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human
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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return rd.nextTensorId++}nextVariableId(){return rd.nextVariableId++}clone(e){let t=P.runKernel(qs,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return P.runKernel(Fs,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,Fc(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 a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Zm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Zm(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Fc(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let A=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,A,y);let x=y.map(v=>{if(v.rank!=null)return v;let{dataId:b,shape:k,dtype:N}=v;return this.makeTensorFromDataId(b,k,N)});if(a){let v=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:d}=e,p=Zm(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(l,u,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=Wm(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Wr(e[0])&&(r=e.map(o=>Qu(o)));let s=a.write(r,t,n),i=new je(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=S5(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new je(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new ad(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*$m(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ad||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*$m(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=Wm(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let p=n[d],c=Xp(p.size,p.dtype);return this.makeTensor(c,p.shape,p.dtype)}return u}),a(l.length>1?l:l[0],r,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=Km(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(O(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 r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));O(r instanceof je,()=>"The result y returned by f() must be a tensor.");let s=sS(this.state.activeTape,t,r);if(!a&&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[r.id]=n==null?AS(r.shape):n,iS(i,s,l=>this.tidy(l),yS);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return O(Br(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{O(t.every(i=>i instanceof je),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,a={};t.forEach((i,o)=>{a[o]=i});let r=(i,o)=>(n=e(...t,o),O(n.value instanceof je,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),O(Br(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),u=Array.isArray(l)?l:[l];O(u.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(...)."),O(u.every(p=>p instanceof je),()=>"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 d={};return u.forEach((p,c)=>{d[c]=()=>p}),d};return this.runKernelFunc({forwardFunc:r,backwardsFunc:s,inputs:a})}}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=Ju(),n=await this.backend.time(e);return n.wallMs=Ju()-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 U5;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}};rd.nextTensorId=0;rd.nextVariableId=0;function AS(e){let t=Om($t(e),"float32");return P.makeTensor(t,e,"float32")}function H5(){let e=M5();if(e._tfengine==null){let t=new R5(e);e._tfengine=new rd(t)}return VI(e._tfengine.ENV),dS(()=>e._tfengine),e._tfengine}var P=H5();function yS(e,t){let n={a:e,b:t};return P.runKernel(Vr,n)}var sd={};_e(sd,{isBrowser:()=>j5,isMobile:()=>bS});function xS(){return typeof navigator!="undefined"&&navigator!=null}function bS(e){if(e||xS()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let n=e;return n.userAgentData&&n.userAgentData.mobile}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(t)||/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(t.substr(0,4))}return!1}function j5(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var $a=te();$a.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. 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a=M(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=M(t,"weights","computeWeightedLoss"));let s=r==null?a:L(a,r);if(n===bn.NONE)return s;if(n===bn.SUM)return be(s);if(n===bn.MEAN){if(r==null)return Mt(s);{let i=a.size/r.size,o=ce(be(s),be(r));return i>1?ce(o,ke(i)):o}}if(n===bn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return ce(be(s),ke(a.size));{let i=L(r,Gn(a.shape)),o=de(be(Wl(i,ke(0))),"float32");return ce(be(s),o)}}throw Error(`Unknown reduction: ${n}`)}var kr=W({computeWeightedLoss_:tF});function nF(e,t,n,a=bn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","absoluteDifference"),s=M(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=M(n,"weights","absoluteDifference")),gn(r.shape,s.shape,"Error in absoluteDifference: ");let o=Ht(fe(r,s));return kr(o,i,a)}var aF=W({absoluteDifference_:nF});function rF(e,t,n,a,r=bn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","cosineDistance"),i=M(t,"predictions","cosineDistance"),o=null;a!=null&&(o=M(a,"weights","cosineDistance")),gn(s.shape,i.shape,"Error in cosineDistance: ");let l=ke(1),u=fe(l,be(L(s,i),n,!0));return kr(u,o,r)}var sF=W({cosineDistance_:rF});function iF(e,t,n,a=bn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","hingeLoss"),s=M(t,"predictions","hingeLoss"),i=null;n!=null&&(i=M(n,"weights","hingeLoss")),gn(r.shape,s.shape,"Error in hingeLoss: ");let o=ke(1);r=fe(L(ke(2),r),o);let l=nr(fe(o,L(r,s)));return kr(l,i,a)}var oF=W({hingeLoss_:iF});function lF(e,t,n,a=1,r=bn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","huberLoss"),i=M(t,"predictions","huberLoss"),o=null;n!=null&&(o=M(n,"weights","huberLoss")),gn(s.shape,i.shape,"Error in huberLoss: ");let l=ke(a),u=Ht(fe(i,s)),d=fd(u,l),p=fe(u,d),c=re(L(ke(.5),ut(d)),L(l,p));return kr(c,o,r)}var uF=W({huberLoss_:lF});function dF(e,t,n,a=1e-7,r=bn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","logLoss"),i=M(t,"predictions","logLoss"),o=null;n!=null&&(o=M(n,"weights","logLoss")),gn(s.shape,i.shape,"Error in logLoss: ");let l=ke(1),u=ke(a),d=Nt(L(s,ua(re(i,u)))),p=L(fe(l,s),ua(re(fe(l,i),u))),c=fe(d,p);return kr(c,o,r)}var pF=W({logLoss_:dF});function cF(e,t,n,a=bn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","meanSquaredError"),s=M(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=M(n,"weights","meanSquaredError")),gn(r.shape,s.shape,"Error in meanSquaredError: ");let o=X1(r,s);return kr(o,i,a)}var hF=W({meanSquaredError_:cF});function fF(e,t){let n=M(e,"labels","sigmoidCrossEntropyWithLogits"),a=M(t,"logits","sigmoidCrossEntropyWithLogits");gn(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=nr(a),s=L(a,n),i=Yc(la(Nt(Ht(a))));return re(fe(r,s),i)}function mF(e,t,n,a=0,r=bn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"multiClassLabels","sigmoidCrossEntropy"),i=M(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","sigmoidCrossEntropy")),gn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let u=ke(a),d=ke(1),p=ke(.5);s=re(L(s,fe(d,u)),L(p,u))}let l=fF(s,i);return kr(l,o,r)}var gF=W({sigmoidCrossEntropy_:mF});function AF(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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s={skipEmpty:n},i={input:a,delimiter:r},o=P.runKernel(Cc,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var MF=W({stringSplit_:RF});function FF(e,t){let n=M(e,"input","stringToHashBucketFast","string"),a={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return P.runKernel(Ec,r,a)}var $F=W({stringToHashBucketFast_:FF}),OF={fft:lh,ifft:Ad,rfft:uh,irfft:q1},zF={hammingWindow:uM,hannWindow:Db,frame:Pb,stft:hM},Me={flipLeftRight:AM,grayscaleToRGB:xM,resizeNearestNeighbor:UM,resizeBilinear:BM,rotateWithOffset:vM,cropAndResize:mM,nonMaxSuppression:kM,nonMaxSuppressionAsync:MM,nonMaxSuppressionWithScore:$M,nonMaxSuppressionWithScoreAsync:zM,nonMaxSuppressionPadded:DM,nonMaxSuppressionPaddedAsync:LM,threshold:GM,transform:XM},Hb={bandPart:ZM,gramSchmidt:JM,qr:eF},_F={absoluteDifference:aF,computeWeightedLoss:kr,cosineDistance:sF,hingeLoss:oF,huberLoss:uF,logLoss:pF,meanSquaredError:hF,sigmoidCrossEntropy:gF,softmaxCrossEntropy:xF},xd={sparseFillEmptyRows:vF,sparseReshape:kF,sparseSegmentMean:SF,sparseSegmentSum:NF},mh={stringNGrams:EF,stringSplit:MF,stringToHashBucketFast:$F},Ir=class extends Nx{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return K(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return db(e,t)}dispose(){this.iterations_!=null&&K(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ir,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var gh=class extends Ir{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:H(()=>Ke(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:H(()=>Ke(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;H(()=>{let l=re(L(i,this.rho),L(ut(s),1-this.rho)),u=L(ce(cn(re(o,this.epsilon)),cn(re(i,this.epsilon))),s),d=re(L(o,this.rho),L(ut(u),1-this.rho));i.assign(l),o.assign(d);let p=re(L(u,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(K(this.accumulatedGrads.map(e=>e.variable)),K(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};gh.className="Adadelta";Zr(gh);var Ah=class extends Ir{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:H(()=>Dl(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;H(()=>{let i=re(s,ut(r));s.assign(i);let o=re(L(ce(r,cn(re(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&K(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)}};Ah.className="Adagrad";Zr(Ah);var yh=class extends Ir{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],H(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=fe(1,this.accBeta1),a=fe(1,this.accBeta2);t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:H(()=>Ke(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:H(()=>Ke(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,p=re(L(u,this.beta1),L(l,1-this.beta1)),c=re(L(d,this.beta2),L(ut(l),1-this.beta2)),h=ce(p,n),m=ce(c,a);u.assign(p),d.assign(c);let f=re(L(ce(h,re(cn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),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&&K(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&K(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),H(()=>{this.accBeta1.assign(es(this.beta1,this.iterations_+1)),this.accBeta2.assign(es(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.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)}};yh.className="Adam";Zr(yh);var xh=class extends Ir{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],H(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=fe(1,this.accBeta1),a=ce(-this.learningRate,re(L(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ke(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ke(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,p=re(L(u,this.beta1),L(l,1-this.beta1)),c=L(d,this.beta2),h=Ht(l),m=wr(c,h);u.assign(p),d.assign(m);let f=re(L(ce(a,n),ce(p,re(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(re(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&K(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&K(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new 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)}};xh.className="Adamax";Zr(xh);var bd=class extends Ir{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 a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=P.registeredVariables[t];H(()=>{let s=re(L(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Jt(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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Pt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Ba=class{constructor(e,t,n,a,r,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=a,this.callArgs=r,this.outputTensorIndex=i,this.id=k3(),s!=null&&(this.originalName=f3(s),this.name=m3(this.originalName)),this.rank=t.length}},p_=0,Ph=class{constructor(e,t){this.callArgs=t,this.id=p_++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},c_=0,Ze=class extends se.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=c_++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Tr(n)+"_"+zh(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let a=e.dtype;a==null&&(a=e.inputDType),a==null&&(a="float32"),this.dtype=a}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Pa(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return On(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return On(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Sr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Use \`getOutputAt(nodeIndex)\` instead.`);return On(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=yt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=yt(this.inputSpec);if(e.length!==t.length)throw new j(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let a=e[n],r=t[n];if(r==null)continue;let s=a.rank;if(r.ndim!=null&&s!==r.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${s}`);if(r.maxNDim!=null&&s>r.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${s}`);if(r.minNDim!=null&&s<r.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${s}.`);if(r.dtype!=null&&a.dtype!==r.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${a.dtype}.`);if(r.axes){let i=a.shape;for(let o in r.axes){let l=Number(o),u=r.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],l=a.shape[i];if(o!=null&&l!=null&&o!==l)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${a.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=yt(e),a=!0;for(let s of n)if(!(s instanceof Ba)){a=!1;break}let r=!0;for(let s of n)if(s instanceof Ba){r=!1;break}if(a===r)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Ui(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of yt(e))s.push(i.shape);this.build(On(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let s=this.call(e,t),i=yt(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=On(o),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=h_(e),i=this.computeOutputShape(s),o,l=f_(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new Ba(l,u,this,yt(e),t,this.name,d)):o=new Ba(l,i,this,yt(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,a)=>{n!=null&&e[a]!=null&&e[a]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Sr(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Sr(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Pa(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Dh(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Sg(e?this.trainableWeights:this.weights)}setWeights(e){H(()=>{let t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],a=Sg(t);for(let r=0;r<a.length;++r){let s=a[r],i=t[r],o=e[r];if(!w.arraysEqual(s.shape,o.shape))throw new j(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Tg(n)})}addWeight(e,t,n,a,r,s,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(a=wt("zeros"));let o=a.apply(t,n),l=new S3(o,n,e,s,i);return o.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),s==null&&(s=!0),s?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=yt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,a,r,s,i=null){let o=yt(e);t=yt(t),n=yt(n),a=yt(a),r=_h(r),s=_h(s);let l=[],u=[],d=[];for(let p of o)l.push(p.sourceLayer),u.push(p.nodeIndex),d.push(p.tensorIndex);new Ph({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:a,inputShapes:r,outputShapes:s},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function h_(e){e=yt(e);let t=[];for(let n of e)t.push(n.shape);return On(t)}function f_(e){return"float32"}function T3(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let a=t.inboundNodes[n];if(a.inboundLayers.length===0)return a.inputTensors;{let r=[];for(let s=0;s<a.inboundLayers.length;s++){let i=a.inputTensors[s],o=a.inboundLayers[s],l=a.nodeIndices[s],u=T3(i,o,l);for(let d of u)r.indexOf(d)===-1&&r.push(d)}return r}}}var Gl=class extends Ze{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:zh("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new j("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let a=new Ba(this.dtype,this.batchInputShape,this,[],{},this.name);a.nodeIndex=0,a.tensorIndex=0,new Ph({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[a],outputTensors:[a],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`Cannot pass any input to an InputLayer's apply() method. 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All inputs should only appear once. Found: ${this.inputs.map(A=>A.name)}`);ns(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(A=>A.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let A of this.outputs){let y=A.sourceLayer,x=A.nodeIndex,v=A.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let A of this.inputs){let y=A.sourceLayer,x=A.nodeIndex,v=A.tensorIndex;rr(x===0,"input layer has >1 nodes"),rr(v===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let y=this.inputLayers[A];if(!(y instanceof Gl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${A} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let A of this.outputLayers)this.outputNames.push(A.name);this.internalInputShapes=this.inputs.map(A=>A.shape),this.internalOutputShapes=this.outputs.map(A=>A.shape);let t={},n={},a={},r={},s={},i=[],o=(A,y,x,v,b,k)=>{(v==null||b==null||k==null)&&(v=A.sourceLayer,b=A.nodeIndex,k=A.tensorIndex);let N=v.inboundNodes[b];if(x.indexOf(N)!==-1)throw new Pa(`The tensor ${A.name} at layer "${v.name}" is part of a cycle.`);if(y.indexOf(N)!==-1)return;this.containerNodes.add(ir.nodeKey(v,b)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(N)===-1&&x.push(N);let C=N.inboundLayers.length;for(let E=0;E<C;E++){let z=N.inputTensors[E],F=N.inboundLayers[E],I=N.nodeIndices[E],_=N.tensorIndices[E];o(z,y,x,F,I,_)}for(y.push(N);x.indexOf(N)>=0;)x.splice(x.indexOf(N),1);i.push(N)},l=[],u=[];for(let A of this.outputs)o(A,l,u);let d=i.slice().reverse();for(let A of d){n[A.id]=A,A.id in t||(t[A.id]=0);let y=t[A.id],x=a[A.outboundLayer.id]==null?0:a[A.outboundLayer.id];y=Math.max(y,x),a[A.outboundLayer.id]=y,r[A.outboundLayer.id]=A.outboundLayer,t[A.id]=y;for(let v=0;v<A.inboundLayers.length;v++){let b=A.inboundLayers[v],k=A.nodeIndices[v],N=b.inboundNodes[k],C=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(y+1,C),n[N.id]=N}}let p={};for(let A in t){let y=t[A];y in p||(p[y]=[]),p[y].push(n[A])}let c={};for(let A in a){let y=a[A];y in c||(c[y]=[]),c[y].push(r[A])}let h=Object.keys(c).map(A=>parseInt(A,10)).sort(kh);this.layers=[];for(let A of h){let y=c[A];y.sort((x,v)=>{let b=s[x.id],k=s[v.id];return b<k?-1:b>k?1:0});for(let x of y)x instanceof ir&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=c,h=Object.keys(p).map(A=>parseInt(A,10)).sort(kh);let m=this.inputs.slice(),f=[];for(let A of h)for(let y of p[A]){let x=y.outboundLayer;if(x!=null){for(let v of y.inputTensors)if(m.indexOf(v)===-1)throw new Pa(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of y.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=p;let g=this.layers.map(A=>A.name);for(let A of g){let y=g.filter(x=>x===A).length;if(y!==1)throw new Pa(`The name "${A}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Ph({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new j("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new j(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new j(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new j(`${s.length} of ${a} weights are not set: ${s}`)}Tg(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Og}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=$g(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return H(()=>{e=yt(e);let n=new Gi;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Ed(this.outputs,n,t)})}computeMask(e,t){return H(()=>{e=yt(e);let n;return t==null?n=Wi(null,e.length):n=yt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=_h(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";n[u]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(kh);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(u.id)!==-1)continue;let d=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],A=l.tensorIndices[m],y=`${f.name}_${g}_${A}`,x=n[y];d.push(x)}let p=u.computeOutputShape(On(d)),c=_h(p),h=u.inboundNodes.indexOf(l);for(let m=0;m<c.length;m++){let f=`${u.name}_${h}_${m}`;n[f]=c[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];rr(o in n),r.push(n[o])}return On(r)}runInternalGraph(e,t){t==null&&(t=Wi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];n[l.id]=[u,d]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(kh);for(let o of a){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,p=u.inputTensors,c=u.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,g,A,y;if(u.callArgs!=null&&(m=u.callArgs),h.length===1){let[x,v]=h[0];m.mask==null&&(m.mask=v),A=yt(d.call(x,m)),y=yt(d.computeMask(x,v)),f=[x],g=[v]}else f=h.map(x=>x[0]),g=h.map(x=>x[1]),m.mask==null&&(m.mask=g),A=yt(d.call(f,m)),y=yt(d.computeMask(f,g));if(d.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<c.length;++x){let v=c[x],b=A[x],k=y[x];n[v.id]=[b,k]}}}}let r=[],s=[],i=[];for(let o of this.outputs){rr(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=n[o.id];i.push(l.shape),r.push(l),s.push(u)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof ir?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=ir.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new j("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return H(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=ir.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let p=s.inboundNodes[d],c=ir.nodeKey(s,d),h={};if(this.containerNodes.has(c)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],A=p.nodeIndices[f],y=p.tensorIndices[f],x=ir.nodeKey(g,A),v=t[x];v==null&&(v=0),m.push([g.name,v,y,h])}l.push(m)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,n.push(u)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=ir.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];a.push([i.name,u,d])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=ir.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];r.push([i.name,u,d])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let A=[],y;for(let x of g){let v=x[0],b=x[1],k=x[2];if(y=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let N=r[v];if(N.inboundNodes.length<=b){i(f,g);return}let C=N.inboundNodes[b];A.push(C.outputTensors[k])}A.length>0&&f.apply(On(A),y)}function l(f){let g=f.name,A=Va(f,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(a),r[g]=A,f.inboundNodes.forEach(y=>{if(!(y instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${y}`);i(A,y)})}let u=t.name,d=t.layers;for(let f of d)l(f);for(;!wz(s);)for(let f of d){let g=r[f.name];if(g.name in s){let A=s[g.name];delete s[g.name];for(let y of A)o(g,y)}}let p=[],c=[],h=t.inputLayers;for(let f of h){let g=f[0],A=f[1],y=f[2];rr(g in r);let x=r[g].inboundNodes[A].outputTensors;p.push(x[y])}let m=t.outputLayers;for(let f of m){let g=f[0],A=f[1],y=f[2];rr(g in r);let x=r[g].inboundNodes[A].outputTensors;c.push(x[y])}return new e({inputs:p,outputs:c,name:u})}get stateful(){if(this._stateful)throw new j("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){H(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function G_(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function j3(e,t){return G_(e,t,"classWeight")}async function G3(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=H(()=>{if(e.shape.length===1)return Oa(e);if(e.shape.length===2){if(e.shape[1]>1)return Qa(e,1);if(e.shape[1]===1)return B(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await r.data());K(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Dt(i,"float32")}else return null}function q_(e,t){return L(e,t)}var X_=32;function q3(e,t){let n,a,r=t;n=r.xs,a=r.ys,w.assert(n!=null&&a!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=X3("input",e.inputNames,n),i=X3("output",e.outputNames,a),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function X3(e,t,n){if(n instanceof je)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new j(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function K_(e){if(e.length===3)throw new ze("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Z_(e,t,n){let a=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,s,i;if(r)if(K3(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=K_(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;r?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let d=$3(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=O3(d,p,n.epochs,null,null,Y_(t,n),null,r,u);c.setModel(e),e.history=h,await c.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await c.onEpochBegin(m);let A=0,y=0;for(a||(f=await t.iterator());a?A<n.batchesPerEpoch:!0;){let x=await f.next();if(a&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${A} batches; interrupting training. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let h=0;h<this.inputs.length;++h)u.push({key:this.inputs[h],value:n[h]});let d=new Gi(u),p=Ed(this.outputs,d,{training:!0}),c;for(let h=0;h<this.lossFunctions.length;++h){let m=this.lossFunctions[h](a[h],p[h]);r[h]!=null&&(m=q_(m,r[h]));let f=Mt(m);t.push(f),h===0?c=m:c=re(c,m)}for(let h=0;h<this.metricsTensors.length;++h){let m;if(this.outputs.length>1&&h<this.outputs.length)m=t[h];else{let f=this.metricsTensors[h][0],g=this.metricsTensors[h][1];m=Mt(f(a[g],p[g]))}Jt(m),s.push(m)}return c=Mt(c),this.calculateLosses().forEach(h=>{c=re(c,h)}),c},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>H(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:a[l]});let i=new Gi(s),o=Ed(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],d=Mt(u(r[l],o[l]));l===0?n=d:n=re(n,d),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],d=this.metricsTensors[l][1],p=Mt(u(r[d],o[d]));t.push(p)}return t})}async fit(e,t,n={}){return tD(this,e,t,n)}async fitDataset(e,t){return Z_(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return K(s),On(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,a=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let s=0;s<a.length;++s)n&&!a[s].trainable||t.push({name:a[s].originalName,tensor:r[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Vc().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Vc().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Tr(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Tr(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let a of t)if(typeof n[a]=="string")e[a]=Tr(n[a]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Tr(jh(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Tr(jh(e)));{let e={};for(let t in this.metrics)e[t]=Tr(jh(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Cd(e.optimizer_config),n=Va(t),a;if(typeof e.loss=="string")a=Bi(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Bi(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Bi(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Bi(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Bi(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=Fn.getSaveHandlers(e);if(i.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new j(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Fn.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:iD,generatedBy:`TensorFlow.js tfjs-layers v${Og}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Fn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=Fn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;B3(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){B3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Nr.className="Model";se.registerClass(Nr);var ev=class extends Nr{};ev.className="Functional";se.registerClass(ev);async function oD(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=Cd(n),r=Va(a,t);if(e.weightsManifest!=null){let s=await Fn.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),K(s)}return r}async function lD(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Fn.getLoadHandlers(e,t);if(n.length===0)n.push(Fn.browserHTTPRequest(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return uD(e,void 0,t)}async function uD(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let a=await e.load(),r=a.modelTopology;r.model_config!=null&&(r=r.model_config);let s=n.strict==null?!0:n.strict,i=a.weightData!=null&&a.weightSpecs!=null&&s,o=Va(Cd(r),t,i),l=a.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),a.userDefinedMetadata!=null&&o.setUserDefinedMetadata(a.userDefinedMetadata),a.weightData!=null){if(a.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:d}=dD(a.weightData,a.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),K(u),K(d.map(p=>p.tensor))}return o}function dD(e,t){let n=Fn.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var Kl=class extends Nr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:zh("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Kl||e instanceof Nr,n;if(t){if(n=e,n.outputs.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new j("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new j("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=N3({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(a)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new j(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=T3(this.outputs[0])}this.inboundNodes=[],new Ph({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Wi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(a=>a.shape),outputShapes:this.outputs[0].shape})}else{let a=e.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(st(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Nr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Pa("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Pa("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 Pa("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 Pa("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={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Kl))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Va(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new j("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 j("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}}};Kl.className="Sequential";se.registerClass(Kl);function pD(e){return new Nr(e)}function cD(e){return new Kl(e)}function hD(e,t){return t==null&&(t={}),lD(e,t)}function tv(e){return N3(e)}function fD(e,t){Ea.registerCallbackConstructor(e,t)}var _n=class extends se.Serializable{getConfig(){return{}}},nv=class extends _n{apply(e,t=1){return Wz(e,t)}};nv.className="elu";se.registerClass(nv);var av=class extends _n{apply(e){return U1(e)}};av.className="selu";se.registerClass(av);var rv=class extends _n{apply(e){return nr(e)}};rv.className="relu";se.registerClass(rv);var sv=class extends _n{apply(e){return H(()=>fd(6,nr(e)))}};sv.className="relu6";se.registerClass(sv);var iv=class extends _n{apply(e){return e}};iv.className="linear";se.registerClass(iv);var ov=class extends _n{apply(e){return Un(e)}};ov.className="sigmoid";se.registerClass(ov);var lv=class extends _n{apply(e){return Vz(e)}};lv.className="hardSigmoid";se.registerClass(lv);var uv=class extends _n{apply(e){return Ll(e)}};uv.className="softplus";se.registerClass(uv);var dv=class extends _n{apply(e){return Bz(e)}};dv.className="softsign";se.registerClass(dv);var pv=class extends _n{apply(e){return Ol(e)}};pv.className="tanh";se.registerClass(pv);var Wg=class extends _n{apply(e,t=-1){return oh(e,t)}};Wg.className="softmax";se.registerClass(Wg);var cv=class extends _n{apply(e,t=-1){return F1(e,t)}};cv.className="logSoftmax";se.registerClass(cv);var hv=class extends _n{apply(e,t=1){return H(()=>L(Un(L(e,t)),e))}};hv.className="swish";se.registerClass(hv);var fv=class extends _n{apply(e){return H(()=>L(e,Ol(Ll(e))))}};fv.className="mish";se.registerClass(fv);function is(e){return e.getClassName()}function Bg(e,t={}){return vd(e,se.SerializationMap.getMap().classNameMap,t,"activation")}function os(e){if(e==null){let t={};return t.className="linear",t.config={},Bg(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Bg(t)}else return e instanceof _n?e:Bg(e)}function Vg(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 mv=class extends se.Serializable{},Md=class extends mv{constructor(e){super();Vg(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 H(()=>{let t=_t([1]);return this.hasL1&&(t=re(t,be(L(this.l1,Ht(e))))),this.hasL2&&(t=re(t,be(L(this.l2,Sd(e))))),B(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Md.className="L1L2";se.registerClass(Md);function mD(e){return Vg(e),new Md({l1:e!