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D1;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(D1||(D1={}));var z1;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(z1||(z1={}));var xk={float32:D1,int32:O1,bool:$1,complex64:z1};function Zn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return xk[e][t]}function jh(e){return Zn(e,"int32")}function gt(e,t){if(e.dtype===t.dtype)return[e,t];let n=Zn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Ag(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function gk(e,t){return t.some(n=>n.id===e.id)}function F1(e){let t=[],n=new Set;return yg(e,t,n),t}function yg(e,t,n){if(e==null)return;if(e instanceof Xe){t.push(e);return}if(!wk(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),yg(s,t,n))}}function wk(e){return Array.isArray(e)||typeof e=="object"}function P1(e){return e.kernelName!=null}var gg=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Tu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new gg}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Po(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof nu)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r(rthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return Tu.nextTensorId++}nextVariableId(){return Tu.nextVariableId++}clone(e){let t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return D.runKernel(Ja,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(Wh(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=P1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(P1(e)){let{kernelName:d,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=Wh(d,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,y,g);let _=g.map(x=>{if(x.rank!=null)return x;let{dataId:w,shape:b,dtype:N}=x;return this.makeTensorFromDataId(w,b,N)});if(r){let x=this.getTensorsForGradient(d,f,_);n=this.saveTensorsForBackwardMode(x)}return _}}else{let{forwardFunc:d}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>d(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:c,attrs:u}=e,h=P1(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=E1(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&ca(e[0])&&(a=e.map(o=>vu(o)));let s=r.write(a,t,n),i=new Xe(t,n,s,this.nextTensorId());if(this.incRef(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=ag(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Xe(t,n,e,this.nextTensorId());return this.incRef(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new Su(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}incRef(e,t){let n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*rg(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:r,refCount:0}),this.state.numBytes+=r}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Su||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;let t=this.state.tensorInfo.get(e.dataId);t.refCount<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):(t.backend.decComplexRef(e.dataId),this.state.tensorInfo.get(e.dataId).refCount--)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},o=E1(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((c,u)=>{if(c==null){let h=n[u],p=lh(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return c}),r(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=F1(e),n=new Set(t.map(a=>a.id));for(let a=0;a{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(a instanceof Xe,()=>"The result y returned by f() must be a tensor.");let s=ck(this.state.activeTape,t,a);if(!r&&s.length===0&&t.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]=n==null?_k(a.shape):n,hk(i,s,l=>this.tidy(l),bk);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:a,grads:o}})}customGrad(e){return F(ha(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof Xe),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((i,o)=>{r[o]=i});let a=(i,o)=>(n=e(...t,o),F(n.value instanceof Xe,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(ha(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),c=Array.isArray(l)?l:[l];F(c.length===t.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(...)."),F(c.every(h=>h instanceof Xe),()=>"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 u={};return c.forEach((h,p)=>{u[p]=()=>h}),u};return this.runKernelFunc({forwardFunc:a,backwardsFunc:s,inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=C1(),n=await this.backend.time(e);return n.wallMs=C1()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new gg;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Tu.nextTensorId=0;Tu.nextVariableId=0;function _k(e){let t=v1(Ct(e),"float32");return D.makeTensor(t,e,"float32")}function xg(){let e=cg();if(e._tfengine==null){let t=new ug(e);e._tfengine=new Tu(t)}return J8(e._tfengine.ENV),mk(()=>e._tfengine),e._tfengine}var D=xg();function bk(e,t){let n={a:e,b:t};return D.runKernel(da,n)}var Gh={};$e(Gh,{isBrowser:()=>wg,isMobile:()=>vk});function kk(){return typeof navigator!="undefined"&&navigator!=null}function vk(){if(kk()){let e=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}return!1}function wg(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Ir=Q();Ir.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. 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D.runKernel(Xi,t)}var Jo=z({elu_:oN});function lN(e){let t=R(e,"x","erf");F(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let n={x:t};return D.runKernel(Ki,n)}var kf=z({erf_:lN});function uN(e){let t={x:R(e,"x","exp")};return D.runKernel(ss,t)}var Ln=z({exp_:uN});function cN(e,t=0){let n=R(e,"x","expandDims","string_or_numeric");F(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},a={dim:t};return D.runKernel(Yi,r,a)}var bn=z({expandDims_:cN});function hN(e){let t={x:R(e,"x","expm1")};return D.runKernel(Ji,t)}var If=z({expm1_:hN});function dN(e,t){let n=R(e,"x","tile","string_or_numeric");F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},a={reps:t};return D.runKernel(fa,r,a)}var ba=z({tile_:dN});function pN(e,t,n,r="float32"){t==null&&(t=e);let a=Pe([e,t],r),s=e<=t?e:t;for(let o=0;o`Error in localResponseNormalization: x must 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Labels / logits was rank ${t.rank} and dim was ${n}`);return Tr((r,a,s)=>{let i=Cf(a,[n],!0),o=Ae(me(a,"float32"),i);s([r,o]);let l=xt(L(o,r));return{value:Ie(l,[n]),gradFunc:(c,u)=>{let[h,p]=u,d=Zs(c.shape,[n]);return[L(q(c,d),Ae(me(h,"float32"),Ln(p))),L(q(c,d),Ae(Ln(p),me(h,"float32")))]}}})(e,t)}function ZE(e,t,n,r=0,a=rn.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"onehotLabels","softmaxCrossEntropy"),i=R(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","softmaxCrossEntropy")),Jt(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let c=ke(r),u=ke(1),h=ke(s.shape[1]);s=se(L(s,Ae(u,c)),be(c,h))}let l=KE(s,i);return Yr(l,o,a)}var YE=z({softmaxCrossEntropy_:ZE}),JE={fft:qu,ifft:al,rfft:Xu,irfft:_d},QE={hammingWindow:ZT,hannWindow:j5,frame:G5,stft:eE},Je={flipLeftRight:aE,resizeNearestNeighbor:Y5,resizeBilinear:Z5,rotateWithOffset:iE,cropAndResize:nE,nonMaxSuppression:lE,nonMaxSuppressionAsync:AE,nonMaxSuppressionWithScore:gE,nonMaxSuppressionWithScoreAsync:wE,nonMaxSuppressionPadded:bE,nonMaxSuppressionPaddedAsync:kE},Q5={bandPart:TE,gramSchmidt:CE,qr:FE},eC={absoluteDifference:$E,computeWeightedLoss:Yr,cosineDistance:zE,hingeLoss:LE,huberLoss:BE,logLoss:UE,meanSquaredError:GE,sigmoidCrossEntropy:XE,softmaxCrossEntropy:YE},Jr=class extends Qg{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return Ne(a),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 k5(e,t)}dispose(){this.iterations_!=null&&Ne(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Jr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Cd=class extends Jr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=D.registeredVariables[t],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:W(()=>Be(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:W(()=>Be(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;W(()=>{let l=se(L(i,this.rho),L(it(s),1-this.rho)),c=L(be(qt(se(o,this.epsilon)),qt(se(i,this.epsilon))),s),u=se(L(o,this.rho),L(it(c),1-this.rho));i.assign(l),o.assign(u);let h=se(L(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ne(this.accumulatedGrads.map(e=>e.variable)),Ne(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Cd.className="Adadelta";xa(Cd);var Rd=class extends Jr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=D.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:W(()=>Pu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;W(()=>{let i=se(s,it(a));s.assign(i);let o=se(L(be(a,qt(se(i,D.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ne(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Rd.className="Adagrad";xa(Rd);var Fd=class extends Jr{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],W(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(n).variable()}),r==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);W(()=>{let n=Ae(1,this.accBeta1),r=Ae(1,this.accBeta2);t.forEach((a,s)=>{let i=D.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:W(()=>Be(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:W(()=>Be(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,h=se(L(c,this.beta1),L(l,1-this.beta1)),p=se(L(u,this.beta2),L(it(l),1-this.beta2)),d=be(h,n),f=be(p,r);c.assign(h),u.assign(p);let m=se(L(be(d,se(qt(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ne(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),W(()=>{this.accBeta1.assign(Zr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Zr(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)}};Fd.className="Adam";xa(Fd);var Md=class extends Jr{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],W(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),r==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);W(()=>{let n=Ae(1,this.accBeta1),r=be(-this.learningRate,se(L(this.iteration,this.decay),1));t.forEach((a,s)=>{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)}};Md.className="Adamax";xa(Md);var Ku=class extends Jr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=D.registeredVariables[t];W(()=>{let s=se(L(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Lt(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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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)}};Od.className="Momentum";xa(Od);var $d=class extends Jr{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=D.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=D.registeredVariables[t],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:W(()=>Be(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:W(()=>Be(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:W(()=>Be(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;W(()=>{let l=se(L(i,this.decay),L(it(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=se(L(c,this.decay),L(s,1-this.decay)),h=be(L(s,this.learningRate),qt(Ae(l,se(it(u),this.epsilon)))),p=se(L(o,this.momentum),h);i.assign(l),c.assign(u),o.assign(p);let d=Ae(r,p);r.assign(d)}else{let c=se(L(i,this.decay),L(it(s),1-this.decay)),u=se(L(o,this.momentum),be(L(s,this.learningRate),qt(se(c,this.epsilon))));i.assign(c),o.assign(u);let h=Ae(r,u);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ne(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ne(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ne(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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)}};$d.className="RMSProp";xa($d);var Js=class{static sgd(e){return new Ku(e)}static momentum(e,t,n=!1){return new Od(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new $d(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new Fd(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Cd(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new Md(e,t,n,r,a)}static adagrad(e,t=.1){return new Rd(e,t)}},Qs={sgd:Js.sgd,momentum:Js.momentum,adadelta:Js.adadelta,adagrad:Js.adagrad,rmsprop:Js.rmsprop,adamax:Js.adamax,adam:Js.adam},tC=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Dd(){return new Promise(e=>tC(()=>e()))}var 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Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))h=Mr({inputs:{x:a},backend:n});else{let p=n.data.get(a.dataId).values,d=k.computeStrides(a.shape),f=om(p,a.shape,a.dtype,d,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var aF={kernelName:Za,backendName:"cpu",kernelFunc:rF};function sF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;we(a,"avgPool3d");let u=C.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,p=Px(h,a.shape,a.dtype,k.computeStrides(a.shape),u,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}var iF={kernelName:iu,backendName:"cpu",kernelFunc:sF};function oF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;we([a,s],"avgPool3DGrad");let u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,p=u.strideHeight,d=u.strideWidth,f=u.filterDepth,m=u.filterHeight,A=u.filterWidth,y=u.dilationDepth,g=u.dilationHeight,_=u.dilationWidth,x=u.effectiveFilterDepth,w=u.effectiveFilterHeight,b=u.effectiveFilterWidth,N=x-1-u.padInfo.front,T=b-1-u.padInfo.left,E=w-1-u.padInfo.top,M=Pe(s.shape,"float32"),$=1/(f*m*A),P=n.bufferSync(a);for(let V=0;V=u.outDepth||Math.floor(ne)!==ne))for(let ce=0;ce=u.outHeight||Math.floor(ue)!==ue))for(let pe=0;pe=u.outWidth||Math.floor(fe)!==fe||(J+=P.get(V,ne,ue,fe,H))}}}M.set(J*$,V,U,K,X,H)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var lF={kernelName:dh,backendName:"cpu",kernelFunc:oF};function uF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;we([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,p=u.strideWidth,d=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,_=g-1-u.padInfo.left,x=y-1-u.padInfo.top,w=Pe(i.shape,"float32"),b=1/(d*f),N=n.data.get(a.dataId).values,T=Pe(a.shape,"float32",N);for(let E=0;E=u.outHeight||Math.floor(X)!==X))for(let ee=0;ee=u.outWidth||Math.floor(Z)!==Z||(U+=T.get(E,X,Z,M))}}w.set(U*b,E,$,P,M)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var cF={kernelName:hh,backendName:"cpu",kernelFunc:uF};function hF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean 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o=s.reduce((y,g)=>y*g),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(u,i,s.length),d=mt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=tr({inputs:{x:d},backend:n,attrs:{perm:c}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=ti({inputs:{x:m},backend:n,attrs:{begin:h,size:p}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var fF={kernelName:ou,backendName:"cpu",kernelFunc:pF};function mF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=Jf(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var AF={kernelName:ph,backendName:"cpu",kernelFunc:mF},yF=at(pa,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,r=new 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mt({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(c.map(m=>m.shape),1);let h=c[0].shape[0]===1,p=Qf(u,i,t[0].dtype,h),d=C.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(d,t[0].dtype,p);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var bF={kernelName:ji,backendName:"cpu",kernelFunc:cl};function Lx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r;we([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),d=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,A=p.dilationWidth,y=p.padInfo.left,g=p.padInfo.top,_=p.dataFormat==="channelsLast",x=new Rt(p.outShape,a.dtype),w=k.computeStrides(a.shape),b=k.computeStrides(s.shape),N=w[0],T=_?w[1]:w[2],E=_?w[2]:1,M=_?1:w[1],$=x.strides[0],P=_?x.strides[1]:x.strides[2],V=_?x.strides[2]:1,H=_?1:x.strides[1],U=n.data.get(a.dataId).values,K=n.data.get(s.dataId).values,X=x.values;for(let ee=0;ee=p.inHeight)continue;let pe=ce*b[0],fe=Z+ue*T;for(let _e=0;_e=p.inWidth)continue;let We=pe+Oe*b[1],tt=fe+He*E,st=We;for(let Ve=0;Ve=c.inDepth)continue;let ee=K*E[0],Z=$+X*T[1];for(let ae=0;ae=c.inHeight)continue;let ue=ee+ne*E[1],pe=Z+ce*T[2];for(let fe=0;fe=c.inWidth)continue;let He=ue+Ce*E[2],We=pe+Oe*c.inChannels,tt=He;for(let st=0;stMath.cos(e)),$F={kernelName:ts,backendName:"cpu",kernelFunc:OF},DF=at(Gi,e=>Math.cosh(e)),zF={kernelName:Gi,backendName:"cpu",kernelFunc:DF};function PF(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,[u,h,p,d]=a.shape,f=s.shape[0],[m,A]=o,y=Pe([f,m,A,d],"float32"),g=n.data.get(s.dataId).values,_=n.data.get(i.dataId).values,x=n.data.get(a.dataId).values,w=k.computeStrides(a.shape),b=k.computeStrides(y.shape);for(let N=0;N=u)continue;let H=m>1?($-E)*(h-1)/(m-1):0,U=A>1?(P-M)*(p-1)/(A-1):0;for(let K=0;K1?E*(h-1)+K*H:.5*(E+$)*(h-1);if(X<0||X>h-1){for(let ee=0;ee1?M*(p-1)+J*U:.5*(M+P)*(p-1);if(oe<0||oe>p-1){for(let pe=0;pe1?M*(p-1)+ee*U:.5*(M+P)*(p-1);if(Z<0||Z>p-1){for(let oe=0;oey+f-g-1:(y,g)=>y+g;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],c=a.shape[2],u=a.shape[3],h=l*s,p=c*s,d=u/(s*s),f=n.data.get(a.dataId).values,m=new Float32Array(o*h*p*d),A=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=C.computeConv2DInfo(a.shape,s.shape,i,p,o,c,!0),{filterHeight:f,filterWidth:m,dilationHeight:A,dilationWidth:y,padInfo:g}=d,_=g.left,x=g.top,w=d.outChannels/d.inChannels,b=new Rt(d.outShape,a.dtype),N=n.data.get(a.dataId).values,T=n.data.get(s.dataId).values,E=b.values;for(let M=0;M=d.inHeight)continue;let ee=K*h[0],Z=$+X*u[1];for(let ae=0;ae=d.inWidth)continue;let ue=ee+ne*h[1],pe=Z+ce*d.inChannels,fe=J,_e=ue;for(let Se=0;Se{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(r.dataId).values,u=r.shape.length,h=l.data.get(a.dataId).values,p=a.shape.length,{batchSize:d,inHeight:f,inWidth:m,inChannels:A,outHeight:y,outWidth:g,padInfo:_,strideHeight:x,strideWidth:w,filterHeight:b,filterWidth:N,dilationHeight:T,dilationWidth:E,outShape:M}=C.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),$=k.sizeFromShape(M),P=M.length,V=k.getArrayFromDType(r.dtype,$);for(let H=0;H=0&&ne=0&&ueae&&(ae=_e)}}}let J=k.locToIndex([H,U,X,Z],P,k.computeStrides(M));V[J]=ae}}}return{dataId:l.write(k.toTypedArray(V,r.dtype),M,r.dtype),shape:M,dtype:r.dtype}}},eM={kernelName:vh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(r.shape,c.data.get(r.dataId).values),h=k.