et={}));var Od;(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"})(Od||(Od={}));var AD;function uce(e){AD=e.wasm.cwrap(uo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function cce(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:c,activation:l,leakyreluAlpha:p}=r,d=n.dataIdMap.get(s.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let E=n.dataIdMap.get(o.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let g=i==null?0:n.dataIdMap.get(i.dataId).id,m=Od[l];if(m==null)throw new Error(`${l} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?s.shape[2]:s.shape[1],y=c?a.shape[1]:a.shape[2],v=dl.assertAndGetBroadcastShape(s.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],s.dtype),k=n.dataIdMap.get(x.dataId).id,S=new Uint8Array(new Int32Array(s.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return AD(d,S,s.shape.length,h,N,a.shape.length,u,c,m,f,g,p||0,k),x}var lce={kernelName:uo,backendName:"wasm",setupFunc:uce,kernelFunc:cce};function Xe(e,t){let n;function r(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function s(a){let{backend:o,inputs:{x:i}}=a,u=o.dataIdMap.get(i.dataId).id,c=o.makeOutput(i.shape,t||i.dtype),l=o.dataIdMap.get(c.dataId).id;return w.sizeFromShape(c.shape)===0||n(u,et[i.dtype],l),c}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var dce=Xe(cc),pce=Xe(Eo),hce=Xe(Ao);function Ut(e,t,n){let r;function s(o){r=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:u}=o,{a:c,b:l}=u,p=i.dataIdMap.get(c.dataId).id,d=i.dataIdMap.get(l.dataId).id,h=n!=null?n:c.dtype,f=T.assertAndGetBroadcastShape(c.shape,l.shape),g=i.makeOutput(f,h);if(w.sizeFromShape(f)===0)return g;let m=new Uint8Array(new Int32Array(c.shape).buffer),b=new Uint8Array(new Int32Array(l.shape).buffer),y=i.dataIdMap.get(g.dataId).id;return r(p,m,c.shape.length,d,b,l.shape.length,et[c.dtype],y),g}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:a}}var fce=!0,mce=Ut(Sa,fce),DD;function gce(e){DD=e.wasm.cwrap(Do,null,["array","number","number","number"])}function bce(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(r.shape)===0)return r;let s=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(s).buffer),o=n.dataIdMap.get(r.dataId).id;return DD(a,s.length,et[r.dtype],o),r}var yce={kernelName:Do,backendName:"wasm",setupFunc:gce,kernelFunc:bce};function sg(e){let{inputs:{x:t},backend:n}=e;if(t.dtype==="string")return yn(n.readSync(t.dataId),t.shape,t.dtype);let r=n.makeOutput(t.shape,t.dtype),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var vce={kernelName:si,backendName:"wasm",kernelFunc:sg},$D;function xce(e){$D=e.wasm.cwrap(_s,null,["number","array","number","number","number","array","number"])}function Ia(e){let{inputs:t,backend:n,attrs:r}=e,[s,a]=Ice(t.x.shape,r.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=wce(t.x.shape,r.perm),u={dataId:t.x.dataId,shape:s,dtype:t.x.dtype};if(o){let f=sg({inputs:t,backend:n});return f.shape=i,f}let c=n.makeOutput(i,u.dtype),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(c.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return $D(l,h,u.shape.length,et[u.dtype],p,d,a.length),c}function wce(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function Ice(e,t){let n=[],r=[];for(let s=0;s<e.length;++s)e[s]!==1&&n.push(e[s]),e[t[s]]!==1&&r.push(t[s]);for(let s=0;s<r.length;++s){let a=-1;for(let o=0;o<r.length;++o)r[o]>=s&&(a===-1||r[a]>r[o])&&(a=o);r[a]=s}return[n,r]}var kce={kernelName:_s,backendName:"wasm",kernelFunc:Ia,setupFunc:xce};function Ma(e,t,n){let r=e.shape,s=e.shape.length,a=w.parseAxisParam(t,r),o=a,i=T.getAxesPermutation(o,s),u=null,c=!1;if(i!=null){let l=new Array(s);for(let h=0;h<l.length;h++)l[h]=r[i[h]];o=T.getInnerMostAxes(o.length,s),u=Ia({inputs:{x:e},attrs:{perm:i},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(c=!0)}return{transposed:u,originalAxes:a,axes:o,inputWasTransposed:c}}var FD;function Sce(e){FD=e.wasm.cwrap(lc,null,["number, number, number"])}function Cce(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,c=o,{transposed:l,axes:p,originalAxes:d,inputWasTransposed:h}=Ma(o,s,t);if(h){let v=t.dataIdMap.get(l.dataId).id;c=l,u=v}let f=c.shape.length;T.assertAxesAreInnerMostDims("all",p,f);let[g,m]=T.computeOutAndReduceShapes(c.shape,p),b=w.sizeFromShape(m),y=t.makeOutput(g,o.dtype);if(w.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;FD(u,b,v)}if(h&&t.disposeData(l.dataId),a){let v=T.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Tce={kernelName:lc,backendName:"wasm",setupFunc:Sce,kernelFunc:Cce},RD;function Nce(e){RD=e.wasm.cwrap(dc,null,["number, number, number"])}function _ce(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,c=o,{transposed:l,axes:p,originalAxes:d,inputWasTransposed:h}=Ma(o,s,t);if(h){let v=t.dataIdMap.get(l.dataId).id;c=l,u=v}let f=c.shape.length;T.assertAxesAreInnerMostDims("any",p,f);let[g,m]=T.computeOutAndReduceShapes(c.shape,p),b=w.sizeFromShape(m),y=t.makeOutput(g,o.dtype);if(w.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;RD(u,b,v)}if(h&&t.disposeData(l.dataId),a){let v=T.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ece={kernelName:dc,backendName:"wasm",setupFunc:Nce,kernelFunc:_ce};function PD(e){let t;function n(s){t=s.wasm.cwrap(e,null,["number","number","number","number","number"])}function r(s){let{backend:a,inputs:o,attrs:i}=s,{axis:u}=i,{x:c}=o,l=a.dataIdMap.get(c.dataId).id,p=l,d=c,{transposed:h,axes:f,inputWasTransposed:g}=Ma(c,u,a);if(g){let k=a.dataIdMap.get(h.dataId).id;k!==l&&(d=h,p=k)}let m=d.shape.slice(0,-1),b=a.makeOutput(m,"int32"),y=a.dataIdMap.get(b.dataId).id,v=w.sizeFromShape(b.shape),x=d.shape[f[0]];return t(p,et[d.dtype],v,x,y),g&&a.disposeData(h.dataId),b}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var Ace=PD(pc),Dce=PD(hc),$ce=Xe($o),Fce=Xe(Fo),Rce=Xe(Ro),Pce=Ut(Oo,!1),Oce=Xe(Po),OD;function Mce(e){OD=e.wasm.cwrap(Mo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Lce(e){let{inputs:t,attrs:n,backend:r}=e,s=t.x,a=r.dataIdMap.get(s.dataId).id,{filterSize:o,strides:i,pad:u,dimRoundingMode:c}=n,l=T.computePool2DInfo(s.shape,o,i,1,u,c),p=l.filterHeight,d=l.filterWidth,h=l.padInfo.top,f=l.padInfo.right,g=l.padInfo.bottom,m=l.padInfo.left,b=l.strideHeight,y=l.strideWidth,v=l.inChannels;if(l.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${l.dataFormat}'. Please use 'channelsLast'.`);if(l.dilationWidth!==1||l.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${l.dilationHeight}, ${l.dilationWidth}].