=null?e.l1:null,l2:0})}function gD(e){return Vg(e),new Md({l2:e!=null?e.l2:null,l1:0})}var gv={l1l2:"L1L2"};function dt(e){return sg(e)}function Av(e,t={}){return vd(e,se.SerializationMap.getMap().classNameMap,t,"regularizer")}function kt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in gv?gv[e]:e,config:{}};return Av(t)}else return e instanceof mv?e:Av(e)}var Ug=class extends Ze{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Pe(e);let n=nr(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}};Ug.className="ReLU";se.registerClass(Ug);var Hg=class extends Ze{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=Pe(e);return Zc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Hg.className="LeakyReLU";se.registerClass(Hg);var jg=class extends Ze{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=wt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=kt(e.alphaRegularizer),this.alphaConstraint=Xt(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 j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=st(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new Pt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Pe(e),rh(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Ct(this.alphaInitializer),alphaRegularizer:dt(this.alphaRegularizer),alphaConstraint:qt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};jg.className="PReLU";se.registerClass(jg);var Gg=class extends Ze{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Pe(e);return cd(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Gg.className="ELU";se.registerClass(Gg);var qg=class extends Ze{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Pe(e);return L(n,de(jn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};qg.className="ThresholdedReLU";se.registerClass(qg);var Xg=class extends Ze{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Wg().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Xg.className="Softmax";se.registerClass(Xg);function Zl(e,t,n){if(typeof e=="number")return Wi(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!_z(r))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Ua(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function or(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+rs([n-t,0]);else if(a==="same")e=e*t;else throw new j(`Unsupport padding mode: ${a}.`);return e}function Kg(e,t){return H(()=>(Ot(t),t==="channelsFirst"?Xe(e,[0,2,3,1]):e))}function yv(e,t){return H(()=>(Ot(t),t==="channelsFirst"?Xe(e,[0,2,3,4,1]):e))}function AD(e,t,n,a=1,r="valid",s,i=1){return H(()=>{if(s==null&&(s=Da()),Ot(s),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Xe(e,[0,2,1])),r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=I1(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Wa(o,n)),o})}function xv(e,t,n,a=[1,1],r="valid",s,i,o=null){return H(()=>{if(s==null&&(s=Da()),Ot(s),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Kg(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ts.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Xe(l,[0,3,1,2])),l})}function yD(e,t,n,a=[1,1,1],r="valid",s,i){return H(()=>{if(s==null&&(s=Da()),Ot(s),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=yv(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=N1(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Wa(o,n)),s==="channelsFirst"&&(o=Xe(o,[0,4,1,2,3])),o})}var Zg=class extends Ze{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Zg.verifyArgs(t),this.rank=e,Qt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Zl(t.kernelSize,e,"kernelSize"),this.strides=Zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,fa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ot(this.dataFormat),this.activation=os(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Xt(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=Zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`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 j(`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 j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(rr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!og(e.kernelSize,"number",1,3))throw new j(`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:is(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Fd=class extends Zg{constructor(e,t){super(e,t);this.kernel=null,Fd.verifyArgs(t),this.filters=t.filters,Qt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Xt(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,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 H(()=>{e=Pe(e);let n,a=this.bias==null?null:this.bias.read(),r=l3(this.activation.getClassName());if(r!=null&&this.rank===2)n=xv(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=AD(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=xv(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=yD(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Ua(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Ct(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:qt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},$d=class extends Fd{constructor(e){super(2,e);$d.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!og(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};$d.className="Conv2D";se.registerClass($d);var Od=class extends Fd{constructor(e){super(3,e);Od.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 j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Od.className="Conv3D";se.registerClass(Od);var Yg=class extends $d{constructor(e){super(e);if(this.inputSpec=[new Pt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Pt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=Pe(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],d=this.kernelSize[1],p=this.strides[0],c=this.strides[1],h=or(o,p,u,this.padding),m=or(l,c,d,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Xe(n,[0,2,3,1]));let g=T1(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Xe(g,[0,3,1,2])),this.bias!=null&&(g=Wa(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=or(t[a],o,s,this.padding),t[r]=or(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Yg.className="Conv2DTranspose";se.registerClass(Yg);var Jg=class extends Od{constructor(e){super(e);if(this.inputSpec=[new Pt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new j("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Pt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=Pe(e);if(n.shape.length!==5)throw new j(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],d=a[i],p=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],A=or(l,m,p,this.padding),y=or(u,f,c,this.padding),x=or(d,g,h,this.padding),v=[r,A,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Xe(n,[0,2,3,4,1]));let b=Qx(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=Xe(b,[0,4,1,2,3])),this.bias!==null&&(b=Wa(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=or(t[a],u,i,this.padding),t[r]=or(t[r],d,o,this.padding),t[s]=or(t[s],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jg.className="Conv3DTranspose";se.registerClass(Jg);var bv=class extends Fd{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 j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("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 j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Xt(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Xt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Pt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{e=Pe(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Xe(e,[0,2,3,1])),n=vb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Wa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Xe(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=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseConstraint),e.pointwiseConstraint=qt(this.pointwiseConstraint),e}};bv.className="SeparableConv";var Qg=class extends bv{constructor(e){super(2,e)}};Qg.className="SeparableConv2D";se.registerClass(Qg);var qh=class extends Fd{constructor(e){super(1,e);qh.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"&&!og(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};qh.className="Conv1D";se.registerClass(qh);var eA=class extends Ze{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return H(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Sh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Sh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Sh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Sh(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};eA.className="Cropping2D";se.registerClass(eA);var tA=class extends Ze{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,$z(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 H(()=>{let n=Pe(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Xe(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Me.resizeNearestNeighbor(n,[r,s]):Me.resizeBilinear(n,[r,s]);return Xe(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Me.resizeNearestNeighbor(n,[r,s]):Me.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};tA.className="UpSampling2D";se.registerClass(tA);function xD(e,t,n=[1,1],a="valid",r,s){return H(()=>{r==null&&(r=Da()),Ot(r);let i=Kg(e,r);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=pd(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Xe(i,[0,3,1,2])),i})}var nA=class extends Zg{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=wt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Xt(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 H(()=>{e=Pe(e);let n=xD(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Wa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ua(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ua(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseRegularizer),e}};nA.className="DepthwiseConv2D";se.registerClass(nA);function vv(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function wv(e,t,n,a=!1,r,s,i=!1,o=!1){return H(()=>{let l=t.shape.length;if(l<3)throw new j(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(La(2,l));if(t=Xe(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=de(de(r,"bool"),"float32"),r.rank===l-1&&(r=zt(r,-1)),r=Xe(r,u)),a&&(t=ca(t,0),r!=null&&(r=ca(r,0)));let d=[],p,c=n,h=t.shape[0],m=ha(t),f;r!=null&&(f=ha(r));for(let A=0;A<h;++A){let y=m[A],x=H(()=>e(y,c));if(r==null)p=x[0],c=x[1];else{let v=H(()=>{let b=f[A],k=fe(pa(b),b),N=re(L(x[0],b),L(c[0],k)),C=c.map((E,z)=>re(L(x[1][z],b),L(E,k)));return{output:N,newStates:C}});p=v.output,c=v.newStates}o&&d.push(p)}let g;return o&&(g=$n(d,1)),[p,g,c]})}var lr=class extends Ze{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zh({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("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 Pt({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 La(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ig(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return H(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new ze("Constants support is not implemented in RNN yet.");Ig(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Pt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new j(`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 Pt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Sr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new j("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(a=>_t([n,a])):this.states_=[_t([n,this.cell.stateSize])];else if(e==null)K(this.states_),this.keptStates!=null&&(K(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>_t([n,a])):this.states_[0]=_t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`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()):K(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!w.arraysEqual(r.shape,i))throw new j(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>Jt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=vv(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.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 Pt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Ba){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Pe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new j(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=wv((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,a);let p=this.returnSequences?u:l;return this.returnState?[p].concat(d):p})}getInitialState(e){return H(()=>{let t=_t(e.shape);return t=be(t,[1,2]),t=Id(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?mg(t,[1,n]):t):this.cell.stateSize>1?[mg(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()===lr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Va(a,n);return new e(Object.assign(t,{cell:r}))}};lr.className="RNN";se.registerClass(lr);var zd=class extends Ze{},Xh=class extends zd{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Qt(this.units,"units"),this.activation=os(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=jl([1,rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(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 H(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>pa(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>pa(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=sr(L(e,s),this.kernel.read()):r=sr(e,this.kernel.read()),this.bias!=null&&(r=Wa(r,this.bias.read())),i!=null&&(n=L(n,i));let o=re(r,sr(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:is(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Xh.className="SimpleRNNCell";se.registerClass(Xh);var aA=class extends lr{constructor(e){e.cell=new Xh(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(K(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(K(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};aA.className="SimpleRNN";se.registerClass(aA);var Kh=class extends zd{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Qt(this.units,"units"),this.activation=os(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=os(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=jl([1,rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(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 H(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>pa(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>pa(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let u=sr(e,this.kernel.read());this.useBias&&(u=Wa(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,s[0]));let d=this.recurrentKernel.read(),[p,c]=sn(d,[2*this.units,this.units],d.rank-1),h=sr(a,p),[m,f,g]=sn(u,3,u.rank-1),[A,y]=sn(h,2,h.rank-1);i=this.recurrentActivation.apply(re(m,A)),o=this.recurrentActivation.apply(re(f,y));let x=sr(L(o,a),c);l=this.activation.apply(re(g,x));let v=re(L(i,a),L(re(1,Nt(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:is(this.activation),recurrentActivation:is(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Kh.className="GRUCell";se.registerClass(Kh);var rA=class extends lr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Kh(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(K(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(K(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};rA.className="GRU";se.registerClass(rA);var _d=class extends zd{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Qt(this.units,"units"),this.activation=os(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=os(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=jl([1,rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,rs([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=st(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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Ca{apply(i,o){let l=r.apply([s]),u=new Nh().apply([s]),d=r.apply([s*2]);return A3(A3(l,u),d)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>pa(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>pa(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let p=sr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=re(p,sr(a,this.recurrentKernel.read())),this.useBias&&(p=Wa(p,this.bias.read()));let[c,h,m,f]=sn(p,4,p.rank-1);o=this.recurrentActivation.apply(c),l=this.recurrentActivation.apply(h),u=re(L(l,r),L(o,this.activation.apply(m))),d=this.recurrentActivation.apply(f);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:is(this.activation),recurrentActivation:is(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};_d.className="LSTMCell";se.registerClass(_d);var sA=class extends lr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new _d(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(K(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(K(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};sA.className="LSTM";se.registerClass(sA);var Zh=class extends zd{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return H(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Ig(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Ui(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Va(r,n));return new e({cells:a})}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 Sg(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Tg(t)}};Zh.className="StackedRNNCells";se.registerClass(Zh);function ls(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>x3(t(),n),i=()=>Td(s,t,a);return!r||r<=1?Jt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Jt(o.clone()))}var bD=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},kv=class extends lr{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Pt({ndim:5})]}call(e,t){return H(()=>{if(this.cell.dropoutMask!=null&&(K(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(K(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return H(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=_t(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Sr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>_t(r)):this.states_=[_t(r)];else if(e==null)K(this.states_),this.keptStates!=null&&(K(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>_t(r)):this.states_[0]=_t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):K(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!w.arraysEqual(i.shape,o))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Jt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=Ua(l,a[0],r,s[0],i[0]),p=Ua(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,d,p]:[d,p,n]]}};kv.className="ConvRNN2D";var Yh=class extends _d{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Qt(this.filters,"filters"),this.kernelSize=Zl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Qt(o,"kernelSize")),this.strides=Zl(a||1,2,"strides"),this.strides.forEach(o=>Qt(o,"strides")),this.padding=r||"valid",fa(this.padding),this.dataFormat=s||"channelsLast",Ot(this.dataFormat),this.dilationRate=Zl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Qt(o,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);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,u=this.filters;o=new(t=class extends Ca{apply(d,p){let c=l.apply([u]),h=Gn([u]),m=l.apply([u*2]);return fg([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return H(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ls({ones:()=>pa(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(J,ne,ee)=>!ne||!ne[ee]?J:L(ne[ee],J),u=l(a,o,0),d=l(a,o,1),p=l(a,o,2),c=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ls({ones:()=>pa(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),A=l(r,h,3),y=3,[x,v,b,k]=sn(this.kernel.read(),i,y),[N,C,E,z]=this.useBias?sn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,N,this.padding),d=this.inputConv(d,v,C,this.padding),p=this.inputConv(p,b,E,this.padding),c=this.inputConv(c,k,z,this.padding);let[F,I,_,D]=sn(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,F),f=this.recurrentConv(f,I),g=this.recurrentConv(g,_),A=this.recurrentConv(A,D);let V=this.recurrentActivation.apply(re(u,m)),q=this.recurrentActivation.apply(re(d,f)),G=re(L(q,s),L(V,this.activation.apply(re(p,g)))),Q=L(this.recurrentActivation.apply(re(c,A)),this.activation.apply(G));return[Q,Q,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=bD(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=Jr(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Wa(r,n,this.dataFormat):r}recurrentConv(e,t){return Jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Yh.className="ConvLSTM2DCell";se.registerClass(Yh);var iA=class extends kv{constructor(e){let t=new Yh(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};iA.className="ConvLSTM2D";se.registerClass(iA);var Jh=class extends Ze{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Td(()=>x3(n,this.rate,r,this.seed),()=>n,a)}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()}};Jh.className="Dropout";se.registerClass(Jh);var oA=class extends Jh{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};oA.className="SpatialDropout1D";se.registerClass(oA);var lA=class extends Ze{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,Qt(this.units,"units"),this.activation=os(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Xt(e.kernelConstraint),this.biasConstraint=Xt(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(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=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=l3(this.activation.getClassName()),r;return a!=null?r=sr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=sr(n,this.kernel.read()),this.bias!=null&&(r=Wa(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:is(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};lA.className="Dense";se.registerClass(lA);var uA=class extends Ze{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],as(e,1)]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=Xe(n,a)}return Lz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};uA.className="Flatten";se.registerClass(uA);var dA=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.activation=os(e.activation)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:is(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};dA.className="Activation";se.registerClass(dA);var pA=class extends Ze{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return H(()=>(e=Pe(e),Dz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};pA.className="RepeatVector";se.registerClass(pA);var cA=class extends Ze{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new j("Can only specifiy one unknown dimension.");else r*=l}let i=as(e);if(s!==null){if(r===0||i%r!=0)throw new j(n);a[s]=i/r}else if(i!==r)throw new j(n);return a}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return B(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};cA.className="Reshape";se.registerClass(cA);var hA=class extends Ze{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=La(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Pt({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return 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Xi=class extends Ze{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[st(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=ns(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};vA.className="Concatenate";se.registerClass(vA);function Dd(e,t){for(;e<0;)e+=t;return e}function vD(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new ze("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return H(()=>{let i;if(a>r){i=a-r;let l=[];for(let u=0;u<i;++u)l.push(1);t=B(t,t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);e=B(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=be(L(e,t),s[0]):o=be(L(Xe(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Ve(e,t,l,u)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=lt(o,u)}return o.shape.length===1&&(o=zt(o,1)),o})}var wA=class extends Xi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new j(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>Dd(r,e[s].shape.length)):a=[Dd(this.axes,t.shape.length),Dd(this.axes,n.shape.length)],this.normalize&&(t=Lh(t,a[0]),n=Lh(n,a[1])),vD(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Dd(this.axes,e.length),Dd(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};wA.className="Dot";se.registerClass(wA);var kA=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Td(()=>re(Th(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};kA.className="GaussianNoise";se.registerClass(kA);var IA=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Td(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return L(n,Th(n.shape,1,a))},()=>n,t.training||!1):n})}};IA.className="GaussianDropout";se.registerClass(IA);var SA=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Td(()=>{let a=Pe(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=zi(Bl(n),this.rate);o=Ih(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=re(L(a,o),L(re(o,-1),i));return re(L(d,l),u)},()=>Pe(e),t.training||!1)}return e})}};SA.className="AlphaDropout";se.registerClass(SA);function Pd(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=Hx(e,t,n,a,r,s);else if(e.rank===3)i=jx(e,t,n,a,r,s);else if(e.rank===4)i=Gx(e,t,n,a,r,s);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function wD(e,t,n,a,r=.001){return H(()=>{let s=nh(e,a),i=s.mean,o=s.variance;return[Pd(e,i,o,n,t,r),i,o]})}function kD(e,t,n,a,r=.001){return H(()=>{let s=nh(e,a),i=s.mean,o=s.variance,l=[];for(let h of La(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let u=B(i,l),d=B(o,l),p=t==null?null:B(t,l),c=n==null?null:B(n,l);return[Pd(e,u,d,c,p,r),i,o]})}function ID(e,t,n,a,r=.001){return w.arraysEqual(a.slice().sort(),La(0,e.rank-1))?wD(e,t,n,a,r):kD(e,t,n,a,r)}var TA=class extends Ze{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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Xt(e.betaConstraint),this.gammaConstraint=Xt(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Pt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training,a=Pe(e),r=a.shape,s=r.length,i=La(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Wi(1,s);l[o]=r[o];let u=i.slice();u.sort();let d=!w.arraysEqual(u,La(0,s).slice(0,s-1)),p=()=>{if(d){let g=B(this.movingMean.read(),l),A=B(this.movingVariance.read(),l),y=this.center?B(this.beta.read(),l):null,x=this.scale?B(this.gamma.read(),l):null;return Pd(a,g,A,y,x,this.epsilon)}else return Pd(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[c,h,m]=ID(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,A,y)=>{H(()=>{let x=1-y,v=g.read(),b=L(fe(v,A),x);g.write(fe(v,b))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),movingMeanInitializer:Ct(this.movingMeanInitializer),movingVarianceInitializer:Ct(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:qt(this.betaConstraint),gammaConstraint:qt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};TA.className="BatchNormalization";se.registerClass(TA);var NA=class extends Ze{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ns(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),a=n.shape,r=a.length;return H(()=>{let s=!0,{mean:i,variance:o}=nh(n,this.axis,s),l=Wi(1,r);for(let m of this.axis)l[m]=a[m];let u=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?B(m,l):m,d=u(this.gamma.read()),p=u(this.beta.read()),c=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(c.push(a[m]),h.push(1)):(c.push(1),h.push(a[m]));return i=Ta(i,c),o=Ta(o,c),d=Ta(d,h),p=Ta(p,h),Pd(n,i,o,p,d,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};NA.className="LayerNormalization";se.registerClass(NA);function SD(e,t,n){return H(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Da()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],Qr(e,a)})}var CA=class extends Ze{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?Da():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Pt({ndim:4})]}computeOutputShape(e){e=st(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return H(()=>SD(Pe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};CA.className="ZeroPadding2D";se.registerClass(CA);function Qh(e,t,n,a,r,s){return H(()=>{Ot(r),c3(s),fa(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Da()),s==null&&(s="max"),e=Kg(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=eh(e,t,n,o):i=Gc(e,t,n,o),r==="channelsFirst"&&(i=Xe(i,[0,3,1,2])),i})}function Iv(e,t,n,a,r,s){return H(()=>{Ot(r),c3(s),fa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Da()),s==null&&(s="max"),e=yv(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=_1(e,t,n,o):i=w1(e,t,n,o),r==="channelsFirst"&&(i=Xe(i,[0,4,1,2,3])),i})}var Sv=class extends Ze{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Qt(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 j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,fa(this.padding),this.inputSpec=[new Pt({ndim:3})]}computeOutputShape(e){e=st(e);let t=Ua(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return H(()=>{this.invokeCallHook(e,t),e=Id(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return lt(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},EA=class extends Sv{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),fa(a),Qh(e,t,n,a,r,"max")}};EA.className="MaxPooling1D";se.registerClass(EA);var RA=class extends Sv{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),fa(a),Qh(e,t,n,a,r,"avg")}};RA.className="AveragePooling1D";se.registerClass(RA);var Tv=class extends Ze{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Qt(this.poolSize,"poolSize"),Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),fa(this.padding),this.inputSpec=[new Pt({ndim:4})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ua(t,this.poolSize[0],this.padding,this.strides[0]),n=Ua(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 H(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(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}},MA=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),fa(a),Qh(e,t,n,a,r,"max")}};MA.className="MaxPooling2D";se.registerClass(MA);var FA=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),fa(a),Qh(e,t,n,a,r,"avg")}};FA.className="AveragePooling2D";se.registerClass(FA);var Nv=class extends Ze{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Qt(this.poolSize,"poolSize"),Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),fa(this.padding),this.inputSpec=[new Pt({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ua(t,this.poolSize[0],this.padding,this.strides[0]),n=Ua(n,this.poolSize[1],this.padding,this.strides[1]),a=Ua(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},$A=class extends Nv{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),fa(a),Iv(e,t,n,a,r,"max")}};$A.className="MaxPooling3D";se.registerClass($A);var OA=class extends Nv{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ot(r),fa(a),Iv(e,t,n,a,r,"avg")}};OA.className="AveragePooling3D";se.registerClass(OA);var Cv=class extends Ze{constructor(e){super(e);this.inputSpec=[new Pt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}},zA=class extends Cv{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=Pe(e);return Mt(n,1)})}};zA.className="GlobalAveragePooling1D";se.registerClass(zA);var _A=class extends Cv{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=Pe(e);return da(n,1)})}};_A.className="GlobalMaxPooling1D";se.registerClass(_A);var Ev=class extends Ze{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),this.inputSpec=[new Pt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},DA=class extends Ev{call(e,t){return H(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?Mt(n,[1,2]):Mt(n,[2,3])})}};DA.className="GlobalAveragePooling2D";se.registerClass(DA);var PA=class extends Ev{call(e,t){return H(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?da(n,[1,2]):da(n,[2,3])})}};PA.className="GlobalMaxPooling2D";se.registerClass(PA);var Rv=class extends Ze{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=Va(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},LA=class extends Rv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return H(()=>(e=Pe(e),wv((n,a)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};LA.className="TimeDistributed";se.registerClass(LA);function TD(e){Vi(Fz,"BidirectionalMergeMode",e)}var ND="concat",WA=class extends Rv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Va(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Va(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?ND:e.mergeMode,TD(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):On(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=vv(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(d=>new Pt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(a!