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:p,inHeight:d,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:x,filterHeight:w,filterWidth:b,dilationHeight:N,dilationWidth:T,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);k.assert(s.rank===E.length,()=>`Error in ${vh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let M=k.toNestedArray(E,c.data.get(s.dataId).values),$=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let 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oO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;we(a,"LRN");let c=a.shape[3],u=c-1,h=n.data.get(a.dataId).values,p=k.sizeFromShape(a.shape),d=new Float32Array(p);function f(m){let A=m%c,y=m-A+Math.max(0,A-s),g=m-A+Math.min(A+s,u),_=0;for(;y<=g;y++){let x=h[y];_+=x*x}return _}for(let m=0;m`Error in maxPool: Either strides or dilations must be 1. 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u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=n.bufferSync(s),p=nF(h,u),d=u.strideDepth,f=u.strideHeight,m=u.strideWidth,A=u.dilationDepth,y=u.dilationHeight,g=u.dilationWidth,_=u.effectiveFilterDepth,x=u.effectiveFilterHeight,w=u.effectiveFilterWidth,b=_-1-u.padInfo.front,N=w-1-u.padInfo.left,T=x-1-u.padInfo.top,E=Pe(s.shape,"float32"),M=n.bufferSync(a);for(let $=0;$=u.outDepth||Math.floor(J)!==J))for(let oe=0;oe=u.outHeight||Math.floor(ne)!==ne))for(let ce=0;ce=u.outWidth||Math.floor(ue)!==ue)continue;let pe=_*x*w-1-p.get($,J,ne,ue,P),fe=ae*x*w+oe*w+ce,_e=pe===fe?1:0;_e!==0&&(Z+=M.get($,J,ne,ue,P)*_e)}}}E.set(Z,$,V,H,U,P)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var yO={kernelName:Rh,backendName:"cpu",kernelFunc:AO};function gO(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;we([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,p=C.computePool2DInfo(o.shape,l,c,1,u,h),d=n.data.get(o.dataId).values,f=Pe(p.outShape,o.dtype,zx(d,o.shape,o.dtype,p).values),m=p.strideHeight,A=p.strideWidth,y=p.dilationHeight,g=p.dilationWidth,_=p.effectiveFilterHeight,x=p.effectiveFilterWidth,w=x-1-p.padInfo.left,b=_-1-p.padInfo.top,N=Pe(o.shape,"float32"),T=n.data.get(a.dataId).values,E=Pe(a.shape,"float32",T);for(let M=0;M=p.outHeight||Math.floor(ee)!==ee))for(let Z=0;Z=p.outWidth||Math.floor(ae)!==ae)continue;let J=_*x-1-f.get(M,ee,ae,$),oe=X*x+Z,ne=J===oe?1:0;ne!==0&&(K+=E.get(M,ee,ae,$)*ne)}}N.set(K,M,P,V,$)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var xO={kernelName:Ch,backendName:"cpu",kernelFunc:gO};function wO(e,t,n,r,a){let s=k.computeStrides(t),i=om(e,t,n,s,a,"max"),o=zx(e,t,n,a,!0,r);return[i.values,o.values]}var 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d=this.pendingRead.get(e);return new Promise(f=>d.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let d;o?d=new yl(r,Zd):d=new Sa(r,Zd);let f=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Q().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&Q().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture,...nc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=d[0],m=d[1];u=C.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let d=k.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(d=>d(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>k.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;to.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.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 Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return Q().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Pn().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=pP){let n=this.getCPUBackend();return!Q().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. 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NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; `,ac=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||k.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(s=` ${ut(a)} coords = getOutputCoords(); `,a===1)s+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let i=sn("coords",a);s+=` bool nextRowOutOfBounds = (${i[a-2]} + 1) >= ${this.outputShape[a-2]}; bool nextColOutOfBounds = (${i[a-1]} + 1) >= ${this.outputShape[a-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); ${s} setOutput(result); } `}};function Tn(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 gP={kernelName:ro,backendName:"webgl",kernelFunc:Tn};function Ta(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Tn({inputs:{x:r},backend:n}),l=n.texData.get(o.dataId);l.complexParentRefCount++;let c=Tn({inputs:{x:a},backend:n}),u=n.texData.get(c.dataId);return u.complexParentRefCount++,i.complexTensorInfos={real:o,imag:c},s}var xP={kernelName:fh,backendName:"webgl",kernelFunc:Ta},e_="return (a < 0.) ? b * a : a;",t_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function wP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new ac(t_,a.shape,i.shape):new gl(e_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var _P={kernelName:cs,backendName:"webgl",kernelFunc:wP},n_="return (a < 0.) ? b * a : a;",r_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function bP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new ac(r_,r.shape,a.shape):new gl(n_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var vP={kernelName:bs,backendName:"webgl",kernelFunc:bP},a_="if (isnan(x)) return x;",kP=` if (isnan(a)) return a; if (isnan(b)) return b; `,IP=` result.r = isNaN.r > 0. ? 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NAN : result.a; `;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),p=n(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let c=Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new yl(i.shape,t):u=new Sa(i.shape,e),o.runWebGLProgram(u,[i],l)}}function Kt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(_=>{let[x,w]=_,b={dataId:x.dataId,dtype:x.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:c.shape},T=new gl(e,l.shape,c.shape);return u.runWebGLProgram(T,[b,N],Zn(x.dtype,w.dtype))}),g=Ta({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||Zn(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),_=u.texData.get(g.dataId);return _.values=A,g}let p=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,d;return p?d=new ac(t,l.shape,c.shape,n):d=new gl(e,l.shape,c.shape),u.runWebGLProgram(d,[l,c],h)}}function Qd(e,t=!1){if(e==="linear")return t?aP:eP;if(e==="relu")return t?iP:nP;if(e==="elu")return t?sP:tP;if(e==="relu6")return t?oP:rP;if(e==="prelu")return t?r_:n_;if(e==="leakyrelu")return t?t_:e_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var s_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",d=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?m=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${i} }`:m=`vec4 activation(vec4 x) { ${i} }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",_="rc.x";e[0]`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. 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setOutput(result); } `}};function np(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Tn({inputs:{x:r[0]},backend:n});if(r.length>Q().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=np({inputs:r.slice(0,o),backend:n}),c=np({inputs:r.slice(o),backend:n});return np({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>Zn(o,l)),s=r.map(o=>o.shape),i=Q().getBool("WEBGL_PACK")?new ZP(r[0].shape,s):new KP(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var YP={kernelName:Xa,backendName:"webgl",kernelFunc:np};function JP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("all",c,o);let[p,d]=C.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(d),m=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=oi(m,m.dtype,"all",n),y;if(i){let g=C.expandShapeToKeepDim(p,l);y=ye({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ye({inputs:{x:A},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var QP={kernelName:uh,backendName:"webgl",kernelFunc:JP};function eL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("any",c,o);let[p,d]=C.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(d),m=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=oi(m,m.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(p,l);y=ye({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ye({inputs:{x:A},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var tL={kernelName:ch,backendName:"webgl",kernelFunc:eL},nL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=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 = ${o}; float candidate = getA(batch, inIdx); if (candidate ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}},rL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ut(o),c=sn("coords",o),u,h;if(s===1){h=o+1;let N=ut(h);u=` ${N} sourceLocR = ${N}(${c.join()}, 0); ++${c[o-1]}; ${N} sourceLocG = ${N}(${c.join()}, 0); ++${c[o-2]}; ${N} sourceLocA = ${N}(${c.join()}, 0); --${c[o-1]}; ${N} sourceLocB = ${N}(${c.join()}, 0); --${c[o-2]};`}else h=o,u=` ${l} sourceLocR = coords; 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} ${b} void main() { ${l} coords = getOutputCoords(); bool hasNextCol = ${c[o-1]} < ${i[o-1]-1}; bool hasNextRow = ${c[o-2]} < ${i[o-2]-1}; ${u} ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d}, sourceLocB${d}, sourceLocA${d}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${w}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${x} vec4 candidate = ${w}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${_}(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 f_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new nL(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=f_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function m_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new rL(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=m_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function A_(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!Q().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=k.sizeFromShape(o),c=ye({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=f_(e,c,r);s.push(u);let h=ye({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(p=>e.disposeIntermediateTensorInfo(p)),h}return m_(e,t,r)}function aL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=A_(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var sL={kernelName:Ka,backendName:"webgl",kernelFunc:aL};function iL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=A_(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var oL={kernelName:su,backendName:"webgl",kernelFunc:iL},lL=hr+` if (abs(x) > 1.) { return NAN; } return asin(x); `,uL=Ke({opSnippet:lL}),cL={kernelName:Pi,backendName:"webgl",kernelFunc:uL},hL=hr+"return log(x + sqrt(x * x + 1.0));",dL=Ke({opSnippet:hL}),pL={kernelName:Li,backendName:"webgl",kernelFunc:dL},fL=hr+` return atan(x); `,mL=Ke({opSnippet:fL}),AL={kernelName:Wi,backendName:"webgl",kernelFunc:mL},yL=kP+` return atan(a, b); `,gL=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+IP+` return result; `,xL=Kt({opSnippet:yL,packedOpSnippet:gL}),wL={kernelName:Vi,backendName:"webgl",kernelFunc:xL},_L=hr+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,bL=Ke({opSnippet:_L}),vL={kernelName:Bi,backendName:"webgl",kernelFunc:bL},sc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${p}, ${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 float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; float avgValue = 0.0; for (int wR = 0; wR < ${u}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${h}; 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?a?m:A:`wR * ${h} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let g="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let x=Math.floor(s/4)*4,w=s%4,b=` if (${f}) { avgValue += dot(values, ones); } else { minMaxValue = ${g}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${p}, ${d}); const float initializationValue = ${y}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xR, int xC, int d) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${y}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${u}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${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) ); ${b} } int xC = xCCorner + ${x}; if (${w===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${b} } else if (${w===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); ${b} } else if (${w===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), initializationValue ); ${b} } } setOutput(${_}); } `}},Sm=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",_="0.0";if(g||(_="-1.0 / 1e-20"),n){let E=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${A}, ${y}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${p}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC += ${h}) { 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 ${E} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${f} + wR * ${f} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let x="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,N=s%4,T=` if (${g}) { avgValue += dot(values, ones); } else { minMaxValue = ${x}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${A}, ${y}); const float initializationValue = ${_}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${_}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${p}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${b}; wC += 4) { int xC = xCCorner + wC * ${h}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${h}, ch), getValue(batch, xD, xR, xC + 2 * ${h}, ch), getValue(batch, xD, xR, xC + 3 * ${h}, ch) ); ${T} } int xC = xCCorner + ${b}; if (${N===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${T} } else if (${N===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${h}, ch), initializationValue, initializationValue ); ${T} } else if (${N===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${h}, ch), getValue(batch, xD, xR, xC + 2 * ${h}, ch), initializationValue ); ${T} } } setOutput(${w}); } } `}};function kL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;hl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Tn({inputs:{x:a},backend:n});let h=new sc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var IL={kernelName:Za,backendName:"webgl",kernelFunc:kL};function NL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,l,c),p=new Sm(h,"avg",!1);return n.runWebGLProgram(p,[a],"float32")}var SL={kernelName:iu,backendName:"webgl",kernelFunc:NL},TL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${c}, ${u}); const float avgMultiplier = float(${h}); 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 < ${o}; wR += ${s}) { 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 < ${l}; wC+= ${i}) { float dyC = float(dyCCorner + wC) / ${a}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},EL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=p-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` const ivec3 pads = ivec3(${d}, ${f}, ${m}); const float avgMultiplier = float(${A}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${u}; wD += ${o}) { float dyD = float(dyDCorner + wD) / ${a}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${h}; wR += ${l}) { float dyR = float(dyRCorner + wR) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${p}; wC += ${c}) { float dyC = float(dyCCorner + wC) / ${i}.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 CL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,c,u),d=new EL(p);return n.runWebGLProgram(d,[a],i.dtype)}var RL={kernelName:dh,backendName:"webgl",kernelFunc:CL};function FL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;hl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=new TL(u);return n.runWebGLProgram(h,[a],i.dtype)}var ML={kernelName:hh,backendName:"webgl",kernelFunc:FL};function OL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return tp({a,b:s,transposeA:i,transposeB:o,backend:n})}var $L={kernelName:Ya,backendName:"webgl",kernelFunc:OL},DL=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${i}; float scale = ${o}; float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}},zL=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${s})); setOutput((x - mean) * inv + offset); } `}},PL=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;k.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let p=Q().getBool("WEBGL_PACK_NORMALIZATION")?new zL(r.shape,a.shape,s.shape,u,h,l):new DL(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(p,c,c[0].dtype)},LL={kernelName:ls,backendName:"webgl",kernelFunc:PL},BL=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,r=WL(this.rank),a,s=e.map((i,o)=>`sourceLoc.${Tm[o]} = start[${o}] + coords.${Tm[o]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` `)} `,this.userCode=` ${n} void main() { ${a} setOutput(getSource(${r})); } `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Tm=["x","y","z","w","u","v"];function WL(e){if(e===1)return"sourceLoc";if(e<=6)return Tm.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var VL=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=sn("coords",this.rank),r=sn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=` result.x = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${r[this.rank-1]}; result.y = ${s}; --${r[this.rank-1]}; } `,o=this.rank===1?"":` --${n[this.rank-1]}; if (++${n[this.rank-2]} < ${e[this.rank-2]}) { ++${r[this.rank-2]}; result.z = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${r[this.rank-1]}; result.w = ${s}; } } `,l=this.rank<=4?`sourceLoc = coords + ${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[u]} = ${n[u]} + start[${u}];`).join(` `);this.userCode=` uniform int start[${this.rank}]; void main() { ${t} coords = getOutputCoords(); ${t} sourceLoc; ${l} vec4 result = vec4(0.); ${i} ${o} setOutput(result); } `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function UL(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.complexParentRefCount=0,i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=nn.computeFlatOffset(t,k.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function ic(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=nn.parseSliceParams(a,s,i);if(nn.assertParamsValid(a,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),p=zz(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,p)}let{isPacked:c}=n.texData.get(a.dataId),u=nn.isSliceContinous(a.shape,o,l);if(c||!u){let h=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VL(l):new BL(l),p=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,p)}return n.uploadToGPU(a.dataId),UL(a,o,l,n)}var jL={kernelName:No,backendName:"webgl",kernelFunc:ic},GL=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;k.