`);let x=r.makeOutput(l.outShape,"float32"),k=r.dataIdMap.get(x.dataId).id;return OD(a,s.shape[0],s.shape[1],s.shape[2],p,d,h,f,g,m,b,y,v,k),x}var Bce={kernelName:Mo,backendName:"wasm",setupFunc:Mce,kernelFunc:Lce},MD;function zce(e){MD=e.wasm.cwrap("AvgPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Wce(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u,dataFormat:c}=r,l=T.computePool3DInfo(s.shape,a,o,1,i,u,c),p=n.makeOutput(l.outShape,s.dtype);return MD(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,l.batchSize,l.inChannels,l.inDepth,l.inHeight,l.inWidth,l.outDepth,l.outHeight,l.outWidth,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),p}var Vce={kernelName:fc,backendName:"wasm",setupFunc:zce,kernelFunc:Wce},LD;function Uce(e){LD=e.wasm.cwrap("AvgPool3DGrad",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"])}function Gce(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,{filterSize:o,strides:i,pad:u,dimRoundingMode:c}=r,l=T.computePool3DInfo(a.shape,o,i,1,u,c),p=n.makeOutput(a.shape,a.dtype);return LD(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,l.batchSize,l.inChannels,l.inDepth,l.inHeight,l.inWidth,l.outDepth,l.outHeight,l.outWidth,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left,l.filterDepth,l.filterHeight,l.filterWidth),p}var Hce={kernelName:zd,backendName:"wasm",setupFunc:Uce,kernelFunc:Gce},BD;function jce(e){BD=e.wasm.cwrap("AvgPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function qce(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,{filterSize:o,strides:i,pad:u}=r,c=T.computePool2DInfo(a.shape,o,i,1,u),l=n.makeOutput(a.shape,a.dtype);return BD(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left,c.filterHeight,c.filterWidth),l}var Kce={kernelName:Bd,backendName:"wasm",setupFunc:jce,kernelFunc:qce};function Ln(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:s}=n,a=w.sizeFromShape(r.shape),o=w.inferFromImplicitShape(s,a);return w.assert(a===w.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:o,dtype:r.dtype}}var Xce={kernelName:qc,backendName:"wasm",kernelFunc:Ln},zD;function Yce(e){zD=e.wasm.cwrap(Lo,null,["number","array","number","number","array","number","number","number","number"])}function Zce(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=s.shape.length,c=a.shape.length,l=o?s.shape[u-2]:s.shape[u-1],p=i?a.shape[c-1]:a.shape[c-2],d=o?s.shape[u-1]:s.shape[u-2],h=i?a.shape[c-2]:a.shape[c-1],f=s.shape.slice(0,-2),g=a.shape.slice(0,-2),m=w.sizeFromShape(f),b=w.sizeFromShape(g),v=dl.assertAndGetBroadcastShape(s.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(l===p,()=>`Error in matMul: inner shapes (${l}) and (${p}) of Tensors with shapes ${s.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let x=o?[m,l,d]:[m,d,l],k=i?[b,h,p]:[b,p,h],S=Ln({inputs:{x:s},backend:n,attrs:{shape:x}}),N=Ln({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(S.dataId).id,$=n.dataIdMap.get(N.dataId).id,F=o?S.shape[2]:S.shape[1],D=i?N.shape[1]:N.shape[2],R=Math.max(m,b),C=n.makeOutput([R,F,D],S.dtype),L=n.dataIdMap.get(C.dataId).id,U=new Uint8Array(new Int32Array(S.shape).buffer),H=new Uint8Array(new Int32Array(N.shape).buffer);return zD(E,U,S.shape.length,$,H,N.shape.length,o,i,L),n.disposeData(S.dataId),n.disposeData(N.dataId),C.shape=v,C}var Jce={kernelName:Lo,backendName:"wasm",setupFunc:Yce,kernelFunc:Zce};function No(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:s}=e,[a,o]=Kt.parseSliceParams(t,n,r),i=Kt.isSliceContinous(t.shape,a,o),u=s.readSync(t.dataId),c=s.makeOutput(o,t.dtype),l=w.computeStrides(t.shape),p=s.dataIdMap.get(c.dataId);if(i){let f=Kt.computeFlatOffset(a,l);return t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(o)):s.typedArrayFromHeap(c).set(u.subarray(f,f+w.sizeFromShape(o))),c}if(t.dtype==="string"){let f=Cf(u,a,o,t.shape,t.dtype);return p.stringBytes=f,c}let d=s.typedArrayFromHeap(c),h=t.shape.length;if(h===2)Qce(u,l[0],d,a,o);else if(h===3)ele(u,l[0],l[1],d,a,o);else if(h===4)tle(u,l[0],l[1],l[2],d,a,o);else{let f=Cf(u,a,o,t.shape,t.dtype);d.set(f)}return c}function Qce(e,t,n,r,s){let a=0,o=r[0],i=r[1],u=o+s[0];for(let c=o;c<u;c++){let l=c*t+i;n.set(e.subarray(l,l+s[1]),a),a+=s[1]}}function ele(e,t,n,r,s,a){let o=0,i=s[0],u=s[1],c=s[2],l=i+a[0],p=u+a[1];for(let d=i;d<l;d++)for(let h=u;h<p;h++){let f=d*t+h*n+c;r.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function tle(e,t,n,r,s,a,o){let i=0,u=a[0],c=a[1],l=a[2],p=u+o[0],d=c+o[1],h=l+o[2],f=a[3];for(let g=u;g<p;g++)for(let m=c;m<d;m++)for(let b=l;b<h;b++){let y=g*t+m*n+b*r+f;s.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var nle={kernelName:el,backendName:"wasm",kernelFunc:No};function rle(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r,i=a.reduce((b,y)=>b*y),u=T.getReshaped(s.shape,a,i),c=T.getPermuted(u.length,a.length),l=T.getReshapedPermuted(s.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(l,o,a.length),h=Ln({inputs:{x:s},backend:n,attrs:{shape:u}}),f=Ia({inputs:{x:h},backend:n,attrs:{perm:c}}),g=Ln({inputs:{x:f},backend:n,attrs:{shape:l}}),m=No({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(g.dataId),m}var sle={kernelName:mc,backendName:"wasm",kernelFunc:rle},WD;function ale(e){WD=e.wasm.cwrap(gc,null,["number","number","boolean","number","number","number"])}function ole(e){let{backend:t,inputs:n,attrs:r}=e,{x:s,weights:a}=n,{size:o}=r,i=a.shape.reduce((p,d)=>p*d,1)!==0,u=s.shape.length===1?[o]:[s.shape[0],o],c=t.makeOutput(u,a.dtype);function l(p){return t.dataIdMap.get(p.dataId).id}return WD(l(s),o,i,l(a),et[a.dtype],l(c)),c}var ile={kernelName:gc,backendName:"wasm",setupFunc:ale,kernelFunc:ole},ule=!0,cle=Ut(bc,ule);function lle(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.typedArrayFromHeap(r),o=n.typedArrayFromHeap(s),i=T.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeOutput([i.length],"int32",void 0,new Int32Array(i))}var dle={kernelName:Wd,backendName:"wasm",kernelFunc:lle};function La(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,s=r.makeOutput(t.shape,n),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(s).set(a),s}var ple={kernelName:Bo,backendName:"wasm",kernelFunc:La},hle=Xe(zo),VD;function fle(e){VD=e.wasm.cwrap(Ca,null,["number","number","number","number"])}function mle(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(s.shape,s.dtype),c=n.dataIdMap.get(u.dataId).id;return VD(i,a,o,c),u}var gle={kernelName:Ca,backendName:"wasm",setupFunc:fle,kernelFunc:mle};function UD(e){let{inputs:t,backend:n}=e,r=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],s=t.map(h=>h.shape);T.assertParamsConsistent(s,r);let a=T.computeOutShape(t.map(h=>h.shape),r),o=t.filter(h=>w.sizeFromShape(h.shape)>0);if(o.length===1)return sg({inputs:{x:o[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(w.sizeFromShape(a)===0)return i;if(o[0].dtype==="string"){let h=o.map(v=>{let k=[-1,w.sizeFromShape(v.shape.slice(r))];return Ln({inputs:{x:v},backend:n,attrs:{shape:k}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));a=T.computeOutShape(h.map(v=>v.shape),1);let g=h[0].shape[0]===1,m=jk(f,a,t[0].dtype,g),b=T.computeOutShape(o.map(v=>v.shape),r);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=T.fromStringArrayToUint8(m),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(o[0].shape.slice(0,r)),c=0,l=o.map(h=>{let f=w.sizeFromShape(h.shape.slice(r));return c+=f,f}),p=o.