=null)throw new ze("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Ba;for(let l of s)if(l instanceof Ba!==o)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=d,p}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=ca(r,1));let i;return this.mergeMode==="concat"?i=fg([a,r]):this.mergeMode==="sum"?i=re(a,r):this.mergeMode==="ave"?i=L(.5,re(a,r)):this.mergeMode==="mul"?i=L(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ui(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ui(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Va(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};WA.className="Bidirectional";se.registerClass(WA);function CD(e){return new Gl(e)}function ED(e){return new Gg(e)}function RD(e){return new Ug(e)}function MD(e){return new Hg(e)}function FD(e){return new jg(e)}function $D(e){return new Xg(e)}function OD(e){return new qg(e)}function zD(e){return new qh(e)}function _D(e){return new $d(e)}function DD(e){return new Yg(e)}function 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implemented`)}},YL=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:a,nGramsSplits:r}=mh.stringNGrams(S("data",e,t,n),S("dataSplits",e,t,n),S("separator",e,t,n),S("nGramWidths",e,t,n),S("leftPad",e,t,n),S("rightPad",e,t,n),S("padWidth",e,t,n),S("preserveShortSequences",e,t,n));return[a,r]}case"StringSplit":{let{indices:a,values:r,shape:s}=mh.stringSplit(S("input",e,t,n),S("delimiter",e,t,n),S("skipEmpty",e,t,n));return[a,r,s]}case"StringToHashBucketFast":return[mh.stringToHashBucketFast(S("input",e,t,n),S("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},JL=(e,t,n)=>{switch(e.op){case"Cast":return[de(S("x",e,t,n),S("dtype",e,t,n))];case"ExpandDims":{let a=S("axis",e,t,n);return[zt(S("x",e,t,n),a)]}case"Squeeze":{let a=S("axis",e,t,n);return[lt(S("x",e,t,n),a)]}case"Reshape":return[B(S("x",e,t,n),S("shape",e,t,n))];case"MirrorPad":return[gb(S("x",e,t,n),S("padding",e,t,n),S("mode",e,t,n))];case"PadV2":case"Pad":return[Qr(S("x",e,t,n),S("padding",e,t,n),S("constantValue",e,t,n))];case"SpaceToBatchND":{let a=S("blockShape",e,t,n),r=S("paddings",e,t,n);return[ah(S("x",e,t,n),a,r)]}case"BatchToSpaceND":{let a=S("blockShape",e,t,n),r=S("crops",e,t,n);return[qc(S("x",e,t,n),a,r)]}case"DepthToSpace":{let a=S("blockSize",e,t,n),r=S("dataFormat",e,t,n).toUpperCase();return[tb(S("x",e,t,n),a,r)]}case"BroadcastTo":return[dd(S("x",e,t,n),S("shape",e,t,n))];case"BroadcastArgs":return[qx(S("s0",e,t,n),S("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function f7(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return H(()=>CL(s,i,o));case"basic_math":return H(()=>EL(s,i,o));case"control":return zL(s,i,o);case"convolution":return H(()=>_L(s,i,o));case"creation":return H(()=>DL(s,i,o));case"dynamic":return PL(s,i,o);case"evaluation":return H(()=>LL(s,i,o));case"image":return H(()=>UL(s,i,o));case"graph":return H(()=>WL(s,i,o));case"logical":return H(()=>HL(s,i,o));case"matrices":return H(()=>jL(s,i,o));case"normalization":return H(()=>GL(s,i,o));case"reduction":return H(()=>qL(s,i,o));case"slice_join":return H(()=>XL(s,i,o));case"sparse":return H(()=>KL(s,i,o));case"spectral":return H(()=>ZL(s,i,o));case"string":return H(()=>YL(s,i,o));case"transformation":return H(()=>JL(s,i,o));case"hash_table":return VL(s,i,o,a);case"custom":let l=Vv(s.op);if(l&&l.customExecutor)return l.customExecutor(new NL(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(s=>[].concat(s)):[].concat(r)}var m7=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function g7(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>qn(c)[0]),d=[];a!=null&&(d=a.map(c=>qn(c.name)[0]));let p=[...t];for(;p.length>0;){let c=p.pop();if((A7(c)||aW(c)||rW(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&d.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function QL(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(d=>qn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{a.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{a.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{a.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(c=>l.has(c.name))&&s.push(p)})}return u}var eW=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],tW=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],nW=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function A7(e){return eW.indexOf(e.op)>=0}function aW(e){return tW.indexOf(e.op)>=0}function rW(e){return nW.indexOf(e.op)>=0}var ry=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 ry(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(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=g7(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return QL(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[qn(d)[0]]),r=t.map(d=>qn(d)[0]),s=r.map(d=>this.graph.nodes[d]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return H(()=>{let d=new m7(this.weightMap,l,u,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=qn(m),A=[];A[g]=e[m],p[f]=A});let c=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let g=f7(f,p,d,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);p[f.name]=g,this.checkTensorForDisposal(f.name,f,p,d,c,r,h)}}return this.parent==null&&d.dispose(c),t.map(m=>vn(m,p,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=iL(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let d=i[u.id];d===1?(u.dispose(),delete i[u.id]):d!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,a={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new m7(this.weightMap,a,r,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(p=>vn(p,i,s)),l=o.map(p=>p.id),u=Object.keys(e).map(p=>e[p].id),d=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(p=>{i[p].forEach(c=>{c&&!c.kept&&!c.isDisposed&&!d.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(d),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(y=>this.graph.nodes[qn(y)[0]]),i=n.map(y=>qn(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:d,syncInputs:p}=g7(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(y=>{let[x,v]=qn(y),b=[];b[v]=e[y],h[x]=b});let m={},f=this.getFrozenTensorIds(h),g={};for(;c.length>0;){let y=this.processStack(s,c,t,h,g,f,i,m,l);await Promise.all(y)}d==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let A=o.filter(y=>!A7(y)&&!vn(y.name,h,t)).map(y=>y.name);if(A.length>0){let y="";throw d!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${y}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();n.currentContext=d.contexts;let p="";if(d.node.op==="Enter"&&S("isConstant",d.node,a,n)&&([p]=Cr(d.node.name,n)),a[d.node.name]==null){let c=f7(d.node,a,n,this._resourceManager);p||([p]=Cr(d.node.name,n));let h=n.currentContext;w.isPromise(c)?u.push(c.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,l),m))):(a[p]=c,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,l))}else this.processChildNodes(d.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Cr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!vn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!vn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=qn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=qn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=qn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},sW=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},iW="?tfjs-format=file",oW="model.json",y7=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new sW}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Fn.browserHTTPRequest(e,this.loadOptions);else{let t=Fn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Fn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=Fn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ry(l7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=l7.Instance.transformGraph(e.modelInitializer);this.initializer=new ry(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Fn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof je)&&!Array.isArray(e))return 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},F7=class extends en{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(te().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new F7(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),pn(n,t)}},$7=class extends en{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=Dt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=_a([s,r,o,i],[1,4])}else this.cropBox=_a([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(te().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 $7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=ia.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return H(()=>{let t=zt(de(e,"float32"),0),n;n=Me.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return B(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},O7=class{},z7=class extends en{split(e){return new zW(this,e)}},zW=class extends z7{constructor(e,t){super();this.upstream=e,this.impl=new _W(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},_W=class extends oy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},DW=class extends en{decodeUTF8(){return new PW(this)}},PW=class extends z7{constructor(e){super();this.upstream=e,this.impl=new LW(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},LW=class extends oy{constructor(e){super();if(this.upstream=e,te().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=y5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return te().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},_7=class extends DW{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(te().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 a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof 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O7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return D7(this.url)?new P7(this.url,this.fileOptions).iterator():WW(this.url,this.fileOptions)}};function VW(e,t={}){return new M7(new L7(e),t)}function UW(e){let t=iy(e);return Xn(async()=>t)}function HW(e){return Xn(async()=>{let t=await e();return iy(()=>t.next())})}async function jW(e,t){return $7.create(e,t)}async function GW(e){return F7.create(e)}var qW="3.9.0";function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var XW=ar.whereImpl,i0=class extends $u{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Hp(this,vr())}nextDataId(){return i0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,te().get("IS_NODE")&&R.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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d=R.computePool3DInfo(s.shape,i,o,1,l,u),p=d.strideDepth,c=d.strideHeight,h=d.strideWidth,m=d.filterDepth,f=d.filterHeight,g=d.filterWidth,A=d.dilationDepth,y=d.dilationHeight,x=d.dilationWidth,v=d.effectiveFilterDepth,b=d.effectiveFilterHeight,k=d.effectiveFilterWidth,N=v-1-d.padInfo.front,C=k-1-d.padInfo.left,E=b-1-d.padInfo.top,z=Be(s.shape,"float32"),F=1/(m*f*g),I=n.bufferSync(r);for(let _=0;_<d.batchSize;++_)for(let D=0;D<d.inChannels;++D)for(let V=0;V<d.inDepth;++V)for(let q=0;q<d.inHeight;++q)for(let G=0;G<d.inWidth;++G){let Q=V-N,J=q-E,ne=G-C,ee=0;for(let ie=0;ie<v;ie+=A){let Z=(Q+ie)/p;if(!(Z<0||Z>=d.outDepth||Math.floor(Z)!==Z))for(let le=0;le<b;le+=y){let oe=(J+le)/c;if(!(oe<0||oe>=d.outHeight||Math.floor(oe)!==oe))for(let ye=0;ye<k;ye+=x){let ge=(ne+ye)/h;ge<0||ge>=d.outWidth||Math.floor(ge)!==ge||(ee+=I.get(_,Z,oe,ge,D))}}}z.set(ee*F,_,V,q,G,D)}return n.makeTensorInfo(z.shape,z.dtype,z.values)}var _V={kernelName:Zp,backendName:"cpu",kernelFunc:zV};function DV(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;we([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=R.computePool2DInfo(i.shape,o,l,1,u),p=d.strideHeight,c=d.strideWidth,h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,A=d.effectiveFilterHeight,y=d.effectiveFilterWidth,x=y-1-d.padInfo.left,v=A-1-d.padInfo.top,b=Be(i.shape,"float32"),k=1/(h*m),N=n.data.get(r.dataId).values,C=Be(r.shape,"float32",N);for(let E=0;E<d.batchSize;++E)for(let z=0;z<d.inChannels;++z)for(let F=0;F<d.inHeight;++F)for(let I=0;I<d.inWidth;++I){let _=F-v,D=I-x,V=0;for(let q=0;q<A;q+=f){let G=(_+q)/p;if(!(G<0||G>=d.outHeight||Math.floor(G)!==G))for(let Q=0;Q<y;Q+=g){let J=(D+Q)/c;J<0||J>=d.outWidth||Math.floor(J)!==J||(V+=C.get(E,G,J,z))}}b.set(V*k,E,F,I,z)}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var PV={kernelName:Kp,backendName:"cpu",kernelFunc:DV};function 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n.makeTensorInfo(r.shape,r.dtype,f)}var WV={kernelName:js,backendName:"cpu",kernelFunc:LV};function BV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;we([r],"batchToSpaceND");let o=s.reduce((A,y)=>A*y),l=R.getReshaped(r.shape,s,o),u=R.getPermuted(l.length,s.length),d=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(d,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ma({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:d}}),g=Zi({inputs:{x:f},backend:n,attrs:{begin:p,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var VV={kernelName:Oo,backendName:"cpu",kernelFunc:BV};function UV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=py(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var HV={kernelName:Yp,backendName:"cpu",kernelFunc:UV};function jV(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=R.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var GV={kernelName:Lm,backendName:"cpu",kernelFunc:jV},qV=it(Ur,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),XV={kernelName:Ur,backendName:"cpu",kernelFunc:qV},KV=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let d=o[u],p=l[u];a[u]=Math.hypot(d,p)}return n.makeOutput(a,t.shape,"float32")},ZV={kernelName:Pu,backendName:"cpu",kernelFunc:KV};function eu(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var YV={kernelName:cc,backendName:"cpu",kernelFunc:eu};function tu(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=R.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>w.sizeFromShape(f.shape)>0);if(o.length===1)return ur({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(R.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(v=>Ki({inputs:{input:v},backend:n})),g=o.map(v=>eu({inputs:{input:v},backend:n})),A=tu({inputs:f,backend:n,attrs:{axis:s}}),y=tu({inputs:g,backend:n,attrs:{axis:s}}),x=Kn({inputs:{real:A,imag:y},backend:n});return 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u6(e){if(x0==null){let t=dr(e);x0=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,x0)}function d6(e){if(e===0)return 0;let t,n=dr(e);return Aa(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Aa(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Aa(e,t){return e.getExtension(t)!=null}function Cy(e){try{if(dr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function p6(e){if(e===0)return!1;let t=dr(e);if(e===1){if(!Aa(t,"OES_texture_float"))return!1}else if(!Aa(t,"EXT_color_buffer_float"))return!1;return Ey(t)}function c6(e){if(e===0)return!1;let t=dr(e);if(e===1){if(!Aa(t,"OES_texture_float")||!Aa(t,"WEBGL_color_buffer_float"))return!1}else{if(Aa(t,"EXT_color_buffer_float"))return Ey(t);let n="EXT_color_buffer_half_float";if(Aa(t,n)){let a=t.getExtension(n);return cq(t,a)}return!1}return Ey(t)}function Ey(e){let t=Sy(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let a=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,a,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function cq(e,t){let n=Sy(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function h6(e){return e!==2?!1:dr(e).fenceSync!=null}function au(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ce=te();Ce.registerFlag("HAS_WEBGL",()=>Ce.getNumber("WEBGL_VERSION")>0);Ce.registerFlag("WEBGL_VERSION",()=>Cy(2)?2:Cy(1)?1:0);Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ce.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ce.get("WEBGL_VERSION")===2);Ce.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ce.registerFlag("WEBGL_PACK",()=>Ce.getBool("HAS_WEBGL"));Ce.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_CLIP",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_REDUCE",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_CONV_IM2COL",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>l6(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>u6(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ce.getNumber("WEBGL_VERSION");return e===0?0:d6(e)});Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!sd.isMobile());Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>p6(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ce.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>c6(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_FENCE_API_ENABLED",()=>h6(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ce.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ce.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>sd.isMobile()&&Ce.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Ce.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Ce.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Ce.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Ce.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function wn(){let e,t,n,a,r,s,i,o,l,u;return te().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,l="",u=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,u=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function eo(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function b0(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function hq(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function fq(e,t,n="index"){let a=e.map((s,i)=>i),r=hq(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function Ry(e){let t=w.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function My(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var f6=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,{getBroadcastDims:m6}=R;function mq(e,t,n){let a=[];if(e.forEach(c=>{let h=w.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Fy(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(`
`),s=e.map(c=>gq(c,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),i=t.texShape,o=wn(),l=xq(o),u,d,p=wq(o);return t.isPacked?(u=Aq(t.logicalShape,i,n.enableShapeUniforms),d=vq(o)):(u=yq(t.logicalShape,i,n.enableShapeUniforms),d=bq(o)),n.packedInputs&&(p+=Tq),[p,l,d,r,u,s,n.userCode].join(`
`)}function ru(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return Pq(e,t);case 1:return Wq(e,t);case 2:return Vq(e,t);case 3:return Hq(e,t);case 4:return Gq(e,t);case 5:return qq(e);case 6:return Xq(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function g6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Dq(e);case 1:return Lq(e,t);case 2:return Bq(e,t);case 3:return Uq(e,t);default:return jq(e,t)}}function gq(e,t,n=!1,a){let r="";n?r+=g6(e,a):r+=ru(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=Kq(e,t):r+=Zq(e,t)),r}function Aq(e,t,n){switch(e.length){case 0:return A6();case 1:return Nq(e,t,n);case 2:return zq(e,t,n);case 3:return Eq(e,t,n);default:return Mq(e,t,n)}}function yq(e,t,n){switch(e.length){case 0:return A6();case 1:return Cq(e,t,n);case 2:return _q(e,t,n);case 3:return Rq(e,t,n);case 4:return Fq(e,t,n);case 5:return $q(e,t);case 6:return Oq(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function xq(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function bq(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function vq(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function wq(e){return`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${kq}
${Iq}
${Sq}
`}var kq=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,Iq=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,Sq=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,Tq=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function A6(){return`
int getOutputCoords() {
return 0;
}
`}function Nq(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${a[1]}.0);
}
`:a[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${a[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${a[0]}, ${a[1]}));
return 2 * (resTexRC.x * ${a[1]} + resTexRC.y);
}
`}function Cq(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function Eq(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${a[0]}, ${a[1]}));
int index = resTexRC.x * ${a[1]} + resTexRC.y;
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function Rq(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${b0(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let a=eo(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${a}
return ivec3(r, c, d);
}
`}function Mq(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u<e.length-1;u++)i*=e[e.length-u-1],o=`
int b${u} = index / ${i};
index -= b${u} * ${i};
`+o,l=`b${u}, `+l;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${a[0]}, ${a[1]}));
int index = resTexRC.x * ${a[1]} + resTexRC.y;
${o}
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${l});
}
`}function Fq(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${b0(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let a=eo(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${a}
return ivec4(r, c, d, d2);
}
`}function $q(e,t){let n=eo(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function Oq(e,t){let n=eo(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function zq(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${a[0]}, ${a[1]}));
}
`;let r=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${a[0]}, ${a[1]}));
int index = resTexRC.x * ${a[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function _q(e,t,n){return w.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function to(e){return`offset${e}`}function Dq(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=wn();return`
vec4 ${n}() {
return ${a.texture2D}(${t}, halfCR);
}
`}function Pq(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return`
float ${a}() {
return sampleTexture(${n}, halfCR);
}
`;let i=to(n);if(t)return`
float ${a}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
return sampleTexture(${n}, uv);
}
`;let[o,l]=e.shapeInfo.texShape;return`
float ${a}() {
vec2 uv = uvFromFlat(${o}, ${l}, ${i});
return sampleTexture(${n}, uv);
}
`}function Lq(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=wn();if(t)return`
vec4 ${a}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${n}, uv);
}
`;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
vec4 ${a}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${s.texture2D}(${n}, uv);
}
`}function Wq(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${a}(int index) {
${su(e)}
}
`;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return`
float ${a}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let o=to(n);return i===1?t?`
float ${a}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
return sampleTexture(${n}, uv);
}
`:s===1?t?`
float ${a}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${a}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int index) {
vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
return sampleTexture(${n}, uv);
}
`}function Bq(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=wn();if(s!=null&&w.arraysEqual(n,s))return t?`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
return ${l.texture2D}(${a}, uv);
}
`:`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
return ${l.texture2D}(${a}, uv);
}
`;if(t)return`
vec4 ${r}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${a}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${a}, uv);
}
`;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],d=Math.ceil(n[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${d}, ${u[0]}, ${u[1]}, row, col);
return ${l.texture2D}(${a}, uv);
}
`}function Vq(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(n,s)){if(t)return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, uv);
}
`;let c=s[0],h=s[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${c}.0);
return sampleTexture(${a}, uv);
}
`}let{newShape:i,keptDims:o}=w.squeezeShape(n),l=i;if(l.length<n.length){let c=iu(e,l),h=["row","col"];return`
${ru(c,t)}
float ${r}(int row, int col) {
return ${r}(${ou(h,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${su(e)}
}
`;let u=s[0],d=s[1],p=to(a);return d===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${a}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${a}TexShape[0]));
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${a}, uv);
}
`:u===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${a}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${a}TexShape[1]), 0.5);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${d}.0, 0.5);
return sampleTexture(${a}, uv);
}
`:t?`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a}Shape[1] + col + ${p};
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${p};
vec2 uv = uvFromFlat(${u}, ${d}, index);
return sampleTexture(${a}, uv);
}
`}function Uq(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=iu(e,c),f=["b","row","col"];return`
${g6(m,t)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${ou(f,h)});
}
`}let o=wn();if(t)return`
vec4 ${r}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${a}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${a}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${o.texture2D}(${a}, uv);
}
`;let l=i[0],u=i[1],d=Math.ceil(n[2]/2),p=d*Math.ceil(n[1]/2);return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${u}, ${p}, ${d}, b, row, col);
return ${o.texture2D}(${a}, uv);
}
`}function Hq(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=w.squeezeShape(n),u=o;if(u.length<n.length){let f=iu(e,u),g=["row","col","depth"];return`
${ru(f,t)}
float ${r}(int row, int col, int depth) {
return ${r}(${ou(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${s}, ${i}, 1)));
${su(e)}
}
`;let d=e.shapeInfo.texShape,p=d[0],c=d[1],h=e.shapeInfo.flatOffset;if(c===s&&h==null)return t?`
float ${r}(int row, int col, int depth) {
int stride1 = ${a}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${a}, uv);
}
`;if(c===i&&h==null)return t?`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${a}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}.0, ${p}.0);
return sampleTexture(${a}, uv);
}
`;let m=to(a);return t?`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${a}Shape[1] * ${a}Shape[2];
int stride1 = ${a}Shape[2];
int index = row * ${s} + col * ${i} + depth + ${m};
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s} + col * ${i} + depth + ${m};
vec2 uv = uvFromFlat(${p}, ${c}, index);
return sampleTexture(${a}, uv);
}
`}function jq(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=wn();if(t)return`
vec4 ${a}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
}
`;let s=e.shapeInfo.logicalShape,i=s.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=l[0],d=l[1],p=Math.ceil(s[i-1]/2),c=p*Math.ceil(s[i-2]/2),h="int b, int row, int col",m=`b * ${c} + (row / 2) * ${p} + (col / 2)`;for(let f=2;f<i-1;f++)h=`int b${f}, `+h,c*=s[i-f-1],m=`b${f} * ${c} + `+m;return`
vec4 ${a}(${h}) {
int index = ${m};
int texR = index / ${d};
int texC = index - texR * ${d};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});
return ${r.texture2D}(${n}, uv);
}
`}function Gq(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[3],i=n[2]*s,o=n[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(n);if(l.length<n.length){let y=iu(e,l),x=["row","col","depth","depth2"];return`
${ru(y,t)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${ou(x,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, 1)));
${su(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],h=p[1],m=`int stride2 = ${a}Shape[3];`,f=`int stride1 = ${a}Shape[2] * stride2;`,g=`int stride0 = ${a}Shape[1] * stride1;`;if(h===o&&d==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
${m}
${f}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${i}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${a}, uv);
}
`;if(h===s&&d==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${a}Shape[1] * ${a}Shape[2], ${a}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}TexShape[1], ${a}TexShape[0]);
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${a}, uv);
}
`;let A=to(a);return t?`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${m}
${f}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index + ${A});
return sampleTexture(${a}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} +
depth * ${s} + depth2;
vec2 uv = uvFromFlat(${c}, ${h}, index + ${A});
return sampleTexture(${a}, uv);
}
`}function qq(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let f=iu(e,l),g=["row","col","depth","depth2","depth3"];return`
${ru(f)}
float ${a}(int row, int col, int depth, int depth2, int depth3) {
return ${a}(${ou(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${r})) +
depth3;
${su(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],h=p[1];if(h===o&&d==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&d==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;let m=to(n);return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${r} + depth3 + ${m};
vec2 uv = uvFromFlat(${c}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function Xq(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=w.squeezeShape(t);if(r.length<t.length){let g=iu(e,r),A=["row","col","depth","depth2","depth3","depth4"];return`
${ru(g)}
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${a}(${ou(A,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,d=t[1]*u;if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${d}, ${u}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${su(e)}
}
`;let p=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],m=c[1];if(m===d&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(m===i&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let f=to(n);return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${d} + col * ${u} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
vec2 uv = uvFromFlat(${h}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function su(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function Kq(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=m6(e.shapeInfo.logicalShape,t.logicalShape),l=pt(i),u=i-s,d,p=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${p[g+u]} = 0;`).join(`
`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,A)=>`coords.${p[A+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,f=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!f)i===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(o.length){let g=s-2,A=s-1;o.indexOf(g)>-1&&o.indexOf(A)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(A)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${l} coords = getOutputCoords();
${d}
vec4 outputValue = get${a}(${c});
${h}
}
`}function Zq(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let u=pt(l),d=m6(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&d.length>=1?c="coords = 0;":c=d.map(f=>`coords.${h[f+p]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
float ${r}() {
${u} coords = getOutputCoords();
${c}
return get${a}(${m});
}