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,_)=>g*_),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(u,i,s.length),d=[],f=ye({inputs:{x:a},backend:n,attrs:{shape:l}}),m=fn({inputs:{x:f},backend:n,attrs:{perm:c}}),A=ye({inputs:{x:m},backend:n,attrs:{shape:u}}),y=ic({inputs:{x:A},backend:n,attrs:{begin:h,size:p}});return d.push(f),d.push(m),d.push(A),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},HL={kernelName:ou,backendName:"webgl",kernelFunc:GL};function qL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=Uw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var XL={kernelName:ph,backendName:"webgl",kernelFunc:qL},KL="return float(a != b);",y_=Kt({opSnippet:KL,dtype:"bool"}),ZL={kernelName:fo,backendName:"webgl",kernelFunc:y_};function oc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Tn({inputs:{x:a.complexTensorInfos.real},backend:n})}var YL={kernelName:Oh,backendName:"webgl",kernelFunc:oc},JL="return float(int(x));";function QL(e,t){let n=new Sa(e.shape,JL),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Em(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Tn({inputs:{x:a},backend:n});let i=Nt(a.shape),o=Em({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Ta({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=oc({inputs:{input:a},backend:n}),o=Em({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(a.dtype,s)){let i=Tn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return QL(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=y_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var eW={kernelName:Ja,backendName:"webgl",kernelFunc:Em},g_="return ceil(x);",tW=Ke({opSnippet:g_,packedOpSnippet:g_,cpuKernelImpl:xz}),nW={kernelName:Ui,backendName:"webgl",kernelFunc:tW},rW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=` uniform float minVal; uniform float maxVal; void main() { float value = getAAtOutCoords(); if (isnan(value)) { setOutput(value); return; } setOutput(clamp(value, minVal, maxVal)); } `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},aW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` uniform float minVal; uniform float maxVal; void main() { vec4 value = getAAtOutCoords(); if (any(isnan(value))) { setOutput(value); return; } setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function sW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;Q().getBool("WEBGL_PACK_CLIP")?o=new aW(a.shape):o=new rW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var iW={kernelName:pa,backendName:"webgl",kernelFunc:sW},oW=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 x_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function lW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new oW(r.shape),i=[x_(r,a.complexTensorInfos.real),x_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var uW={kernelName:lu,backendName:"webgl",kernelFunc:lW},cW=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f= ${o[f-1]}) { return getChannel( getT${f}(${rp(i,l,m)}), vec2(${rp(c,l,m)})); }`}let p=o.length,d=o[o.length-1];h+=` return getChannel( getT${p}(${rp(i,l,d)}), vec2(${rp(c,l,d)}));`,this.userCode=` float getValue(${i.map(f=>"int "+f)}) { ${h} } void main() { ${a} coords = getOutputCoords(); vec4 result = vec4(getValue(${s}), 0., 0., 0.); ${s[r-1]} = ${s[r-1]} + 1; if (${s[r-1]} < ${n[r-1]}) { result.g = getValue(${s}); } ${s[r-2]} = ${s[r-2]} + 1; if (${s[r-2]} < ${n[r-2]}) { result.a = getValue(${s}); } ${s[r-1]} = ${s[r-1]} - 1; if (${s[r-2]} < ${n[r-2]} && ${s[r-1]} < ${n[r-1]}) { result.b = getValue(${s}); } setOutput(result); } `}};function rp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function ap(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Tn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var dW={kernelName:Sh,backendName:"webgl",kernelFunc:ap};function xl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>oc({inputs:{input:f},backend:n})),u=e.map(f=>ap({inputs:{input:f},backend:n})),h=xl(c,t,n),p=xl(u,t,n),d=Ta({inputs:{real:h,imag:p},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),d}if(r==="string"){let{tensors2D:c,outShape:u}=w_(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=c[0].shape[0]===1,d=wz(h,u,r,p),f=C.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,d);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>Q().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=xl(e.slice(0,c),t,n),h=xl(e.slice(c),t,n),p=xl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),p}if(Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new hW(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=w_(e,t,n),i=new cW(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=ye({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function w_(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ye({inputs:{x:a},attrs:{shape:[-1,k.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function __(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=k.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Tn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return C.assertParamsConsistent(l,s),xl(o,s,n)}var pW={kernelName:ji,backendName:"webgl",kernelFunc:__},b_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,d=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,_="",x="";n&&(r?_=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?_=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:_=` float activation(float x) { ${n} } `,x="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${_} const ivec2 strides = ivec2(${o}, ${l}); const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${g}]; ivec2 xRCCorner = ivec2(coords[${A}], coords[${y}]) * 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 < ${h}; wR++) { int xR = xRCorner + wR * ${c}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${p}; wC++) { int xC = xCCorner + wC * ${u}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${d}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${m}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), getX(batch, xR, xC, d1 + 2), getX(batch, xR, xC, d1 + 3) ); dotProd += dot(xValues, wValues); } else { vec4 xValues = vec4( getX(batch, d1, xR, xC), getX(batch, d1 + 1, xR, xC), getX(batch, d1 + 2, xR, xC), getX(batch, d1 + 3, xR, xC) ); dotProd += dot(xValues, wValues); } } if (${f===1}) { if (${m}) { dotProd += getX(batch, xR, xC, ${d}) * getW(wR, wC, ${d}, d2); } else { dotProd += getX(batch, ${d}, xR, xC) * getW(wR, wC, ${d}, d2); } } else if (${f===2}) { vec2 wValues = vec2( getW(wR, wC, ${d}, d2), getW(wR, wC, ${d} + 1, d2) ); if (${m}) { vec2 xValues = vec2( getX(batch, xR, xC, ${d}), getX(batch, xR, xC, ${d} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${d}, xR, xC), getX(batch, ${d} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${f===3}) { vec3 wValues = vec3( getW(wR, wC, ${d}, d2), getW(wR, wC, ${d} + 1, d2), getW(wR, wC, ${d} + 2, d2) ); if (${m}) { vec3 xValues = vec3( getX(batch, xR, xC, ${d}), getX(batch, xR, xC, ${d} + 1), getX(batch, xR, xC, ${d} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${d}, xR, xC), getX(batch, ${d} + 1, xR, xC), getX(batch, ${d} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${w} ${x} setOutput(result); } `}},fW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,p=e.filterWidth,d=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${a}, ${s}, ${i}); const ivec3 pads = ivec3(${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 < ${u}; wF++) { int xF = xFCorner + wF * ${o}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR++) { int xR = xRCorner + wR * ${l}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${p}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${d}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${f===1}) { dotProd += getX(batch, xF, xR, xC, ${d}) * getW(wF, wR, wC, ${d}, d2); } else if (${f===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${d}), getX(batch, xF, xR, xC, ${d} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${d}, d2), getW(wF, wR, wC, ${d} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${f===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${d}), getX(batch, xF, xR, xC, ${d} + 1), getX(batch, xF, xR, xC, ${d} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${d}, d2), getW(wF, wR, wC, ${d} + 1, d2), getW(wF, wR, wC, ${d} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}},mW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:p,top:d}=o,f=a*r,m=an(),A=h==="channelsLast",y=A?0:1,g=A?1:2,_="";for(let x=0;x<=1;x++)for(let w=0;w<=1;w++)_+=` blockIndex = rc.y + ${w}; pos = rc.x + ${x}; if(blockIndex < ${e[1]} && pos < ${e[0]}) { offsetY = int(blockIndex / (${l})) * ${i} - ${d}; d0 = offsetY + ${u} * (pos / ${f}); if(d0 < ${t[y]} && d0 >= 0) { offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${p}.); d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.)); if(d1 < ${t[g]} && d1 >= 0) { ch = int(mod(float(pos), ${a}.)); if (${A}) { innerDims = vec2(d1, ch); result[${x*2+w}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${x*2+w}] = getChannel( getA(ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec2 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${_} ${m.output} = result; } `}};function v_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],p=n.outChannels,d=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||p===1)&&u>h_,_=l[2]%2!=0&&!!c.isPacked;if(g||!Q().getBool("WEBGL_LAZILY_UNPACK")||!Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let x=d?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=ye({inputs:{x:e},backend:r,attrs:{shape:[1,x,n.inChannels]}}),b=ye({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=tp({a:w,b,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=ye({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(b),y.push(N)}else{let x=d?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},b=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(Qu(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let N=ye({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=tp({a:w,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);k.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=b,E.shape=n.outShape,A=Tn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let x of y)r.disposeIntermediateTensorInfo(x);return A}function k_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:p,dataFormat:d}=n,f=d==="channelsLast",m=l*c*u,A=p*h,y=[m,A],g=!0,_=!1,x=[],w=ye({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),b=ye({inputs:{x:t},backend:r,attrs:{shape:[1,m,k.sizeFromShape(t.shape)/m]}});x.push(w),x.push(b);let N=new mW(y,w.shape,n),T=r.runWebGLProgram(N,[w],"float32"),E=ye({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});x.push(T),x.push(E);let M=a!=null,$=s!=null,P=o==="leakyrelu",V=o?Qd(o,!0):null,H=new s_(E.shape,b.shape,[1,A,n.outChannels],g,_,M,V,$,P),U=[E,b];if(a&&U.push(a),$&&U.push(s),P){let Z=r.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));U.push(Z),x.push(Z)}let K=r.runWebGLProgram(H,U,"float32"),X=f?[1,p,h,n.outChannels]:[1,n.outChannels,p,h],ee=ye({inputs:{x:K},backend:r,attrs:{shape:X}});x.push(K);for(let Z of x)r.disposeIntermediateTensorInfo(Z);return ee}function AW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),d;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))d=v_({x:a,filter:s,convInfo:p,backend:n});else if(Q().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)d=k_({x:a,filter:s,convInfo:p,backend:n});else{let m=new b_(p);d=n.runWebGLProgram(m,[a,s],"float32")}let f=ye({inputs:{x:d},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(d),f}var yW={kernelName:Qa,backendName:"webgl",kernelFunc:AW},gW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=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} - ${a}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } if (${s}) { float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } else { float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},xW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${u}]; ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${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) / ${a}.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 < ${e.outChannels}; d2++) { if (${s}) { 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); } `}},wW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=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} - ${a}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${n} - ${s}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${r} - ${i}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},_W=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${c}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${t}; wF++) { float dyF = float(dyFCorner + wF) / ${a}.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) / ${s}.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) / ${i}.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 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function bW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),d=new gW(p);return n.runWebGLProgram(d,[a,s],"float32")}var vW={kernelName:mh,backendName:"webgl",kernelFunc:bW};function kW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(c),p=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),d=new xW(p);return n.runWebGLProgram(d,[a,s],"float32")}var IW={kernelName:es,backendName:"webgl",kernelFunc:kW};function NW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new fW(c);return n.runWebGLProgram(u,[a,s],"float32")}var SW={kernelName:uu,backendName:"webgl",kernelFunc:NW};function TW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=C.computeConv3DInfo(a.shape,l,i,1,o),u=new wW(c);return n.runWebGLProgram(u,[a,s],"float32")}var EW={kernelName:Ah,backendName:"webgl",kernelFunc:TW};function CW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=C.computeConv3DInfo(l,s.shape,o,1,i),u=new _W(c);return n.runWebGLProgram(u,[a,s],"float32")}var RW={kernelName:yh,backendName:"webgl",kernelFunc:CW},FW=a_+` return cos(x); `,MW=Ke({opSnippet:FW}),OW={kernelName:ts,backendName:"webgl",kernelFunc:MW},$W=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,DW=Ke({opSnippet:$W}),zW={kernelName:Gi,backendName:"webgl",kernelFunc:DW},PW=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let p=r==="bilinear"?1:0,[d,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[g,_,x]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` const float height_ratio = float(${m}); const float width_ratio = float(${g}); 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 >= ${s}) { return; } float height_scale = ${A}; float width_scale = ${_}; float in_y = ${y}; if( in_y < 0.0 || in_y > ${d} ) { setOutput(float(${a})); return; } float in_x = ${x}; if( in_x < 0.0 || in_x > ${f} ) { setOutput(float(${a})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${p} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},LW=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new PW(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},WW={kernelName:Hi,backendName:"webgl",kernelFunc:LW},S_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${I_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` uniform float index; void main() { ${ut(r)} coords = getOutputCoords(); int end = ${N_(r,"coords")}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${o}; ${N_(r,"coords")} = idx; val += getX(${I_(r,"coords")}); } setOutput(val); } `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function I_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function N_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function BW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=C.getAxesPermutation([s],l),u=a;c!=null&&(u=fn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=a.shape[h],d=Tn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new S_(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=d;d=n.runWebGLProgram(m,[d],d.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new S_(u.shape,i,o),m=d;d=n.runWebGLProgram(f,[d],d.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=C.getUndoAxesPermutation(c),m=fn({inputs:{x:d},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(u),m}return d}var VW={kernelName:ns,backendName:"webgl",kernelFunc:BW};function UW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=Uw(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=gz(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var jW={kernelName:gh,backendName:"webgl",kernelFunc:UW},GW=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 HW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=c*s,d=u/(s*s),f=i==="NHWC"?[o,h,p,d]:[o,d,h,p],m=new GW(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var qW={kernelName:qi,backendName:"webgl",kernelFunc:HW},T_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?A=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:A=` float activation(float x) { ${n} } `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${A} const ivec2 strides = ivec2(${c}, ${u}); const ivec2 pads = ivec2(${o}, ${l}); void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${m}; int q = d2 - d1 * ${m}; 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 < ${d}; wR++) { int xR = xRCorner + wR * ${h}; if (xR < 0 || xR >= ${s}) { continue; } for (int wC = 0; wC < ${f}; wC++) { int xC = xCCorner + wC * ${p}; if (xC < 0 || xC >= ${i}) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${g} ${y} setOutput(result); } `}},E_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let x=0;x= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${x}C${b} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if(xCOffset + 1 >= ${i}) { xTexelR${x}C${b}.zw = vec2(0.); } } else { xTexelR${x}C${b} = vec4(0.); } xCOffset = xC + 1 - 2; if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { vec4 previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if(xCOffset + 1 >= ${i}) { previous.zw = vec2(0.); } xR${x}C${b} = vec4(previous.zw, xTexelR${x}C${b}.xy); } else { xR${x}C${b} = vec4(0, 0, xTexelR${x}C${b}.xy); } `:A+=` if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) { xTexelR${x}C${b} = getX(batch, xR, xC, d1); } else { xTexelR${x}C${b} = vec4(0.); } xR${x}C${b} = xTexelR${x}C${b}; `,b+1= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${x}C${b+2} = getX(batch, xR, xCOffset, d1); } `,p>1&&(A+=` xCOffset -= 2; if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${x}C${b} = getX(batch, xR, xCOffset, d1); } else { xTexelR${x}C${b} = vec4(0.); } `),A+=` xR${x}C${b+1} = vec4( xTexelR${x}C${b}.zw, xTexelR${x}C${b+2}.xy); `):A+=` xCOffset = xC + ${N}; if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${x}C${b+2} = getX(batch, xR, xCOffset, d1); } xR${x}C${b+1} = xTexelR${x}C${b+2}; `}}else b= 0 && xR < ${s}) { `,l%2==1?