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*c;for(let g=0;g<p.length;g++){let m=l[g],b=h*m,y=p[g].subarray(b,b+m);d.set(y,f),f+=m}}return i}var ble={kernelName:yc,backendName:"wasm",kernelFunc:UD},GD;function yle(e){GD=e.wasm.cwrap(Wo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function vle(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:u,dilations:c,pad:l,dimRoundingMode:p,dataFormat:d}=n,h=T.convertConv2DDataFormat(d),f=T.computeConv2DInfo(s.shape,a.shape,u,c,l,p,!1,h),g=f.filterHeight,m=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,S=f.dilationWidth,N=f.strideHeight,E=f.strideWidth,$=f.inChannels,F=f.outChannels,D=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let R=r.makeOutput(f.outShape,"float32"),C=r.dataIdMap.get(R.dataId).id;return GD(o,s.shape[0],s.shape[1],s.shape[2],i,g,m,b,y,v,x,D,k,S,N,E,$,F,C),R}var xle={kernelName:Wo,backendName:"wasm",setupFunc:yle,kernelFunc:vle},HD;function wle(e){HD=e.wasm.cwrap(Vo,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 Ile(e){let{backend:t,inputs:n,attrs:r}=e,{dy:s,filter:a}=n,{strides:o,pad:i,dataFormat:u,dimRoundingMode:c,inputShape:l}=r,p=1,d=T.convertConv2DDataFormat(u),h=T.computeConv2DInfo(l,a.shape,o,p,i,c,!1,d),{batchSize:f,filterHeight:g,filterWidth:m,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:S,strideHeight:N,strideWidth:E}=h,$=g-1-h.padInfo.top,F=m-1-h.padInfo.left,D=h.dataFormat==="channelsLast",R=w.computeStrides(h.inShape),C=w.computeStrides(s.shape),[L,U,H]=w.computeStrides(a.shape),K=R[0],q=D?R[1]:R[2],Z=D?R[2]:1,J=D?1:R[1],ee=C[0],se=D?C[1]:C[2],te=D?C[2]:1,oe=D?1:C[1],ne=t.makeOutput(h.inShape,"float32"),de=t.dataIdMap.get(ne.dataId).id,ce=t.dataIdMap.get(s.dataId).id,we=t.dataIdMap.get(a.dataId).id;return HD(ce,we,f,g,m,y,v,b,k,S,x,N,E,$,F,L,U,H,K,q,Z,J,ee,se,te,oe,de),ne}var kle={kernelName:Vo,backendName:"wasm",setupFunc:wle,kernelFunc:Ile},jD;function Sle(e){jD=e.wasm.cwrap(Uo,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"])}function Cle(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u}=r;if(s.dtype!=="float32")throw new Error(`Tensor x must have dtype float32, got ${s.dtype}`);if(a.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${a.dtype}`);let c=T.computeConv3DInfo(s.shape,a.shape,o,u,i),l=n.makeOutput(c.outShape,s.dtype);return jD(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(l.dataId).id,c.batchSize,c.inDepth,c.inHeight,c.inWidth,c.inChannels,c.outDepth,c.outHeight,c.outWidth,c.outChannels,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.filterDepth,c.filterHeight,c.filterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var Tle={kernelName:Uo,backendName:"wasm",setupFunc:Sle,kernelFunc:Cle},qD;function Nle(e){qD=e.wasm.cwrap(vc,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"])}function _le(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:u}=r;if(s.dtype!=="float32")throw new Error(`Tensor dy must have dtype float32, got ${s.dtype}`);if(a.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${a.dtype}`);let c=T.computeConv3DInfo(s.shape,u,o,1,i),l=n.makeOutput(c.filterShape,a.dtype);return qD(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(l.dataId).id,c.batchSize,c.inDepth,c.inHeight,c.inWidth,c.inChannels,c.outDepth,c.outHeight,c.outWidth,c.outChannels,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.filterDepth,c.filterHeight,c.filterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var Ele={kernelName:vc,backendName:"wasm",setupFunc:Nle,kernelFunc:_le},KD;function Ale(e){KD=e.wasm.cwrap(xc,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"])}function Dle(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:u}=r;if(s.dtype!=="float32")throw new Error(`Tensor dy must have dtype float32, got ${s.dtype}`);if(a.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${a.dtype}`);let c=T.computeConv3DInfo(u,a.shape,i,1,o),l=n.makeOutput(c.inShape,s.dtype);return KD(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(l.dataId).id,c.batchSize,c.inDepth,c.inHeight,c.inWidth,c.inChannels,c.outDepth,c.outHeight,c.outWidth,c.outChannels,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.filterDepth,c.filterHeight,c.filterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var $le={kernelName:xc,backendName:"wasm",setupFunc:Ale,kernelFunc:Dle},Fle=Xe(Go),Rle=Xe(Ho),Sx;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Sx||(Sx={}));var XD;function Ple(e){XD=e.wasm.cwrap(Ic,null,["number","number","number","number","array","number","number","number","number","number"])}function Ole(e){let{backend:t,inputs:n,attrs:r}=e,{method:s,extrapolationValue:a,cropSize:o}=r,{image:i,boxes:u,boxInd:c}=n,l=u.shape[0],[p,d]=o,h=[l,p,d,i.shape[3]],f=t.dataIdMap.get(i.dataId),g;i.dtype!=="float32"&&(g=La({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let m=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(c.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(i.shape).buffer);return XD(m,b,y,l,k,p,d,Sx[s],a,x),g!=null&&t.disposeData(g.dataId),v}var Mle={kernelName:Ic,backendName:"wasm",setupFunc:Ple,kernelFunc:Ole},YD;function Lle(e){YD=e.wasm.cwrap(wc,null,["number","number","number","number","number","number"])}function Ble(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,u=s.shape.length;w.assert(s.dtype==="float32"||s.dtype==="int32",()=>`cumprod does not support ${s.dtype} tensors in the WASM backend`);let c=T.getAxesPermutation([a],u),l=s;c!==null&&(l=Ia({inputs:{x:s},attrs:{perm:c},backend:n}));let p=T.getInnerMostAxes(1,u)[0];T.assertAxesAreInnerMostDims("cumprod",[p],u);let d=n.makeOutput(l.shape,l.dtype),h=l.shape[p],f=n.dataIdMap.get(l.dataId).id,g=n.dataIdMap.get(d.dataId).id;YD(f,o?1:0,i?1:0,h,g,et[s.dtype]);let m=d;if(c!