`}function pt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Fy(e,t,n){let{newShape:a,keptDims:r}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!w.arraysEqual(t,n)&&a.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function iu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ou(e,t){return t.map(n=>e[n]).join(", ")}function Yq(e,t,n,a){let r=n.map((x,v)=>{let b={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(b.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[v],shapeInfo:b}}),s=r.map(x=>x.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=mq(r,i,t),l=e.createProgram(o),u=null,d=e.getUniformLocation(l,"NAN",!1);te().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(l,"INFINITY",!1));let p=!1,c={},h={},m={};for(let x=0;x<t.variableNames.length;x++){let v=t.variableNames[x];c[v]=e.getUniformLocation(l,v,p),c[`offset${v}`]=e.getUniformLocation(l,`offset${v}`,p),t.enableShapeUniforms&&(h[`${v}Shape`]=e.getUniformLocation(l,`${v}Shape`,p),m[`${v}TexShape`]=e.getUniformLocation(l,`${v}TexShape`,p))}let f,g,A;t.enableShapeUniforms&&(f=e.getUniformLocation(l,"outShape",p),A=e.getUniformLocation(l,"outShapeStrides",p),g=e.getUniformLocation(l,"outTexShape",p));let y=[];return t.customUniforms&&t.customUniforms.forEach((x,v)=>{y[v]=e.getUniformLocation(l,x.name,p)}),{program:t,source:o,webGLProgram:l,uniformLocations:c,customUniformLocations:y,inShapeInfos:s,outShapeInfo:i,infLoc:u,nanLoc:d,inShapesLocations:h,inTexShapesLocations:m,outShapeLocation:f,outShapeStridesLocation:A,outTexShapeLocation:g}}function y6(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function Jq(e,t,n,a,r){t.program.enableShapeUniforms||(y6(t.inShapeInfos,n),y6([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),te().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let d=t.program.variableNames[u],p=t.uniformLocations[d],c=t.uniformLocations[`offset${d}`],h=t.inShapesLocations[`${d}Shape`],m=t.inTexShapesLocations[`${d}TexShape`];if(h){let{uniformShape:f}=Fy(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(h,new Int32Array(f));break;case 2:e.gl.uniform2iv(h,new Int32Array(f));break;case 3:e.gl.uniform3iv(h,new Int32Array(f));break;case 4:e.gl.uniform4iv(h,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),p!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(p,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(p,f)}return}l.texData.slice!=null&&c!=null&&e.gl.uniform1i(c,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,p,u)}});let o=t.outShapeLocation;if(o)switch(a.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(a.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(a.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(a.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(a.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(a.shape);switch(a.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,a.texData.texShape[0],a.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],p=r[u];if(l.type==="float")e.gl.uniform1fv(d,p);else if(l.type==="vec2")e.gl.uniform2fv(d,p);else if(l.type==="vec3")e.gl.uniform3fv(d,p);else if(l.type==="vec4")e.gl.uniform4fv(d,p);else if(l.type==="int")e.gl.uniform1iv(d,p);else if(l.type==="ivec2")e.gl.uniform2iv(d,p);else if(l.type==="ivec3")e.gl.uniform3iv(d,p);else if(l.type==="ivec4")e.gl.uniform4iv(d,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function Qq(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:p}=Fy(e.packedInputs,i.shape,l),c="",h="",m="";if(d.length===1&&e.packedInputs){let b=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${b[0]>1}_${b[1]>1}`}else if(d.length===2&&!e.packedInputs)h=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let b=w.computeStrides(d);m=`${b[0]===l[1]}_${b[b.length-1]===l[1]}`}let f=i.shape.length,g=d.length===2&&w.arraysEqual(i.shape,l),A=w.sizeFromShape(i.shape)===1,y=R.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&w.arraysEqual(l,n.texData.texShape),v=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?p:""}_${d.length}_${A}_${y}_${g}_${c}_${h}_${m}_${v}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${te().getNumber("WEBGL_VERSION")}`,s}function ya(e){return te().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var eX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Gd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=wn();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?b0(["r","c","d"],e):eo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}},tX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Gd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=wn();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?b0(["r","c","d"],e):eo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}},nX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ga.DOWNLOAD;let t=wn();this.outputShape=e,this.userCode=`
${f6}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},aX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ga.DOWNLOAD;let t=wn();this.outputShape=e,this.userCode=`
${f6}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},rX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=wn();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let a="result";t&&(a="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?My():Ry(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${a}, 0., 0., 0.);
}
`}},sX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=wn();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let a="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;a+=`
localCoords = coords;
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${i};
if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${s};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${o}] = values[0];
} else if (offset == 1) {
result[${o}] = values[1];
} else if (offset == 2) {
result[${o}] = values[2];
} else {
result[${o}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?My():Ry(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${a}
${n.output} = ${r};
}
`}},x6={};_e(x6,{bindVertexProgramAttributeStreams:()=>C6,createBufferFromOutputTexture:()=>M6,createFloat16MatrixTexture:()=>I6,createFloat16PackedMatrixTexture:()=>N6,createFloat32MatrixTexture:()=>k6,createIndexBuffer:()=>w6,createPackedMatrixTexture:()=>T6,createUnsignedBytesMatrixTexture:()=>S6,createVertexBuffer:()=>v6,createVertexShader:()=>b6,downloadByteEncodedFloatMatrixFromOutputTexture:()=>$6,downloadFloat32MatrixFromBuffer:()=>F6,downloadMatrixFromPackedOutputTexture:()=>z6,downloadPackedMatrixFromBuffer:()=>O6,getInternalFormatForFloat16MatrixTexture:()=>Oy,getInternalFormatForFloat16PackedMatrixTexture:()=>Dy,getInternalFormatForFloat32MatrixTexture:()=>$y,getInternalFormatForPackedMatrixTexture:()=>_y,getInternalFormatForUnsignedBytesMatrixTexture:()=>zy,uploadDenseMatrixToTexture:()=>E6,uploadPixelDataToTexture:()=>R6});function b6(e){let t=wn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return Gw(e,n)}function v6(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Zw(e,t)}function w6(e){let t=new Uint16Array([0,1,2,2,1,3]);return Yw(e,t)}function Yd(e,t,n,a,r,s){Qw(t,n);let i=Jw(e),o=e.TEXTURE_2D;return xe(e,()=>e.bindTexture(o,i)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),xe(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function $y(e){return e.internalFormatFloat}function k6(e,t,n,a){let[r,s]=qd(t,n);return Yd(e,r,s,$y(a),a.textureFormatFloat,e.FLOAT)}function Oy(e){return e.internalFormatHalfFloat}function I6(e,t,n,a){let[r,s]=qd(t,n);return Yd(e,r,s,Oy(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function zy(e){return e.downloadTextureFormat}function S6(e,t,n,a){let[r,s]=qd(t,n);return Yd(e,r,s,zy(a),e.RGBA,e.UNSIGNED_BYTE)}function _y(e){return e.internalFormatPackedFloat}function T6(e,t,n,a){let[r,s]=nu(t,n);return Yd(e,r,s,_y(a),e.RGBA,e.FLOAT)}function Dy(e){return e.internalFormatPackedHalfFloat}function N6(e,t,n,a){let[r,s]=nu(t,n);return Yd(e,r,s,Dy(a),e.RGBA,a.textureTypeHalfFloat)}function C6(e,t,n){let a=0,r=3*4,s=3*4+2*4;return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Ty(e,t,"clipSpacePos",n,3,s,a)&&Ty(e,t,"uv",n,2,s,r)}function E6(e,t,n,a,r,s){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function R6(e,t,n){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function M6(e,t,n,a){let r=e.createBuffer();xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return xe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function F6(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function $6(e,t,n,a){let[r,s]=qd(t,n),i=4,o=new Uint8Array(eq(t*n,i));return xe(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function O6(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(tq(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function z6(e,t,n){let a=new Float32Array(t*n*4);return xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var v0=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=te().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,c0(t,e)):this.gl=dr(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(te().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Xd(this.gl,r),Aa(this.gl,s))this.textureHalfFloatExtension=Xd(this.gl,s);else if(te().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Aa(this.gl,a))this.colorBufferHalfFloatExtension=Xd(this.gl,a);else if(te().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Aa(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Aa(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=v6(this.gl),this.indexBuffer=w6(this.gl),this.framebuffer=e6(this.gl),this.textureConfig=Sy(this.gl,this.textureHalfFloatExtension)}get debug(){return te().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),N6(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),T6(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Ny(this.gl,this.framebuffer),this.outputTexture=null),xe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>$6(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return O6(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return F6(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=M6(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(te().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Xd(this.gl,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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GX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=kn("rc",t),a=pt(t),r=XX(t,e,n),s=KX(t,e[e.length-1],e[e.length-2],n),i=ZX(e,n);this.userCode=`
void main() {
${a} rc = getOutputCoords();
if(${r}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${i}));
}
}
`}}};function qX(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function XX(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function KX(e,t,n,a){if(e===1)return"";let r=a.slice(-2);return`
int r = ${r[0]};
int c = ${r[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function ZX(e,t){let n=e.length,a=qX(n,t);return n===1?`getA(rc),
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${a[0]}),
cEdge ? 0. : getA(${a[1]}),
rEdge ? 0. : getA(${a[2]}),
rEdge || cEdge ? 0. : getA(${a[3]})`}var W6=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
${r}
${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${a}] =
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${a>0?"}":""}
`}this.userCode=`
${YX(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?My():Ry(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function YX(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?fq(["r","c","d"],"inputShape"):eo(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var JX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=V6(t,n),r=U6(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=B6(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===on.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===on.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===on.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===on.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===on.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=V6(n,a),s=U6(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=B6(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=te().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function QX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function B6(e,t,n,a,r){let s=eK(t,a),i;if(r){let[l,u]=nu(e[0],e[1]);i=l*u}else{let[l,u]=qd(e[0],e[1]);i=l*u}let o=QX(n,s);return i*o}function eK(e,t){switch(e){case on.PACKED_2X2_FLOAT32:return _y(t);case on.PACKED_2X2_FLOAT16:return Dy(t);case on.UNPACKED_FLOAT32:return $y(t);case on.UNPACKED_FLOAT16:return Oy(t);case on.PACKED_4X1_UNSIGNED_BYTE:return zy(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function tK(e){return te().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?on.PACKED_2X2_FLOAT32:on.UNPACKED_FLOAT32:e?on.PACKED_2X2_FLOAT16:on.UNPACKED_FLOAT16}function V6(e,t){if(e===ga.UPLOAD)return on.PACKED_2X2_FLOAT32;if(e===ga.RENDER||e==null)return tK(t);if(e===ga.DOWNLOAD||e===ga.PIXELS)return on.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function U6(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var cs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},ja="if (isnan(x)) return x;",nK="return x;",H6="return abs(x);",aK="return (x >= 0.0) ? x : (exp(x) - 1.0);",rK=ja+`
return (x < 0.0) ? 0.0 : x;
`,sK=ja+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,w0="return x;",iK="return 1.0 / (1.0 + exp(-1.0 * x));",oK="return x;",lK=`
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;
`,uK=`
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;
`,dK=`
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;
`,pK="return 1.0 / (1.0 + exp(-1.0 * x));",lu=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},cK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=kn("rc",t),a=pt(t),r=jX(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${i}));
}
`}},hK=ar.whereImpl,fK=1e-7,mK=1e-4,Ly={};function gK(e){return e in Ly||(Ly[e]={}),Ly[e]}var AK=te().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),yK=600;function xK(){return te().global.screen==null?1024:te().global.screen.height*te().global.screen.width*window.devicePixelRatio*yK/1024/1024}var uu=class extends $u{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!te().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=dr(te().getNumber("WEBGL_VERSION"));this.binaryCache=gK(te().getNumber("WEBGL_VERSION")),this.gpgpu=new v0(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new JX(this.gpgpu),this.numMBBeforeWarning=xK(),this.texData=new Hp(this,vr())}nextDataId(){return uu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((te().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||te().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:ga.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(te().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:ga.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new lu(i,w0):p=new cs(i,w0);let c=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=w.now());let d;if(a==="complex64"){let p=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);d=R.mergeRealAndImagArrays(p,c)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new lu(a,w0):h=new cs(a,w0);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&te().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,u;if(s!=="complex64"&&te().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...h0(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];d=R.mergeRealAndImagArrays(m,f)}else if(l==null)d=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(a);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;xe(h,()=>h.deleteBuffer(l))}let p=this.convertAndCacheOnCPU(e,d),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&vr().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>w.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Hw(n))throw te().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=w.sizeFromShape(t);if(te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),c=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture,...h0(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=te().getBool("WEBGL_PACK")&&a===!0,i=s?g0(t):t,o=s?new aX(i):new nX(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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 te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,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(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=AK){return te().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return hK(e.shape,t)}packedUnaryOp(e,t,n){let a=new lu(e.shape,t),r=this.compileAndRun(a,[e],n);return vr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=D6(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(te().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,H6,e.dtype);let t=new cs(e.shape,H6),n=this.compileAndRun(t,[e]);return vr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(s=>w.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return vr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new cK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new GX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ji(e.shape),...Qi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Ji(t),...Qi(t)],s=new W6(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=g0(a),i,o=h0(s);n?i=new tX(s):i=new eX(s);let l=!0,u=[o],d=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,u,l);return{dtype:r,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Gd.DENSE){let f=h0(e.outputShape);i.texShape=f.map(g=>g*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),w.sizeFromShape(s.shape)===0)return i.values=w.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(f.dataId);if(g.texture==null){if(!e.packedInputs&&w.sizeFromShape(f.shape)<=te().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=f.shape)}else if(!!g.isPacked!=!!e.packedInputs)f=g.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),g=this.texData.get(f.dataId);else if(g.isPacked&&!Zd(g.shape,f.shape)){let A=f,y=f.shape;f.shape=g.shape,f=this.packedReshape(f,y),o.push(f),g=this.texData.get(f.dataId),A.shape=y}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:g,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},d=Qq(e,l,u),p=this.getAndSaveBinary(d,()=>Yq(this.gpgpu,e,l,u)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),Jq(this.gpgpu,p,l,u,a),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),c&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let m=te().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=w.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!te().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(te().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=H(()=>{if(!te().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=te().getBool("DEBUG");te().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(te().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?fK:mK}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let d=t.texShape;if(d==null&&(d=o6(n,o),t.texShape=d),r!=null){let p=g0(n),c,h=d[1],m=d[0],f=r instanceof Uint8Array;o?([h,m]=nu(d[0],d[1]),c=new sX(p,f)):c=new rX(p,f);let g=this.makeTensorInfo([m,h],a);f?this.texData.get(g.dataId).usage=ga.PIXELS:this.texData.get(g.dataId).usage=ga.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,m,r);let A=[[m,h]],y=!0,x=this.runWebGLProgram(c,[g],a,A,y),v=this.texData.get(x.dataId);t.texture=v.texture,t.texShape=v.texShape,t.isPacked=v.isPacked,t.usage=v.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(x.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-u)}else{let p=this.acquireTexture(d,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=bK(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}};uu.nextDataId=0;function bK(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var vK="3.9.0";function j6(){te().set("WEBGL_FORCE_F16_TEXTURES",!0)}sd.isBrowser()&&Fl("webgl",()=>new uu,2);var wK={forceHalfFloat:j6},G6=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,du=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},k0=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`,Jd=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=ya(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${pt(r)} coords = getOutputCoords();
`,r===1)this.enableShapeUniforms?s+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=kn("coords",r);this.enableShapeUniforms?s+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= outShape[${r} - 2];
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= outShape[${r} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:s+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${s}
setOutput(result);
}
`}};function Zn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var kK={kernelName:qs,backendName:"webgl",kernelFunc:Zn};function hs(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Zn({inputs:{x:a},backend:n}),l=Zn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var IK={kernelName:Jp,backendName:"webgl",kernelFunc:hs},q6="return (a < 0.) ? b * a : a;",X6=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function SK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jd(X6,r.shape,i.shape):new du(q6,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var TK={kernelName:Xs,backendName:"webgl",kernelFunc:SK},K6="return (a < 0.) ? b * a : a;",Z6=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function NK(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jd(Z6,a.shape,r.shape):new du(K6,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var CK={kernelName:oi,backendName:"webgl",kernelFunc:NK},Y6="if (isnan(x)) return x;",EK=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,RK=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Je({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),c=n(p.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=te().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new lu(i.shape,t):d=new cs(i.shape,e),o.runWebGLProgram(d,[i],l)}}function ln({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(a&&l.dtype==="complex64"){let m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[g,A]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,b]=x,k={dataId:v.dataId,dtype:v.dtype,shape:l.shape},N={dataId:b.dataId,dtype:b.dtype,shape:u.shape},C=new du(e,l.shape,u.shape);return d.runWebGLProgram(C,[k,N],Sa(v.dtype,b.dtype))}),y=hs({inputs:{real:g,imag:A},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(A),y}let p=s||Sa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&r!=null){let m=d.texData.get(l.dataId).values,f=d.texData.get(u.dataId).values,g=l.dtype==="string"?R.fromUint8ToStringArray(m):m,A=l.dtype==="string"?R.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,A,p),v=d.makeTensorInfo(x,p),b=d.texData.get(v.dataId);return b.values=y,v}let c=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Jd(t,l.shape,u.shape,n):h=new du(e,l.shape,u.shape),d.runWebGLProgram(h,[l,u],p)}}function I0(e,t=!1){if(e==="linear")return t?oK:nK;if(e==="relu")return t?uK:rK;if(e==="elu")return t?lK:aK;if(e==="relu6")return t?dK:sK;if(e==="prelu")return t?Z6:K6;if(e==="leakyrelu")return t?X6:q6;if(e==="sigmoid")return t?pK:iK;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var J6=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=ya(this.outputShape.length);let u=a?e[1]:e[2],d=Math.ceil(u/2),p=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:l?f=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:f=`vec4 activation(vec4 x) {
${i}
}`,g="result = activation(result);");let A=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let y="rc.x",x="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${f}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${d}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${d}; i++) {
int batchA = ${y};
int batchB = ${x};
vec4 a = getMatrixA(batchA, ${p});
vec4 b = getMatrixB(batchB, ${c});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${m[0]});
result += (${h[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${A}
${g}
setOutput(result);
}
`}},Q6={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},e4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},t4="return a * b;";function Wy(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=R.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new e4(Q6.REAL,a.shape,r.shape),d=new e4(Q6.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=hs({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,d]=TX(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(d,s),c=n.texData.get(p.dataId);return c.values=u,p}let i;return te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Jd(t4,a.shape,r.shape):i=new du(t4,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var MK={kernelName:ai,backendName:"webgl",kernelFunc:Wy};function FK(e,t,n){let a=[Ji(e.shape),...Qi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[Ji(t),...Qi(t)],i=new W6(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(r.dataId);return d.isPacked&&!Zd(r.shape,l)&&!(d.texture!==null&&Zd(d.shape,l))?FK(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var $K={kernelName:ul,backendName:"webgl",kernelFunc:Ae},n4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${w.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";r%n>0&&(u=`
if (inIdx < 0 || inIdx >= ${r}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${u}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},OK=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,d=n%4,p=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${o}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,c="vec4";t==="all"?(i="1.0",p=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,c="bvec4"):t==="any"&&(i="0.0",p=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,c="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${p}
}
int inIdx = inOffset + ${u};
if (${d===1}) {
${c} values = ${c}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${p}
} else if (${d===2}) {
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${p}
} else if (${d===3}) {
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${p}
}
setOutput(${l});
}
`}};function zK(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function no(e,t,n,a){let r=zK(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],d,p;n==="mean"?d=i===0?new n4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new n4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new OK({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),p=s,s=a.runWebGLProgram(d,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var _K=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=pt(this.rank),r=DK(t);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function DK(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var PK=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=pt(this.rank),r=L6("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function S0(e,t,n){let a=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PK(e.shape,t):new _K(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function LK(e,t,n,a){let r=t,s=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,l=R.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=S0(e,l,a),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[p,c]=R.computeOutAndReduceShapes(d.shape,o),h=p;n&&(h=R.expandShapeToKeepDim(p,i));let m=w.sizeFromShape(c),f=w.sizeFromShape(e.shape)/m,g=Ae({inputs:{x:d},attrs:{shape:[f,m]},backend:a}),A=Dc(e.dtype),y=no(g,A,"sum",a),x=Ae({inputs:{x:y},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(y),u&&a.disposeIntermediateTensorInfo(d),x}function T0(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return LK(r,s,i,n)}var WK={kernelName:Ai,backendName:"webgl",kernelFunc:T0};function In(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=r.shape[s[d]];let u;if(i.shouldExecuteOnCPU([r])){let d=i.texData.get(r.dataId).values,p=Py(d,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let c=i.texData.get(u.dataId);c.values=p}else u=S0(r,s,i);return u}var BK={kernelName:ki,backendName:"webgl",kernelFunc:In},a4=1e3;function N0({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],c=a?t.shape[d-1]:t.shape[d-2],h=n?e.shape[u-1]:e.shape[u-2],m=a?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=w.sizeFromShape(f),y=w.sizeFromShape(g),x=A===y||A===1||y===1;w.assert(u>=2&&d>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let v=(A>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);w.assert(p===c,()=>`Error in matMul: inner shapes (${p}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=n?[A,p,h]:[A,h,p],k=a?[y,m,c]:[y,c,m],N=Ae({inputs:{x:e},backend:r,attrs:{shape:b}}),C=Ae({inputs:{x:t},backend:r,attrs:{shape:k}}),E=[N,C],z=Math.max(A,y),F=n?N.shape[1]:N.shape[2],I=s!=null,_=i!=null,D=l==="leakyrelu",V=l!=null?I0(l,!0):null,q=I||_||D||V!=null,G;if((h===1||m===1)&&F>a4&&q===!1){let J=N,ne=C;n&&(J=In({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),E.push(J)),a&&(ne=In({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let ee=m!==1,ie=m===1,Z=J;ee&&(Z=Ae({inputs:{x:J},backend:r,attrs:{shape:[z,F,1]}}),E.push(Z));let le=m===1?2:1,oe=ne;ie&&(oe=Ae({inputs:{x:ne},backend:r,attrs:{shape:[z,1,F]}}),E.push(oe));let ye=Wy({inputs:{a:Z,b:oe},backend:r});G=T0({inputs:{x:ye},backend:r,attrs:{axis:le,keepDims:!0}}),E.push(ye)}else{let J=Sa(e.dtype,t.dtype),ne=new J6(b,k,[z,h,m],n,a,I,V,_,D),ee=[N,C];if(s!=null&&ee.push(s),_&&ee.push(i),D){let ie=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(ie),E.push(ie)}G=r.runWebGLProgram(ne,ee,J)}let Q=Ae({inputs:{x:G},backend:r,attrs:{shape:v}});E.push(G);for(let J of E)r.disposeIntermediateTensorInfo(J);return Q}function VK(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:p}=a;return N0({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:d})}var UK={kernelName:Ii,backendName:"webgl",kernelFunc:VK},r4="return abs(x);";function HK(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=D6(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new lu(a.shape,r4):r=new cs(a.shape,r4),n.runWebGLProgram(r,[a],a.dtype)}var jK={kernelName:Io,backendName:"webgl",kernelFunc:HK},GK=ja+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,qK=Je({opSnippet:GK}),XK={kernelName:So,backendName:"webgl",kernelFunc:qK},KK=ja+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,ZK=Je({opSnippet:KK}),YK={kernelName:To,backendName:"webgl",kernelFunc:ZK},s4="return a + b;",JK=ln({opSnippet:s4,packedOpSnippet:s4,supportsComplex:!0,cpuKernelImpl:oX}),QK={kernelName:Vr,backendName:"webgl",kernelFunc:JK},eZ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${a};
setOutput(result);
}
`}},tZ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${a};
setOutput(result);
}
`}};function C0(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Zn({inputs:{x:a[0]},backend:n});if(a.length>te().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=C0({inputs:a.slice(0,o),backend:n}),u=C0({inputs:a.slice(o),backend:n});return C0({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>Sa(o,l)),s=a.map(o=>o.shape),i=te().getBool("WEBGL_PACK")?new tZ(a[0].shape,s):new eZ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var nZ={kernelName:Cs,backendName:"webgl",kernelFunc:C0};function aZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,d=R.getAxesPermutation(u,o),p=r;d!=null&&(p=In({inputs:{x:r},backend:n,attrs:{perm:d}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("all",u,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,u),m=w.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=no(f,f.dtype,"all",n),A;if(i){let y=R.expandShapeToKeepDim(c,l);A=Ae({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=Ae({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(p),A}var rZ={kernelName:No,backendName:"webgl",kernelFunc:aZ};function sZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,d=R.getAxesPermutation(u,o),p=r;d!=null&&(p=In({inputs:{x:r},backend:n,attrs:{perm:d}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("any",u,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,u),m=w.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=no(f,f.dtype,"any",n),A;if(i){let y=R.expandShapeToKeepDim(c,l);A=Ae({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=Ae({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(p),A}var iZ={kernelName:Co,backendName:"webgl",kernelFunc:sZ},oZ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,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 * ${a};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${a}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},lZ=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=pt(o),u=kn("coords",o),d,p;if(s===1){p=o+1;let N=pt(p);d=`