(A+=` xCOffset = xC + 1 - ${u}; if(xCOffset >= 0 && xCOffset < ${i}) { xTexelR${x}C${b} = getX(batch, xR, xCOffset, d1); } else { xTexelR${x}C${b} = vec4(0.); } if(xC + 1 >= 0 && xC + 1 < ${i}) { xTexelR${x}C${b+2} = getX(batch, xR, xC + 1, d1); } else { xTexelR${x}C${b+2} = vec4(0.); } xR${x}C${b} = vec4( xTexelR${x}C${b}.zw, xTexelR${x}C${b+2}.zw); `,b+1= 0 && xCOffset < ${i}) { final = getX(batch, xR, xCOffset, d1); } xR${x}C${b+1} = vec4(xTexelR${x}C${b+2}.xy, final.xy); `)):(A+=` if(xC >= 0 && xC < ${i}) { xTexelR${x}C${b} = getX(batch, xR, xC, d1); } else { xTexelR${x}C${b} = vec4(0.); } xCOffset = xC + ${u}; if(xCOffset >= 0 && xCOffset < ${i}) { xTexelR${x}C${b+2} = getX(batch, xR, xCOffset, d1); } else { xTexelR${x}C${b+2} = vec4(0.); } xR${x}C${b} = vec4( xTexelR${x}C${b}.xy, xTexelR${x}C${b+2}.xy); `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),p;return Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?p=new E_(h):p=new T_(h),n.runWebGLProgram(p,[a,s],"float32")}var KW={kernelName:rs,backendName:"webgl",kernelFunc:XW},ZW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=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 * ${s} + 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} - ${a}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},YW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); 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) / ${a}.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 < ${o}; dm++) { int d2 = d1 * ${o} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function JW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),p=new ZW(h);return n.runWebGLProgram(p,[a,s],"float32")}var QW={kernelName:xh,backendName:"webgl",kernelFunc:JW};function eB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),p=new YW(h);return n.runWebGLProgram(p,[a,s],"float32")}var tB={kernelName:wh,backendName:"webgl",kernelFunc:eB},nB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function rB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=k.sizeFromShape(r.shape),i=ye({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new nB(s),l=n.runWebGLProgram(o,[i],i.dtype),c=ye({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var aB={kernelName:_h,backendName:"webgl",kernelFunc:rB},sB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=` const ivec2 strides = ivec2(${a}, ${s}); const ivec2 pads = ivec2(${u}, ${h}); 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 < ${i}; h++) { int hIn = hBeg + h * ${l}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${o}; 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 iB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new sB(c);u=n.runWebGLProgram(h,[a,s],"float32");let p=ye({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var oB={kernelName:cu,backendName:"webgl",kernelFunc:iB},lB="return (x >= 0.0) ? x : (exp(x) - 1.0);",uB=` 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; `,cB=Ke({opSnippet:lB,packedOpSnippet:uB}),hB={kernelName:Xi,backendName:"webgl",kernelFunc:cB},dB="return (b >= 1.0) ? a : a * (b + 1.0);",pB=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,fB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new ac(pB,r.shape,a.shape):new gl(dB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},mB={kernelName:kh,backendName:"webgl",kernelFunc:fB},AB=` return vec4(equal(a, b)); `,yB="return float(a == b);",gB=Kt({opSnippet:yB,packedOpSnippet:AB,dtype:"bool"}),xB={kernelName:Zi,backendName:"webgl",kernelFunc:gB},wB=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${C.ERF_P}; float a1 = ${C.ERF_A1}; float a2 = ${C.ERF_A2}; float a3 = ${C.ERF_A3}; float a4 = ${C.ERF_A4}; float a5 = ${C.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,_B=Ke({opSnippet:wB}),bB={kernelName:Ki,backendName:"webgl",kernelFunc:_B},C_="return exp(x);",R_=Ke({opSnippet:C_,packedOpSnippet:C_,cpuKernelImpl:_z}),vB={kernelName:ss,backendName:"webgl",kernelFunc:R_};function Cm(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(k.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ye({inputs:{x:s},backend:r,attrs:{shape:o}})}var kB={kernelName:Yi,backendName:"webgl",kernelFunc:Cm},F_="return exp(x) - 1.0;",IB=Ke({opSnippet:F_,packedOpSnippet:F_,cpuKernelImpl:bz}),NB={kernelName:Ji,backendName:"webgl",kernelFunc:IB},M_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${a}; float unaryOpComplex(float real, float expR, float imag, float expI) { ${i} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${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) / ${s}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function O_(e,t,n){let r=n.texData.get(e.dataId),a=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ye({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new M_("real",l,t),u=new M_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(c,h,"float32"),d=n.runWebGLProgram(u,h,"float32"),f=Ta({inputs:{real:p,imag:d},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d);let m=ye({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function SB(e){let{inputs:t,backend:n}=e,{input:r}=t;return O_(r,!1,n)}var TB={kernelName:Ih,backendName:"webgl",kernelFunc:SB},EB=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=` uniform float value; void main() { // Input can be obtained from uniform value. setOutput(value); } `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Rm(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||k.inferDtype(a),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new EB(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var CB={kernelName:hu,backendName:"webgl",kernelFunc:Rm},RB=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; float outputValue; if(coordX >= 0 && coordX < ${t}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}},FB={kernelName:Qi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new RB(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},$_="return floor(x);",MB=Ke({opSnippet:$_,packedOpSnippet:$_,cpuKernelImpl:vz}),OB={kernelName:is,backendName:"webgl",kernelFunc:MB},$B=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); if (ib != 0) { // Windows (D3D) wants guaranteed non-zero int division at compile-time. return float(idiv(ia, ib, s)); } else { return NAN; } `,DB=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); ivec4 result = ivec4(0); vec4 s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { result[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { result[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { result[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); `,zB=Kt({opSnippet:$B,packedOpSnippet:DB,dtype:"int32"}),PB={kernelName:os,backendName:"webgl",kernelFunc:zB},LB=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 = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}},WB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=an(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${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; } `}},VB={kernelName:Lh,backendName:"webgl",kernelFunc:BB},wl;function BB(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[c,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[u,c],p=[u,c,s];(o||i||l)&&(wl==null&&(wl=document.createElement("canvas").getContext("2d")),wl.canvas.width=c,wl.canvas.height=u,wl.drawImage(a,0,0,c,u),a=wl.canvas);let d=n.makeTensorInfo(h,"int32");n.texData.get(d.dataId).usage=Vn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let f=Q().getBool("WEBGL_PACK")?new WB(p):new LB(p),m=n.runWebGLProgram(f,[d],"int32");return n.disposeData(d.dataId),m}function UB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(u),A=C.computeConv2DInfo(a.shape,s.shape,l,h,c,p,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=v_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:f});else if(Q().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=k_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,w=o!=null,b=d==="leakyrelu",N=d?Qd(d,!1):null,T=new b_(A,x,N,w,b),E=[a,s];if(i&&E.push(i),o&&E.push(o),b){let M=n.makeTensorInfo([],"float32",k.createScalarValue(f,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let _=ye({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),_}var jB={kernelName:Ls,backendName:"webgl",kernelFunc:UB};function GB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:p,leakyreluAlpha:d}=r,f=[],m=u;m==null&&(m=[1,1]),k.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=p?Qd(p,y):null,_=[a,s],x=i!=null,w=o!=null,b=p==="leakyrelu";if(x&&_.push(i),w&&_.push(o),b){let E=n.makeTensorInfo([],"float32",k.createScalarValue(d,"float32"));_.push(E),f.push(E)}let N;y?N=new E_(A,x,g,w,b):N=new T_(A,x,g,w,b);let T=n.runWebGLProgram(N,_,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var HB={kernelName:Ws,backendName:"webgl",kernelFunc:GB},qB=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ut(t.length),a=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=` ${r} strides = ${r}(${this.strides}); void main() { ${a} coords = getOutputCoords(); int flattenIndex = 0; for (int j = 0; j < ${this.sliceDim}; j++) { int index = round(getIndices(coords[0], j)); flattenIndex += index * ${s}; } setOutput(getX(flattenIndex, coords[1])); } `}};function XB(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=C.prepareAndValidate(r,a),h=ye({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),p=ye({inputs:{x:r},backend:n,attrs:{shape:[k.sizeFromShape(r.shape)/c,c]}}),d=new qB(i,u,[l,c]),f=n.runWebGLProgram(d,[p,h],p.dtype),m=ye({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}var KB={kernelName:to,backendName:"webgl",kernelFunc:XB},YB=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),r=ZB(e,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); setOutput(getA(${r})); } `}};function ZB(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;an.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let m=new YB(p.shape,f),A=n.runWebGLProgram(m,[p,d],p.dtype);h.push(A);let y=ye({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var QB={kernelName:eo,backendName:"webgl",kernelFunc:JB},eV="return float(a > b);",tV=` return vec4(greaterThan(a, b)); `,nV=Kt({opSnippet:eV,packedOpSnippet:tV,cpuKernelImpl:Iz,dtype:"bool"}),rV={kernelName:no,backendName:"webgl",kernelFunc:nV},aV="return float(a >= b);",sV=` return vec4(greaterThanEqual(a, b)); `,iV=Kt({opSnippet:aV,packedOpSnippet:sV,dtype:"bool"}),oV={kernelName:us,backendName:"webgl",kernelFunc:iV};function lV(e){let{inputs:t,backend:n}=e,{input:r}=t;return O_(r,!0,n)}var uV={kernelName:Nh,backendName:"webgl",kernelFunc:lV},cV="return float(!isnan(x) && !isinf(x));",hV=Ke({opSnippet:cV,dtype:"bool"}),dV={kernelName:ao,backendName:"webgl",kernelFunc:hV},pV="return float(isinf(x));",fV=Ke({opSnippet:pV,dtype:"bool"}),mV={kernelName:so,backendName:"webgl",kernelFunc:fV},AV="return float(isnan(x));",yV=Ke({opSnippet:AV,dtype:"bool"}),gV={kernelName:io,backendName:"webgl",kernelFunc:yV},xV="return float(a < b);",wV=` return vec4(lessThan(a, b)); `,_V=Kt({opSnippet:xV,packedOpSnippet:wV,cpuKernelImpl:Nz,dtype:"bool"}),bV={kernelName:oo,backendName:"webgl",kernelFunc:_V},vV="return float(a <= b);",kV=` return vec4(lessThanEqual(a, b)); `,IV=Kt({opSnippet:vV,packedOpSnippet:kV,dtype:"bool"}),NV={kernelName:lo,backendName:"webgl",kernelFunc:IV};function SV(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=Sz(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var TV={kernelName:Th,backendName:"webgl",kernelFunc:SV},EV=`if (x < 0.0) return NAN; return log(x);`,CV=` vec4 result = log(x); vec4 isNaN = vec4(lessThan(x, vec4(0.0))); result.r = isNaN.r == 1.0 ? NAN : result.r; result.g = isNaN.g == 1.0 ? NAN : result.g; result.b = isNaN.b == 1.0 ? NAN : result.b; result.a = isNaN.a == 1.0 ? NAN : result.a; return result; `,RV=Ke({opSnippet:EV,packedOpSnippet:CV,cpuKernelImpl:Tz}),FV={kernelName:hs,backendName:"webgl",kernelFunc:RV},MV="return log(1.0 + x);",OV=Ke({opSnippet:MV}),$V={kernelName:uo,backendName:"webgl",kernelFunc:OV},DV="return float(a >= 1.0 && b >= 1.0);",zV=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,PV=Kt({opSnippet:DV,packedOpSnippet:zV,dtype:"bool"}),LV={kernelName:co,backendName:"webgl",kernelFunc:PV},WV="return float(!(x >= 1.0));",BV=Ke({opSnippet:WV}),VV={kernelName:du,backendName:"webgl",kernelFunc:BV},UV="return float(a >= 1.0 || b >= 1.0);",jV=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,GV=Kt({opSnippet:UV,packedOpSnippet:jV,dtype:"bool"}),HV={kernelName:pu,backendName:"webgl",kernelFunc:GV},qV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${s}; j <= ${s}; j++) { int idx = d + j; if (idx >= 0 && idx <= ${i}) { float z = getX(b, r, c, idx); sum += z * z; } } float val = x * ${o}; setOutput(val); } `}},XV=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,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 - ${s}; 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 = - ${s}; j <= ${s}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); 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 * ${o}; setOutput(result); } `}},KV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=Q().getBool("WEBGL_PACK_NORMALIZATION")?new XV(a.shape,s,i,o,l):new qV(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},ZV={kernelName:fu,backendName:"webgl",kernelFunc:KV},YV=class{constructor(e,t,n,r,a){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=a,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(${a}) * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${a}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}},JV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new YV(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},QV={kernelName:Eh,backendName:"webgl",kernelFunc:JV};function eU(e,t,n,r){let a=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/a,i=ye({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=oi(i,e.dtype,"max",r),l=ye({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function D_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,p=n.shouldExecuteOnCPU([a]),d=a;if(h){if(p){let g=n.texData.get(d.dataId).values,_=new Array(o);for(let b=0;b<_.length;b++)_[b]=a.shape[u[b]];let x=km(g,a.shape,a.dtype,u,_);d=n.makeTensorInfo(_,a.dtype);let w=n.texData.get(d.dataId);w.values=x}else d=ep(a,u,n);c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("max",c,o);let[f,m]=C.computeOutAndReduceShapes(d.shape,c),A=f;i&&(A=C.expandShapeToKeepDim(f,l));let y;if(p){let g=n.texData.get(d.dataId).values,_=Ez(g,k.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let x=n.texData.get(y.dataId);x.values=_}else y=eU(d,m,A,n);return h&&n.disposeIntermediateTensorInfo(d),y}var tU={kernelName:ds,backendName:"webgl",kernelFunc:D_},nU=Qw+` return max(a, b); `,rU=` vec4 result = vec4(max(a, b)); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+Jd+` return result; `,aU=Kt({opSnippet:nU,packedOpSnippet:rU,cpuKernelImpl:Cz}),sU={kernelName:ps,backendName:"webgl",kernelFunc:aU};function iU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;hl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Tn({inputs:{x:a},backend:n});let h=new sc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var oU={kernelName:fs,backendName:"webgl",kernelFunc:iU};function lU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,c,l),p=new Sm(h,"max",!1);return n.runWebGLProgram(p,[a],a.dtype)}var uU={kernelName:mu,backendName:"webgl",kernelFunc:lU},cU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); 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 < ${a}; 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 < ${s}; wC++) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${s} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},hU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=c-1-e.padInfo.left,d=o*l*c-1;this.userCode=` const ivec3 pads = ivec3(${u}, ${h}, ${p}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${o}; wD += ${a}) { float dyD = float(dyDCorner + wD) / ${t}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${l}; wR += ${s}) { 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 += ${i}) { 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 = ${d} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wD * ${l} * ${c} + wR * ${c} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function dU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,c,u),d=new Sm(p,"max",!0),f=n.runWebGLProgram(d,[i],i.dtype),m=new hU(p),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var pU={kernelName:Rh,backendName:"webgl",kernelFunc:dU};function fU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;hl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,p=C.computePool2DInfo(o.shape,l,c,1,u,h),d=!0,f=new sc(p,"max",d),m=n.runWebGLProgram(f,[o],o.dtype),A=new cU(p),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var mU={kernelName:Ch,backendName:"webgl",kernelFunc:fU};function AU(e,t,n,r){let a=new sc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new sc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var yU={kernelName:Fh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];k.assert(C.