==null){let b=T.getUndoAxesPermutation(c);m=Ia({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(l.dataId),n.disposeData(d.dataId)}return m}var zle={kernelName:wc,backendName:"wasm",setupFunc:Lle,kernelFunc:Ble},ZD;function Wle(e){ZD=e.wasm.cwrap(jo,null,["number","number","number","number","number","number"])}function Vle(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,u=s.shape.length;w.assert(s.dtype==="float32"||s.dtype==="int32",()=>`cumsum does not support ${s.dtype} tensors in the WASM backend`);let c=T.getAxesPermutation([a],u),l=s;c!==null&&(l=Ia({inputs:{x:s},attrs:{perm:c},backend:n}));let p=T.getInnerMostAxes(1,u)[0];T.assertAxesAreInnerMostDims("cumsum",[p],u);let d=n.makeOutput(l.shape,l.dtype),h=l.shape[p],f=n.dataIdMap.get(l.dataId).id,g=n.dataIdMap.get(d.dataId).id;ZD(f,o?1:0,i?1:0,h,g,et[s.dtype]);let m=d;if(c!==null){let b=T.getUndoAxesPermutation(c);m=Ia({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(l.dataId),n.disposeData(d.dataId)}return m}var Ule={kernelName:jo,backendName:"wasm",setupFunc:Wle,kernelFunc:Vle},JD;function Gle(e){JD=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function Hle(e){let{backend:t,inputs:n,attrs:r}=e,{x:s,weights:a}=n,{size:o,binaryOutput:i}=r,u=a.shape.reduce((d,h)=>d*h,1)!==0,c=s.shape.length===1?[o]:[s.shape[0],o],l=t.makeOutput(c,a.dtype);function p(d){return t.dataIdMap.get(d.dataId).id}return JD(p(s),new Uint8Array(new Int32Array(s.shape).buffer),s.shape.length,o,u,p(a),et[a.dtype],i,p(l)),l}var jle={kernelName:Ud,backendName:"wasm",setupFunc:Gle,kernelFunc:Hle},QD;function qle(e){QD=e.wasm.cwrap(kc,null,["number","number","number","array","number","array","array","number","number"])}function Kle(e){let{backend:t,inputs:n,attrs:r}=e,{x:s}=n,{blockSize:a,dataFormat:o}=r,i=s.shape[0],u=o==="NHWC"?s.shape[1]:s.shape[2],c=o==="NHWC"?s.shape[2]:s.shape[3],l=o==="NHWC"?s.shape[3]:s.shape[1],p=u*a,d=c*a,h=l/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],g=t.makeOutput(f,"float32"),b=t.dataIdMap.get(s.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(s.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(g.dataId).id;return QD(b,a,o==="NHWC"?1:0,y,s.shape.length-1,v,x,f.length,k),g}var Xle={kernelName:kc,backendName:"wasm",setupFunc:qle,kernelFunc:Kle},e$;function Yle(e){e$=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zle(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:u,dilations:c,pad:l,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=T.computeConv2DInfo(s.shape,a.shape,u,d,l,p,!0),f=h.filterHeight,g=h.filterWidth,m=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,S=h.strideHeight,N=h.strideWidth,E=h.inChannels,$=h.outChannels,F=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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ape={kernelName:Fc,backendName:"wasm",setupFunc:rpe,kernelFunc:spe},ope=Xe(ci),ipe=Xe(li),upe=!1,cpe=Ut(Rc,upe,"bool"),lpe=Xe(Pc),dpe=!1,ppe=Ut(Oc,dpe,"bool"),hpe=!1,fpe=Ut(gC,hpe,"bool"),m$;function mpe(e){m$=e.wasm.cwrap(di,null,["number","number","number","number","number","number","number"])}function gpe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{depthRadius:a,bias:o,alpha:i,beta:u}=r;if(s.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let c=n.makeOutput(s.shape,s.dtype);return m$(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(c.dataId).id,s.shape[3],a,o,i,u),c}var bpe={kernelName:di,backendName:"wasm",setupFunc:mpe,kernelFunc:gpe},g$;function ype(e){g$=e.wasm.cwrap(Mc,null,["number","number","number","number","number","number","number","number","number"])}function vpe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:u,alpha:c,beta:l}=r;if(s.dtype!=="float32"||a.dtype!=="float32"||o.dtype!=="float32")throw new Error("LRNGrad 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this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(r=>new Float32Array(r));return new e(t.label,n)}};var $0=class extends Dl{static assertIsValidPredictedBox(t,n){if(Dl.assertIsValidLabeledBox(t,n),!El(t.score)||!El(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,r,s){super(t,n),this._score=r,this._classScore=s}get score(){return this._score}get classScore(){return this._classScore}};function vs(e){return e.detection instanceof Tt}function du(e,t){return{...e,...{detection:t}}}function F0(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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implementation for nodejs environment")},s=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},a=()=>{if(n)return new n;throw new Error("createVideoElement - missing Video implementation for nodejs environment")},o=global.fetch,i=ig();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:r,createImageElement:s,createVideoElement:a,fetch:o,...i}}function P0(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var un;function Qme(){if(!un)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return un}function O0(e){un=e}function M0(){return P0()?O0(F0()):zp()?O0(R0()):null}function ege(e){if(un||M0(),!un)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=un.Canvas,Image:n=un.Image}=e;un.Canvas=t,un.Image=n,un.createCanvasElement=e.createCanvasElement||(()=>new t),un.createImageElement=e.createImageElement||(()=>new n),un.ImageData=e.ImageData||un.ImageData,un.Video=e.Video||un.Video,un.fetch=e.fetch||un.fetch,un.readFile=e.readFile||un.readFile}var nt={getEnv:Qme,setEnv:O0,initialize:M0,createBrowserEnv:F0,createFileSystem:ig,createNodejsEnv:R0,monkeyPatch:ege,isBrowser:P0,isNodejs:zp};M0();function pu(e){return!nt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Gn(e){let{Canvas:t,CanvasRenderingContext2D:n}=nt.getEnv();if(e instanceof n)return e;let r=pu(e);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let s=r.getContext("2d",{willReadFrequently:!0});if(!s)throw new Error("resolveContext2d - canvas 2d context is null");return s}var L0=(s=>(s.TOP_LEFT="TOP_LEFT",s.TOP_RIGHT="TOP_RIGHT",s.BOTTOM_LEFT="BOTTOM_LEFT",s.BOTTOM_RIGHT="BOTTOM_RIGHT",s))(L0||{}),$l=class{constructor(t={}){let{anchorPosition:n,backgroundColor:r,fontColor:s,fontSize:a,fontStyle:o,padding:i}=t;this.anchorPosition=n||"TOP_LEFT",this.backgroundColor=r||"rgba(0, 0, 0, 0.5)",this.fontColor=s||"rgba(255, 255, 255, 1)",this.fontSize=a||14,this.fontStyle=o||"Georgia",this.padding=i||4}},hu=class e{constructor(t,n,r={}){this.text=typeof t=="string"?