${N} sourceLocR = ${N}(${u.join()}, 0);
++${u[o-1]};
${N} sourceLocG = ${N}(${u.join()}, 0);
++${u[o-2]};
${N} sourceLocA = ${N}(${u.join()}, 0);
--${u[o-1]};
${N} sourceLocB = ${N}(${u.join()}, 0);
--${u[o-2]};`}else p=o,d=`
${l} sourceLocR = coords;
++${u[o-1]};
${l} sourceLocG = coords;
++${u[o-2]};
${l} sourceLocA = coords;
--${u[o-1]};
${l} sourceLocB = coords;
--${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,p),h="."+c[p-1],m=c.map(N=>"int "+N),f=kn("sourceLocR",p-1).concat("inIdx.r"),g=kn("sourceLocG",p-1).concat("inIdx.g"),A=kn("sourceLocB",p-1).concat("inIdx.b"),y=kn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":`
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${y.join()})));`,b=`vec4(
getAChannel(${f.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${A.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,k=a?"":`
float getBestIndicesAChannel(${m.join()}) {
return getChannel(getBestIndicesA(${c.join()}),
vec2(${c.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${c.join()}),
vec2(${c.slice(-2).join()}));
}
${k}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
${d}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${b};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${v}
vec4 candidate = ${b};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function i4(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new oZ(o,n,a==null),u=[t];a!=null&&u.push(a);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let p=i4(e,t,n,d);return e.disposeIntermediateTensorInfo(d),p}function o4(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=R.computeOptimalWindowSize(s),o=new lZ(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=o4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}return u}function l4(e,t,n,a){let r=[n];if(R.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!te().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=R.computeOutAndReduceShapes(l.shape,r),p=w.sizeFromShape(d),c=Ae({inputs:{x:l},backend:e,attrs:{shape:[-1,p]}});s.push(c);let h=i4(e,c,a);s.push(h);let m=Ae({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return o4(e,t,a)}function uZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=In({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=l4(n,l,i[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var dZ={kernelName:Es,backendName:"webgl",kernelFunc:uZ};function pZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=In({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=l4(n,l,i[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var cZ={kernelName:_u,backendName:"webgl",kernelFunc:pZ},hZ=ja+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,fZ=Je({opSnippet:hZ}),mZ={kernelName:Eo,backendName:"webgl",kernelFunc:fZ},gZ=ja+"return log(x + sqrt(x * x + 1.0));",AZ=Je({opSnippet:gZ}),yZ={kernelName:Ro,backendName:"webgl",kernelFunc:AZ},xZ=ja+`
return atan(x);
`,bZ=Je({opSnippet:xZ}),vZ={kernelName:Mo,backendName:"webgl",kernelFunc:bZ},wZ=EK+`
return atan(a, b);
`,kZ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+RK+`
return result;
`,IZ=ln({opSnippet:wZ,packedOpSnippet:kZ}),SZ={kernelName:$o,backendName:"webgl",kernelFunc:IZ},TZ=ja+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NZ=Je({opSnippet:TZ}),CZ={kernelName:Fo,backendName:"webgl",kernelFunc:NZ},Qd=class{constructor(e,t,n,a=!1,r=!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,u=e.dilationWidth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(m||(A="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${c}, ${h});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${d};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p};
wC += ${u}) {
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 = ${a?r?f:g:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,b=s%4,k=`
if (${m}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${y}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${c}, ${h});
const float initializationValue = ${A};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${A});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${d};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${v}; wC += 4) {
int xC = xCCorner + wC * ${u};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
getValue(batch, xR, xC + 3 * ${u}, d)
);
${k}
}
int xC = xCCorner + ${v};
if (${b===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${k}
} else if (${b===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
initializationValue,
initializationValue
);
${k}
} else if (${b===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
initializationValue
);
${k}
}
}
setOutput(${x});
}
`}},By=class{constructor(e,t,n,a=!1,r=!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,u=e.dilationDepth,d=e.dilationHeight,p=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${A});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${c};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${d}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${p}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${E} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} +
wR * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let v="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let k=Math.floor(s/4)*4,N=s%4,C=`
if (${y}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${v}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${A});
const float initializationValue = ${x};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${x});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${c};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${d}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; wC += 4) {
int xC = xCCorner + wC * ${p};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
);
${C}
}
int xC = xCCorner + ${k};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${C}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${C}
} else if (${N===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
initializationValue
);
${C}
}
}
setOutput(${b});
}
}
`}};function EZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;au(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=R.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return Zn({inputs:{x:r},backend:n});let p=new Qd(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var RZ={kernelName:Rs,backendName:"webgl",kernelFunc:EZ};function MZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,d=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,d,o,l,u),c=new By(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var FZ={kernelName:Du,backendName:"webgl",kernelFunc:MZ},$Z=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${u}, ${d});
const float avgMultiplier = float(${p});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},OZ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=d-1-e.padInfo.front,m=p-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
const ivec3 pads = ivec3(${h}, ${m}, ${f});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${d};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${r}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${p};
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 < ${c};
wC += ${u}) {
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 zZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,l,p,u,d),h=new OZ(c);return n.runWebGLProgram(h,[r],i.dtype)}var _Z={kernelName:Zp,backendName:"webgl",kernelFunc:zZ};function DZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;au([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=R.computePool2DInfo(i.shape,o,l,1,u),p=new $Z(d);return n.runWebGLProgram(p,[r],i.dtype)}var PZ={kernelName:Kp,backendName:"webgl",kernelFunc:DZ};function LZ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return N0({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var WZ={kernelName:Ms,backendName:"webgl",kernelFunc:LZ},BZ=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),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)));
}
`}},VZ=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),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);
}
`}},UZ=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.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 u=[a,r,s],d=null;i!=null&&(d=i.shape,u.push(i));let p=null;o!=null&&(p=o.shape,u.push(o));let c=te().getBool("WEBGL_PACK_NORMALIZATION")?new VZ(a.shape,r.shape,s.shape,d,p,l):new BZ(a.shape,r.shape,s.shape,d,p,l);return t.runWebGLProgram(c,u,u[0].dtype)},HZ={kernelName:js,backendName:"webgl",kernelFunc:UZ},jZ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=pt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=GZ(this.rank),a,r=e.map((s,i)=>`sourceLoc.${Vy[i]} = start[${i}] + coords.${Vy[i]};`);a=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${r.join(`
`)}
`,this.userCode=`
void main() {
${a}
setOutput(getSource(${n}));
}
`}},Vy=["x","y","z","w","u","v"];function GZ(e){if(e===1)return"sourceLoc";if(e<=6)return Vy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var qZ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=pt(this.rank),n=kn("coords",this.rank),a=kn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
result.x = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.y = ${s};
--${a[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${a[this.rank-2]};
result.z = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((u,d)=>`start[${d}]`).join()});`:e.map((u,d)=>`${a[d]} = ${n[d]} + start[${d}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}};function XZ(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=yn.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function pu(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=yn.parseSliceParams(r,s,i);if(yn.assertParamsValid(r,o,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),c=$X(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),d=yn.isSliceContinous(r.shape,o,l);if(u||!d){let p=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qZ(l):new jZ(l),c=[o];return n.runWebGLProgram(p,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),XZ(r,o,l,n)}var KZ={kernelName:hl,backendName:"webgl",kernelFunc:pu},ZZ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=R.getReshaped(r.shape,s,o),u=R.getPermuted(l.length,s.length),d=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(d,i,s.length),h=[],m=Ae({inputs:{x:r},backend:n,attrs:{shape:l}}),f=In({inputs:{x:m},backend:n,attrs:{perm:u}}),g=Ae({inputs:{x:f},backend:n,attrs:{shape:d}}),A=pu({inputs:{x:g},backend:n,attrs:{begin:p,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},YZ={kernelName:Oo,backendName:"webgl",kernelFunc:ZZ};function JZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=_6(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var QZ={kernelName:Yp,backendName:"webgl",kernelFunc:JZ},eY="return float(a != b);",u4=ln({opSnippet:eY,cpuKernelImpl:CX,dtype:"bool"}),tY={kernelName:tl,backendName:"webgl",kernelFunc:u4};function ep(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Zn({inputs:{x:r.complexTensorInfos.real},backend:n})}var nY={kernelName:xc,backendName:"webgl",kernelFunc:ep},aY="return float(int(x));";function rY(e,t){let n=new cs(e.shape,aY),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Uy(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Zn({inputs:{x:r},backend:n});let i=_t(r.shape),o=Uy({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=hs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=ep({inputs:{input:r},backend:n}),o=Uy({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,s)){let i=Zn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return rY(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=u4({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var sY={kernelName:Fs,backendName:"webgl",kernelFunc:Uy},d4="return ceil(x);",iY=Je({opSnippet:d4,packedOpSnippet:d4,cpuKernelImpl:uX}),oY={kernelName:$s,backendName:"webgl",kernelFunc:iY},lY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}},uY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function dY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;te().getBool("WEBGL_PACK_CLIP")?o=new uY(r.shape):o=new lY(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var pY={kernelName:Ur,backendName:"webgl",kernelFunc:dY},cY=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 p4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function hY(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new cY(a.shape),i=[p4(a,r.complexTensorInfos.real),p4(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var fY={kernelName:Pu,backendName:"webgl",kernelFunc:hY},mY=class{constructor(e){this.outputShape=[],this.outputShape=R.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.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},gY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=pt(a),s=kn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],u=i.slice(-2),d=i.join(),p=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${d}), vec2(${u.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
return getChannel(
getT${m}(${E0(i,l,f)}),
vec2(${E0(u,l,f)}));
}`}let c=o.length,h=o[o.length-1];p+=`
return getChannel(
getT${c}(${E0(i,l,h)}),
vec2(${E0(u,l,h)}));`,this.userCode=`
float getValue(${i.map(m=>"int "+m)}) {
${p}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[a-1]} = ${s[a-1]} + 1;
if (${s[a-1]} < ${n[a-1]}) {
result.g = getValue(${s});
}
${s[a-2]} = ${s[a-2]} + 1;
if (${s[a-2]} < ${n[a-2]}) {
result.a = getValue(${s});
}
${s[a-1]} = ${s[a-1]} - 1;
if (${s[a-2]} < ${n[a-2]} &&
${s[a-1]} < ${n[a-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function E0(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function R0(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Zn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var AY={kernelName:cc,backendName:"webgl",kernelFunc:R0};function cu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let d=e.map(f=>ep({inputs:{input:f},backend:n})),p=e.map(f=>R0({inputs:{input:f},backend:n})),c=cu(d,t,n),h=cu(p,t,n),m=hs({inputs:{real:c,imag:h},backend:n});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),p.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let d=e.map(A=>{let y=w.sizeFromShape(A.shape.slice(t));return Ae({inputs:{x:A},backend:n,attrs:{shape:[-1,y]}})}),p=d.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),c=R.computeOutShape(d.map(A=>A.shape),1),h=d[0].shape[0]===1,m=dX(p,c,a,h),f=R.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(f,a,m);return d.forEach(A=>n.disposeIntermediateTensorInfo(A)),g}if(e.length>te().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),p=cu(e.slice(0,d),t,n),c=cu(e.slice(d),t,n),h=cu([p,c],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),h}if(te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new gY(e.map(p=>p.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:s,outShape:i}=yY(e,t,n),o=new mY(s.map(d=>d.shape)),l=n.runWebGLProgram(o,s,a);s.forEach(d=>n.disposeIntermediateTensorInfo(d));let u=Ae({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),u}function yY(e,t,n){let a=R.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ae({inputs:{x:r},attrs:{shape:[-1,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function c4(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=R.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return Zn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return R.assertParamsConsistent(l,s),cu(o,s,n)}var xY={kernelName:zo,backendName:"webgl",kernelFunc:c4},h4=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,A=f?2:3,y=f?3:1,x="",v="";n&&(a?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:x=`
float activation(float x) {
${n}
}
`,v="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${y}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${A}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${c}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${f}) {
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 (${m===1}) {
if (${f}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${m===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${f}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${m===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${f}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${b}
${v}
setOutput(result);
}
`}},bY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${a});
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 < ${d}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${c}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${m===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${m===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${m===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},vY=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let{dataFormat:n}=t,a=wn(),r=n==="channelsLast",s=r?0:1,i=r?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let d=0;d<=1;d++)l+=`
blockIndex = rc.y + ${d};
pos = rc.x + ${u};
${o}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${s}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${i}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${r}) {
innerDims = vec2(d1, ch);
result[${u*2+d}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${u*2+d}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${l}
${a.output} = result;
}
`}};function f4({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),d=n.inChannels,p=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,A=[];if(!((p===1||c===1)&&d>a4)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!=0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Zd(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let b=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(b);let k=N0({a:x,b,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),N=a.texData.get(k.dataId);w.assert(N.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,N.shape=n.outShape,g=Zn({inputs:{x:k},backend:a}),g.shape=n.outShape,A.push(k)}else{let y=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=Ae({inputs:{x:e},backend:a,attrs:{shape:[1,y,n.inChannels]}}),v=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),b=N0({a:x,b:v,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=Ae({inputs:{x:b},backend:a,attrs:{shape:n.outShape}}),A.push(x),A.push(v),A.push(b)}for(let y of A)a.disposeIntermediateTensorInfo(y);return g}function m4({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:p,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*d,g=c*p,A=[f,g],y=!0,x=!1,v=[],b=Ae({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),k=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(b),v.push(k);let N=new vY(A,n),C=[b.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(N,[b],"float32",C),z=Ae({inputs:{x:E},backend:a,attrs:{shape:[1,A[0],A[1]]}});v.push(E),v.push(z);let F=r!=null,I=s!=null,_=o==="leakyrelu",D=o?I0(o,!0):null,V=new J6(z.shape,k.shape,[1,g,n.outChannels],y,x,F,D,I,_),q=[z,k];if(r&&q.push(r),I&&q.push(s),_){let ne=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));q.push(ne),v.push(ne)}let G=a.runWebGLProgram(V,q,"float32"),Q=m?[1,c,p,n.outChannels]:[1,n.outChannels,c,p],J=Ae({inputs:{x:G},backend:a,attrs:{shape:Q}});v.push(G);for(let ne of v)a.disposeIntermediateTensorInfo(ne);return J}function wY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=a,p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,p),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=f4({x:r,filter:s,convInfo:c,backend:n});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=m4({x:r,filter:s,convInfo:c,backend:n});else{let f=new h4(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=Ae({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var kY={kernelName:Os,backendName:"webgl",kernelFunc:wY},IY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=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} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${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);
}
`}},SY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${d}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - 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) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${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);
}
`}},TY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=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} - ${r};
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 * ${a} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},NY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${l}, ${u});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${r}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${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 < ${a}; 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 = ${a} - 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 CY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=a,p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,d,i,1,o,u,!1,p),h=new IY(c);return n.runWebGLProgram(h,[r,s],"float32")}var EY={kernelName:Qp,backendName:"webgl",kernelFunc:CY};function RY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=a,p=R.convertConv2DDataFormat(u),c=R.computeConv2DInfo(i,s.shape,o,1,l,d,!1,p),h=new SY(c);return n.runWebGLProgram(h,[r,s],"float32")}var MY={kernelName:zs,backendName:"webgl",kernelFunc:RY};function FY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=R.computeConv3DInfo(r.shape,s.shape,i,l,o),d=new bY(u);return n.runWebGLProgram(d,[r,s],"float32")}var $Y={kernelName:Lu,backendName:"webgl",kernelFunc:FY};function OY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=R.computeConv3DInfo(r.shape,l,i,1,o),d=new TY(u);return n.runWebGLProgram(d,[r,s],"float32")}var zY={kernelName:ec,backendName:"webgl",kernelFunc:OY};function _Y(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=R.computeConv3DInfo(l,s.shape,o,1,i),d=new NY(u);return n.runWebGLProgram(d,[r,s],"float32")}var DY={kernelName:tc,backendName:"webgl",kernelFunc:_Y},PY=Y6+`
return cos(x);
`,LY=Je({opSnippet:PY}),WY={kernelName:_s,backendName:"webgl",kernelFunc:LY},BY=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,VY=Je({opSnippet:BY}),UY={kernelName:Ds,backendName:"webgl",kernelFunc:VY},HY=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,p]=n;this.outputShape=[u,d,p,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,A]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,v]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
const float height_ratio = float(${f});
const float width_ratio = float(${y});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${s}) {
return;
}
float height_scale = ${g};
float width_scale = ${x};
float in_y = ${A};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${v};
if( in_x < 0.0 || in_x > ${m} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${c} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},jY=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,d=new HY(r.shape,s.shape,o,l,u);return n.runWebGLProgram(d,[r,s,i],"float32")},GY={kernelName:_o,backendName:"webgl",kernelFunc:jY},g4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${A4(a,"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=`
void main() {
${pt(a)} coords = getOutputCoords();
int end = ${y4(a,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${y4(a,"coords")} = idx;
val += getX(${A4(a,"coords")});
}
setOutput(val);
}
`}};function A4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function y4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function qY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=R.getAxesPermutation([s],l),d=r;u!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:u}}));let p=R.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=d.shape[p],h=Zn({inputs:{x:d},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new g4(d.shape,!1,o),g=[[m]],A=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(i){let m=new g4(d.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=R.getUndoAxesPermutation(u),f=In({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),f}return h}var XY={kernelName:Ps,backendName:"webgl",kernelFunc:qY};function KY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=_6(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),d=lX(l,u,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var ZY={kernelName:nc,backendName:"webgl",kernelFunc:KY},YY=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 JY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;w.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,c=u*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=new YY(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var QY={kernelName:Do,backendName:"webgl",kernelFunc:JY},x4=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:l=`
float activation(float x) {
${n}
}
`,u="result = activation(result);");let d=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${l}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${o};
int q = d2 - d1 * ${o};
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 < ${s}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${i}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${d}
${u}
setOutput(result);
}
`}},b4=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,p=d,c=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<d;g++)c+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;for(let g=0;g<u;g++){for(let A=0;A<d;A++)c+=`
xTexelC${A*2} = vec4(0.0);
xTexelC${A*2}Ready = 0;
xTexelC${A*2+1} = vec4(0.0);
xTexelC${A*2+1}Ready = 0;
xC${A} = vec4(0.0);`;c+=`
xR = xRCorner + ${g} * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let A=0;A<(p+1)/2;A++){let y=A*2;if(c+=`
xC = xCCorner + ${y*l};
`,o===1){if(y<d&&(i%2==1?(c+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
`,l===1&&y>0?c+=`
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
`:c+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${y} = vec4(previous.zw, xTexelC${y}.xy);
} else {
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
}
`):c+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xC${y} = xTexelC${y};
`,y+1<d)){let x=i%2==0?w.nearestLargerEven(l):l;l%2==0&&i%2==1||l%2!=0&&i%2!=1?(c+=`
xCOffset = xC + imod(pads[1], 2) + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
`,l>1&&(c+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
xTexelC${y}Ready = 1;
}
`),c+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
`):x===1?c+=`
xC${y+1} = xTexelC${y};
`:c+=`
xCOffset = xC + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y+1} = xTexelC${y+1};
`}}else y<d&&(i%2==1?(c+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`,y+1<d&&(c+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
`)):(c+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(
xTexelC${y}.xy, xTexelC${y+1}.xy);
`,y+1<d&&(c+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`)));y<d&&(c+=`
wTexel = getW(${g}, ${y}, d1, q);
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
`,y+1<d&&(c+=`
wTexel = getW(${g}, ${y+1}, d1, q);
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
`))}c+=`
}
`}let h="",m="";n&&(a?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,m="result = activation(result);");let f=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${s};
int q = d2 - d1 * ${s};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${c}
vec4 result = dotProd - vec4(0.000000000000001);
${f}
${m}
setOutput(result);
}
`}};function eJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,d=l;d==null&&(d=[1,1]),w.assert(R.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=R.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),c;te().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new b4(p):c=new x4(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var tJ={kernelName:Ls,backendName:"webgl",kernelFunc:eJ},nJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=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} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},aJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function rJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=a,p=R.computeConv2DInfo(r.shape,d,i,o,l,u,!0),c=new nJ(p);return n.runWebGLProgram(c,[r,s],"float32")}var sJ={kernelName:ac,backendName:"webgl",kernelFunc:rJ};function iJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=a,p=R.computeConv2DInfo(d,s.shape,i,o,l,u,!0),c=new aJ(p);return n.runWebGLProgram(c,[r,s],"float32")}var oJ={kernelName:rc,backendName:"webgl",kernelFunc:iJ},lJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function uJ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=Ae({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new lJ(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var dJ={kernelName:sc,backendName:"webgl",kernelFunc:uJ},pJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:p}=a;this.userCode=`
const ivec2 strides = ivec2(${r}, ${s});
const ivec2 pads = ivec2(${d}, ${p});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${u};
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 cJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=R.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),d,p=new pJ(u);d=n.runWebGLProgram(p,[r,s],"float32");let c=Ae({inputs:{x:d},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(d),c}var hJ={kernelName:Wu,backendName:"webgl",kernelFunc:cJ};function fJ(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=R.decodeEinsumEquation(r,s.length);R.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=R.getEinsumComputePath(o,l),p=d.length,c=null,h=i.length,m=[];for(let f=0;f<p;++f){for(let g of d[f]){let{permutationIndices:A,expandDims:y}=R.getEinsumPermutation(h,l[g]),x;R.isIdentityPermutation(A)?x=s[g]:(x=In({inputs:{x:s[g]},backend:n,attrs:{perm:A}}),m.push(x));let v=x.shape.slice();for(let b=0;b<y.length;++b)v.splice(y[b],0,1);w.arraysEqual(x.shape,v)||(x=Ae({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),c===null?c=x:(c=Wy({inputs:{a:x,b:c},backend:n}),m.push(c))}f<p-1&&(u[f]>=0&&(c=T0({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var mJ={kernelName:lc,backendName:"webgl",kernelFunc:fJ},gJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",AJ=`
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;
`,yJ=Je({opSnippet:gJ,packedOpSnippet:AJ}),xJ={kernelName:Bs,backendName:"webgl",kernelFunc:yJ},bJ="return (b >= 1.0) ? a : a * (b + 1.0);",vJ=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,wJ=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jd(vJ,a.shape,r.shape):new du(bJ,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},kJ={kernelName:uc,backendName:"webgl",kernelFunc:wJ},IJ=`
return vec4(equal(a, b));
`,SJ="return float(a == b);",TJ=ln({opSnippet:SJ,packedOpSnippet:IJ,dtype:"bool",cpuKernelImpl:pX}),NJ={kernelName:Lo,backendName:"webgl",kernelFunc:TJ},CJ=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${R.ERF_P};
float a1 = ${R.ERF_A1};
float a2 = ${R.ERF_A2};
float a3 = ${R.ERF_A3};
float a4 = ${R.ERF_A4};