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=C.computePool2DInfo(r.shape,a,s,c,i),[h,p]=AU(r,o,u,l);return[h,p]}};function gU(e,t,n,r){let a=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/a,i=ye({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=oi(i,"float32","mean",r),l=ye({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var xU={kernelName:ms,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,p=i.shouldExecuteOnCPU([r]),d=[],f=r;if(h){if(p){let _=i.texData.get(f.dataId).values,x=new Array(o);for(let N=0;Nc[0]+e[u]+c[1]);let r=e.length,a=ut(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=` int start = ${s}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${l}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${l}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${a} start = ${a}(${s}); ${a} end = ${a}(${i}); void main() { ${a} outC = getOutputCoords(); for (int i = 0; i < ${r}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${l}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; } } ${a} coords = outC - start; setOutput(getX(${o})); } `}},SU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,f)=>d[0]+e[f]+d[1]);let r=e.length,a=ut(r),s=t.map(d=>d[0]).join(","),i=t.map((d,f)=>d[0]+e[f]).join(","),o=sn("rc",r),l=sn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,p="";if(r===1){let d=` ${a} source = rc; if (source < start) { source = start * 2 - source - ${h}; } else if (source >= end) { source = (end - 1) * 2 - source + ${h}; } source -= start; `;p=` ${a} rc = outputLoc; ${d} result[0] = getChannel(getX(${l.join()}), ${u}); ${o[r-1]} += 1; if(${c}) { ${d} result[1] = getChannel(getX(${l.join()}), ${u}); } `}else{let d=` ${a} source = rc; ${a} lt = ${a}(lessThan(source, start)); ${a} gte = ${a}(greaterThanEqual(source, end)); ${a} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${h}) + gte * ((end - 1) * 2 - source + ${h}); source -= start; `;p=` ${a} rc = outputLoc; ${d} result[0] = getChannel(getX(${l.join()}), ${u}); ${o[r-1]} += 1; if(${c}) { ${d} result[1] = getChannel(getX(${l.join()}), ${u}); } rc = outputLoc; ${o[r-2]} += 1; if(${o[r-2]} < ${this.outputShape[r-2]}) { ${d} result[2] = getChannel(getX(${l.join()}), ${u}); ${o[r-1]} += 1; if(${c}) { ${d} result[3] = getChannel(getX(${l.join()}), ${u}); } } `}this.userCode=` const ${a} start = ${a}(${s}); const ${a} end = ${a}(${i}); void main() { ${a} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${p} setOutput(result); } `}},TU=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SU(r.shape,a,s):new NU(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},EU={kernelName:Au,backendName:"webgl",kernelFunc:TU},CU=`if (b == 0.0) return NAN; return mod(a, b);`,RU=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+Jd+` return result; `,FU=Kt({opSnippet:CU,packedOpSnippet:RU}),MU={kernelName:ho,backendName:"webgl",kernelFunc:FU},OU=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=` uniform float seed; 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})); } `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},$U=` if (a == b) { return 1.0; }; return a / b;`,DU=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; if(a.x == b.x) { result.x = 1.; } if(a.y == b.y) { result.y = 1.; } if(a.z == b.z) { result.z = 1.; } if(a.w == b.w) { result.w = 1.; } return result; `,z_=Kt({opSnippet:$U,packedOpSnippet:DU,checkOutOfBounds:!0}),zU={kernelName:as,backendName:"webgl",kernelFunc:z_},P_="return a - b;",L_=Kt({opSnippet:P_,packedOpSnippet:P_,supportsComplex:!0,cpuKernelImpl:Lz}),PU={kernelName:$s,backendName:"webgl",kernelFunc:L_};function W_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=k.parseAxisParam([s],a.shape),o=D_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),c=ye({inputs:{x:o},backend:n,attrs:{shape:l}}),u=L_({inputs:{a,b:c},backend:n}),h=R_({inputs:{x:u},backend:n}),p=Nm({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),d=ye({inputs:{x:p},backend:n,attrs:{shape:l}}),f=z_({inputs:{a:h,b:d},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),f}var LU={kernelName:Ms,backendName:"webgl",kernelFunc:W_};function WU(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:W_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new OU(c,u,s),p=h.getCustomSetupFunc(i),d=n.runWebGLProgram(h,[l],"int32",p);return o||n.disposeIntermediateTensorInfo(l),d}var BU={kernelName:Mh,backendName:"webgl",kernelFunc:WU},B_="return -x;";function VU(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=Mz(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new yl(r.shape,B_):a=new Sa(r.shape,B_),n.runWebGLProgram(a,[r],r.dtype)}var UU={kernelName:po,backendName:"webgl",kernelFunc:VU},jU=Fr.nonMaxSuppressionV3Impl;function GU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=jU(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var HU={kernelName:mo,backendName:"webgl",kernelFunc:GU},qU=Fr.nonMaxSuppressionV4Impl;function XU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:p,validOutputs:d}=qU(u,h,i,o,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([d]))]}var KU={kernelName:Ao,backendName:"webgl",kernelFunc:XU},ZU=Fr.nonMaxSuppressionV5Impl;function YU(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),p=i,d=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=ZU(u,h,p,d,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var JU={kernelName:yo,backendName:"webgl",kernelFunc:YU},QU=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(${n}), float(index == coords.y))); } `}},ej=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=k.sizeFromShape(a.shape),c=new QU(l,s,i,o),u=ye({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let p=[...a.shape,s],d=ye({inputs:{x:h},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(h),d},tj={kernelName:xs,backendName:"webgl",kernelFunc:ej};function sp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=oc({inputs:{input:r},backend:n}),s=sp({inputs:{x:a},backend:n}),i=ap({inputs:{input:r},backend:n}),o=sp({inputs:{x:i},backend:n}),l=Ta({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Rm({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var nj={kernelName:$o,backendName:"webgl",kernelFunc:sp};function V_(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 a=oc({inputs:{input:r},backend:n}),s=V_({inputs:{x:a},backend:n}),i=ap({inputs:{input:r},backend:n}),o=sp({inputs:{x:i},backend:n}),l=Ta({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Rm({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var rj={kernelName:go,backendName:"webgl",kernelFunc:V_};function aj(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Cm({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=Cm({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=__({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var sj={kernelName:xo,backendName:"webgl",kernelFunc:aj},ij=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=ut(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=` int start = ${s}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(float(${n})); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${a} start = ${a}(${s}); ${a} end = ${a}(${i}); void main() { ${a} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(float(${n})); } else { ${a} coords = outC - start; setOutput(getX(${o})); } } `}},oj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ut(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=sn("rc",r),l=sn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1; if(${c}) { `,r===1?"":`} rc = outputLoc; ${o[r-2]} += 1; if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1; if(${c}) {`],p=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let f=0,m=r===1?2:4;f{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oj(a.shape,s,i):new ij(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},lj={kernelName:ws,backendName:"webgl",kernelFunc:U_},uj=` 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); `,cj=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); vec4 result = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS bvec4 isExpZero = equal(b, vec4(0.0)); result.r = isExpZero.r ? 1.0 : result.r; result.g = isExpZero.g ? 1.0 : result.g; result.b = isExpZero.b ? 1.0 : result.b; result.a = isExpZero.a ? 1.0 : result.a; vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b)); `+Jd+` return result; `,hj=Kt({opSnippet:uj,packedOpSnippet:cj}),dj={kernelName:_s,backendName:"webgl",kernelFunc:hj};function pj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=k.parseAxisParam(s,a.shape),u=c,h=C.getAxesPermutation(u,o),p=a;h!=null&&(p=fn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=C.getInnerMostAxes(u.length,o),l.push(p)),C.assertAxesAreInnerMostDims("prod",u,o);let d;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:A,outDtype:y}=Oz(p.shape,p.dtype,f,u);d=n.makeTensorInfo(A,y,m)}else{let[f,m]=C.computeOutAndReduceShapes(p.shape,u),A=k.sizeFromShape(m),y=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,A]}}),g=jh(a.dtype),_=oi(y,g,"prod",n);d=ye({inputs:{x:_},backend:n,attrs:{shape:f}}),l.push(y),l.push(_)}if(i){l.push(d);let f=C.expandShapeToKeepDim(d.shape,c);d=ye({inputs:{x:d},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),d}var fj={kernelName:wo,backendName:"webgl",kernelFunc:pj},j_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=$z(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},mj={kernelName:yu,backendName:"webgl",kernelFunc:j_},Aj="return 1.0 / x;",yj=Ke({opSnippet:Aj}),gj={kernelName:_o,backendName:"webgl",kernelFunc:yj},xj=hr+` return (x < 0.0) ? 0.0 : x; `,wj=` 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; `,_j=Ke({opSnippet:xj,packedOpSnippet:wj}),bj={kernelName:vs,backendName:"webgl",kernelFunc:_j},vj=hr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,kj=` 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; `,Ij=Ke({opSnippet:vj,packedOpSnippet:kj}),Nj={kernelName:Is,backendName:"webgl",kernelFunc:Ij},Sj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/u[0]}, ${c[1]/u[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${h}; // 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); } `}},Tj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/u[0]}, ${c[1]/u[1]}, ${c[1]/u[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, ${o}.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 = ${h}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); ivec3 sourceCeilRC = ivec3( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; // In parallel, construct four corners for all four components in // packed 2x2 cell. vec4 topLeft = vec4( getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 bottomLeft = vec4( getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 topRight = vec4( getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0); vec4 bottomRight = vec4( getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0); vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC); vec4 top = mix(topLeft, topRight, fracRC.yyzz); vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz); vec4 newValue = mix(top, bottom, fracRC.x); setOutput(newValue); } `}};function Ej(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=Q().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Tj(a.shape,l,c,s,i):new Sj(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var Cj={kernelName:ks,backendName:"webgl",kernelFunc:Ej},Rj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,p=1/u,d=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${u}); const float invHeightScale = float(${h}); const float invWidthScale = float(${p}); const int winHeight = int(${d}); 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 >= ${s}) { 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 >= ${i}) { 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), ${a-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 Fj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Rj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Mj={kernelName:Dh,backendName:"webgl",kernelFunc:Fj},Oj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/u[0]}, ${c[1]/u[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${p}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}};function $j(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new Oj(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var Dj={kernelName:gu,backendName:"webgl",kernelFunc:$j},zj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,p=1/u,d=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${u}); const float invHeightScale = float(${h}); const float invWidthScale = float(${p}); const int winHeight = int(${d}); const int winWidth = int(${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 >= ${s}) { 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 >= ${i}) { continue; } float sourceFracRow = float(${o[0]}) * (float(dyR) / float(${l[0]})); float sourceFracCol = float(${o[1]}) * (float(dyC) / float(${l[1]})); int sourceNearestRow = int(min( float(int(${r}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${a}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function Pj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new zj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Lj={kernelName:$h,backendName:"webgl",kernelFunc:Pj},Wj=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=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=ut(n);this.userCode=` void main() { ${s} coords = getOutputCoords(); setOutput(getX(${a})); } `}},Bj=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-${n} tensor is not yet supported`);this.outputShape=e;let r=sn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${e[0]} - rc - 1), ${e[0]} - rc - 1); if(${a}){ result.g = getChannel(getX(${e[0]} - (rc + 1) - 1), ${e[0]} - (rc + 1) - 1); } setOutput(result); } `:this.userCode=` void main() { ${i} rc = getOutputCoords(); vec4 result = vec4(0.); result.r = ${o(r.slice())}; if(${a}){ result.g = ${l(r.slice())}; } if(${s}) { result.b = ${c(r.slice())}; if(${a}) { result.a = ${u(r.slice())}; } } setOutput(result); } `;function o(d){return h(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",h(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",h(d)}function u(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",h(d)}function h(d){let f=e.map((y,g)=>p(g,d)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function p(d,f){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${f[d]} - 1`:`${f[d]}`}}};function Vj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=k.parseAxisParam(s,a.shape);if(i===0)return Tn({inputs:{x:a},backend:n});let l=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Bj(a.shape,o):new Wj(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Uj={kernelName:Ns,backendName:"webgl",kernelFunc:Vj},jj=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=C.getImageCenter(r,a,s),u=l.toFixed(3),h=c.toFixed(3),p="";typeof n=="number"?p=`float outputValue = ${n.toFixed(2)};`:p=` vec3 fill = vec3(${n.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) - ${u}) * ${o} - (float(y) - ${h}) * ${i}; float coordYFloat = (float(x) - ${u}) * ${i} + (float(y) - ${h}) * ${o}; int coordX = int(round(coordXFloat + ${u})); int coordY = int(round(coordYFloat + ${h})); ${p} if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${a}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}},Gj={kernelName:Do,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new jj(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},Hj=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,qj=Ke({opSnippet:Hj}),Xj={kernelName:Ss,backendName:"webgl",kernelFunc:qj},Kj="return inversesqrt(x);",Zj=Ke({opSnippet:Kj,cpuKernelImpl:Dz}),Yj={kernelName:Ts,backendName:"webgl",kernelFunc:Zj},G_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(a.length),l=ut(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,d=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${a}); void main() { ${l} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${t}; j++) { int index = round(${u}); flattenedIndex += index * ${d}; } if (flattenedIndex == coords[0]) { sum += ${p}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function Jj(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=C.calculateShapes(s,a,i),p=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let d=ye({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=ye({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new G_(l,o,d.shape.length,f.shape.length,u,p),y=n.runWebGLProgram(A,[f,d,m],f.dtype),g=ye({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var Qj={kernelName:vo,backendName:"webgl",kernelFunc:Jj},eG=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c= 1.0) { setOutput(getA(${a})); } else { setOutput(getB(${a})); } } `}};function tG(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new eG(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],Zn(a.dtype,s.dtype))}var nG={kernelName:ko,backendName:"webgl",kernelFunc:tG},rG=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${C.SELU_SCALEALPHA}; float scale = ${C.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,aG=Ke({opSnippet:rG}),sG={kernelName:Io,backendName:"webgl",kernelFunc:aG},iG="return 1.0 / (1.0 + exp(-1.0 * x));",oG=Ke({opSnippet:iG}),lG={kernelName:Cs,backendName:"webgl",kernelFunc:oG},uG=` if (isnan(x)) { return 0.0; } return sign(x); `,cG=Ke({opSnippet:uG}),hG={kernelName:To,backendName:"webgl",kernelFunc:cG},dG=a_+` return sin(x); `,pG=Ke({opSnippet:dG}),fG={kernelName:Es,backendName:"webgl",kernelFunc:pG},mG=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,AG=Ke({opSnippet:mG}),yG={kernelName:So,backendName:"webgl",kernelFunc:AG},gG=` 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; `,xG=Ke({opSnippet:gG}),wG={kernelName:Eo,backendName:"webgl",kernelFunc:xG},_G=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;k.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;yn.disposeIntermediateTensorInfo(y)),A},bG={kernelName:xu,backendName:"webgl",kernelFunc:_G};function vG(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=C.calculateShapes(s,a,o),p=!1,d=new G_(c,l,a.shape.length,s.shape.length,u,[h,1],p),f=n.runWebGLProgram(d,[s,a,i],s.dtype),m=ye({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var kG={kernelName:zh,backendName:"webgl",kernelFunc:vG};function IG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=k.