[t]:t instanceof e?t.text:t,this.anchor=n,this.options=new $l(r)}measureWidth(t){let{padding:n}=this.options;return this.text.map(r=>t.measureText(r).width).reduce((r,s)=>r<s?s:r,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:r}=this.options,s=r==="BOTTOM_RIGHT"||r==="TOP_RIGHT",a=r==="BOTTOM_LEFT"||r==="BOTTOM_RIGHT",o=this.measureWidth(t),i=this.measureHeight(),u=s?this.anchor.x-o:this.anchor.x,c=a?this.anchor.y-i:this.anchor.y;if(n){let{width:l,height:p}=n,d=Math.max(Math.min(u,l-o),0),h=Math.max(Math.min(c,p-i),0);return{x:d,y:h}}return{x:u,y:c}}draw(t){let n=pu(t),r=Gn(n),{backgroundColor:s,fontColor:a,fontSize:o,fontStyle:i,padding:u}=this.options;r.font=`${o}px ${i}`;let c=this.measureWidth(r),l=this.measureHeight();r.fillStyle=s;let p=this.getUpperLeft(r,n);r.fillRect(p.x,p.y,c,l),r.fillStyle=a,this.text.forEach((d,h)=>{let f=u+p.x,g=u+p.y+(h+1)*o;r.fillText(d,f,g)})}};var ug=class{constructor(t={}){let{boxColor:n,lineWidth:r,label:s,drawLabelOptions:a}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=s;let o={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new $l({...o,...a})}},Wp=class{constructor(t,n={}){this.box=new on(t),this.options=new ug(n)}draw(t){let n=Gn(t),{boxColor:r,lineWidth:s}=this.options,{x:a,y:o,width:i,height:u}=this.box;n.strokeStyle=r,n.lineWidth=s,n.strokeRect(a,o,i,u);let{label:c}=this.options;c&&new hu([c],{x:a-s/2,y:o},this.options.drawLabelOptions).draw(t)}};function tge(e,t){(Array.isArray(t)?t:[t]).forEach(r=>{let s=r instanceof Tt?r.score:vs(r)?r.detection.score:void 0,a=r instanceof Tt?r.box:vs(r)?r.detection.box:new on(r),o=s?`${au(s)}`:void 0;new Wp(a,{label:o}).draw(e)})}function Vp(e){let{Image:t,Video:n}=nt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function B0(e){return new Promise((t,n)=>{(e instanceof nt.getEnv().Canvas||Vp(e))&&t(null);function r(a){a.currentTarget&&(a.currentTarget.removeEventListener("load",s),a.currentTarget.removeEventListener("error",r),n(a))}function s(a){a.currentTarget&&(a.currentTarget.removeEventListener("load",s),a.currentTarget.removeEventListener("error",r),t(a))}e.addEventListener("load",s),e.addEventListener("error",r)})}function z0(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let s=nt.getEnv().createImageElement();s.onload=()=>t(s),s.onerror=n,s.src=r.result},r.onerror=n,r.readAsDataURL(e)})}function fu(e){let{Image:t,Video:n}=nt.getEnv();return e instanceof t?new Un(e.naturalWidth,e.naturalHeight):e instanceof n?new Un(e.videoWidth,e.videoHeight):new Un(e.width,e.height)}function mu({width:e,height:t}){let{createCanvasElement:n}=nt.getEnv(),r=n();return r.width=e,r.height=t,r}function Up(e,t){let{ImageData:n}=nt.getEnv();if(!(e instanceof n)&&!Vp(e))throw new Error("createCanvasFromMedia - media has not finished 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xs=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((r,s)=>{if(zs(r)){this._imageTensors[s]=r,this._inputDimensions[s]=r.shape;return}if(xr(r)){let o=r.shape[0];if(o!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${o} passed, but not supported in input array`);this._imageTensors[s]=r,this._inputDimensions[s]=r.shape.slice(1);return}let a=r instanceof nt.getEnv().Canvas?r:Up(r);this._canvases[s]=a,this._inputDimensions[s]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return ys(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),r=this.getInputHeight(t);return C0({width:n,height:r},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,O(()=>{let r=ys(this.batchSize,0,1).map(a=>{let o=this.getInput(a);if(o instanceof Ne){let i=xr(o)?o:Gt(o);return i=A0(i,n),(i.shape[1]!==t||i.shape[2]!==t)&&(i=er.resizeBilinear(i,[t,t],!1,!1)),i.as3D(t,t,3)}if(o instanceof nt.getEnv().Canvas)return Yi.fromPixels(V0(o,t,n));throw new 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gu(e,t,n="same",r=!1){return O(()=>{let s=X(Ft(e,t.filters,[1,1],n),t.bias);return r?Ke(s):s})}function Dn(e,t){Object.keys(e).forEach(n=>{t.some(r=>r.originalPath===n)||e[n].dispose()})}function Pl(e,t){return(n,r,s,a)=>{let o=Rr(e(n*r*s*s),[s,s,n,r]),i=He(e(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:o,bias:i}}}function pg(e,t){return(n,r,s)=>{let a=Dr(e(n*r),[n,r]),o=He(e(r));return t.push({paramPath:`${s}/weights`},{paramPath:`${s}/bias`}),{weights:a,bias:o}}}var Hp=class{constructor(t,n,r){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=r}};function Ol(e,t){return(n,r,s)=>{let a=Rr(e(9*n),[3,3,n,1]),o=Rr(e(n*r),[1,1,n,r]),i=He(e(r));return t.push({paramPath:`${s}/depthwise_filter`},{paramPath:`${s}/pointwise_filter`},{paramPath:`${s}/bias`}),new Hp(a,o,i)}}function Ml(e){return t=>{let n=e(`${t}/depthwise_filter`,4),r=e(`${t}/pointwise_filter`,4),s=e(`${t}/bias`,1);return new Hp(n,r,s)}}function ir(e,t){return(n,r,s)=>{let 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r=ae(t.toBatchTensor(112,!0),"float32"),a=Zr(r,[122.782,117.001,104.298]).div(255),o=Gp(a,n.dense0,!0);return o=Gp(o,n.dense1),o=Gp(o,n.dense2),o=Gp(o,n.dense3),o=br(o,[7,7],[2,2],"valid"),o})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return lF(t)}extractParams(t){return cF(t)}};function jp(e,t){return O(()=>X(Fe(e,t.weights),t.bias))}function dF(e,t,n){let r=[],{extractWeights:s,getRemainingWeights:a}=$n(e),i=pg(s,r)(t,n,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function pF(e){let t=[],n=ir(e,t);function r(a){let o=n(`${a}/weights`,2),i=n(`${a}/bias`,1);return{weights:o,bias:i}}let s={fc:r("fc")};return Dn(e,t),{params:s,paramMappings:t}}function gg(e){let t={},n={};return Object.keys(e).forEach(r=>{let s=r.startsWith("fc")?n:t;s[r]=e[r]}),{featureExtractorMap:t,classifierMap:n}}var Bl=class extends cn{constructor(t,n){super(t),this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let r=t instanceof xs?this.faceFeatureExtractor.forwardInput(t):t;return jp(r.as2D(r.