float a5 = ${R.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,EJ=Je({opSnippet:CJ}),RJ={kernelName:Po,backendName:"webgl",kernelFunc:EJ},v4="return exp(x);",w4=Je({opSnippet:v4,packedOpSnippet:v4,cpuKernelImpl:cX}),MJ={kernelName:Vs,backendName:"webgl",kernelFunc:w4};function Hy(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:a,attrs:{shape:o}})}var FJ={kernelName:Wo,backendName:"webgl",kernelFunc:Hy},k4="return exp(x) - 1.0;",$J=Je({opSnippet:k4,packedOpSnippet:k4,cpuKernelImpl:hX}),OJ={kernelName:Bo,backendName:"webgl",kernelFunc:$J},I4=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.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 = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${a});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${a}; 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 S4(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new I4("real",l,t),d=new I4("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=hs({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function zJ(e){let{inputs:t,backend:n}=e,{input:a}=t;return S4(a,!1,n)}var _J={kernelName:dc,backendName:"webgl",kernelFunc:zJ},DJ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function tp(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new DJ(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var PJ={kernelName:Bu,backendName:"webgl",kernelFunc:tp},LJ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},WJ={kernelName:Vo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new LJ(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},T4="return floor(x);",BJ=Je({opSnippet:T4,packedOpSnippet:T4,cpuKernelImpl:fX}),VJ={kernelName:Us,backendName:"webgl",kernelFunc:BJ},UJ=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,HJ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,jJ=ln({opSnippet:UJ,packedOpSnippet:HJ,dtype:"int32"}),GJ={kernelName:Hs,backendName:"webgl",kernelFunc:jJ},qJ=class{constructor(e){this.variableNames=["A"];let t=wn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},XJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=wn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}.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;
}
`}},KJ={kernelName:Mc,backendName:"webgl",kernelFunc:ZJ},hu;function ZJ(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[u,l],p=[u,l,s];(o||i)&&(hu==null&&(hu=document.createElement("canvas").getContext("2d")),hu.canvas.width=l,hu.canvas.height=u,hu.drawImage(r,0,0,l,u),r=hu.canvas);let c=n.makeTensorInfo(d,"int32");n.texData.get(c.dataId).usage=ga.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=te().getBool("WEBGL_PACK")?new XJ(p):new qJ(p),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function YJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=R.convertConv2DDataFormat(d),g=R.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!1,f),A,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))A=f4({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=m4({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,b=o!=null,k=h==="leakyrelu",N=h?I0(h,!1):null,C=new h4(g,v,N,b,k),E=[r,s];if(i&&E.push(i),o&&E.push(o),k){let z=n.makeTensorInfo([],"float32",w.createScalarValue(m,"float32"));E.push(z),y.push(z)}A=n.runWebGLProgram(C,E,"float32")}let x=Ae({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return y.push(A),y.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var JJ={kernelName:Si,backendName:"webgl",kernelFunc:YJ};function QJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:p,activation:c,leakyreluAlpha:h}=a,m=[],f=d;f==null&&(f=[1,1]),w.assert(R.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=R.computeConv2DInfo(r.shape,s.shape,l,f,u,p,!0),A=te().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=c?I0(c,A):null,x=[r,s],v=i!=null,b=o!=null,k=c==="leakyrelu";if(v&&x.push(i),b&&x.push(o),k){let z=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));x.push(z),m.push(z)}let N;A?N=new b4(g,v,y,b,k):N=new x4(g,v,y,b,k);let C=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],E=n.runWebGLProgram(N,x,"float32",C);return m.forEach(z=>n.disposeIntermediateTensorInfo(z)),E}var eQ={kernelName:Ti,backendName:"webgl",kernelFunc:QJ},tQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=pt(t.length),r=pt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${a} strides = ${a}(${this.strides});
void main() {
${r} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function nQ(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=w.sizeFromShape(a.shape),[l,u,d,p]=R.prepareAndValidate(a,r),c=Ae({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape)/d,d]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let A=n.readSync(r.dataId),y=n.bufferSync(a),x=mX(A,y,a.dtype,u,i,d,p,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new tQ(i,p,[u,d]),f=n.runWebGLProgram(m,[h,c],h.dtype),g=Ae({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var aQ={kernelName:Ho,backendName:"webgl",kernelFunc:nQ},rQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=pt(this.rank),a=sQ(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function sQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function N4(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=w.sizeFromShape(s.shape),p=[],c=Ae({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=Ae({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,d/u.batchSize]}});p.push(c),p.push(h);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let y=n.bufferSync(h),x=n.bufferSync(c),v=gX(x,y,m);return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new rQ(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);p.push(g);let A=Ae({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}var iQ={kernelName:Uo,backendName:"webgl",kernelFunc:N4},oQ="return float(a > b);",lQ=`
return vec4(greaterThan(a, b));
`,uQ=ln({opSnippet:oQ,packedOpSnippet:lQ,cpuKernelImpl:AX,dtype:"bool"}),dQ={kernelName:jo,backendName:"webgl",kernelFunc:uQ},pQ="return float(a >= b);",cQ=`
return vec4(greaterThanEqual(a, b));
`,hQ=ln({opSnippet:pQ,packedOpSnippet:cQ,dtype:"bool",cpuKernelImpl:yX}),fQ={kernelName:Gs,backendName:"webgl",kernelFunc:hQ};function mQ(e){let{inputs:t,backend:n}=e,{input:a}=t;return S4(a,!0,n)}var gQ={kernelName:pc,backendName:"webgl",kernelFunc:mQ},AQ="return float(!isnan(x) && !isinf(x));",yQ=Je({opSnippet:AQ,dtype:"bool"}),xQ={kernelName:Go,backendName:"webgl",kernelFunc:yQ},bQ="return float(isinf(x));",vQ=Je({opSnippet:bQ,dtype:"bool"}),wQ={kernelName:qo,backendName:"webgl",kernelFunc:vQ},kQ="return float(isnan(x));",IQ=Je({opSnippet:kQ,dtype:"bool"}),SQ={kernelName:Xo,backendName:"webgl",kernelFunc:IQ},TQ="return float(a < b);",NQ=`
return vec4(lessThan(a, b));
`,CQ=ln({opSnippet:TQ,packedOpSnippet:NQ,cpuKernelImpl:xX,dtype:"bool"}),EQ={kernelName:Ko,backendName:"webgl",kernelFunc:CQ},RQ="return float(a <= b);",MQ=`
return vec4(lessThanEqual(a, b));
`,FQ=ln({opSnippet:RQ,packedOpSnippet:MQ,cpuKernelImpl:bX,dtype:"bool"}),$Q={kernelName:Zo,backendName:"webgl",kernelFunc:FQ};function OQ(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=vX(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var zQ={kernelName:hc,backendName:"webgl",kernelFunc:OQ},_Q=`if (x < 0.0) return NAN;
return log(x);`,DQ=`
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;
`,PQ=Je({opSnippet:_Q,packedOpSnippet:DQ,cpuKernelImpl:wX}),LQ={kernelName:Ks,backendName:"webgl",kernelFunc:PQ},WQ="return log(1.0 + x);",BQ=Je({opSnippet:WQ}),VQ={kernelName:Yo,backendName:"webgl",kernelFunc:BQ},UQ="return float(a >= 1.0 && b >= 1.0);",HQ=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,jQ=ln({opSnippet:UQ,packedOpSnippet:HQ,dtype:"bool"}),GQ={kernelName:Jo,backendName:"webgl",kernelFunc:jQ},qQ="return float(!(x >= 1.0));",XQ=Je({opSnippet:qQ}),KQ={kernelName:Vu,backendName:"webgl",kernelFunc:XQ},ZQ="return float(a >= 1.0 || b >= 1.0);",YQ=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,JQ=ln({opSnippet:ZQ,packedOpSnippet:YQ,dtype:"bool"}),QQ={kernelName:Uu,backendName:"webgl",kernelFunc:JQ},eee=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${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 * ${o};
setOutput(val);
}
`}},tee=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${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);
}
`}},nee=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=te().getBool("WEBGL_PACK_NORMALIZATION")?new tee(r.shape,s,i,o,l):new eee(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},aee={kernelName:Hu,backendName:"webgl",kernelFunc:nee},ree=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${a}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${a})
* float(${r})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},see=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=a,p=new ree(r.shape,o,l,u,d);return n.runWebGLProgram(p,[r,s,i],r.dtype)},iee={kernelName:fc,backendName:"webgl",kernelFunc:see};function oee(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=no(i,e.dtype,"max",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function C4(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,d=R.getAxesPermutation(u,o),p=d!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let k=0;k<x.length;k++)x[k]=r.shape[d[k]];let v=Py(y,r.shape,r.dtype,d,x);h=n.makeTensorInfo(x,r.dtype);let b=n.texData.get(h.dataId);b.values=v}else h=S0(r,d,n);u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("max",u,o);let[m,f]=R.computeOutAndReduceShapes(h.shape,u),g=m;i&&(g=R.expandShapeToKeepDim(m,l));let A;if(c){let y=n.texData.get(h.dataId).values,x=kX(y,w.sizeFromShape(f),g,r.dtype);A=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(A.dataId);v.values=x}else A=oee(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),A}var lee={kernelName:Zs,backendName:"webgl",kernelFunc:C4},uee=G6+`
return max(a, b);
`,dee=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+k0+`
return result;
`,pee=ln({opSnippet:uee,packedOpSnippet:dee,cpuKernelImpl:IX}),cee={kernelName:Ys,backendName:"webgl",kernelFunc:pee};function hee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;au(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=R.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return Zn({inputs:{x:r},backend:n});let p=new Qd(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var fee={kernelName:Js,backendName:"webgl",kernelFunc:hee};function mee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,d=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,d,o,u,l),c=new By(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var gee={kernelName:ju,backendName:"webgl",kernelFunc:mee},Aee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*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 < ${r};
wR += ${a}) {
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 * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},yee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,p=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=`
const ivec3 pads = ivec3(${d}, ${p}, ${c});
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 += ${r}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${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 < ${u};
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(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${u} +
wR * ${u} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function xee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,l,p,u,d),h=new By(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new yee(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var bee={kernelName:gc,backendName:"webgl",kernelFunc:xee};function vee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;au([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:p}=a,c=R.computePool2DInfo(o.shape,l,u,1,d,p),h=!0,m=new Qd(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Aee(c),A=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),A}var wee={kernelName:mc,backendName:"webgl",kernelFunc:vee};function kee(e,t,n,a){let r=new Qd(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Qd(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Iee={kernelName:Ac,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.assert(R.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=R.computePool2DInfo(a.shape,r,s,u,i),[p,c]=kee(a,o,d,l);return[p,c]}};function See(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=no(i,"float32","mean",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Tee={kernelName:Qs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=R.getAxesPermutation(u,o),p=d!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let N=0;N<v.length;N++)v[N]=a.shape[d[N]];let b=Py(x,a.shape,a.dtype,d,v);m=i.makeTensorInfo(v,a.dtype);let k=i.texData.get(m.dataId);k.values=b}else m=S0(a,d,i);h.push(m),u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=R.computeOutAndReduceShapes(m.shape,u),A=f;r&&(A=R.expandShapeToKeepDim(f,l));let y=See(m,g,A,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return y}};function Nee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,d=R.getAxesPermutation(u,o),p=r;d!=null&&(p=In({inputs:{x:r},backend:n,attrs:{perm:d}}),u=R.getInnerMostAxes(u.length,r.shape.length)),R.assertAxesAreInnerMostDims("min",u,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,u),m=w.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=no(f,f.dtype,"min",n),A;if(i){let y=R.expandShapeToKeepDim(c,l);A=Ae({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=Ae({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(p),A}var Cee={kernelName:ei,backendName:"webgl",kernelFunc:Nee},Eee=G6+`
return min(a, b);
`,Ree=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+k0+`
return result;
`,Mee=ln({opSnippet:Eee,packedOpSnippet:Ree,cpuKernelImpl:SX}),Fee={kernelName:ti,backendName:"webgl",kernelFunc:Mee},$ee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let a=e.length,r=pt(a),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===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=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${a}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${r} coords = outC - start;
setOutput(getX(${o}));
}
`}},Oee=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=pt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=kn("rc",a),l=kn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,c="";if(a===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${p};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${p};
}
source -= start;
`;c=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${d});
${o[a-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${d});
}
`}else{let h=`
${r} source = rc;
${r} lt = ${r}(lessThan(source, start));
${r} gte = ${r}(greaterThanEqual(source, end));
${r} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${p}) +
gte * ((end - 1) * 2 - source + ${p});
source -= start;
`;c=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${d});
${o[a-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${d});
}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${d});
${o[a-1]} += 1;
if(${u}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${d});
}
}
`}this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${c}
setOutput(result);
}
`}},zee=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Oee(a.shape,r,s):new $ee(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},_ee={kernelName:ni,backendName:"webgl",kernelFunc:zee},Dee=`if (b == 0.0) return NAN;
return mod(a, b);`,Pee=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+k0+`
return result;
`,Lee=ln({opSnippet:Dee,packedOpSnippet:Pee}),Wee={kernelName:Qo,backendName:"webgl",kernelFunc:Lee},Bee=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}},Vee=`
if (a == b) {
return 1.0;
};
return a / b;`,Uee=`
// 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;
`,E4=ln({opSnippet:Vee,packedOpSnippet:Uee,checkOutOfBounds:!0}),Hee={kernelName:Ws,backendName:"webgl",kernelFunc:E4},R4="return a - b;",M4=ln({opSnippet:R4,packedOpSnippet:R4,supportsComplex:!0,cpuKernelImpl:BX}),jee={kernelName:bi,backendName:"webgl",kernelFunc:M4};function F4(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=C4({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),d=M4({inputs:{a:r,b:u},backend:n}),p=w4({inputs:{x:d},backend:n}),c=T0({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Ae({inputs:{x:c},backend:n,attrs:{shape:l}}),m=E4({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Gee={kernelName:yi,backendName:"webgl",kernelFunc:F4};function qee(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:F4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],d=l.shape[1],p=new Bee(u,d,s),c=[[i]],h=n.runWebGLProgram(p,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Xee={kernelName:yc,backendName:"webgl",kernelFunc:qee},$4="return -x;";function Kee(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=NX(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new lu(a.shape,$4):r=new cs(a.shape,$4),n.runWebGLProgram(r,[a],a.dtype)}var Zee={kernelName:el,backendName:"webgl",kernelFunc:Kee},Yee=ar.nonMaxSuppressionV3Impl;function Jee(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:p}=Yee(u,d,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Qee={kernelName:nl,backendName:"webgl",kernelFunc:Jee},ete=ar.nonMaxSuppressionV4Impl;function tte(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=ete(d,p,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var nte={kernelName:al,backendName:"webgl",kernelFunc:tte},ate=ar.nonMaxSuppressionV5Impl;function rte(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:A}=ate(d,p,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var ste={kernelName:rl,backendName:"webgl",kernelFunc:rte},ite=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${a}), float(${n}),
float(index == coords.y)));
}
`}},ote=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=w.sizeFromShape(r.shape),u=new ite(l,s,i,o),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(u,[d],r.dtype);n.disposeIntermediateTensorInfo(d);let c=[...r.shape,s],h=Ae({inputs:{x:p},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),h},lte={kernelName:ri,backendName:"webgl",kernelFunc:ote};function M0(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=ep({inputs:{input:a},backend:n}),s=M0({inputs:{x:r},backend:n}),i=R0({inputs:{input:a},backend:n}),o=M0({inputs:{x:i},backend:n}),l=hs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return tp({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var ute={kernelName:kl,backendName:"webgl",kernelFunc:M0};function O4(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=ep({inputs:{input:a},backend:n}),s=O4({inputs:{x:r},backend:n}),i=R0({inputs:{input:a},backend:n}),o=M0({inputs:{x:i},backend:n}),l=hs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return tp({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var dte={kernelName:sl,backendName:"webgl",kernelFunc:O4};function pte(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Hy({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let p=Hy({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),u=c4({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var cte={kernelName:il,backendName:"webgl",kernelFunc:pte},hte=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=pt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},fte=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=pt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=kn("rc",a),l=kn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
if(${u}) {
`,a===1?"":`}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
if(${u}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
${p[m]}
if (${c}) {
result[${m}] = float(value);
} else {
${r} source = rc - start;
result[${m}] = getChannel(getX(${l.join()}), ${d});
}
`;h+=a===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},z4=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((d,p)=>d[0]+r.shape[p]+d[1]);return tp({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fte(r.shape,s,i):new hte(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},mte={kernelName:si,backendName:"webgl",kernelFunc:z4},gte=`
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);
`,Ate=`
// 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));
`+k0+`
return result;
`,yte=ln({opSnippet:gte,packedOpSnippet:Ate}),xte={kernelName:ii,backendName:"webgl",kernelFunc:yte};function bte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.parseAxisParam(s,r.shape),d=u,p=R.getAxesPermutation(d,o),c=r;p!=null&&(c=In({inputs:{x:r},backend:n,attrs:{perm:p}}),d=R.getInnerMostAxes(d.length,o),l.push(c)),R.assertAxesAreInnerMostDims("prod",d,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:A}=EX(c.shape,c.dtype,m,d);h=n.makeTensorInfo(g,A,f)}else{let[m,f]=R.computeOutAndReduceShapes(c.shape,d),g=w.sizeFromShape(f),A=Ae({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Dc(r.dtype),x=no(A,y,"prod",n);h=Ae({inputs:{x},backend:n,attrs:{shape:m}}),l.push(A),l.push(x)}if(i){l.push(h);let m=R.expandShapeToKeepDim(h.shape,u);h=Ae({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var vte={kernelName:ol,backendName:"webgl",kernelFunc:bte},_4=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=RX(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},wte={kernelName:Gu,backendName:"webgl",kernelFunc:_4},kte="return 1.0 / x;",Ite=Je({opSnippet:kte}),Ste={kernelName:ll,backendName:"webgl",kernelFunc:Ite},Tte=ja+`
return (x < 0.0) ? 0.0 : x;
`,Nte=`
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;
`,Cte=Je({opSnippet:Tte,packedOpSnippet:Nte}),Ete={kernelName:li,backendName:"webgl",kernelFunc:Cte},Rte=ja+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Mte=`
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;
`,Fte=Je({opSnippet:Rte,packedOpSnippet:Mte}),$te={kernelName:di,backendName:"webgl",kernelFunc:Fte},Ote=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/d[0]},
${u[1]/d[1]});
const vec2 inputShapeRC = vec2(${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 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);
}
`}},zte=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/d[0]},
${u[1]/d[1]},
${u[1]/d[1]});
const vec3 inputShapeRC = vec3(${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 = ${p};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${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 _te(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new zte(r.shape,l,u,s,i):new Ote(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],"float32")}var Dte={kernelName:ui,backendName:"webgl",kernelFunc:_te},Pte=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*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(${u});
const float widthScale = float(${d});
const float invHeightScale = float(${p});
const float invWidthScale = float(${c});
const int winHeight = int(${h});
const int winWidth = int(${m});
// 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), ${a-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Lte(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Pte(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Wte={kernelName:vc,backendName:"webgl",kernelFunc:Lte},Bte=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/d[0]},
${u[1]/d[1]});
const vec2 inputShapeRC = vec2(${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 = ${c};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},Vte=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/d[0]},
${u[1]/d[1]},
${u[1]/d[1]});
const vec3 inputShapeRC = vec3(${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 = ${c};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function Ute(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Vte(r.shape,l,u,s,i):new Bte(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],r.dtype)}var Hte={kernelName:qu,backendName:"webgl",kernelFunc:Ute},jte=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${d});
const float invHeightScale = float(${p});
const float invWidthScale = float(${c});
const int winHeight = int(${h});
const int winWidth = int(${m});
// 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(${l[1]}));
int sourceNearestRow = int(min(
float(int(${a}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Gte(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new jte(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var qte={kernelName:bc,backendName:"webgl",kernelFunc:Gte},Xte=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=pt(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},Kte=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-${n} tensor is not yet supported`);this.outputShape=e;let a=kn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=pt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${r}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(a.slice())};
if(${r}){
result.g = ${l(a.slice())};
}
if(${s}) {
result.b = ${u(a.slice())};
if(${r}) {
result.a = ${d(a.slice())};
}
}
setOutput(result);
}
`;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function d(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((A,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Zte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return Zn({inputs:{x:r},backend:n});let l=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kte(r.shape,o):new Xte(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Yte={kernelName:pi,backendName:"webgl",kernelFunc:Zte},Jte=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${r}
if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},Qte={kernelName:Il,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new Jte(a.shape,s),[u,d]=R.getImageCenter(i,a.shape[1],a.shape[2]),p=[[u,d,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,p)}},ene=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,tne=Je({opSnippet:ene}),nne={kernelName:ci,backendName:"webgl",kernelFunc:tne},ane="return inversesqrt(x);",rne=Je({opSnippet:ane,cpuKernelImpl:MX}),sne={kernelName:hi,backendName:"webgl",kernelFunc:rne},D4=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=pt(r.length),l=pt(s.length),u="";n===1?u="i":n===2&&(u="i, j");let d=`getIndices(${u})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let c=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${r});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${d});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${c};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function ine(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:p}=R.calculateShapes(s,r,i),c=[p/u,u];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Ae({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new D4(l,o,h.shape.length,m.shape.length,d,c),A=n.runWebGLProgram(g,[m,h,f],m.dtype),y=Ae({inputs:{x:A},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(f),y}var one={kernelName:dl,backendName:"webgl",kernelFunc:ine},lne=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);a=o.join(),r=l.join()}let s=pt(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${a});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function une(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new lne(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],Sa(r.dtype,s.dtype))}var dne={kernelName:pl,backendName:"webgl",kernelFunc:une},pne=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${R.SELU_SCALEALPHA};
float scale = ${R.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,cne=Je({opSnippet:pne}),hne={kernelName:cl,backendName:"webgl",kernelFunc:cne},P4="return 1.0 / (1.0 + exp(-1.0 * x));",fne=Je({opSnippet:P4,packedOpSnippet:P4,cpuKernelImpl:FX}),mne={kernelName:mi,backendName:"webgl",kernelFunc:fne},gne=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Ane=Je({opSnippet:gne}),yne={kernelName:ml,backendName:"webgl",kernelFunc:Ane},xne=Y6+`
return sin(x);
`,bne=Je({opSnippet:xne}),vne={kernelName:fi,backendName:"webgl",kernelFunc:bne},wne=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,kne=Je({opSnippet:wne}),Ine={kernelName:fl,backendName:"webgl",kernelFunc:kne},Sne=`
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;
`,Tne=Je({opSnippet:Sne}),Nne={kernelName:gl,backendName:"webgl",kernelFunc:Tne},Cne=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,y)=>A*y),l=[[0,0]];l.push(...i);for(let A=1+s.length;A<r.shape.length;++A)l.push([0,0]);let u=[],d=z4({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=R.getReshaped(d.shape,s,o,!1),c=R.getPermuted(p.length,s.length,!1),h=R.getReshapedPermuted(d.shape,s,o,!1),m=Ae({inputs:{x:d},backend:n,attrs:{shape:p}}),f=In({inputs:{x:m},backend:n,attrs:{perm:c}}),g=Ae({inputs:{x:f},backend:n,attrs:{shape:h}});return u.push(d),u.push(m),u.push(f),u.forEach(A=>n.disposeIntermediateTensorInfo(A)),g},Ene={kernelName:Al,backendName:"webgl",kernelFunc:Cne};function Rne(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=n.readSync(i.dataId)[0],[p,c,h,m,f]=OX(o,a.shape,a.dtype,l,r.dtype,u,d);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var Mne={kernelName:wc,backendName:"webgl",kernelFunc:Rne};function Fne(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,d,p]=zX(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(d,a.dtype,u),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var $ne={kernelName:kc,backendName:"webgl",kernelFunc:Fne};function One(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,d]=P6(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(d,a.dtype,u)}var zne={kernelName:Ic,backendName:"webgl",kernelFunc:One};function _ne(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,d]=P6(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(d,a.dtype,u)}var Dne={kernelName:Sc,backendName:"webgl",kernelFunc:_ne};function Pne(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,strides:d,outputSize:p}=R.calculateShapes(s,r,o),c=!1,h=new D4(u,l,r.shape.length,s.shape.length,d,[p,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var Lne={kernelName:Tc,backendName:"webgl",kernelFunc:Pne};function Wne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=R.prepareSplitSize(r,s,o),u=r.shape.length,d=new Array(u).fill(0),p=r.shape.slice();return l.map(c=>{let h=[...p];h[o]=c;let m=pu({inputs:{x:r},backend:n,attrs:{begin:d,size:h}});return d[o]+=c,m})}var Bne={kernelName:yl,backendName:"webgl",kernelFunc:Wne},L4="return sqrt(x);",Vne=Je({opSnippet:L4,packedOpSnippet:L4,cpuKernelImpl:_X}),Une={kernelName:gi,backendName:"webgl",kernelFunc:Vne},Hne="return x * x;",jne=Je({opSnippet:Hne}),Gne={kernelName:Xu,backendName:"webgl",kernelFunc:jne},W4="return (a - b) * (a - b);",qne=ln({opSnippet:W4,packedOpSnippet:W4}),Xne={kernelName:xi,backendName:"webgl",kernelFunc:qne};function Kne({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=ja+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new cs(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Zne={kernelName:jr,backendName:"webgl",kernelFunc:Kne},Yne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=pt(n.length),s=pt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function Jne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a,{nonStrided:h,$begin:m,$strides:f,size:g,newShape:A,outShape:y}=yn.sliceInfo(r.shape,s,i,o,l,u,d,p,c),x=Ae({inputs:{x:r},backend:n,attrs:{shape:A}}),v;if(h){let k=pu({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=Ae({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))v=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let k=n.texData.get(x.dataId).values,N=Be(x.shape,x.dtype,k),C=DX(y,N,f,m);v=n.makeTensorInfo(y,x.dtype,C.values)}else{let k=new Yne(m,f,y);v=n.runWebGLProgram(k,[x],x.dtype)}let b=Ae({inputs:{x:v},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),b}var Qne={kernelName:xl,backendName:"webgl",kernelFunc:Jne};function eae(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:d,dataSplits:p}=t,c=n.readSync(d.dataId),h=n.readSync(p.dataId),[m,f]=PX(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(p.shape,"int32",f)]}var tae={kernelName:Nc,backendName:"webgl",kernelFunc:eae};function nae(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,d,p]=LX(o,l,r),c=d.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",d),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var aae={kernelName:Cc,backendName:"webgl",kernelFunc:nae};function rae(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=WX(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var sae={kernelName:Ec,backendName:"webgl",kernelFunc:rae},iae="return tan(x);",oae=Je({opSnippet:iae}),lae={kernelName:vi,backendName:"webgl",kernelFunc:oae},uae=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,dae=Je({opSnippet:uae}),pae={kernelName:wi,backendName:"webgl",kernelFunc:dae},cae=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=pt(this.rank),r=hae(e);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function hae(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function B4(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(p=>w.decodeString(p)):o,u=Be(r.shape,r.dtype,l),d=VX(u,s);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new cae(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var fae={kernelName:Hr,backendName:"webgl",kernelFunc:B4},mae=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},gae=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function ao(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function V4(e){let t=1;for(;t<e;)t*=2;return t}function Aae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=te().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=te().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,d=u[u.length-1];if(n.shouldExecuteOnCPU([r])||d<o||s>l){let E=n.readSync(r.dataId),[z,F]=UX(E,u,r.dtype,s,i);return[n.makeTensorInfo(z.shape,z.dtype,z.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(d===1)return[r,tp({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),c=p!==null&&p.isPacked,h=c?n.unpackTensor(r):r,m=w.sizeFromShape(u)/d,f=Ae({inputs:{x:h},attrs:{shape:[m,d]},backend:n});c&&ao(n,h);let g=V4(s),A=V4(d),y=null,x=()=>y===null?[f,f]:[f,y],v=(E,z,F)=>{let I=x(),_=new mae(F),D=[[d],[y===null?1:0],[Number.NEGATIVE_INFINITY],[E],[z]],V=y;y=n.runWebGLProgram(_,I,"int32",D),ao(n,V)};for(let E=1;E<g;E*=2){let z=E*2;for(let F=E;F>=1;F/=2)v(z,F,[m,A])}for(let E=A;E>g;E/=2){let z=x(),F=new gae([m,E/2]),I=[[d],[y===null?1:0],[g]],_=y;y=n.runWebGLProgram(F,z,"int32",I),ao(n,_);let D=g/2,V=D*2;for(let q=D;q>=1;q/=2)v(V,q,y.shape)}let b=y;y=pu({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,s]}}),ao(n,b);let k=N4({inputs:{x:f,indices:y},backend:n,attrs:{axis:1,batchDims:1}});ao(n,f);let N=u.slice(0,-1);N.push(s),b=y,y=Ae({inputs:{x:y},attrs:{shape:N},backend:n}),ao(n,b);let C=k;return k=Ae({inputs:{x:k},attrs:{shape:N},backend:n}),ao(n,C),[k,y]}var yae={kernelName:bl,backendName:"webgl",kernelFunc:Aae},xae=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${o} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${r});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${r});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function bae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[p,c],g=[d,m,f,h],A=new xae(p,c,i,o,l,g);return n.runWebGLProgram(A,[r,s],"float32")}var vae={kernelName:vl,backendName:"webgl",kernelFunc:bae};function wae(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;au(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=HX(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var kae={kernelName:Rc,backendName:"webgl",kernelFunc:wae};function Iae(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let p=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){c[s]=f;let g=pu({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),A=Ae({inputs:{x:g},backend:n,attrs:{shape:u}});m[f]=A,p.push(g)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var Sae={kernelName:wl,backendName:"webgl",kernelFunc:Iae},Tae=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,d=n%4,p=`