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(p=>{let d=[...h];d[o]=p;let f=ic({inputs:{x:a},backend:n,attrs:{begin:u,size:d}});return u[o]+=p,f})}var NG={kernelName:Co,backendName:"webgl",kernelFunc:IG},SG="return sqrt(x);",TG=Ke({opSnippet:SG}),EG={kernelName:Rs,backendName:"webgl",kernelFunc:TG},CG="return x * x;",RG=Ke({opSnippet:CG}),FG={kernelName:wu,backendName:"webgl",kernelFunc:RG},H_="return (a - b) * (a - b);",MG=Kt({opSnippet:H_,packedOpSnippet:H_}),OG={kernelName:Os,backendName:"webgl",kernelFunc:MG};function $G({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=hr+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new Sa(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var DG={kernelName:ma,backendName:"webgl",kernelFunc:$G},zG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ut(n.length),s=ut(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` ${a} begin = ${a}(${e}); ${a} strides = ${a}(${t}); void main() { ${s} coords = getOutputCoords(); setOutput(getX(${i})); } `}};function PG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:p}=r,{nonStrided:d,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=nn.sliceInfo(a.shape,s,i,o,l,c,u,h,p),_=ye({inputs:{x:a},backend:n,attrs:{shape:y}}),x;if(d){let b=ic({inputs:{x:_},backend:n,attrs:{begin:f,size:A}});x=ye({inputs:{x:b},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(b)}else if(g.some(b=>b===0))x=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([_])){let b=n.texData.get(_.dataId).values,N=Pe(_.shape,_.dtype,b),T=Pz(g,N,m,f);x=n.makeTensorInfo(g,_.dtype,T.values)}else{let b=new zG(f,m,g);x=n.runWebGLProgram(b,[_],_.dtype)}let w=ye({inputs:{x},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(_),n.disposeIntermediateTensorInfo(x),w}var LG={kernelName:Ro,backendName:"webgl",kernelFunc:PG},WG="return tan(x);",BG=Ke({opSnippet:WG}),VG={kernelName:Fo,backendName:"webgl",kernelFunc:BG},UG=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,jG=Ke({opSnippet:UG}),GG={kernelName:Ds,backendName:"webgl",kernelFunc:jG},qG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)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 a=0;ak.decodeString(u)),l=Pe(a.shape,a.dtype,o),c=Wz(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new qG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var XG={kernelName:fa,backendName:"webgl",kernelFunc:q_};function KG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=Bz(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var ZG={kernelName:Mo,backendName:"webgl",kernelFunc:KG};function YG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;hl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=Vz(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var JG={kernelName:Ph,backendName:"webgl",kernelFunc:YG};function QG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],c=new Array(o-1),u=0;for(let m=0;mn.disposeIntermediateTensorInfo(m)),f}var eH={kernelName:Oo,backendName:"webgl",kernelFunc:QG},tH=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,h=` sumValue += dot(values, segFilter); `,p="";a%n>0&&(p=` if (inIdx < 0 || inIdx >= ${a}) { return initializationValue; } `);let d="";a%n>0&&(d=` if (inIdx < 0 || inIdx >= ${a}) { return -1.0; } `),this.userCode=` const float initializationValue = ${o}; float getValue(int batch, int inIdx) { ${p} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { ${d} return getSegmentIds(inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = int(floor(float(outIdx) / float( ${s})) * float(${n})); int currentSeg = int(mod(float(outIdx), float(${s}))); 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 ); ${h} } int inIdx = inOffset + ${c}; if (${u===1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); int inIdxSeg = int(getSegmentIdAtIndex(inIdx)); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, 0, 0, 0 ); ${h} } else if (${u===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, 0, 0 ); ${h} } else if (${u===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, 0 ); ${h} } setOutput(${l}); } `}};function nH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=C.getAxesPermutation([c],o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=C.getInnerMostAxes(1,o)[0]);let p=C.segment_util.computeOutShape(h.shape,c,i),d=k.sizeFromShape([h.shape[c]]),f=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,d]}});l.push(f);let m=jh(a.dtype),A=(x,w,b,N,T)=>{let E=x.shape[0],M=x.shape[1],$=C.segment_util.segOpComputeOptimalWindowSize(M,T),P={windowSize:$,inSize:M,batchSize:E,numSegments:T},V=new tH(P,w),H=n.compileAndRun(V,[x,b],N);if(l.push(H),H.shape[1]===T)return H;let U=j_({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),K=q_({inputs:{x:U},backend:n,attrs:{reps:[M/$]}});return l.push(U),l.push(K),A(H,w,K,N,T)},y=A(f,"unsortedSegmentSum",s,m,i),g=ye({inputs:{x:y},backend:n,attrs:{shape:p}}),_=g;if(u!=null){l.push(g);let x=C.getUndoAxesPermutation(u);_=fn({inputs:{x:_},backend:n,attrs:{perm:x}})}return 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sH(e){X_=e.wasm.cwrap(Ps,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function iH(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r,p=n.dataIdMap.get(a.dataId).id,d=n.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let T=n.dataIdMap.get(i.dataId);if(T.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${T.shape.length}.`);f=T.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,A=lc[u];if(A==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=c?s.shape[1]:s.shape[2],_=a.shape[0],x=n.makeOutput([_,y,g],a.dtype),w=n.dataIdMap.get(x.dataId).id,b=new Uint8Array(new Int32Array(a.shape).buffer),N=new 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ip(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),a=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(a),r}var fH={kernelName:ro,backendName:"wasm",kernelFunc:ip},Z_;function mH(e){Z_=e.wasm.cwrap(zs,null,["number","array","number","number","number","array","number"])}function op(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=yH(t.x.shape,r.perm),i=!0;for(let f=0;f=a&&(s===-1||r[s]>r[i])&&(s=i);r[s]=a}return[n,r]}var gH={kernelName:zs,backendName:"wasm",kernelFunc:op,setupFunc:mH};function _l(e,t,n){let r=e.shape,a=e.shape.length,s=k.parseAxisParam(t,r),i=s,o=C.getAxesPermutation(i,a),l=null,c=!1;if(o!=null){let u=new Array(a);for(let p=0;p`new shape: ${i}, old shape: ${r.shape}. 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Please use 'channelsLast'.`);let $=r.makeOutput(d.outShape,"float32"),P=r.dataIdMap.get($.dataId).id;return ob(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,A,y,g,_,M,x,w,b,N,T,E,P),$}var JH={kernelName:rs,backendName:"wasm",setupFunc:ZH,kernelFunc:YH},QH=!1,eq=on(Zi,QH,"bool"),tq=Cn(ss);function Mm(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),dr({inputs:{x:a},backend:r,attrs:{shape:o}})}var nq={kernelName:Yi,backendName:"wasm",kernelFunc:Mm};function rq(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var aq={kernelName:hu,backendName:"wasm",kernelFunc:rq},lb;function sq(e){lb=e.wasm.cwrap(Qi,null,["number","number","number","number","number","number"])}function iq(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return lb(s,o,l,c,u,i),a}var oq={kernelName:Qi,backendName:"wasm",kernelFunc:iq,setupFunc:sq},lq=Cn(is),uq=!1,cq=on(os,uq),ub;function hq(e){ub=e.wasm.cwrap(ls,null,["number","number","number","number","number","number","number"])}function dq(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,d=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return ub(u,h,p,d,f,a,A),m}var pq={kernelName:ls,backendName:"wasm",setupFunc:hq,kernelFunc:dq},cb;function fq(e){cb=e.wasm.cwrap(Ls,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,p),A=lc[d];if(A==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=m.outChannels,x=0;if(i!=null){let ne=r.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${_})`);x=ne.id}let w=m.filterHeight,b=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,$=m.dilationHeight,P=m.dilationWidth,V=m.strideHeight,H=m.strideWidth,U=m.inChannels,K=m.padInfo.type==="SAME"?1:0,X=m.batchSize,ee=m.inHeight,Z=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. 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uX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,p=wb(u,h,s,a,i,o),{pSelectedIndices:d,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=Om(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",d),g=t.makeOutput([],"int32",A);return[y,g]}var cX={kernelName:Ao,backendName:"wasm",setupFunc:lX,kernelFunc:uX},_b;function hX(e){_b=e.wasm.cwrap(yo,"number",["number","number","number","number","number","number"])}function dX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,p=_b(u,h,s,a,i,o),{pSelectedIndices:d,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=Om(t,p);t.wasm._free(A);let y=t.makeOutput([f],"int32",d),g=t.makeOutput([f],"float32",m);return[y,g]}var pX={kernelName:yo,backendName:"wasm",setupFunc:hX,kernelFunc:dX},fX=!1,mX=on(fo,fX,"bool"),bb;function AX(e){bb=e.wasm.cwrap(xs,null,["number","number","number","number","number"])}function yX(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=n.makeOutput([...a.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(a.dataId).id;return bb(u,s,i,o,c),l}var gX={kernelName:xs,backendName:"wasm",setupFunc:AX,kernelFunc:yX};function xX(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var wX={kernelName:go,backendName:"wasm",kernelFunc:xX};function _X(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Mm({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let 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MX={kernelName:wo,backendName:"wasm",setupFunc:RX,kernelFunc:FX},OX=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=nm(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},$X={kernelName:yu,backendName:"wasm",kernelFunc:OX},DX=!0,zX=on(as,DX),PX=Cn(vs),LX=Cn(Is),Nb;function WX(e){Nb=e.wasm.cwrap(ks,null,["number","number","number","number","number","number","number","number","number","number"])}function BX(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,[u,h,p,d]=a.shape,f=[u,l,c,d],m=t.dataIdMap.get(a.dataId),A;m.dtype!=="float32"&&(A=lp({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let y=m.id,g=t.makeOutput(f,"float32");if(k.sizeFromShape(a.shape)===0)return g;let _=t.dataIdMap.get(g.dataId).id;return Nb(y,u,h,p,d,l,c,s?1:0,i?1:0,_),A!=null&&t.disposeData(A.dataId),g}var VX={kernelName:ks,backendName:"wasm",setupFunc:WX,kernelFunc:BX},Sb;function 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new fr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new fr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new fr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");a=t}else k.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Sl))throw new Me(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=yr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Sl.className="Sequential";re.registerClass(Sl);function Jee(e){return new na(e)}function Qee(e){return new Sl(e)}function ete(e,t){return t==null&&(t={}),Zee(e,t)}function y3(e){return N3(e)}function tte(e,t){ar.registerCallbackConstructor(e,t)}var Rn=class extends re.Serializable{getConfig(){return{}}},e7=class extends Rn{apply(e,t=1){return MJ(e,t)}};e7.className="elu";re.registerClass(e7);var t7=class extends Rn{apply(e){return Ad(e)}};t7.className="selu";re.registerClass(t7);var n7=class extends Rn{apply(e){return Rr(e)}};n7.className="relu";re.registerClass(n7);var r7=class extends Rn{apply(e){return W(()=>nl(6,Rr(e)))}};r7.className="relu6";re.registerClass(r7);var a7=class extends Rn{apply(e){return e}};a7.className="linear";re.registerClass(a7);var s7=class extends Rn{apply(e){return _n(e)}};s7.className="sigmoid";re.registerClass(s7);var i7=class extends Rn{apply(e){return $J(e)}};i7.className="hardSigmoid";re.registerClass(i7);var o7=class extends Rn{apply(e){return el(e)}};o7.className="softplus";re.registerClass(o7);var l7=class extends Rn{apply(e){return OJ(e)}};l7.className="softsign";re.registerClass(l7);var u7=class extends Rn{apply(e){return Ko(e)}};u7.className="tanh";re.registerClass(u7);var bA=class extends Rn{apply(e,t=-1){return Hu(e,t)}};bA.className="softmax";re.registerClass(bA);var c7=class extends Rn{apply(e,t=-1){return ud(e,t)}};c7.className="logSoftmax";re.registerClass(c7);var h7=class extends Rn{apply(e,t=1){return W(()=>_n(e.mul(t)).mul(e))}};h7.className="swish";re.registerClass(h7);function Ma(e){return e.getClassName()}function vA(e,t={}){return dc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function Oa(e){if(e==null){let t={};return t.className="linear",t.config={},vA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},vA(t)}else return e instanceof Rn?e:vA(e)}function kA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var d7=class extends re.Serializable{},kc=class extends d7{constructor(e){super();kA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return W(()=>{let t=Nt([1]);return this.hasL1&&(t=se(t,Ie(L(this.l1,Ft(e))))),this.hasL2&&(t=se(t,Ie(L(this.l2,yc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};kc.className="L1L2";re.registerClass(kc);function nte(e){return kA(e),new kc({l1:e!=null?e.l1:null,l2:0})}function rte(e){return kA(e),new kc({l2:e!=null?e.l2:null,l1:0})}var p7={l1l2:"L1L2"};function ht(e){return Pm(e)}function f7(e,t={}){return dc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function yt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in p7?p7[e]:e,config:{}};return f7(t)}else return e instanceof d7?e:f7(e)}var IA=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=De(e);let n=Rr(e);return this.maxValue!=null&&(n=hn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};IA.className="ReLU";re.registerClass(IA);var NA=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=De(e);return Lu(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};NA.className="LeakyReLU";re.registerClass(NA);var SA=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=At(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=yt(e.alphaRegularizer),this.alphaConstraint=Dt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ct(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r(kt(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function m7(e,t){return W(()=>(kt(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function ate(e,t,n,r=1,a="valid",s,i=1){return W(()=>{if(s==null&&(s=pr()),kt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=td(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=zr(o,n)),o})}function A7(e,t,n,r=[1,1],a="valid",s,i,o=null){return W(()=>{if(s==null&&(s=pr()),kt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=RA(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ia.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function ste(e,t,n,r=[1,1,1],a="valid",s,i){return W(()=>{if(s==null&&(s=pr()),kt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=m7(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=wf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=zr(o,n)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var FA=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",FA.verifyArgs(t),this.rank=e,Vt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Me(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Tl(t.kernelSize,e,"kernelSize"),this.strides=Tl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,kt(this.dataFormat),this.activation=Oa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=At(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Dt(t.biasConstraint),this.biasRegularizer=yt(t.biasRegularizer),this.activityRegularizer=yt(t.activityRegularizer),this.dilationRate=Tl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`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 B(`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 B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if($r("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Wm(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ma(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),biasConstraint:$t(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Ic=class extends FA{constructor(e,t){super(e,t);this.kernel=null,Ic.verifyArgs(t),this.filters=t.filters,Vt(this.filters,"filters"),this.kernelInitializer=At(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Dt(t.kernelConstraint),this.kernelRegularizer=yt(t.kernelRegularizer)}build(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return W(()=>{e=De(e);let n,r=this.bias==null?null:this.bias.read(),a=t3(this.activation.getClassName());if(a!=null&&this.rank===2)n=A7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=ate(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=A7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=ste(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Me("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ct(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a 0 but got ${JSON.stringify(e.filters)}`)}},Nc=class extends Ic{constructor(e){super(2,e);Nc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Wm(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Nc.className="Conv2D";re.registerClass(Nc);var $p=class extends Ic{constructor(e){super(3,e);$p.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};$p.className="Conv3D";re.registerClass($p);var MA=class extends Nc{constructor(e){super(e);if(this.inputSpec=[new Ut({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ct(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ut({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return W(()=>{let n=De(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],p=this.strides[1],d=Op(o,h,c,this.padding),f=Op(l,p,u,this.padding),m=[a,d,f,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let A=nd(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=nt(A,[0,3,1,2])),this.bias!