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:r}=this.extractClassifierParams(t);this._params=n,this._paramMappings=r}extractClassifierParams(t){return dF(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:r}=gg(t);return this.faceFeatureExtractor.loadFromWeightMap(n),pF(r)}extractParams(t){let n=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*n+r,a=t.slice(0,t.length-s),o=t.slice(t.length-s);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(o)}};var 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d=a("exit_flow/reduction_block"),h=s("exit_flow/separable_conv"),f={reduction_block:d,separable_conv:h};return Dn(e,n),{params:{entry_flow:l,middle_flow:p,exit_flow:f},paramMappings:n}}function gF(e,t,n){return X(Ft(e,t.filters,n,"same"),t.bias)}function K0(e,t,n=!0){let r=n?Ke(e):e;return r=Hn(r,t.separable_conv0,[1,1]),r=Hn(Ke(r),t.separable_conv1,[1,1]),r=Rt(r,[3,3],[2,2],"same"),r=X(r,gF(e,t.expansion_conv,[2,2])),r}function hge(e,t){let n=Hn(Ke(e),t.separable_conv0,[1,1]);return n=Hn(Ke(n),t.separable_conv1,[1,1]),n=Hn(Ke(n),t.separable_conv2,[1,1]),n=X(n,e),n}var xg=class extends cn{constructor(t){super("TinyXception"),this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return O(()=>{let r=ae(t.toBatchTensor(112,!0),"float32"),a=Zr(r,[122.782,117.001,104.298]).div(255),o=Ke(gF(a,n.entry_flow.conv_in,[2,2]));return o=K0(o,n.entry_flow.reduction_block_0,!1),o=K0(o,n.entry_flow.reduction_block_1),ys(this._numMainBlocks,0,1).forEach(i=>{o=hge(o,n.middle_flow[`main_block_${i}`])}),o=K0(o,n.exit_flow.reduction_block),o=Ke(Hn(o,n.exit_flow.separable_conv,[1,1])),o})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return mF(t,this._numMainBlocks)}extractParams(t){return fF(t,this._numMainBlocks)}};function bF(e){let t=[],{extractWeights:n,getRemainingWeights:r}=$n(e),s=pg(n,t),a=s(512,1,"fc/age"),o=s(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:o}}}}function yF(e){let t=[],n=ir(e,t);function r(a){let o=n(`${a}/weights`,2),i=n(`${a}/bias`,1);return{weights:o,bias:i}}let s={fc:{age:r("fc/age"),gender:r("fc/gender")}};return Dn(e,t),{params:s,paramMappings:t}}var wg=(n=>(n.FEMALE="female",n.MALE="male",n))(wg||{});var Kp=class extends cn{constructor(t=new xg(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let r=t instanceof xs?this.faceFeatureExtractor.forwardInput(t):t,s=br(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=jp(s,n.fc.age).as1D(),o=jp(s,n.fc.gender);return{age:a,gender:o}})}forwardInput(t){return O(()=>{let{age:n,gender:r}=this.runNet(t);return{age:n,gender:Kr(r)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),r=await this.forwardInput(n),s=pt(r.age),a=pt(r.gender),o=s.map((u,c)=>({ageTensor:u,genderTensor:a[c]})),i=await Promise.all(o.map(async({ageTensor:u,genderTensor:c})=>{let l=u.dataSync()[0],p=c.dataSync()[0],d=p>.5,h=d?"male":"female",f=d?p:1-p;return u.dispose(),c.dispose(),{age:l,gender:h,genderProbability:f}}));return r.age.dispose(),r.gender.dispose(),n.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:r}=this.extractClassifierParams(t);this._params=n,this._paramMappings=r}extractClassifierParams(t){return bF(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:r}=gg(t);return this.faceFeatureExtractor.loadFromWeightMap(n),yF(r)}extractParams(t){let r=t.slice(0,t.length-1539),s=t.slice(t.length-1539);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(s)}};var Wl=class extends Bl{postProcess(t,n,r){let s=r.map(({width:o,height:i})=>{let u=n/Math.max(i,o);return{width:o*u,height:i*u}}),a=s.length;return O(()=>{let o=(p,d)=>Dt([vn([68],p,"float32"),vn([68],d,"float32")],1).as2D(1,136).as1D(),i=(p,d)=>{let{width:h,height:f}=s[p];return d(h,f)?Math.abs(h-f)/2:0},u=p=>i(p,(d,h)=>d<h),c=p=>i(p,(d,h)=>h<d);return t.mul(vn([a,136],n,"float32")).sub(Dt(Array.from(Array(a),(p,d)=>o(u(d),c(d))))).div(Dt(Array.from(Array(a),(p,d)=>o(s[d].width,s[d].height))))})}forwardInput(t){return O(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([r,s])=>({height:r,width:s})))})}async forward(t){return this.forwardInput(await vt(t))}async detectLandmarks(t){let n=await vt(t),r=O(()=>pt(this.forwardInput(n))),s=await Promise.all(r.map(async(a,o)=>{let i=Array.from(a.dataSync()),u=i.filter((l,p)=>ag(p)),c=i.filter((l,p)=>!ag(p));return new lu(Array(68).fill(0).map((l,p)=>new Ue(u[p],c[p])),{height:n.getInputHeight(o),width:n.getInputWidth(o)})}));return r.forEach(a=>a.dispose()),n.isBatchInput?s:s[0]}getClassifierChannelsOut(){return 136}};var yu=class extends Wl{constructor(t=new Ll){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function vF(e){let 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r=Mr(e,n.conv_0,[1,1]),s=Mr(r,n.conv_1,[2,2]),a=Mr(s,n.conv_2,[1,1]),o=Mr(a,n.conv_3,[2,2]),i=Mr(o,n.conv_4,[1,1]),u=Mr(i,n.conv_5,[2,2]),c=Mr(u,n.conv_6,[1,1]),l=Mr(c,n.conv_7,[2,2]),p=xu(t,n.box_predictor_0),d=xu(e,n.box_predictor_1),h=xu(s,n.box_predictor_2),f=xu(o,n.box_predictor_3),g=xu(u,n.box_predictor_4),m=xu(l,n.box_predictor_5),b=tt([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding,m.boxPredictionEncoding],1),y=tt([p.classPrediction,d.classPrediction,h.classPrediction,f.classPrediction,g.classPrediction,m.classPrediction],1);return{boxPredictions:b,classPredictions:y}})}var wr=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var za=class extends cn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let r=ae(t.toBatchTensor(512,!1),"float32"),s=le(fe(r,127.5),1),a=NF(s,n.mobilenetv1),{boxPredictions:o,classPredictions:i}=AF(a.out,a.conv11,n.prediction_layer);return EF(o,i,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:r,minConfidence:s}=new wr(n),a=await vt(t),{boxes:o,scores:i}=this.forwardInput(a),u=o[0],c=i[0];for(let v=1;v<o.length;v++)o[v].dispose(),i[v].dispose();let l=Array.from(c.dataSync()),d=_F(u,l,r,.5,s),h=a.getReshapedInputDimensions(0),f=a.inputSize,g=f/h.width,m=f/h.height,b=u.arraySync(),y=d.map(v=>{let[x,k]=[Math.max(0,b[v][0]),Math.min(1,b[v][2])].map(E=>E*m),[S,N]=[Math.max(0,b[v][1]),Math.min(1,b[v][3])].map(E=>E*g);return new Tt(l[v],new cu(S,x,N-S,k-x),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return u.dispose(),c.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return TF(t)}extractParams(t){return CF(t)}};function DF(e){let t=new za;return t.extractWeights(e),t}function Nge(e){return DF(e)}var J0=class extends za{};var $F=.4,FF=[new Ue(.738768,.874946),new Ue(2.42204,2.65704),new Ue(4.30971,7.04493),new Ue(10.246,4.59428),new Ue(12.6868,11.8741)],RF=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],PF=[117.001,114.697,97.404],OF="tiny_yolov2_model",MF="tiny_yolov2_separable_conv_model";var Ng=e=>typeof e=="number";function Q0(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Ng(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Ng(t.x)&&Ng(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Ng)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Vl(e){return O(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(le(e,t)),t)})}function Us(e,t){return O(()=>{let n=yr(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=le(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Vl(n)})}function Gs(e,t){return O(()=>{let n=yr(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Fa(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Vl(n)})}function _ge(e,t){let n=Pl(e,t);function