sumValue += dot(values, segFilter);
`,c="";r%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${s})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${p}
}
int inIdx = inOffset + ${u};
if (${d===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${p}
} else if (${d===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${p}
} else if (${d===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${p}
}
setOutput(${l});
}
`}};function Nae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,d=R.getAxesPermutation([u],o),p=r;d!=null&&(p=In({inputs:{x:r},backend:n,attrs:{perm:d}}),l.push(p),u=R.getInnerMostAxes(1,o)[0]);let c=R.segment_util.computeOutShape(p.shape,u,i),h=w.sizeFromShape([p.shape[u]]),m=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Dc(r.dtype),g=(v,b,k,N,C)=>{let E=v.shape[0],z=v.shape[1],F=R.segment_util.segOpComputeOptimalWindowSize(z,C),I={windowSize:F,inSize:z,batchSize:E,numSegments:C},_=new Tae(I,b),D=n.compileAndRun(_,[v,k],N);if(l.push(D),D.shape[1]===C)return D;let V=_4({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),q=B4({inputs:{x:V},backend:n,attrs:{reps:[z/F]}});return l.push(V),l.push(q),g(D,b,q,N,C)},A=g(m,"unsortedSegmentSum",s,f,i),y=Ae({inputs:{x:A},backend:n,attrs:{shape:c}}),x=y;if(d!=null){l.push(y);let v=R.getUndoAxesPermutation(d);x=In({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var Cae={kernelName:Ku,backendName:"webgl",kernelFunc:Nae},Eae=[aee,iee,UK,jK,XK,YK,QK,nZ,rZ,iZ,dZ,cZ,mZ,yZ,SZ,vZ,CZ,FZ,RZ,_Z,PZ,WZ,HZ,YZ,QZ,sY,oY,pY,fY,IK,xY,EY,MY,kY,zY,DY,$Y,WY,UY,GY,XY,ZY,QY,sJ,oJ,tJ,dJ,hJ,mJ,xJ,kJ,NJ,RJ,MJ,FJ,OJ,_J,PJ,WJ,VJ,GJ,KJ,JJ,eQ,aQ,iQ,dQ,fQ,kK,gQ,AY,xQ,wQ,SQ,TK,EQ,$Q,zQ,VQ,LQ,GQ,KQ,QQ,lee,gee,fee,bee,wee,Iee,cee,Tee,Cee,Fee,_ee,Wee,Xee,MK,Zee,Qee,nte,ste,tY,lte,dte,cte,mte,xte,CK,vte,wte,nY,Hee,Ste,$te,Ete,$K,Dte,Wte,Hte,qte,Yte,Qte,nne,sne,one,dne,hne,mne,yne,vne,Ine,KZ,Gee,Nne,Ene,Mne,$ne,zne,Dne,Lne,Bne,Une,Gne,Xne,Zne,Qne,tae,aae,sae,jee,WK,lae,pae,fae,yae,vae,BK,kae,Sae,Cae,ute];for(let e of Eae)Ni(e);var Dn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Dn||(Dn={}));var np;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(np||(np={}));var U4;function Rae(e){U4=e.wasm.cwrap(Ii,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Mae(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:p}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let C=n.dataIdMap.get(i.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);m=C.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=np[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the 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ap(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=yn.parseSliceParams(t,n,a),o=yn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),d=w.computeStrides(t.shape),p=r.dataIdMap.get(u.dataId);if(o){let m=yn.computeFlatOffset(s,d);return t.dtype==="string"?p.stringBytes=l.slice(m,m+w.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+w.sizeFromShape(i))),u}if(t.dtype==="string"){let m=u0(l,s,i,t.shape,t.dtype);return p.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)ire(l,d[0],c,s,i);else if(h===3)ore(l,d[0],d[1],c,s,i);else if(h===4)lre(l,d[0],d[1],d[2],c,s,i);else{let m=u0(l,s,i,t.shape,t.dtype);c.set(m)}return u}function ire(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u<l;u++){let d=u*t+o;n.set(e.subarray(d,d+r[1]),s),s+=r[1]}}function ore(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],u=r[2],d=o+s[0],p=l+s[1];for(let c=o;c<d;c++)for(let h=l;h<p;h++){let m=c*t+h*n+u;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function lre(e,t,n,a,r,s,i){let o=0,l=s[0],u=s[1],d=s[2],p=l+i[0],c=u+i[1],h=d+i[2],m=s[3];for(let f=l;f<p;f++)for(let g=u;g<c;g++)for(let A=d;A<h;A++){let y=f*t+g*n+A*a+m;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var ure={kernelName:hl,backendName:"wasm",kernelFunc:ap};function dre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a,o=s.reduce((A,y)=>A*y),l=R.getReshaped(r.shape,s,o),u=R.getPermuted(l.length,s.length),d=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(d,i,s.length),h=Pn({inputs:{x:r},backend:n,attrs:{shape:l}}),m=fu({inputs:{x:h},backend:n,attrs:{perm:u}}),f=Pn({inputs:{x:m},backend:n,attrs:{shape:d}}),g=ap({inputs:{x:f},backend:n,attrs:{begin:p,size:c}});return n.disposeData(h.dataId),n.disposeData(m.dataId),n.disposeData(h.dataId),g}var pre={kernelName:Oo,backendName:"wasm",kernelFunc:dre};function $0(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return 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xre(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:p,dataFormat:c}=n,h=R.convertConv2DDataFormat(c),m=R.computeConv2DInfo(r.shape,s.shape,l,u,d,p,!1,h),f=m.filterHeight,g=m.filterWidth,A=m.padInfo.top,y=m.padInfo.right,x=m.padInfo.bottom,v=m.padInfo.left,b=m.dilationHeight,k=m.dilationWidth,N=m.strideHeight,C=m.strideWidth,E=m.inChannels,z=m.outChannels,F=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. 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qoe=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Xoe=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Koe=[33,133,362,263,1,78,308],Hle=qoe.map(e=>pp[e]),jle=Xoe.map(e=>pp[e]),Gle=Koe.map(e=>pp[e]);var e2=pr.leftEyeLower0,t2=pr.rightEyeLower0,mu={leftBounds:[e2[0],e2[e2.length-1]],rightBounds:[t2[0],t2[t2.length-1]]},ek={count:468,mouth:13,symmetryLine:[13,pr.midwayBetweenEyes[0]]},Zoe={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},gu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function P0(e,t,n,a){for(let r=0;r<Qy.length;r++){let{key:s,indices:i}=Qy[r],o=pr[`${n}${s}`];if(!a||a.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var n2=class{constructor(t,n,a){var r,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=a,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(a==null?void 0:a.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,a,r){let s=lp({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(p=>[s[0]/this.meshSize*(p[0]-this.meshSize/2),s[1]/this.meshSize*(p[1]-this.meshSize/2),p[2]]),o=a!==0?Jy(a,[0,0]):D0,l=a!==0?i.map(p=>[...K8(p,o),p[2]]):i,u=a!==0?X8(r):D0,d=[...up({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(p=>[Math.round(p[0]+ms(d,u[0])),Math.round(p[1]+ms(d,u[1])),Math.round(p[2])])}getLeftToRightEyeDepthDifference(t){let n=t[mu.leftBounds[0]][2],a=t[mu.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=_0(z0(Yy([t[a],t[r]]),this.irisEnlarge)),o=lp(i),l=Me.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);if(s&&ra.flags.IS_BROWSER){let u=Me.flipLeftRight(l);K(l),l=u}return{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i<gu.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],u=t[i*3+2];s.push([(r?1-o/this.irisSize:o/this.irisSize)*a[0]+n.startPoint[0],l/this.irisSize*a[1]+n.startPoint[1],u])}return{rawCoords:s,iris:s.slice(gu.index)}}getAdjustedIrisCoords(t,n,a){let r=t[pr[`${a}EyeUpper0`][gu.upperCenter]][2],s=t[pr[`${a}EyeLower0`][gu.lowerCenter]][2],i=(r+s)/2;return n.map((o,l)=>{let u=i;return l===2?u=r:l===4&&(u=s),[o[0],o[1],u]})}correctFaceRotation(t,n,a){let[r,s]=n.landmarks.length>=ek.count?ek.symmetryLine:Zoe.symmetryLine,i=j8(n.landmarks[r],n.landmarks[s]),o=up({startPoint:n.startPoint,endPoint:n.endPoint}),l=[o[0]/a.shape[2],o[1]/a.shape[1]],u=Me.rotateWithOffset(a,i,0,l),d=Jy(-i,o),p=t.face.mesh.enabled?dp({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.meshSize,this.meshSize]):dp({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.boxSize,this.boxSize]),c=ce(p,255);return K(p),K(u),[i,d,c]}async augmentIris(t,n){let{box:a,boxSize:r,crop:s}=this.getEyeBox(t,n,mu.leftBounds[0],mu.leftBounds[1],!0),{box:i,boxSize:o,crop:l}=this.getEyeBox(t,n,mu.rightBounds[0],mu.rightBounds[1]),u=ht([s,l]);K(s),K(l);let d=this.irisModel.predict(u);K(u);let p=await d.data();K(d);let c=p.slice(0,gu.numCoordinates*3),{rawCoords:h,iris:m}=this.getEyeCoords(c,a,r,!0),f=p.slice(gu.numCoordinates*3),{rawCoords:g,iris:A}=this.getEyeCoords(f,i,o),y=this.getLeftToRightEyeDepthDifference(t);Math.abs(y)<30?(P0(t,h,"left",null),P0(t,g,"right",null)):y<1?P0(t,h,"left",["EyeUpper0","EyeLower0"]):P0(t,g,"right",["EyeUpper0","EyeLower0"]);let x=this.getAdjustedIrisCoords(t,m,"left"),v=this.getAdjustedIrisCoords(t,A,"right");return t.concat(x).concat(v)}async predict(t,n){let a=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t,n),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let o of r.boxes){let l=await o.box.startPoint.data(),u=await o.box.endPoint.data(),d=await o.landmarks.array();this.storedBoxes.push({startPoint:l,endPoint:u,landmarks:d,confidence:o.confidence})}this.storedBoxes.length>0&&(a=!0)}if(a){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let o=0;o<this.storedBoxes.length;o++){let l=U8({startPoint:this.storedBoxes[o].startPoint,endPoint:this.storedBoxes[o].endPoint},r.scaleFactor),u=z0(l),d=_0(u),p=this.storedBoxes[o].landmarks,c=this.storedBoxes[o].confidence;this.storedBoxes[o]={...d,confidence:c,landmarks:p}}}r&&r.boxes&&r.boxes.forEach(o=>{K(o.box.startPoint),K(o.box.endPoint),K(o.landmarks)});let s=[],i=[];for(let o of this.storedBoxes){let l,u=0,d;if(n.face.detector.rotation&&n.face.mesh.enabled&&ra.flags.IS_BROWSER)[u,d,l]=this.correctFaceRotation(n,o,t);else{d=D0;let p=t.clone(),c=n.face.mesh.enabled?dp({startPoint:o.startPoint,endPoint:o.endPoint},p,[this.meshSize,this.meshSize]):dp({startPoint:o.startPoint,endPoint:o.endPoint},p,[this.boxSize,this.boxSize]);l=ce(c,255),K(c),K(p)}if(!n.face.mesh.enabled)s.push({mesh:[],box:o,faceConfidence:null,boxConfidence:o.confidence,confidence:o.confidence,image:l});else{let[p,c,h]=this.meshDetector.execute(l);K(p);let m=(await c.data())[0];K(c);let f=B(h,[-1,3]),g=await f.array();if(K(h),K(f),m<n.face.detector.minConfidence)o.confidence=m,K(l);else{n.face.iris.enabled&&(g=await this.augmentIris(g,l));let A=this.transformRawCoords(g,o,u,d);o={...z0(Yy(A),1.5),confidence:o.confidence},n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&ra.flags.IS_BROWSER&&([u,d,l]=this.correctFaceRotation(n,o,t)),s.push({mesh:A,box:o,faceConfidence:m,boxConfidence:o.confidence,confidence:m,image:l}),o={..._0(o),confidence:o.confidence,faceConfidence:m}}}i.push(o)}return n.face.mesh.enabled&&(this.storedBoxes=i.filter(o=>o.confidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}};var Ft=[null,null,null],a2;async function tk(e,t){let n=await a2.predict(e,t),a=[],r=0;for(let s of n||[]){if(!s||s.isDisposedInternal)continue;let i=s.mesh.map(d=>[d[0]/(e.shape[2]||0),d[1]/(e.shape[1]||0),d[2]/a2.meshSize]),o={};if(s.mesh&&s.mesh.length>0)for(let d of Object.keys(pr))o[d]=pr[d].map(p=>s.mesh[p]);let l=s.box?[Math.trunc(Math.max(0,s.box.startPoint[0])),Math.trunc(Math.max(0,s.box.startPoint[1])),Math.trunc(Math.min(e.shape[2]||0,s.box.endPoint[0])-Math.max(0,s.box.startPoint[0])),Math.trunc(Math.min(e.shape[1]||0,s.box.endPoint[1])-Math.max(0,s.box.startPoint[1]))]:[0,0,0,0],u=s.box?[s.box.startPoint[0]/(e.shape[2]||0),s.box.startPoint[1]/(e.shape[1]||0),(s.box.endPoint[0]-s.box.startPoint[0])/(e.shape[2]||0),(s.box.endPoint[1]-s.box.startPoint[1])/(e.shape[1]||0)]:[0,0,0,0];a.push({id:r++,score:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxScore:Math.round(100*s.boxConfidence)/100,faceScore:Math.round(100*s.faceConfidence)/100,box:l,boxRaw:u,mesh:s.mesh,meshRaw:i,annotations:o,tensor:s.image}),s.coords&&K(s.coords)}return a}async function r2(e){return!Ft[0]&&e.face.enabled||!Ft[1]&&e.face.mesh.enabled||!Ft[2]&&e.face.iris.enabled?(Ft=await Promise.all([!Ft[0]&&e.face.enabled?Q8(e):null,!Ft[1]&&e.face.mesh.enabled?ft(gt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Ft[2]&&e.face.iris.enabled?ft(gt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Ft[1]||!Ft[1].modelUrl?ue("load model failed:",e.face.mesh.modelPath):e.debug&&ue("load model:",Ft[1].modelUrl)),e.face.iris.enabled&&(!Ft[2]||!Ft[2].modelUrl?ue("load model failed:",e.face.iris.modelPath):e.debug&&ue("load model:",Ft[2].modelUrl))):e.debug&&(Ft[0]&&ue("cached model:",Ft[0].model.modelUrl),Ft[1]&&ue("cached model:",Ft[1].modelUrl),Ft[2]&&ue("cached model:",Ft[2].modelUrl)),a2=new n2(Ft[0],Ft[1],Ft[2]),Ft}var nk=ro,ak=pp;var Ga,L0=[],rk=0,s2=Number.MAX_SAFE_INTEGER;async function i2(e){let t=gt(e.modelBasePath,e.face.description.modelPath);return Ga?e.debug&&ue("cached model:",t):(Ga=await ft(t),Ga?e.debug&&ue("load model:",t):ue("load model failed:",e.face.description.modelPath)),Ga}function o2(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let a=5*e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(0,100-a)/100}function sk(e,t,n=0){let a={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return a;for(let r of t)if(r.embedding&&r.name){let s=o2(e,r.embedding);s>n&&s>a.similarity&&(a={...r,similarity:s})}return a}function l2(e){return H(()=>{let n=e.image||e.tensor||e;if(!(n instanceof je))return null;let a=[[.05,.15,.85,.85]];if(!Ga.inputs[0].shape)return null;let r=n.shape.length===3?Me.cropAndResize(zt(n,0),a,[0],[Ga.inputs[0].shape[2],Ga.inputs[0].shape[1]]):Me.cropAndResize(n,a,[0],[Ga.inputs[0].shape[2],Ga.inputs[0].shape[1]]);return L(r,255)})}async function u2(e,t,n,a){var r,s;return Ga?s2<t.face.description.skipFrames&&t.skipFrame&&rk===a&&((r=L0[n])==null?void 0:r.age)&&((s=L0[n])==null?void 0:s.age)>0?(s2++,L0[n]):(s2=0,new Promise(async i=>{let o=l2(e),l,u={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(t.face.description.enabled&&(l=await Ga.predict(o)),K(o),l){let d=await l.find(A=>A.shape[1]===1).data(),p=Math.trunc(200*Math.abs(d[0]-.5))/100;p>t.face.description.minConfidence&&(u.gender=d[0]<=.5?"female":"male",u.genderScore=Math.min(.99,p));let h=(await Qa(l.find(A=>A.shape[1]===100),1).data())[0],m=await l.find(A=>A.shape[1]===100).data();u.age=Math.round(m[h-1]>m[h+1]?10*h-100*m[h-1]:10*h+100*m[h+1])/10;let g=await l.find(A=>A.shape[1]===1024).data();u.descriptor=[...g],l.forEach(A=>K(A))}L0[n]=u,rk=a,i(u)})):null}var Yoe=["angry","disgust","fear","happy","sad","surprise","neutral"],qa,W0=[],ik=0,d2=Number.MAX_SAFE_INTEGER,p2=[.2989,.587,.114];async function c2(e){return qa?e.debug&&ue("cached model:",qa.modelUrl):(qa=await ft(gt(e.modelBasePath,e.face.emotion.modelPath)),!qa||!qa.modelUrl?ue("load model failed:",e.face.emotion.modelPath):e.debug&&ue("load model:",qa.modelUrl)),qa}async function h2(e,t,n,a){return qa?d2<t.face.emotion.skipFrames&&t.skipFrame&&ik===a&&W0[n]&&W0[n].length>0?(d2++,W0[n]):(d2=0,new Promise(async r=>{let s=Me.resizeBilinear(e,[qa.inputs[0].shape[2],qa.inputs[0].shape[1]],!1),[i,o,l]=sn(s,3,3);K(s);let u=L(i,p2[0]),d=L(o,p2[1]),p=L(l,p2[2]);K(i),K(o),K(l);let c=Uc([u,d,p]);K(u),K(d),K(p);let h=H(()=>L(fe(c,.5),2));K(c);let m=[];if(t.face.emotion.enabled){let f=await qa.predict(h),g=await f.data();K(f);for(let A=0;A<g.length;A++)g[A]>t.face.emotion.minConfidence&&m.push({score:Math.min(.99,Math.trunc(100*g[A])/100),emotion:Yoe[A]});m.sort((A,y)=>y.score-A.score)}K(h),W0[n]=m,ik=a,r(m)})):null}var cp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ok=cp.length,hp=cp.reduce((e,t,n)=>(e[t]=n,e),{}),Joe=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Qoe=Joe.map(([e,t])=>[hp[e],hp[t]]),lk=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function uk(e){let t=e.reduce(({maxX:n,maxY:a,minX:r,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(n,i),maxY:Math.max(a,o),minX:Math.min(r,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function dk(e,[t,n],[a,r]){let s=t/a,i=n/r,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/r,u.box[1]/a,u.box[2]/r,u.box[3]/a],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:p,part:c,position:h})=>({score:p,part:c,position:[Math.trunc(h.x*i),Math.trunc(h.y*s)],positionRaw:[h.x/a,h.y/a]}))});return e.map((u,d)=>o(u,d))}var f2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let a=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=a}};function m2(e,t,n,a){return{y:a.get(e,t,n),x:a.get(e,t,n+ok)}}function g2(e,t,n){let{heatmapY:a,heatmapX:r,id:s}=e,{y:i,x:o}=m2(a,r,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function A2(e,t,n){return e<t?t:e>n?n:e}function pk(e,t,n,a){let r=n-e,s=a-t;return r*r+s*s}function y2(e,t){return{x:e.x+t.x,y:e.y+t.y}}var B0=1,Au=16,ele=50**2;function ck(e,t,n,a,r,s,i=2){let o=A=>({y:s.get(A.y,A.x,e),x:s.get(A.y,A.x,s.shape[2]/2+e)}),l=(A,y,x)=>({y:A2(Math.round(A.y/Au),0,y-1),x:A2(Math.round(A.x/Au),0,x-1)}),[u,d]=a.shape,p=l(t.position,u,d),c=o(p),m=y2(t.position,c);for(let A=0;A<i;A++){let y=l(m,u,d),x=m2(y.y,y.x,n,r);m=y2({x:y.x*Au,y:y.y*Au},{x:x.x,y:x.y})}let f=l(m,u,d),g=a.get(f.y,f.x,n);return{position:m,part:cp[n],score:g}}function tle(e,t,n,a,r){let s=lk.map(([c,h])=>[hp[c],hp[h]]),i=s.map(([,c])=>c),o=s.map(([c])=>c),l=t.shape[2],u=i.length,d=new Array(l),p=g2(e.part,Au,n);d[e.part.id]={score:e.score,part:cp[e.part.id],position:p};for(let c=u-1;c>=0;--c){let h=i[c],m=o[c];d[h]&&!d[m]&&(d[m]=ck(c,d[h],m,t,n,r))}for(let c=0;c<u;++c){let h=o[c],m=i[c];d[h]&&!d[m]&&(d[m]=ck(c,d[h],m,t,n,a))}return d}function nle(e,t,n,a,r){let[s,i]=r.shape,o=!0,l=Math.max(n-B0,0),u=Math.min(n+B0+1,s);for(let d=l;d<u;++d){let p=Math.max(a-B0,0),c=Math.min(a+B0+1,i);for(let h=p;h<c;++h)if(r.get(d,h,e)>t){o=!1;break}if(!o)break}return o}function ale(e,t){let[n,a,r]=t.shape,s=new f2(n*a*r,({score:i})=>i);for(let i=0;i<n;++i)for(let o=0;o<a;++o)for(let l=0;l<r;++l){let u=t.get(i,o,l);u<e||nle(l,u,i,o,t)&&s.enqueue({score:u,part:{heatmapY:i,heatmapX:o,id:l}})}return s}function hk(e,{x:t,y:n},a){return e.some(({keypoints:r})=>{var i;let s=(i=r[a])==null?void 0:i.position;return s?pk(n,t,s.y,s.x)<=ele:!1})}function rle(e,t){return t.reduce((a,{position:r,score:s},i)=>(hk(e,r,i)||(a+=s),a),0)/t.length}function fk(e,t,n,a,r,s){let i=[],o=ale(s,t);for(;i.length<r&&!o.empty();){let l=o.dequeue(),u=g2(l.part,Au,e);if(hk(i,u,l.part.id))continue;let d=tle(l,t,e,n,a);d=d.filter(h=>h.score>s);let p=rle(i,d),c=uk(d);p>s&&i.push({keypoints:d,box:c,score:Math.round(100*p)/100})}return i}var 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Me.nonMaxSuppressionAsync(a.norm,a.scores,10*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let s=await a.nms.array(),i=[];for(let o of s){let l=Re(a.norm,[o,0],[1,-1]),u=H(()=>B(this.normalizeLandmarks(Re(a.predictions,[o,5],[1,14]),o),[-1,2]));i.push({box:l,palmLandmarks:u,confidence:r[o]})}for(let o of Object.keys(a))K(a[o]);return i}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=H(()=>fe(ce(Me.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),i=await this.getBoxes(s,n);K(s);let o=[];if(!i||i.length===0)return o;for(let l of i){let u=await l.box.data(),d=u.slice(0,2),p=u.slice(2,4),c=await l.palmLandmarks.array();K(l.box),K(l.palmLandmarks),o.push(gk({startPoint:d,endPoint:p,palmLandmarks:c,confidence:l.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function ile(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function yk(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return ile(n)}var xk=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function gs(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function ole(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function bk(e,t){let n=[],a=e.length;for(let r=0;r<a;r++){n.push([]);for(let s=0;s<a;s++)n[r].push(gs(e[r],ole(t,s)))}return n}function w2(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=xk(t[0],t[1]),i=bk(s,r),o=xk(-t[0],-t[1]);return bk(i,o)}function vk(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-gs(t[0],n),-gs(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function k2(e,t){return[gs(e,t[0]),gs(e,t[1])]}var lle=5,wk=1.65,kk=[0,5,9,13,17,1,2],ule=0,dle=2,I2=class{constructor(t,n){var a;this.handDetector=t,this.handPoseModel=n,this.inputSize=(a=this.handPoseModel)==null?void 0:a.inputs[0].shape[2],this.storedBoxes=[],this.skipped=0,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),a=t.map(i=>i[1]),r=[Math.min(...n),Math.min(...a)],s=[Math.max(...n),Math.max(...a)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let a=t.map(s=>k2([...s,1],n)),r=this.calculateLandmarksBoundingBox(a);return U0(H0(r),lle)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),a=U0(H0(n),wk);a.palmLandmarks=[];for(let r=0;r<kk.length;r++)a.palmLandmarks.push(t[kk[r]].slice(0,2));return a}transformRawCoords(t,n,a,r){let s=V0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(h=>[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=w2(a,[0,0]),u=o.map(h=>[...k2(h,l),h[2]]),d=vk(r),p=[...fp(n),1],c=[gs(p,d[0]),gs(p,d[1])];return u.map(h=>[Math.trunc(h[0]+c[0]),Math.trunc(h[1]+c[1]),Math.trunc(h[2])])}async estimateHands(t,n){let a=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await 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t){let s=t[r],i=this.curls[r];if(typeof i=="undefined"){a+=this.weightsRelative[r];continue}for(let[o,l]of i)if(s===o){a+=l*this.weightsRelative[r];break}}for(let r in n){let s=n[r],i=this.directions[r];if(typeof i=="undefined"){a+=this.weightsRelative[r];continue}for(let[o,l]of i)if(s===o){a+=l*this.weightsRelative[r];break}}return a/10}};var As=new mp("thumbs up");As.addCurl(Ue.thumb,Tn.none,1);As.addDirection(Ue.thumb,We.verticalUp,1);As.addDirection(Ue.thumb,We.diagonalUpLeft,.25);As.addDirection(Ue.thumb,We.diagonalUpRight,.25);for(let e of[Ue.index,Ue.middle,Ue.ring,Ue.pinky])As.addCurl(e,Tn.full,1),As.addDirection(e,We.horizontalLeft,1),As.addDirection(e,We.horizontalRight,1);var Wt=new 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t=S2(e),n=[];for(let a of Ek){let r=a.matchAgainst(t.curls,t.directions);r>=fle&&n.push({name:a.name,confidence:r})}return n}var Fk={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},ys,xs,$k;async function T2(e,t){let n=await $k.estimateHands(e,t);if(!n)return[];let a=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let d of Object.keys(Fk))s[d]=Fk[d].map(p=>n[r].landmarks[p]);let i=n[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let u=Rk(i);a.push({id:r,score:Math.round(100*n[r].confidence)/100,box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return a}async function N2(e){!ys||!xs?([ys,xs]=await Promise.all([e.hand.enabled?ft(gt(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?ft(gt(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!ys||!ys.modelUrl?ue("load model failed:",e.hand.detector.modelPath):e.debug&&ue("load model:",ys.modelUrl),!xs||!xs.modelUrl?ue("load model failed:",e.hand.skeleton.modelPath):e.debug&&ue("load model:",xs.modelUrl))):(e.debug&&ue("cached model:",ys.modelUrl),e.debug&&ue("cached model:",xs.modelUrl));let t=new v2(ys);return $k=new I2(t,xs),[ys,xs]}var Ok=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],zk=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var Wn;async function j0(e){return Wn?e.debug&&ue("cached model:",Wn.modelUrl):(Wn=await ft(gt(e.modelBasePath,e.body.modelPath)),Wn.width=parseInt(Wn.signature.inputs["input_1:0"].tensorShape.dim[2].size),Wn.height=parseInt(Wn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!Wn||!Wn.modelUrl?ue("load model failed:",e.body.modelPath):e.debug&&ue("load model:",Wn.modelUrl)),Wn}async function C2(e,t){if(!Wn)return[];if(!t.body.enabled)return[];let n={width:e.shape[2]||0,height:e.shape[1]||0},a=Me.resizeBilinear(e,[Wn.width,Wn.height],!1),r=ce(a,[255]);K(a);let s=await Wn.predict(r),i=s.find(g=>g.size===195||g.size===155),o=await(i==null?void 0:i.data())||[];s.forEach(g=>K(g)),K(r);let l=[],u=(o==null?void 0:o.length)===195?Ok:zk,d=5;for(let g=0;g<o.length/d;g++)l.push({id:g,part:u[g],position:[Math.trunc(n.width*o[d*g+0]/255),Math.trunc(n.height*o[d*g+1]/255),Math.trunc(o[d*g+2])+0],positionRaw:[o[d*g+0]/255,o[d*g+1]/255,o[d*g+2]+0],score:(100-Math.trunc(100/(1+Math.exp(o[d*g+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(o[d*g+4]))))/100});let p=l.map(g=>g.position[0]),c=l.map(g=>g.position[1]),h=[Math.min(...p),Math.min(...c),Math.max(...p)-Math.min(...p),Math.max(...c)-Math.min(...p)],m=[0,0,0,0],f=l.reduce((g,A)=>A.score>g?A.score:g,0);return[{id:0,score:f,box:h,boxRaw:m,keypoints:l}]}var Bn,cr=[],E2=[0,0,0,0],R2=[0,0,0,0],G0=0,M2=Number.MAX_SAFE_INTEGER,mle=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function _k(e){return Bn?e.debug&&ue("cached model:",Bn.modelUrl):(Bn=await ft(gt(e.modelBasePath,e.body.modelPath)),!Bn||!Bn.modelUrl?ue("load model failed:",e.body.modelPath):e.debug&&ue("load model:",Bn.modelUrl)),Bn}function gle(e,t){let[n,a]=e.shape;return H(()=>{let r=(o,l)=>fe(o,L(ce(o,ke(l,"int32")),ke(l,"int32"))),s=B(e,[a*n]),i=da(s,0).dataSync()[0];if(i>t){let o=Qa(s,0),l=r(o,n).dataSync()[0],u=ce(o,ke(n,"int32")).dataSync()[0];return[l,u,i]}return[0,0,i]})}async function F2(e,t){return M2<t.body.skipFrames&&t.skipFrame&&Object.keys(cr).length>0?(M2++,[{id:0,score:G0,box:E2,boxRaw:R2,keypoints:cr}]):(M2=0,new Promise(async n=>{let a=H(()=>{if(!Bn.inputs[0].shape)return null;let u=Me.resizeBilinear(e,[Bn.inputs[0].shape[2],Bn.inputs[0].shape[1]],!1);return L(u,2).sub(1)}),r;if(t.body.enabled&&(r=await Bn.predict(a)),K(a),r){cr.length=0;let u=r.squeeze();K(r);let d=u.unstack(2);K(u);for(let p=0;p<d.length;p++){let[c,h,m]=gle(d[p],t.body.minConfidence);G0>t.body.minConfidence&&cr.push({score:Math.round(100*m)/100,part:mle[p],positionRaw:[c/Bn.inputs[0].shape[2],h/Bn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*c/Bn.inputs[0].shape[2]),Math.round(e.shape[1]*h/Bn.inputs[0].shape[1])]})}d.forEach(p=>K(p))}G0=cr.reduce((u,d)=>d.score>u?d.score:u,0);let s=cr.map(u=>u.position[0]),i=cr.map(u=>u.position[1]);E2=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=cr.map(u=>u.positionRaw[0]),l=cr.map(u=>u.positionRaw[1]);R2=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)],n([{id:0,score:G0,box:E2,boxRaw:R2,keypoints:cr}])}))}var hr,xa=[],$2=[0,0,0,0],Mr=[0,0,0,0],Fr=0,O2=Number.MAX_SAFE_INTEGER,Dk=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function z2(e){return hr?e.debug&&ue("cached model:",hr.modelUrl):(hr=await ft(gt(e.modelBasePath,e.body.modelPath)),!hr||!hr.modelUrl?ue("load model failed:",e.body.modelPath):e.debug&&ue("load model:",hr.modelUrl)),hr}async function Ale(e,t,n){xa.length=0;let a=e[0][0];for(let u=0;u<a.length;u++)Fr=a[u][2],Fr>t.body.minConfidence&&xa.push({score:Math.round(100*Fr)/100,part:Dk[u],positionRaw:[a[u][1],a[u][0]],position:[Math.round((n.shape[2]||0)*a[u][1]),Math.round((n.shape[1]||0)*a[u][0])]});Fr=xa.reduce((u,d)=>d.score>u?d.score:u,0);let r=xa.map(u=>u.position[0]),s=xa.map(u=>u.position[1]);$2=[Math.min(...r),Math.min(...s),Math.max(...r)-Math.min(...r),Math.max(...s)-Math.min(...s)];let i=xa.map(u=>u.positionRaw[0]),o=xa.map(u=>u.positionRaw[1]);Mr=[Math.min(...i),Math.min(...o),Math.max(...i)-Math.min(...i),Math.max(...o)-Math.min(...o)];let l=[];return l.push({id:0,score:Fr,box:$2,boxRaw:Mr,keypoints:xa}),l}async function yle(e,t,n){let a=[];for(let r=0;r<e[0].length;r++){let s=e[0][r];if(Fr=Math.round(100*s[51+4])/100,!(Fr<t.body.minConfidence)){xa.length=0;for(let i=0;i<17;i++){let o=Math.round(100*s[3*i+2])/100;o>t.body.minConfidence&&xa.push({part:Dk[i],score:o,positionRaw:[s[3*i+1],s[3*i+0]],position:[Math.trunc(s[3*i+1]*(n.shape[2]||0)),Math.trunc(s[3*i+0]*(n.shape[1]||0))]})}Mr=[s[51+1],s[51+0],s[51+3]-s[51+1],s[51+2]-s[51+0]],a.push({id:r,score:Fr,boxRaw:Mr,box:[Math.trunc(Mr[0]*(n.shape[2]||0)),Math.trunc(Mr[1]*(n.shape[1]||0)),Math.trunc(Mr[2]*(n.shape[2]||0)),Math.trunc(Mr[3]*(n.shape[1]||0))],keypoints:xa})}}return a}async function _2(e,t){return O2<t.body.skipFrames&&t.skipFrame&&Object.keys(xa).length>0?(O2++,[{id:0,score:Fr,box:$2,boxRaw:Mr,keypoints:xa}]):(O2=0,new Promise(async n=>{let a=H(()=>{if(!hr.inputs[0].shape)return null;let o=hr.inputs[0].shape[2];o===-1&&(o=256);let l=Me.resizeBilinear(e,[o,o],!1);return de(l,"int32")}),r;t.body.enabled&&(r=await hr.predict(a)),K(a),r||n([]);let s=await r.array(),i;r.shape[2]===17?i=await Ale(s,t,e):r.shape[2]===56&&(i=await yle(s,t,e)),K(r),n(i)}))}var yu=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball 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phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var Jn,D2=[],P2=Number.MAX_SAFE_INTEGER,q0=2.5;async function L2(e){if(Jn)e.debug&&ue("cached model:",Jn.modelUrl);else{Jn=await ft(gt(e.modelBasePath,e.object.modelPath));let t=Object.values(Jn.modelSignature.inputs);if(Jn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Jn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!Jn||!Jn.modelUrl?ue("load model failed:",e.object.modelPath):e.debug&&ue("load model:",Jn.modelUrl)}return Jn}async function xle(e,t,n,a){let r=0,s=[];for(let u of[1,2,4])H(async()=>{var g,A;let d=u*13,p=(g=e.find(y=>y.shape[1]===d**2&&y.shape[2]===yu.length))==null?void 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`),p.brightness=function(b){let k=(b||0)+1;p.colorMatrix([k,0,0,0,0,0,k,0,0,0,0,0,k,0,0,0,0,0,1,0])},p.saturation=function(b){let k=(b||0)*2/3+1,N=(k-1)*-.5;p.colorMatrix([k,N,N,0,0,N,k,N,0,0,N,N,k,0,0,0,0,0,1,0])},p.desaturate=function(){p.saturation(-1)},p.contrast=function(b){let k=(b||0)+1,N=-128*(k-1);p.colorMatrix([k,0,0,0,N,0,k,0,0,N,0,0,k,0,N,0,0,0,1,0])},p.negative=function(){p.contrast(-2)},p.hue=function(b){b=(b||0)/180*Math.PI;let k=Math.cos(b),N=Math.sin(b),C=.213,E=.715,z=.072;p.colorMatrix([C+k*(1-C)+N*-C,E+k*-E+N*-E,z+k*-z+N*(1-z),0,0,C+k*-C+N*.143,E+k*(1-E)+N*.14,z+k*-z+N*-.283,0,0,C+k*-C+N*-(1-C),E+k*-E+N*E,z+k*(1-z)+N*z,0,0,0,0,0,1,0])},p.desaturateLuminance=function(){p.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},p.sepia=function(){p.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},p.brownie=function(){p.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},p.vintagePinhole=function(){p.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},p.kodachrome=function(){p.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},p.technicolor=function(){p.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},p.polaroid=function(){p.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},p.shiftToBGR=function(){p.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},p.convolution=function(b){let k=new Float32Array(b),N=1/o,C=1/l,E=v(p.convolution.SHADER);f.uniform1fv(E.uniform.m,k),f.uniform2f(E.uniform.px,N,C),x()},p.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