=null&&(A=zr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ct(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Op(t[r],o,s,this.padding),t[a]=Op(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};MA.className="Conv2DTranspose";re.registerClass(MA);var y7=class extends Ic{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("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 B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=At(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=yt(t.depthwiseRegularizer),this.depthwiseConstraint=Dt(t.depthwiseConstraint),this.pointwiseInitializer=At(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=yt(t.pointwiseRegularizer),this.pointwiseConstraint=Dt(t.pointwiseConstraint)}build(e){if(e=ct(e),e.length{e=De(e);let n;if(this.rank===1)throw new Me("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=Pf(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=zr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.pointwiseRegularizer=ht(this.pointwiseRegularizer),e.depthwiseConstraint=$t(this.depthwiseConstraint),e.pointwiseConstraint=$t(this.pointwiseConstraint),e}};y7.className="SeparableConv";var OA=class extends y7{constructor(e){super(2,e)}};OA.className="SeparableConv2D";re.registerClass(OA);var Dp=class extends Ic{constructor(e){super(1,e);Dp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Wm(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Dp.className="Conv1D";re.registerClass(Dp);var $A=class extends Ge{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return W(()=>{if(e=De(e),this.dataFormat==="channelsLast"){let n=dp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return dp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=dp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return dp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$A.className="Cropping2D";re.registerClass($A);var DA=class extends Ge{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,kt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,NJ(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 W(()=>{let n=De(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};DA.className="UpSampling2D";re.registerClass(DA);function ite(e,t,n=[1,1],r="valid",a,s){return W(()=>{a==null&&(a=pr()),kt(a);let i=RA(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Yo(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var zA=class extends FA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=At(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Dt(e.depthwiseConstraint),this.depthwiseRegularizer=yt(e.depthwiseRegularizer)}build(e){if(e=ct(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 W(()=>{e=De(e);let n=ite(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=zr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ct(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,a=gr(t,this.kernelSize[0],this.padding,this.strides[0]),s=gr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.depthwiseConstraint=$t(this.depthwiseRegularizer),e}};zA.className="DepthwiseConv2D";re.registerClass(zA);function g7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("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 a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function x7(e,t,n,r=!1,a,s,i=!1,o=!1){return W(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(mr(2,l));if(t=nt(t,c),s!=null)throw new Me("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=bn(a,-1)),a=nt(a,c)),r&&(t=In(t,0),a!=null&&(a=In(a,0)));let u=[],h,p=n,d=t.shape[0],f=er(t),m;a!=null&&(m=er(a));for(let y=0;ye(g,p));if(a==null)h=_[0],p=_[1];else{let x=W(()=>{let w=m[y],b=kn(w).sub(w),N=_[0].mul(w).add(p[0].mul(b)),T=p.map((E,M)=>_[1][M].mul(w).add(E.mul(b)));return{output:N,newStates:T}});h=x.output,p=x.newStates}o&&u.push(h)}let A;return o&&(A=Nn(u,1)),[h,A,p]})}var Pr=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new zp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ut({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return mr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){iA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return W(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new B(`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=s.map(i=>new Ut({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){W(()=>{if(!this.stateful)throw new ea("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Nt([n,r])):this.states_=[Nt([n,this.cell.stateSize])];else if(e==null)Ne(this.states_),this.keptStates!=null&&(Ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Nt([n,r])):this.states_[0]=Nt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ne(this.states_);for(let r=0;rLt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=g7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ut({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Ar){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return W(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=De(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} 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:r},o=x7((p,d)=>{let f=this.cell.call([p].concat(d),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return W(()=>{let t=Nt(e.shape);return t=Ie(t,[1,2]),t=Ac(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Hm(t,[1,n]):t):this.cell.stateSize>1?[Hm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Pr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=yr(r,n);return new e(Object.assign(t,{cell:a}))}};Pr.className="RNN";re.registerClass(Pr);var xc=class extends Ge{},Pp=class extends xc{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,Vt(this.units,"units"),this.activation=Oa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Dt(e.kernelConstraint),this.recurrentConstraint=Dt(e.recurrentConstraint),this.biasConstraint=Dt(e.biasConstraint),this.dropout=vl([1,Ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,Ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ct(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 W(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0kn(e),rate:this.dropout,training:r})),0kn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Dr(L(e,s),this.kernel.read()):a=Dr(e,this.kernel.read()),this.bias!=null&&(a=zr(a,this.bias.read())),i!=null&&(n=L(n,i));let o=se(a,Dr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ma(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:$t(this.kernelConstraint),recurrentConstraint:$t(this.recurrentConstraint),biasConstraint:$t(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Pp.className="SimpleRNNCell";re.registerClass(Pp);var PA=class extends Pr{constructor(e){e.cell=new Pp(e),super(e)}call(e,t){return W(()=>{this.cell.dropoutMask!=null&&(Ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};PA.className="SimpleRNN";re.registerClass(PA);var Lp=class extends xc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Vt(this.units,"units"),this.activation=Oa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Oa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Dt(e.kernelConstraint),this.recurrentConstraint=Dt(e.recurrentConstraint),this.biasConstraint=Dt(e.biasConstraint),this.dropout=vl([1,Ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,Ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ct(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 W(()=>{if(e=e,e.length!==2)throw new B(`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],0kn(e),rate:this.dropout,training:n,count:3})),0kn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};LA.className="GRU";re.registerClass(LA);var Sc=class extends xc{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,Vt(this.units,"units"),this.activation=Oa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Oa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Dt(e.kernelConstraint),this.recurrentConstraint=Dt(e.recurrentConstraint),this.biasConstraint=Dt(e.biasConstraint),this.dropout=vl([1,Ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=vl([1,Ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ct(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 a=this.biasInitializer,s=this.units;r=new(t=class extends rr{apply(i,o){let l=a.apply([s]),c=new fp().apply([s]),u=a.apply([s*2]);return h3(h3(l,c),u)}},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 W(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0kn(e),rate:this.dropout,training:n,count:4})),0kn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0{this.cell.dropoutMask!=null&&(Ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};WA.className="LSTM";re.registerClass(WA);var zp=class extends xc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return W(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{hi(`RNNCell_${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=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(yr(a,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 oA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;sp3(t(),n),i=()=>gc(s,t,r);return!a||a<=1?Lt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Lt(o.clone()))}var ote=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 a=0,r=Object.getOwnPropertySymbols(e);a{if(this.cell.dropoutMask!=null&&(Ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}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 W(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Nt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){W(()=>{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),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. 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Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends rr{apply(u,h){let p=l.apply([c]),d=Cr([c]),f=l.apply([c*2]);return Xm([p,d,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return W(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0kn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Z,ae,J)=>!ae||!ae[J]?Z:L(ae[J],Z),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),p=l(r,o,3);0kn(a),rate:this.recurrentDropout,training:n,count:i}));let d=this.recurrentDropoutMask,f=l(a,d,0),m=l(a,d,1),A=l(a,d,2),y=l(a,d,3),g=3,[_,x,w,b]=Ht(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?Ht(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,N,this.padding),u=this.inputConv(u,x,T,this.padding),h=this.inputConv(h,w,E,this.padding),p=this.inputConv(p,b,M,this.padding);let[$,P,V,H]=Ht(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,$),m=this.recurrentConv(m,P),A=this.recurrentConv(A,V),y=this.recurrentConv(y,H);let U=this.recurrentActivation.apply(se(c,f)),K=this.recurrentActivation.apply(se(u,m)),X=se(L(K,s),L(U,this.activation.apply(se(h,A)))),ee=L(this.recurrentActivation.apply(se(p,y)),this.activation.apply(X));return[ee,ee,X]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ote(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=Xr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?zr(a,n,this.dataFormat):a}recurrentConv(e,t){return Xr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Wp.className="ConvLSTM2DCell";re.registerClass(Wp);var BA=class extends w7{constructor(e){let t=new Wp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};BA.className="ConvLSTM2D";re.registerClass(BA);var Bp=class extends Ge{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.invokeCallHook(e,t);let n=De(e);if(0p3(n,this.rate,a,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()}};Bp.className="Dropout";re.registerClass(Bp);var VA=class extends Bp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};VA.className="SpatialDropout1D";re.registerClass(VA);var UA=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Vt(this.units,"units"),this.activation=Oa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Dt(e.kernelConstraint),this.biasConstraint=Dt(e.biasConstraint),this.kernelRegularizer=yt(e.kernelRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.activityRegularizer=yt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ct(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=ct(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e),r=t3(this.activation.getClassName()),a;return r!=null?a=Dr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Dr(n,this.kernel.read()),this.bias!=null&&(a=zr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ma(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:$t(this.kernelConstraint),biasConstraint:$t(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};UA.className="Dense";re.registerClass(UA);var jA=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ct(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Ge{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 W(()=>{this.invokeCallHook(e,t);let n=De(e);return gc(()=>pp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};ay.className="GaussianNoise";re.registerClass(ay);var sy=class extends Ge{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 W(()=>{this.invokeCallHook(e,t);let n=De(e);return this.rate>0&&this.rate<1?gc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(pp(n.shape,1,r))},()=>n,t.training||!1):n})}};sy.className="GaussianDropout";re.registerClass(sy);var iy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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Ge{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=At(e.betaInitializer||"zeros"),this.gammaInitializer=At(e.gammaInitializer||"ones"),this.movingMeanInitializer=At(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=At(e.movingVarianceInitializer||"ones"),this.betaConstraint=Dt(e.betaConstraint),this.gammaConstraint=Dt(e.gammaConstraint),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer)}build(e){e=ct(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ut({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return W(()=>{let n=t.training==null?!1:t.training,r=De(e),a=r.shape,s=a.length,i=mr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=li(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,mr(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,_=this.scale?this.gamma.read().reshape(l):null;return Ec(r,A,y,g,_,this.epsilon)}else return Ec(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[p,d,f]=hte(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{W(()=>{let _=1-g,x=A.read(),w=x.sub(y).mul(_);A.write(x.sub(w))})};return(()=>{m(this.movingMean,d,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),movingMeanInitializer:_t(this.movingMeanInitializer),movingVarianceInitializer:_t(this.movingVarianceInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer),betaConstraint:$t(this.betaConstraint),gammaConstraint:$t(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};oy.className="BatchNormalization";re.registerClass(oy);var ly=class extends Ge{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=At(e.betaInitializer||"zeros"),this.gammaInitializer=At(e.gammaInitializer||"ones"),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ct(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a=t)throw new Error(`Invalid axis: 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s==="max"?i=Bu(e,t,n,o):i=Ou(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function _7(e,t,n,r,a,s){return W(()=>{kt(a),s3(s),Un(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=pr()),s==null&&(s="max"),e=m7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Rf(e,t,n,o):i=yf(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var b7=class extends Ge{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 B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Vt(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 B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Vt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Un(this.padding),this.inputSpec=[new Ut({ndim:3})]}computeOutputShape(e){e=ct(e);let t=gr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return W(()=>{this.invokeCallHook(e,t),e=Ac(De(e),2);let n=this.poolingFunction(De(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ka(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},cy=class extends b7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Un(r),Vp(e,t,n,r,a,"max")}};cy.className="MaxPooling1D";re.registerClass(cy);var hy=class extends b7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return 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t=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return W(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(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}},dy=class extends v7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Un(r),Vp(e,t,n,r,a,"max")}};dy.className="MaxPooling2D";re.registerClass(dy);var py=class extends v7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Un(r),Vp(e,t,n,r,a,"avg")}};py.className="AveragePooling2D";re.registerClass(py);var k7=class extends Ge{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 B(`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];Vt(this.poolSize,"poolSize"),Vt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,kt(this.dataFormat),Un(this.padding),this.inputSpec=[new Ut({ndim:5})]}computeOutputShape(e){e=ct(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=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(n,this.poolSize[1],this.padding,this.strides[1]),r=gr(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 W(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(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}},fy=class extends k7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Un(r),_7(e,t,n,r,a,"max")}};fy.className="MaxPooling3D";re.registerClass(fy);var my=class extends k7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return kt(a),Un(r),_7(e,t,n,r,a,"avg")}};my.className="AveragePooling3D";re.registerClass(my);var I7=class extends Ge{constructor(e){super(e);this.inputSpec=[new Ut({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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Cc(s,t,r)}function lne(e,t,n){return new Cc([],e,t,n)}function une(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let a=Math.max(...t);if(r!=null&&r!==-1&&a>=r)throw new Error(`Max index must be < array size (${a} vs. ${r})`);let s=new Cc([],n,e.dtype,r),i=er(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function cne(e,t,n){let r=0,a=t.map(l=>(r+=l,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${r}, and tensor's shape is: ${e.shape}`);let s=r===0?0:e.size/r,i=W(()=>{let l=[];e=q(e,[1,r,s]);for(let c=0;c{switch(e.op){case"If":case"StatelessIf":{let r=I("thenBranch",e,t,n),a=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await 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r=I("boxes",e,t,n),a=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var fne=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=Oy(e,t,n),c=await Je.nonMaxSuppressionWithScoreAsync(r,a,s,i,o,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=Oy(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await Je.nonMaxSuppressionPaddedAsync(r,a,s,i,o,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=Oy(e,t,n);return[await Je.nonMaxSuppressionAsync(r,a,s,i,o)]}case"Where":{let r=me(I("condition",e,t,n),"bool"),a=[await Gf(r)];return 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I("x",e,t,n).map(c=>Wt(c.shape));case"Size":return[ke(I("x",e,t,n).size,"int32")];case"Rank":return[ke(I("x",e,t,n).rank,"int32")];case"NoOp":return[ke(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),W(()=>{let r=er(t),a=n.length,s=r.length;k.