r(o,i){let u=He(e(o)),c=He(e(o));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:u,truediv:c}}function s(o,i,u){let c=n(o,i,3,`${u}/conv`),l=r(i,`${u}/bn`);return{conv:c,bn:l}}let a=Ol(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function LF(e,t,n,r){let{extractWeights:s,getRemainingWeights:a}=$n(e),o=[],{extractConvParams:i,extractConvWithBatchNormParams:u,extractSeparableConvParams:c}=_ge(s,o),l;if(t.withSeparableConvs){let[p,d,h,f,g,m,b,y,v]=r,x=t.isFirstLayerConv2d?i(p,d,3,"conv0"):c(p,d,"conv0"),k=c(d,h,"conv1"),S=c(h,f,"conv2"),N=c(f,g,"conv3"),E=c(g,m,"conv4"),$=c(m,b,"conv5"),F=y?c(b,y,"conv6"):void 0,D=v?c(y,v,"conv7"):void 0,R=i(v||y||b,5*n,1,"conv8");l={conv0:x,conv1:k,conv2:S,conv3:N,conv4:E,conv5:$,conv6:F,conv7:D,conv8:R}}else{let[p,d,h,f,g,m,b,y,v]=r,x=u(p,d,"conv0"),k=u(d,h,"conv1"),S=u(h,f,"conv2"),N=u(f,g,"conv3"),E=u(g,m,"conv4"),$=u(m,b,"conv5"),F=u(b,y,"conv6"),D=u(y,v,"conv7"),R=i(v,5*n,1,"conv8");l={conv0:x,conv1:k,conv2:S,conv3:N,conv4:E,conv5:$,conv6:F,conv7:D,conv8:R}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:l,paramMappings:o}}function Ege(e,t){let n=ir(e,t);function r(i){let u=n(`${i}/sub`,1),c=n(`${i}/truediv`,1);return{sub:u,truediv:c}}function s(i){let u=n(`${i}/filters`,4),c=n(`${i}/bias`,1);return{filters:u,bias:c}}function a(i){let u=s(`${i}/conv`),c=r(`${i}/bn`);return{conv:u,bn:c}}let o=Ml(n);return{extractConvParams:s,extractConvWithBatchNormParams:a,extractSeparableConvParams:o}}function BF(e,t){let n=[],{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}=Ege(e,n),o;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else o={conv0:s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:s("conv6"),conv7:s("conv7"),conv8:r("conv8")};return Dn(e,n),{params:o,paramMappings:n}}var Qr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var _g=class _g extends cn{constructor(t){super("TinyYolov2"),Q0(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let r=Us(t,n.conv0);return r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv1),r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv2),r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv3),r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv4),r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv5),r=Rt(r,[2,2],[1,1],"same"),r=Us(r,n.conv6),r=Us(r,n.conv7),gu(r,n.conv8,"valid",!1)}runMobilenet(t,n){let r=this.config.isFirstLayerConv2d?Vl(gu(t,n.conv0,"valid",!1)):Gs(t,n.conv0);return r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv1),r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv2),r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv3),r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv4),r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv5),r=Rt(r,[2,2],[1,1],"same"),r=n.conv6?Gs(r,n.conv6):r,r=n.conv7?Gs(r,n.conv7):r,gu(r,n.conv8,"valid",!1)}forwardInput(t,n){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let s=ae(t.toBatchTensor(n,!1),"float32");return s=this.config.meanRgb?Zr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:r,scoreThreshold:s}=new Qr(n),a=await vt(t),o=await this.forwardInput(a,r),i=O(()=>pt(o)[0].expandDims()),u={width:a.getInputWidth(0),height:a.getInputHeight(0)},c=await this.extractBoxes(i,a.getReshapedInputDimensions(0),s);o.dispose(),i.dispose();let l=c.map(m=>m.box),p=c.map(m=>m.score),d=c.map(m=>m.classScore),h=c.map(m=>this.config.classes[m.label]);return E0(l.map(m=>m.rescale(r)),p,this.config.iouThreshold,!0).map(m=>new uu(p[m],d[m],h[m],l[m],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return BF(t,this.config)}extractParams(t){let n=this.config.filterSizes||_g.DEFAULT_FILTER_SIZES,r=n?n.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return LF(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,r){let{width:s,height:a}=n,o=Math.max(s,a),i=o/s,u=o/a,c=t.shape[1],l=this.config.anchors.length,[p,d,h]=O(()=>{let b=t.reshape([c,c,l,this.boxEncodingSize]),y=b.slice([0,0,0,0],[c,c,l,4]),v=b.slice([0,0,0,4],[c,c,l,1]),x=this.withClassScores?Kr(b.slice([0,0,0,5],[c,c,l,this.config.classes.length]),3):xe(0);return[y,v,x]}),f=[],g=await d.array(),m=await p.array();for(let b=0;b<c;b++)for(let y=0;y<c;y++)for(let v=0;v<l;v++){let x=Bp(g[b][y][v][0]);if(!r||x>r){let k=(y+Bp(m[b][y][v][0]))/c*i,S=(b+Bp(m[b][y][v][1]))/c*u,N=Math.exp(m[b][y][v][2])*this.config.anchors[v].x/c*i,E=Math.exp(m[b][y][v][3])*this.config.anchors[v].y/c*u,$=k-N/2,F=S-E/2,D={row:b,col:y,anchor:v},{classScore:R,label:C}=this.withClassScores?await this.extractPredictedClass(h,D):{classScore:1,label:0};f.push({box:new iu($,F,$+N,F+E),score:x,classScore:x*R,label:C,...D})}}return p.dispose(),d.dispose(),h.dispose(),f}async extractPredictedClass(t,n){let{row:r,col:s,anchor:a}=n,o=await t.array();return Array(this.config.classes.length).fill(0).map((i,u)=>o[r][s][a][u]).map((i,u)=>({classScore:i,label:u})).reduce((i,u)=>i.classScore>u.classScore?i:u)}};_g.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Ul=_g;var wu=class extends Ul{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:$F,classes:["face"],...t?{anchors:RF,meanRgb:PF}:{anchors:FF,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new Tt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?MF:OF}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Age(e,t=!0){let n=new wu(t);return n.extractWeights(e),n}var Zp=class extends Qr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Ir=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Iu(e,t,n,r,s=({alignedRect:a})=>a){let a=e.map(u=>bu(u)?s(u):u.detection),o=r||(t instanceof Ne?await Rl(t,a):await Fl(t,a)),i=await n(o);return o.forEach(u=>u instanceof Ne&&u.dispose()),i}async function Gl(e,t,n,r,s){return Iu([e],t,async a=>n(a[0]),r,s)}var zF=.4,WF=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],VF=[117.001,114.697,97.404];var ku=class extends Ul{constructor(){let t={withSeparableConvs:!0,iouThreshold:zF,classes:["face"],anchors:WF,meanRgb:VF,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new Tt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new za,tinyFaceDetector:new ku,tinyYolov2:new wu,faceLandmark68Net:new yu,faceLandmark68TinyNet:new Xp,faceRecognitionNet:new vu,faceExpressionNet:new qp,ageGenderNet:new