`),p.detectEdges=function(){p.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},p.sobelX=function(){p.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},p.sobelY=function(){p.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},p.sharpen=function(b){let k=b||1;p.convolution.call(this,[0,-1*k,0,-1*k,1+4*k,-1*k,0,-1*k,0])},p.emboss=function(b){let k=b||1;p.convolution.call(this,[-2*k,-1*k,0,-1*k,1,1*k,0,1*k,2*k])},p.blur=function(b){let k=b/7/o,N=b/7/l,C=v(p.blur.SHADER);f.uniform2f(C.uniform.px,0,N),x(m.INTERMEDIATE),f.uniform2f(C.uniform.px,k,0),x()},p.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(`
`),p.pixelate=function(b){let k=b/o,N=b/l,C=v(p.pixelate.SHADER);f.uniform2f(C.uniform.size,k,N),x()},p.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
`)}var X0=2048,Ee,It,Bt;function io(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof je)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof je)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Oa(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let r=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!r||!s)return{tensor:null,canvas:Ee};let i=r,o=s;if(i>X0&&(i=X0,o=i*s/r),o>X0&&(o=X0,i=o*r/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=r*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/r)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Ee||(Ee==null?void 0:Ee.width)!==i||(Ee==null?void 0:Ee.height)!==o)&&(Ee=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Ee==null?void 0:Ee.width)!==i&&(Ee.width=i),(Ee==null?void 0:Ee.height)!==o&&(Ee.height=o));let l=Ee.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):t.filter.flip&&typeof l.translate!="undefined"?(l.translate(r,0),l.scale(-1,1),l.drawImage(e,0,0,r,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),l.setTransform(1,0,0,1,0,0)):l.drawImage(e,0,0,r,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),t.filter.enabled){if((!Bt||!It||Ee.width!==It.width||(Ee==null?void 0:Ee.height)!==(It==null?void 0:It.height))&&(It=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height):document.createElement("canvas"),(It==null?void 0:It.width)!==(Ee==null?void 0:Ee.width)&&(It.width=Ee==null?void 0:Ee.width),(It==null?void 0:It.height)!==(Ee==null?void 0:Ee.height)&&(It.height=Ee==null?void 0:Ee.height),Bt=ra.flags.IS_BROWSER?new Pk({canvas:It}):null),!Bt)return{tensor:null,canvas:Ee};Bt.reset(),Bt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Bt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Bt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Bt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Bt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Bt.addFilter("hue",t.filter.hue),t.filter.negative&&Bt.addFilter("negative"),t.filter.sepia&&Bt.addFilter("sepia"),t.filter.vintage&&Bt.addFilter("brownie"),t.filter.sepia&&Bt.addFilter("sepia"),t.filter.kodachrome&&Bt.addFilter("kodachrome"),t.filter.technicolor&&Bt.addFilter("technicolor"),t.filter.polaroid&&Bt.addFilter("polaroid"),t.filter.pixelate!==0&&Bt.addFilter("pixelate",t.filter.pixelate),Bt.apply(Ee)}else It=Ee,Bt&&(Bt=null);if(!n){let u;if(It.data){let d=[It.height,It.width,3];u=Wc(It.data,d,"int32")}else if(It instanceof ImageData)u=ia?ia.fromPixels(It):null;else if(t.backend==="webgl"||t.backend==="humangl"){let d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");d.width=i,d.height=o;let p=d.getContext("2d");p==null||p.drawImage(It,0,0),u=ia?ia.fromPixels(d):null}else{let d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");d.width=i,d.height=o;let p=d.getContext("2d");p==null||p.drawImage(It,0,0);let c=p==null?void 0:p.getImageData(0,0,i,o);u=ia?ia.fromPixels(c):null}if(u){let d=de(u,"float32");n=zt(d,0),K(u),K(d)}}}let a=t.filter.return?It:null;return{tensor:n,canvas:a}}var ba,j2=!1;async function K0(e){return ba?e.debug&&ue("cached model:",ba.modelUrl):(ba=await ft(gt(e.modelBasePath,e.segmentation.modelPath)),!ba||!ba.modelUrl?ue("load model failed:",e.segmentation.modelPath):e.debug&&ue("load model:",ba.modelUrl)),ba}async function G2(e){var m,f;let t=((m=e.tensor)==null?void 0:m.shape[1])||0,n=((f=e.tensor)==null?void 0:f.shape[2])||0;if(!e.tensor||!ba||!ba.inputs[0].shape)return null;let a=Me.resizeBilinear(e.tensor,[ba.inputs[0].shape[1],ba.inputs[0].shape[2]],!1),r=ce(a,255),s=ba.predict(r);K(a),K(r);let i=lt(s,0),o;if(i.shape[2]===2){let g=i.softmax(),[A,y]=ha(g,2),x=zt(y,2),v=zt(x,0);K(g),K(A),K(y);let b=Me.cropAndResize(v,[[0,0,.5,.5]],[0],[t,n]);o=lt(b,0),K(b),K(x),K(v)}else o=Me.resizeBilinear(i,[t,n]);if(typeof document=="undefined")return o.data();let l=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");l.width=t,l.height=n,ia&&await ia.toPixels(o,l),K(o),K(i),K(s);let u=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");u.width=t,u.height=n;let d=u.getContext("2d");d.filter="blur(8px",await d.drawImage(l,0,0);let p=d.getImageData(0,0,t,n).data,c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t,n):document.createElement("canvas");c.width=t,c.height=n;let h=c.getContext("2d");return e.canvas&&await h.drawImage(e.canvas,0,0),h.globalCompositeOperation="darken",h.filter="blur(8px)",await h.drawImage(l,0,0),h.globalCompositeOperation="source-over",h.filter="none",e.canvas=c,p}async function Lk(e,t,n){var s;if(j2)return null;j2=!0,ba||await K0(n);let a=io(e,n),r=await G2(a);if(K(a.tensor),t&&r){let i=io(t,n),o=i.canvas;K(i.tensor);let l=a.canvas,u=(s=l.getContext("2d"))==null?void 0:s.getImageData(0,0,l.width,l.height).data,d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(l.width,l.height):document.createElement("canvas");d.width=l.width,d.height=l.height;let p=d.getContext("2d");p.globalCompositeOperation="copy",p.drawImage(o,0,0,d.width,d.height);let c=p.getImageData(0,0,d.width,d.height);for(let h=0;h<d.width*d.height;h++)c.data[4*h+0]=(255-r[4*h+0])/255*c.data[4*h+0]+r[4*h+0]/255*u[4*h+0],c.data[4*h+1]=(255-r[4*h+1])/255*c.data[4*h+1]+r[4*h+1]/255*u[4*h+1],c.data[4*h+2]=(255-r[4*h+2])/255*c.data[4*h+2]+r[4*h+2]/255*u[4*h+2],c.data[4*h+3]=(255-r[4*h+3])/255*c.data[4*h+3]+r[4*h+3]/255*u[4*h+3];p.putImageData(c,0,0),a.canvas=d}return j2=!1,a.canvas}async function Wk(e){e.config.async?[e.models.face,e.models.emotion,e.models.handpose,e.models.posenet,e.models.blazepose,e.models.efficientpose,e.models.movenet,e.models.nanodet,e.models.centernet,e.models.faceres,e.models.segmentation]=await Promise.all([e.models.face||(e.config.face.enabled?r2(e.config):null),e.models.emotion||(e.config.face.enabled&&e.config.face.emotion.enabled?c2(e.config):null),e.models.handpose||(e.config.hand.enabled?N2(e.config):null),e.models.posenet||(e.config.body.enabled&&e.config.body.modelPath.includes("posenet")?b2(e.config):null),e.models.blazepose||(e.config.body.enabled&&e.config.body.modelPath.includes("blazepose")?j0(e.config):null),e.models.efficientpose||(e.config.body.enabled&&e.config.body.modelPath.includes("efficientpose")?_k(e.config):null),e.models.movenet||(e.config.body.enabled&&e.config.body.modelPath.includes("movenet")?z2(e.config):null),e.models.nanodet||(e.config.object.enabled&&e.config.object.modelPath.includes("nanodet")?L2(e.config):null),e.models.centernet||(e.config.object.enabled&&e.config.object.modelPath.includes("centernet")?U2(e.config):null),e.models.faceres||(e.config.face.enabled&&e.config.face.description.enabled?i2(e.config):null),e.models.segmentation||(e.config.segmentation.enabled?K0(e.config):null)]):(e.config.face.enabled&&!e.models.face&&(e.models.face=await r2(e.config)),e.config.face.enabled&&e.config.face.emotion.enabled&&!e.models.emotion&&(e.models.emotion=await c2(e.config)),e.config.hand.enabled&&!e.models.handpose&&(e.models.handpose=await N2(e.config)),e.config.body.enabled&&!e.models.posenet&&e.config.body.modelPath.includes("posenet")&&(e.models.posenet=await b2(e.config)),e.config.body.enabled&&!e.models.blazepose&&e.config.body.modelPath.includes("blazepose")&&(e.models.blazepose=await j0(e.config)),e.config.body.enabled&&!e.models.efficientpose&&e.config.body.modelPath.includes("efficientpose")&&(e.models.efficientpose=await j0(e.config)),e.config.body.enabled&&!e.models.movenet&&e.config.body.modelPath.includes("movenet")&&(e.models.movenet=await z2(e.config)),e.config.object.enabled&&!e.models.nanodet&&e.config.object.modelPath.includes("nanodet")&&(e.models.nanodet=await L2(e.config)),e.config.object.enabled&&!e.models.centernet&&e.config.object.modelPath.includes("centernet")&&(e.models.centernet=await U2(e.config)),e.config.face.enabled&&e.config.face.description.enabled&&!e.models.faceres&&(e.models.faceres=await i2(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=await K0(e.config)))}var wle=e=>{let t=(p,c)=>Math.atan2(p[1]-c[1],p[0]-c[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],a=1,r=e.mesh[33][2]>e.mesh[263][2],s=r?e.mesh[473]:e.mesh[468],i=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-n[0],a*(s[1]-i[1])/o[1]-n[1]],u=Math.sqrt(l[0]**2+l[1]**2);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},kle=(e,t)=>{let n=g=>{let A=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=A,g[1]/=A,g[2]/=A,g},a=(g,A)=>{let y=g[0]-A[0],x=g[1]-A[1],v=g[2]-A[2];return[y,x,v]},r=(g,A)=>{let y=g[1]*A[2]-g[2]*A[1],x=g[2]*A[0]-g[0]*A[2],v=g[0]*A[1]-g[1]*A[0];return[y,x,v]},s=g=>{let[A,y,x,v,b,k,N,C,E]=g,z,F,I;return v<1?v>-1?(I=Math.asin(v),F=Math.atan2(-N,A),z=Math.atan2(-k,b)):(I=-Math.PI/2,F=-Math.atan2(C,E),z=0):(I=Math.PI/2,F=Math.atan2(C,E),z=0),isNaN(z)&&(z=0),isNaN(F)&&(F=0),isNaN(I)&&(I=0),{pitch:2*-z,yaw:2*-F,roll:2*-I}},i=g=>{let A=(x,v,b,k)=>Math.atan2(k-v,b-x);return{pitch:A(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:A(g[33][0],g[33][2],g[263][0],g[263][2]),roll:A(g[33][0],g[33][1],g[263][0],g[263][1])}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[o[10],o[152],o[234],o[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),d=n(a(u[1],u[0])),p=n(a(u[3],u[2])),c=n(r(p,d));p=r(d,c);let h=[p[0],p[1],p[2],d[0],d[1],d[2],c[0],c[1],c[2]],m=s(h),f=o.length===478?wle(e):{bearing:0,strength:0};return{angle:m,matrix:h,gaze:f}},q2=async(e,t)=>{var p,c,h,m,f,g;let n,a,r,s,i,o,l,u=[];e.state="run:face",n=Ye();let d=await tk(t,e.config);if(e.performance.face=Math.trunc(Ye()-n),!t.shape||t.shape.length!==4)return[];if(!d)return[];for(let A=0;A<d.length;A++){if(e.analyze("Get Face"),!d[A].tensor||d[A].tensor.isDisposedInternal){ue("Face object is disposed:",d[A].tensor);continue}let y=kle(d[A],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?i=e.config.face.emotion.enabled?h2(d[A].tensor||pn([]),e.config,A,d.length):{}:(e.state="run:emotion",n=Ye(),i=e.config.face.emotion.enabled?await h2(d[A].tensor||pn([]),e.config,A,d.length):{},e.performance.emotion=Math.trunc(Ye()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?l=e.config.face.description.enabled?u2(d[A].tensor||pn([]),e.config,A,d.length):[]:(e.state="run:description",n=Ye(),l=e.config.face.description.enabled?await u2(d[A].tensor||pn([]),e.config,A,d.length):[],e.performance.embedding=Math.trunc(Ye()-n)),e.analyze("End Description:"),e.config.async&&([a,s,i,o,l,r]=await Promise.all([a,s,i,o,l,r])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((c=(p=d[A])==null?void 0:p.annotations)==null?void 0:c.leftEyeIris)&&((m=(h=d[A])==null?void 0:h.annotations)==null?void 0:m.rightEyeIris)&&(delete d[A].annotations.leftEyeIris,delete d[A].annotations.rightEyeIris);let x=((f=d[A].annotations)==null?void 0:f.leftEyeIris)&&((g=d[A].annotations)==null?void 0:g.rightEyeIris)?Math.max(Math.abs(d[A].annotations.leftEyeIris[3][0]-d[A].annotations.leftEyeIris[1][0]),Math.abs(d[A].annotations.rightEyeIris[4][1]-d[A].annotations.rightEyeIris[2][1]))/t.shape[2]:0,v=e.config.face.detector.return?lt(d[A].tensor):null;K(d[A].tensor),d[A].tensor&&delete d[A].tensor,u.push({...d[A],id:A,age:l.age,gender:l.gender,genderScore:l.genderScore,embedding:l.descriptor,emotion:i,iris:x!==0?Math.trunc(500/x/11.7)/100:0,rotation:y,tensor:v}),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),u};var Bk=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let a=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&a&&r&&a.position.y<s.position.y&&r.position.y<s.position.y?t.push({body:n,gesture:"i give up"}):s&&a&&a.position.y<s.position.y?t.push({body:n,gesture:"raise left hand"}):s&&r&&r.position.y<s.position.y&&t.push({body:n,gesture:"raise right hand"});let i=e[n].keypoints.find(l=>l.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},Vk=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let a=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(a)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${a<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let 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 ${o<0?"up":"down"}`})}return t},Uk=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let a=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(a*r),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:n,gesture:"facing center"}));let p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],c=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(c>.06||p>.06)&&(u=!1),c>.06&&t.push({iris:n,gesture:"looking right"}),p>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],m=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(m<.01||h<.01||m>.022||h>.022)&&(u=!1),(m<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(m>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},Hk=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let a=[];for(let[s,i]of Object.entries(e[n].annotations))s!=="palmBase"&&Array.isArray(i)&&a.push({name:s.toLowerCase(),position:i[0]});if(a&&a.length>0){let s=a.reduce((o,l)=>o.position[2]<l.position[2]?o:l);t.push({hand:n,gesture:`${s.name} forward`});let i=a.reduce((o,l)=>o.position[1]<l.position[1]?o:l);t.push({hand:n,gesture:`${i.name} up`})}let r=Mk(e[n].keypoints);for(let s of r)t.push({hand:n,gesture:s.name})}return t};var Z2={};p5(Z2,{all:()=>Tle,body:()=>qk,canvas:()=>Sle,face:()=>Gk,gesture:()=>jk,hand:()=>Xk,object:()=>Kk,options:()=>bs,person:()=>Ile});var bs={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},vs=e=>{if(e&&e.getContext)return e.getContext("2d");throw new Error("Human: Invalid Canvas")},Z0=e=>Math.round(e*180/Math.PI);function X2(e,t,n,a=0,r){e.fillStyle=r.useDepth&&a?`rgba(${127.5+2*a}, ${127.5-2*a}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function gp(e,t,n,a,r,s){if(e.beginPath(),s.useCurves){let i=(t+t+a)/2,o=(n+n+r)/2;e.ellipse(i,o,a/2,r/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+a-s.roundRect,n),e.quadraticCurveTo(t+a,n,t+a,n+s.roundRect),e.lineTo(t+a,n+r-s.roundRect),e.quadraticCurveTo(t+a,n+r,t+a-s.roundRect,n+r),e.lineTo(t+s.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function K2(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let a of t){let r=a[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(a[0],Math.round(a[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function Ap(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){K2(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let a=0;a<t.length-2;a++){let r=(t[a][0]+t[a+1][0])/2,s=(t[a][1]+t[a+1][1])/2;e.quadraticCurveTo(t[a][0],t[a][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function jk(e,t,n){let a=mn(bs,n);if(!t||!e)return;let r=vs(e);r.font=a.font,r.fillStyle=a.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d,8,2+s*a.lineHeight)),r.fillStyle=a.labelColor,r.fillText(d,6,0+s*a.lineHeight),s+=1}}}async function Gk(e,t,n){var s,i,o,l;let a=mn(bs,n);if(!t||!e)return;let r=vs(e);for(let u of t){r.font=a.font,r.strokeStyle=a.color,r.fillStyle=a.color,a.drawBoxes&&gp(r,u.box[0],u.box[1],u.box[2],u.box[3],a);let d=[];if(d.push(`face: ${Math.trunc(100*u.score)}%`),u.genderScore&&d.push(`${u.gender||""} ${Math.trunc(100*u.genderScore)}%`),u.age&&d.push(`age: ${u.age||""}`),u.iris&&d.push(`distance: ${u.iris}`),u.emotion&&u.emotion.length>0){let p=u.emotion.map(c=>`${Math.trunc(100*c.score)}% ${c.emotion}`);p.length>3&&(p.length=3),d.push(p.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.gaze&&(u.rotation.angle.roll&&d.push(`roll: ${Z0(u.rotation.angle.roll)}\xB0 yaw:${Z0(u.rotation.angle.yaw)}\xB0 pitch:${Z0(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&d.push(`gaze: ${Z0(u.rotation.gaze.bearing)}\xB0`)),d.length===0&&d.push("face"),r.fillStyle=a.color;for(let p=d.length-1;p>=0;p--){let c=Math.max(u.box[0],0),h=p*a.lineHeight+u.box[1];a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d[p],c+5,h+16)),r.fillStyle=a.labelColor,r.fillText(d[p],c+4,h+15)}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(a.drawPoints)for(let p of u.mesh)X2(r,p[0],p[1],p[2],a);if(a.drawPolygons){r.lineWidth=1;for(let p=0;p<ro.length/3;p++){let c=[ro[p*3+0],ro[p*3+1],ro[p*3+2]].map(h=>u.mesh[h]);K2(r,c,a)}if(u.annotations&&u.annotations.leftEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,c=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,c=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(a.drawGaze&&((i=(s=u.rotation)==null?void 0:s.gaze)==null?void 0:i.strength)&&((l=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:l.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.beginPath();let p=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),r.lineTo(p[0],p[1]);let c=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),r.lineTo(c[0],c[1]),r.stroke()}}}}}async function qk(e,t,n){var s;let a=mn(bs,n);if(!t||!e)return;let r=vs(e);r.lineJoin="round";for(let i=0;i<t.length;i++){if(r.strokeStyle=a.color,r.fillStyle=a.color,r.lineWidth=a.lineWidth,r.font=a.font,a.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(gp(r,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+a.lineHeight,t[i].box[2])),r.fillStyle=a.labelColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+a.lineHeight,t[i].box[2]))),a.drawPoints)for(let o=0;o<t[i].keypoints.length;o++)r.fillStyle=a.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:a.color,X2(r,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,a);if(a.drawLabels&&(r.font=a.font,t[i].keypoints))for(let o of t[i].keypoints)r.fillStyle=a.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:a.color,r.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4);if(a.drawPolygons&&t[i].keypoints){let o,l=[];l.length=0,o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position[0],o.position[1]]),Ap(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position[0],o.position[1]]),l.length===4&&K2(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftKnee"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftAnkle"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftHeel"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftFoot"),o&&l.push([o.position[0],o.position[1]]),Ap(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightKnee"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightAnkle"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightHeel"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightFoot"),o&&l.push([o.position[0],o.position[1]]),Ap(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftElbow"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftWrist"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftPalm"),o&&l.push([o.position[0],o.position[1]]),Ap(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightElbow"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightWrist"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightPalm"),o&&l.push([o.position[0],o.position[1]]),Ap(r,l,a)}}}async function Xk(e,t,n){let a=mn(bs,n);if(!t||!e)return;let r=vs(e);r.lineJoin="round",r.font=a.font;for(let s of t){if(a.drawBoxes&&(r.strokeStyle=a.color,r.fillStyle=a.color,gp(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText("hand",s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText("hand",s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])),r.stroke()),a.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)r.fillStyle=a.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:a.color,X2(r,i[0],i[1],0,a);if(a.drawLabels){let i=(o,l)=>{!o||(r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 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s=Promise.all([Gk(e,t.face,r),qk(e,t.body,r),Xk(e,t.hand,r),Kk(e,t.object,r),jk(e,t.gesture,r)]);return t.performance.draw=Math.trunc(Ye()-a),s}function Zk(e,t,n,a,r){var o,l,u,d,p,c,h,m,f,g,A,y,x,v,b,k;let s=0,i=[];for(let N of e){let C={id:s++,face:N,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let D of t)N.box[0]>D.box[0]&&N.box[0]<D.box[0]+D.box[2]&&N.box[1]+N.box[3]>D.box[1]&&N.box[1]+N.box[3]<D.box[1]+D.box[3]&&(C.body=D);if(C.body)for(let D of n)D.box[0]+D.box[2]>C.body.box[0]&&D.box[0]+D.box[2]<C.body.box[0]+C.body.box[2]&&D.box[1]+D.box[3]>C.body.box[1]&&D.box[1]+D.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=D),D.box[0]<C.body.box[0]+C.body.box[2]&&D.box[0]>C.body.box[0]&&D.box[1]+D.box[3]>C.body.box[1]&&D.box[1]+D.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=D);for(let D of a)D.face!==void 0&&D.face===N.id?(o=C.gestures)==null||o.push(D):D.iris!==void 0&&D.iris===N.id?(l=C.gestures)==null||l.push(D):D.body!==void 0&&D.body===((u=C.body)==null?void 0:u.id)?(d=C.gestures)==null||d.push(D):D.hand!==void 0&&D.hand===((c=(p=C.hands)==null?void 0:p.left)==null?void 0:c.id)?(h=C.gestures)==null||h.push(D):D.hand!==void 0&&D.hand===((f=(m=C.hands)==null?void 0:m.right)==null?void 0:f.id)&&((g=C.gestures)==null||g.push(D));let E=[],z=[],F=D=>{D&&D.length===4&&(E.push(D[0],D[0]+D[2]),z.push(D[1],D[1]+D[3]))};F((A=C.face)==null?void 0:A.box),F((y=C.body)==null?void 0:y.box),F((v=(x=C.hands)==null?void 0:x.left)==null?void 0:v.box),F((k=(b=C.hands)==null?void 0:b.right)==null?void 0:k.box);let I=Math.min(...E),_=Math.min(...z);C.box=[I,_,Math.max(...E)-I,Math.max(...z)-_],r&&r[1]&&r[2]&&(C.boxRaw=[C.box[0]/r[2],C.box[1]/r[1],C.box[2]/r[2],C.box[3]/r[1]]),i.push(C)}return i}var Fe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function Yk(e){var a,r,s,i,o,l,u,d,p,c,h,m,f,g,A,y,x,v,b,k,N;if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t+1):1;if(Fe.canvas=e.canvas,!Fe.body||e.body.length!==Fe.body.length)Fe.body=JSON.parse(JSON.stringify(e.body));else for(let C=0;C<e.body.length;C++){let 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backend set to tensorflow while running in browser"),this.config.backend="humangl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(ue("override: backend set to webgl while running in nodejs"),this.config.backend="tensorflow"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="webgpu")if(typeof navigator=="undefined"||typeof navigator.gpu=="undefined")ue("override: backend set to webgpu but browser does not support webgpu"),this.config.backend="humangl";else{let s=await navigator.gpu.requestAdapter();this.config.debug&&ue("enumerated webgpu adapter:",s)}this.config.backend==="humangl"&&V8();let r=Object.keys(this.tf.engine().registryFactory);if(this.config.debug&&ue("available backends:",r),r.includes(this.config.backend)||(ue(`error: backend ${this.config.backend} not found in registry`),this.config.backend=this.tf.ENV.flags.IS_NODE?"tensorflow":"humangl",ue(`override: using backend ${this.config.backend} instead`)),this.config.debug&&ue("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&ue("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let s=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),i=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&ue(`wasm execution: ${s?"SIMD":"no SIMD"} ${i?"multithreaded":"singlethreaded"}`),this.config.debug&&!s&&ue("warning: wasm simd support is not enabled")}try{await this.tf.setBackend(this.config.backend)}catch(s){ue("error: cannot set backend:",this.config.backend,s)}}if(this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!1),this.tf.ENV.set("WEBGL_USE_SHAPES_UNIFORMS",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(ue("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&ue(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}this.tf.enableProdMode(),await this.tf.ready(),this.performance.backend=Math.trunc(Ye()-a)}});this.next=t=>Yk(t||this.result);aa(this,ef,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32;if(!t.shape[1]||!t.shape[2])return!1;let a=Me.resizeBilinear(t,[Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=await a.data(),s=0;for(let l=0;l<r.length/3;l++)s+=r[3*l+2];a.dispose();let i=100*(Math.max(s,fn(this,lo))/Math.min(s,fn(this,lo))-1);Ma(this,lo,s);let o=i<Math.max(this.config.cacheSensitivity,fn(this,bu));return Ma(this,bu,i>10*this.config.cacheSensitivity?0:i),o});aa(this,tf,async()=>{let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),n,a;switch(this.config.warmup){case"face":n=await t(Y0);break;case"full":n=await t(J0);break;default:n=null}if(n){let r=await createImageBitmap(n);a=await this.detect(r,this.config),r.close()}return a});aa(this,nf,async()=>new Promise(t=>{let n,a=0;switch(this.config.warmup){case"face":a=256,n="data:image/jpeg;base64,"+Y0;break;case"full":case"body":a=1200,n="data:image/jpeg;base64,"+J0;break;default:n=null}let r=new Image;r.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(a,a):document.createElement("canvas");s.width=r.naturalWidth,s.height=r.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(r,0,0);let o=await this.detect(s,this.config);t(o)},n?r.src=n:t(null)}));aa(this,af,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(Y0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(J0)),!n)return null;let a;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),s=r.expandDims(0);this.tf.dispose(r),a=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&ue("Warmup tfjs-node not loaded");return a});this.version=Y2,Object.defineProperty(this,"version",{value:Y2}),Fm.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Mx}/dist/`,this.config=mn(Fm,t||{}),this.tf=op,this.draw=Z2,this.state="idle",Ma(this,xu,0),Ma(this,yp,!1),Ma(this,xp,!1),Ma(this,oo,!0),Ma(this,bu,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null,segmentation:null},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.image=n=>io(n,this.config),this.faceTriangulation=nk,this.faceUVMap=ak,this.sysinfo=h5(),Ma(this,lo,1)}similarity(t,n){return o2(t,n)}segmentation(t,n){return Lk(t,n,this.config)}enhance(t){return l2(t)}match(t,n,a=0){return sk(t,n,a)}async load(t){this.state="load";let n=Ye();t&&(this.config=mn(this.config,t)),fn(this,oo)&&(this.config.debug&&ue(`version: ${this.version}`),this.config.debug&&ue(`tfjs version: ${this.tf.version_core}`),this.config.debug&&ue("platform:",this.sysinfo.platform),this.config.debug&&ue("agent:",this.sysinfo.agent),await fn(this,bp).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&ue("configuration:",this.config),this.config.debug&&ue("tf flags:",this.tf.ENV.flags))),await Wk(this),fn(this,oo)&&(this.config.debug&&ue("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ma(this,oo,!1));let a=Math.trunc(Ye()-n);a>(this.performance.load||0)&&(this.performance.load=a)}async detect(t,n){return new Promise(async a=>{this.state="config";let r,s;this.config=mn(this.config,n),this.state="check";let i=fn(this,Q0).call(this,t);i&&(ue(i,t),a({error:i}));let o=Ye();await fn(this,bp).call(this),await this.load(),r=Ye();let l=io(t,this.config);if(this.performance.image=Math.trunc(Ye()-r),this.analyze("Get Image:"),this.config.segmentation.enabled&&l&&l.tensor&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",r=Ye(),await G2(l),s=Math.trunc(Ye()-r),s>0&&(this.performance.segmentation=s),l.canvas&&(K(l.tensor),l=io(l.canvas,this.config)),this.analyze("End Segmentation:")),!l||!l.tensor){ue("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}r=Ye(),this.config.skipFrame=await fn(this,ef).call(this,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(Ye()-r),this.analyze("Check Changed:");let u=[],d=[],p=[],c=[];this.config.async?(u=this.config.face.enabled?q2(this,l.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Ye(),u=this.config.face.enabled?await q2(this,l.tensor):[],s=Math.trunc(Ye()-r),s>0&&(this.performance.face=s)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?d=this.config.body.enabled?x2(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?d=this.config.body.enabled?C2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?d=this.config.body.enabled?F2(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(d=this.config.body.enabled?_2(l.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Ye(),this.config.body.modelPath.includes("posenet")?d=this.config.body.enabled?await x2(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?d=this.config.body.enabled?await C2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?d=this.config.body.enabled?await F2(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(d=this.config.body.enabled?await _2(l.tensor,this.config):[]),s=Math.trunc(Ye()-r),s>0&&(this.performance.body=s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(p=this.config.hand.enabled?T2(l.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Ye(),p=this.config.hand.enabled?await T2(l.tensor,this.config):[],s=Math.trunc(Ye()-r),s>0&&(this.performance.hand=s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?c=this.config.object.enabled?W2(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(c=this.config.object.enabled?H2(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Ye(),this.config.object.modelPath.includes("nanodet")?c=this.config.object.enabled?await W2(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(c=this.config.object.enabled?await H2(l.tensor,this.config):[]),s=Math.trunc(Ye()-r),s>0&&(this.performance.object=s)),this.analyze("End Object:"),this.config.async&&([u,d,p,c]=await Promise.all([u,d,p,c]));let h=[];this.config.gesture.enabled&&(r=Ye(),h=[...Vk(u),...Bk(d),...Hk(p),...Uk(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Ye()-r)),this.performance.total=Math.trunc(Ye()-o),this.state="idle",this.result={face:u,body:d,hand:p,gesture:h,object:c,performance:this.performance,canvas:l.canvas,timestamp:Date.now(),get persons(){var m;return Zk(u,d,p,h,(m=l==null?void 0:l.tensor)==null?void 0:m.shape)}},K(l.tensor),a(this.result)})}async warmup(t){let n=Ye();if(t&&(this.config=mn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let a;typeof createImageBitmap=="function"?a=await fn(this,tf).call(this):typeof Image!="undefined"?a=await fn(this,nf).call(this):a=await fn(this,af).call(this);let r=Ye();return this.config.debug&&ue("Warmup",this.config.warmup,Math.round(r-n),"ms",a),a}};xu=new WeakMap,yp=new WeakMap,xp=new WeakMap,oo=new WeakMap,lo=new WeakMap,bu=new WeakMap,Q0=new WeakMap,bp=new WeakMap,ef=new WeakMap,tf=new WeakMap,nf=new WeakMap,af=new WeakMap;})();
/**
* @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
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
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