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i{let r=[];for(let a=0;a{switch(e.op){case"HashTable":case"HashTableV2":{let a=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new yne(a,s);return 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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 ov(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(p=>Fn(p)[0]),u=[];r!=null&&(u=r.map(p=>Fn(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((iv(p)||Sne(p)||Tne(p))&&i==null&&(i=p,o=i.children.map(d=>d.name).filter(d=>a.has(d))),a.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(d=>{l.has(d.name)||(l.add(d.name),h.push(d))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Ene(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Fn(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return c}var Cne=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Rne=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Fne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function iv(e){return Cne.indexOf(e.op)>=0}function Sne(e){return Rne.indexOf(e.op)>=0}function Tne(e){return Fne.indexOf(e.op)>=0}var $y=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new $y(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=ov(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. 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in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Fn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Fn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Mne=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=cn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(cn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=cn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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Array(e),n=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new ore(this,e,t)}columnMajorBatch(e,t=!0,n=mv){return this.rowMajorBatch(e,t).map(r=>Xne(r,n))}concatenate(e,t){return new xv(gv([this,e]),t)}take(e){return e<0||e==null?this:new ire(this,e)}skip(e){return e<0||e==null?this:new sre(this,e)}prefetch(e){return new _v(this,e)}shuffle(e,t){return new dre(this,e,t)}serial(){return new are(this)}},Qne=class extends jt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Jne(e),done:!1}}},ere=class extends jt{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: ${e.message}`,e}}},are=class extends jt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},sre=class extends jt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},ore=class extends jt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},lre=class extends jt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ne(e.value)}}},ure=class extends jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=lr.getTensorsInContainer(e.value),n=this.transform(e.value),r=lr.getTensorsInContainer(n);for(let a of t)lr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},cre=class extends jt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},wv=class extends jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=lr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=lr.getTensorsInContainer(n);for(let a of t)lr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Py=class extends jt{constructor(){super();this.outputQueue=new Dy,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}}},hre=class extends Py{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=lr.getTensorsInContainer(e.value),n=this.transform(e.value),r=lr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)lr.isTensorInList(a,r)||a.dispose();return!0}},xv=class extends jt{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}},Da;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Da||(Da={}));var nre=class extends jt{constructor(e,t=Da.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(s){return s instanceof jt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await Av(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Da.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Da.SHORTEST:return{value:null,done:!0};case Da.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},_v=class extends jt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new yv(t)}summary(){return`${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()}},dre=class extends _v{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Hne.alea(n||k.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}}},El=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Mn(async()=>(await n.iterator()).columnMajorBatch(e,t,pre),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Mn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Mn(async()=>(await t.iterator()).filter(r=>W(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Mn(async()=>(await t.iterator()).map(n=>W(()=>e(n))),this.size)}mapAsync(e){let t=this;return Mn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Mn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Mn(async()=>{let r=zy(async()=>({value:await t.iterator(),done:!1}));return tre(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Gne.alea(t||k.now().toString());return Mn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Mn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};El.MAX_BUFFER_SIZE=1e4;function Mn(e,t=null){return new class extends El{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function zne(e){return Mn(async()=>gv(e),e.length)}function Pne(e){if(!Cl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await Av(e,r=>{if(r instanceof El)return{value:r.iterator(),recurse:!1};if(Cl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return rre(n,Da.SHORTEST)},t)}function pre(e){if(e===null)return null;let t=e[0];return Zne(t)?{value:fre(e),recurse:!1}:{value:null,recurse:!0}}function fre(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Xe?Nn(e):ur(e)}var cv=class extends El{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` `).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},qp='"',Rc=Symbol("out"),bv=Symbol("field"),Xp=Symbol("quote"),Ly=Symbol("quoteafterquote"),vv=Symbol("quoteinquote"),hv=class extends El{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new cv(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Q().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new kv(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),ur(n,t)}},Iv=class extends jt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Wt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=pn([s,a,o,i],[1,4])}else this.cropBox=pn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Q().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new Iv(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=jo.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return W(()=>{let t=bn(me(e,"float32"),0),n;n=Je.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return q(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Nv=class{},Sv=class extends jt{split(e){return new mre(this,e)}},mre=class extends Sv{constructor(e,t){super();this.upstream=e,this.impl=new Are(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Are=class extends Py{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},gre=class extends jt{decodeUTF8(){return new yre(this)}},yre=class extends Sv{constructor(e){super();this.upstream=e,this.impl=new xre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},xre=class extends Py{constructor(e){super();if(this.upstream=e,Q().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=N8();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Q().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},Tv=class extends gre{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(Q().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof 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Nv{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Ev(this.url)?new dv(this.url,this.fileOptions).iterator():_re(this.url,this.fileOptions)}};function Lne(e,t={}){return new hv(new pv(e),t)}function Wne(e){let t=zy(e);return Mn(async()=>t)}function Bne(e){return Mn(async()=>{let t=await e();return zy(()=>t.next())})}async function Vne(e,t){return Iv.create(e,t)}async function Une(e){return kv.create(e)}var jne="3.0.0",bre={tfjs:S8,"tfjs-core":T8,"tfjs-data":E8,"tfjs-layers":C8,"tfjs-converter":R8,"tfjs-backend-cpu":Cx,"tfjs-backend-webgl":Yw,"tfjs-backend-wasm":Vb};var ln={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function Cv(){if(!af(ln.name)){Te("backend registration:",ln.name);try{ln.canvas=typeof OffscreenCanvas!="undefined"?new 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this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];for(let i=0;i=n.hand.minConfidence){let _=q(y,[-1,3]),x=_.arraySync();y.dispose(),_.dispose();let w=this.transformRawCoords(x,d,l,p),b=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=b;let N={landmarks:w,confidence:g,box:{topLeft:b.startPoint,bottomRight:b.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=o0(l0(o),d6),c={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(c)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t},y6=e=>{if(!e)return[];let t=[];for(let n=0;n{if(!e)return[];let t=[];for(let n=0;n0){let a=r.reduce((i,o)=>i.position[2]i.position[1]0,a=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,s=r,i=a;if(t.filter.width>0?s=t.filter.width:t.filter.height>0&&(s=r*(t.filter.height/a)),t.filter.height>0?i=t.filter.height:t.filter.width>0&&(i=a*(t.filter.width/r)),!s||!i)return Te("Human: invalid input",e),null;(!It||It.width!==s||It.height!==i)&&(It=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(s,i):document.createElement("canvas"),It.width!==s&&(It.width=s),It.height!==i&&(It.height=i));let o=It.getContext("2d");if(e instanceof ImageData?o.putImageData(e,0,0):o.drawImage(e,0,0,r,a,0,0,It.width,It.height),t.filter.enabled){if((!this.fx||!Zt||It.width!==Zt.width||It.height!==Zt.height)&&(Zt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(It.width,It.height):document.createElement("canvas"),Zt.width!==It.width&&(Zt.width=It.width),Zt.height!==It.height&&(Zt.height=It.height),this.fx=tn.flags.IS_BROWSER?new _6.GLImageFilter({canvas:Zt}):null),!this.fx)return 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L2=class{constructor(t={}){this.tf=rh,this.version=v6,this.config=Ol(b6,t),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=k6,this.age=e2,this.gender=r2,this.emotion=u2,this.body=S2,this.hand=O2}profile(){return this.config.profile?Yy:{}}analyze(...t){if(!this.analyzeMemoryLeaks)return;let n=Pn().state.numTensors,r=this.numTensors;this.numTensors=n;let a=n-r;a!==0&&Te(...t,a)}sanity(t){if(!this.checkSanity)return null;if(!t)return"input is not defined";if(tn.flags.IS_NODE&&!(t instanceof Xe))return"input must be a tensor";try{Yh()}catch(n){return"backend not loaded"}return null}simmilarity(t,n){return this.config.face.embedding.enabled?jv(t,n):0}async load(t=null){this.state="load";let n=dt();t&&(this.config=Ol(this.config,t)),this.firstRun&&(Te(`version: ${this.version} TensorFlow/JS version: ${r5}`),await this.checkBackend(!0),tn.flags.IS_BROWSER&&(Te("configuration:",this.config),Te("tf flags:",tn.flags)));let r=this.config.face.detector.modelPath.includes("faceboxes")?Jy:k6;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.face||(this.config.face.enabled?r.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?t2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?o2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?d2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?f2(this.config):null),this.models.posenet||(this.config.body.enabled?E2(this.config):null),this.models.handpose||(this.config.hand.enabled?z2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await r.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await t2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await o2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await d2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await f2(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await E2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await z2(this.config))),this.firstRun&&(Te("tf engine state:",Pn().state.numBytes,"bytes",Pn().state.numTensors,"tensors"),this.firstRun=!1);let a=Math.trunc(dt()-n);a>(this.perf.load||0)&&(this.perf.load=a)}async checkBackend(t=!1){if(this.config.backend&&this.config.backend!==""&&t||Yh()!==this.config.backend){let n=dt();this.state="backend",Te("setting backend:",this.config.backend),this.config.backend==="wasm"&&(Te("settings wasm path:",this.config.wasmPath),Bb(this.config.wasmPath),await Q().getAsync("WASM_HAS_SIMD_SUPPORT")||Te("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&Cv();try{await s5(this.config.backend)}catch(r){Te("error: cannot set backend:",this.config.backend,r)}if(a5(),Yh()==="webgl"){this.config.deallocate&&(Te("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tn.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tn.set("WEBGL_FORCE_F16_TEXTURES",!0),tn.set("WEBGL_PACK_DEPTHWISECONV",!0);let r=await sf().getGPGPUContext().gl;Te(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await i5(),this.perf.backend=Math.trunc(dt()-n)}}async detectFace(t){var c,u,h,p,d,f;let n,r,a,s,i,o=[];this.state="run:face",n=dt();let l=await((c=this.models.face)==null?void 0:c.estimateFaces(t,this.config));this.perf.face=Math.trunc(dt()-n);for(let m of l){if(this.analyze("Get Face"),!m.image||m.image.isDisposedInternal){Te("Face object is disposed:",m.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?n2(m.image,this.config):{}:(this.state="run:age",n=dt(),r=this.config.face.age.enabled?await n2(m.image,this.config):{},this.perf.age=Math.trunc(dt()-n)),this.analyze("Start Gender:"),this.config.async?a=this.config.face.gender.enabled?l2(m.image,this.config):{}:(this.state="run:gender",n=dt(),a=this.config.face.gender.enabled?await l2(m.image,this.config):{},this.perf.gender=Math.trunc(dt()-n)),this.analyze("Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?p2(m.image,this.config):{}:(this.state="run:emotion",n=dt(),s=this.config.face.emotion.enabled?await p2(m.image,this.config):{},this.perf.emotion=Math.trunc(dt()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?m2(m.image,this.config):{}:(this.state="run:embedding",n=dt(),i=this.config.face.embedding.enabled?await m2(m.image,this.config):{},this.perf.embedding=Math.trunc(dt()-n)),this.analyze("End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((u=m==null?void 0:m.annotations)==null?void 0:u.leftEyeIris)&&((h=m==null?void 0:m.annotations)==null?void 0:h.rightEyeIris)&&(delete m.annotations.leftEyeIris,delete m.annotations.rightEyeIris);let A=((p=m.annotations)==null?void 0:p.leftEyeIris)&&((d=m.annotations)==null?void 0:d.rightEyeIris)?11.7*Math.max(Math.abs(m.annotations.leftEyeIris[3][0]-m.annotations.leftEyeIris[1][0]),Math.abs(m.annotations.rightEyeIris[4][1]-m.annotations.rightEyeIris[2][1])):0;o.push({confidence:m.confidence,box:m.box,mesh:m.mesh,boxRaw:m.boxRaw,meshRaw:m.meshRaw,annotations:m.annotations,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:A!==0?Math.trunc(A)/100:0}),(f=m.image)==null||f.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),o}async image(t,n={}){var a;this.state="image",this.config=Ol(this.config,n);let r=P2(t,this.config);return(a=r==null?void 0:r.tensor)==null||a.dispose(),r==null?void 0:r.canvas}async detect(t,n={}){return new Promise(async r=>{var p,d,f,m;this.state="config";let a;this.config=Ol(this.config,n),this.state="check";let s=this.sanity(t);s&&(Te(s,t),r({error:s}));let i,o,l,c=dt();await this.checkBackend(),await this.load(),this.config.scoped&&Pn().startScope(),this.analyze("Start Scope:"),a=dt();let u=P2(t,this.config);if(!u||!u.tensor){Te("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(dt()-a),this.analyze("Get Image:"),this.config.async?(l=this.config.face.enabled?this.detectFace(u.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=dt(),l=this.config.face.enabled?await this.detectFace(u.tensor):[],this.perf.face=Math.trunc(dt()-a)),this.analyze("Start Body:"),this.config.async?(i=this.config.body.enabled?(p=this.models.posenet)==null?void 0:p.estimatePoses(u.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",a=dt(),i=this.config.body.enabled?await((d=this.models.posenet)==null?void 0:d.estimatePoses(u.tensor,this.config)):[],this.perf.body=Math.trunc(dt()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(o=this.config.hand.enabled?(f=this.models.handpose)==null?void 0:f.estimateHands(u.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=dt(),o=this.config.hand.enabled?await((m=this.models.handpose)==null?void 0:m.estimateHands(u.tensor,this.config)):[],this.perf.hand=Math.trunc(dt()-a)),this.analyze("End Hand:"),this.config.async&&([l,i,o]=await Promise.all([l,i,o])),u.tensor.dispose(),this.config.scoped&&Pn().endScope(),this.analyze("End Scope:");let h=[];this.config.gesture.enabled&&(a=dt(),h=[...A6(l),...m6(i),...g6(o),...y6(l)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(dt()-a)),this.perf.total=Math.trunc(dt()-c),this.state="idle",r({face:l,body:i,hand:o,gesture:h,performance:this.perf,canvas:u.canvas})})}async warmupBitmap(){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(u0);break;case"full":n=await t(c0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r}async warmupCanvas(){return new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+u0;break;case"full":r=1200,n="data:image/jpeg;base64,"+c0;break;default:n=null}let a=new Image(r,r);a.onload=()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=r,s.height=r;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=i==null?void 0:i.getImageData(0,0,r,r);this.detect(o,this.config).then(l=>t(l))},n?a.src=n:t(null)})}async warmupNode(){let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(u0):t(c0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);Ne(r);let s=await this.detect(a,this.config);return Ne(a),s}async warmup(t){let n=dt();t&&(this.config=Ol(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await this.warmupBitmap():typeof Image!="undefined"?a=await this.warmupCanvas():a=await this.warmupNode(),this.config.videoOptimized=r;let s=dt();return Te("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};return mae;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the License); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an AS IS BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** @license See the LICENSE file. */ //# sourceMappingURL=human.ts.map