Kp},UF=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),Dge=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),$ge=(e,t)=>rt.tinyYolov2.locateFaces(e,t),GF=e=>rt.faceLandmark68Net.detectLandmarks(e),Fge=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),Rge=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),Pge=e=>rt.faceExpressionNet.predictExpressions(e),Oge=e=>rt.ageGenderNet.predictAgeAndGender(e),HF=e=>rt.ssdMobilenetv1.load(e),Mge=e=>rt.tinyFaceDetector.load(e),Lge=e=>rt.tinyYolov2.load(e),Bge=e=>rt.faceLandmark68Net.load(e),zge=e=>rt.faceLandmark68TinyNet.load(e),Wge=e=>rt.faceRecognitionNet.load(e),Vge=e=>rt.faceExpressionNet.load(e),Uge=e=>rt.ageGenderNet.load(e),Gge=HF,Hge=UF,jge=GF;var Eg=class extends Ir{constructor(n,r,s){super();this.parentTask=n;this.input=r;this.extractedFaces=s}},Su=class extends Eg{async run(){let t=await this.parentTask,n=await Iu(t,this.input,async r=>Promise.all(r.map(s=>rt.faceExpressionNet.predictExpressions(s))),this.extractedFaces);return t.map((r,s)=>bg(r,n[s]))}withAgeAndGender(){return new Tu(this,this.input)}},Cu=class extends Eg{async run(){let t=await this.parentTask;if(!t)return;let n=await Gl(t,this.input,r=>rt.faceExpressionNet.predictExpressions(r),this.extractedFaces);return bg(t,n)}withAgeAndGender(){return new Nu(this,this.input)}},Wa=class extends Su{withAgeAndGender(){return new Ua(this,this.input)}withFaceDescriptors(){return new Hs(this,this.input)}},Va=class extends Cu{withAgeAndGender(){return new Ga(this,this.input)}withFaceDescriptor(){return new js(this,this.input)}};var Ag=class extends Ir{constructor(n,r,s){super();this.parentTask=n;this.input=r;this.extractedFaces=s}},Tu=class extends Ag{async run(){let t=await this.parentTask,n=await Iu(t,this.input,async r=>Promise.all(r.map(s=>rt.ageGenderNet.predictAgeAndGender(s))),this.extractedFaces);return t.map((r,s)=>{let{age:a,gender:o,genderProbability:i}=n[s];return Cg(Tg(r,o,i),a)})}withFaceExpressions(){return new Su(this,this.input)}},Nu=class extends Ag{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:r,genderProbability:s}=await Gl(t,this.input,a=>rt.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Cg(Tg(t,r,s),n)}withFaceExpressions(){return new Cu(this,this.input)}},Ua=class extends Tu{withFaceExpressions(){return new Wa(this,this.input)}withFaceDescriptors(){return new Hs(this,this.input)}},Ga=class extends Nu{withFaceExpressions(){return new Va(this,this.input)}withFaceDescriptor(){return new js(this,this.input)}};var Jp=class extends Ir{constructor(n,r){super();this.parentTask=n;this.input=r}},Hs=class extends Jp{async run(){let t=await this.parentTask;return(await Iu(t,this.input,r=>Promise.all(r.map(s=>rt.faceRecognitionNet.computeFaceDescriptor(s))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,s)=>Sg(t[s],r))}withFaceExpressions(){return new Wa(this,this.input)}withAgeAndGender(){return new Ua(this,this.input)}},js=class extends Jp{async run(){let t=await this.parentTask;if(!t)return;let n=await Gl(t,this.input,r=>rt.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Sg(t,n)}withFaceExpressions(){return new Va(this,this.input)}withAgeAndGender(){return new Ga(this,this.input)}};var Qp=class extends Ir{constructor(n,r,s){super();this.parentTask=n;this.input=r;this.useTinyLandmarkNet=s}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},eh=class extends Qp{async run(){let t=await this.parentTask,n=t.map(o=>o.detection),r=this.input instanceof Ne?await Rl(this.input,n):await Fl(this.input,n),s=await Promise.all(r.map(o=>this.landmarkNet.detectLandmarks(o)));return r.forEach(o=>o instanceof Ne&&o.dispose()),t.filter((o,i)=>s[i]).map((o,i)=>zl(o,s[i]))}withFaceExpressions(){return new Wa(this,this.input)}withAgeAndGender(){return new Ua(this,this.input)}withFaceDescriptors(){return new Hs(this,this.input)}},th=class extends Qp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,r=this.input instanceof Ne?await Rl(this.input,[n]):await Fl(this.input,[n]),s=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ne&&a.dispose()),zl(t,s)}withFaceExpressions(){return new Va(this,this.input)}withAgeAndGender(){return new Ga(this,this.input)}withFaceDescriptor(){return new js(this,this.input)}};var nh=class extends Ir{constructor(n,r=new wr){super();this.input=n;this.options=r}},Hl=class extends nh{async run(){let{input:t,options:n}=this,r;if(n instanceof Zp)r=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof wr)r=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Qr)r=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(r=>t(r.map(s=>du({},s)))).catch(r=>n(r))})}withFaceLandmarks(t=!1){return new eh(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Su(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Tu(this.runAndExtendWithFaceDetections(),this.input)}},rh=class extends nh{async run(){let t=await new Hl(this.input,this.options),n=t[0];return t.forEach(r=>{r.score>n.score&&(n=r)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?du({},n):void 0)})}withFaceLandmarks(t=!1){return new th(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Cu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Nu(this.runAndExtendWithFaceDetection(),this.input)}};function qge(e,t=new wr){return new rh(e,t)}function Dg(e,t=new wr){return new Hl(e,t)}async function jF(e,t){return Dg(e,new wr(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Kge(e,t={}){return Dg(e,new Qr(t)).withFaceLandmarks().withFaceDescriptors()}var Xge=jF;function eS(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),r=Array.from(t);return Math.sqrt(n.map((s,a)=>s-r[a]).reduce((s,a)=>s+a*a,0))}var tS=class e{constructor(t,n=.6){this._distanceThreshold=n;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let s=1,a=()=>`person ${s++}`;this._labeledDescriptors=r.map(o=>{if(o instanceof Ba)return o;if(o instanceof Float32Array)return new Ba(a(),[o]);if(o.descriptor&&o.descriptor instanceof Float32Array)return new Ba(a(),[o.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(r=>eS(r,t)).reduce((r,s)=>r+s,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:r})=>new Al(r,this.computeMeanDistance(t,n))).reduce((n,r)=>n.distance<r.distance?n:r)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this._distanceThreshold?n:new Al("unknown",n.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(r=>Ba.fromJSON(r));return new e(n,t.distanceThreshold)}};function Yge(e){let t=new ku;return t.extractWeights(e),t}function qF(e,t){let{width:n,height:r}=new Un(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(s=>qF(s,{width:n,height:r}));if(bu(e)){let s=e.detection.forSize(n,r),a=e.unshiftedLandmarks.forSize(s.box.width,s.box.height);return zl(du(e,s),a)}return vs(e)?du(e,e.detection.forSize(n,r)):e instanceof or||e instanceof Tt?e.forSize(n,r):e}var Zge=hF;return $R(Jge);})();