https://www.joseph-woman.jp/js/pages/room-components-detection.js?id=487107b65cd0ba44

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2)}if(ut().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let t=0;t<r.length;t++){let n=r[t];n.data().then((t=>{ys(t,n.dtype,e)}))}return{kernelName:e,outputs:r,inputs:t,timeMs:a.then((e=>e.kernelMs)),extraInfo:a.then((e=>null!=e.getExtraProfileInfo?e.getExtraProfileInfo():""))}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:r,inputs:a,extraInfo:s}=e;n.forEach((e=>{Promise.all([e.data(),r,s]).then((n=>{this.logger.logKernelProfile(t,e,n[0],n[1],a,n[2])}))}))}};function ys(e,t,n){if("float32"!==t)return!1;for(let t=0;t<e.length;t++){let r=e[t];if(isNaN(r)||!isFinite(r))return console.warn(`Found ${r} in the result of '${n}'`),!0}return!1}var bs=class{logKernelProfile(e,t,n,r,a,s){let i="number"==typeof r?Ae(`${r}ms`,9):r.error,o=Ae(e,25),l=t.rank,u=t.size,d=Ae(t.shape.toString(),14),p="";for(let e in a){let n=a[e];if(null!=n){let r=n.shape||t.shape,a=r.length;p+=`${e}: ${a}D ${a>0?r:""} `}}console.log(`%c${o}\t%c${i}\t%c${l}D ${d}\t%c${u}\t%c${p}\t%c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};var xs=20,ws=3,vs=7;function ks(e,t,n,r){let a=Xe(t),s=function(e,t,n,r){let a=Ie(t),s=r[r.length-1],i=new Array(s).fill(0),o=t.length,l="complex64"===n?Ts(e):e;if(o>1)for(let e=0;e<a/s;e++){let t=e*s;for(let e=0;e<s;e++)i[e]=Math.max(i[e],Is(l[t+e],0,n).length)}return i}(e,t,n,a),i=t.length,o=Ns(e,t,n,a,s),l=["Tensor"];return r&&(l.push(`  dtype: ${n}`),l.push(`  rank: ${i}`),l.push(`  shape: [${t}]`),l.push("  values:")),l.push(o.map((e=>"    "+e)).join("\n")),l.join("\n")}function Is(e,t,n){let r;return r=Array.isArray(e)?`${parseFloat(e[0].toFixed(vs))} + ${parseFloat(e[1].toFixed(vs))}j`:Ue(e)?`'${e}'`:"bool"===n?Ss(e):parseFloat(e.toFixed(vs)).toString(),Ae(r,t)}function Ss(e){return 0===e?"false":"true"}function Ns(e,t,n,r,a,s=!0){let i="complex64"===n?2:1,o=t[0],l=t.length;if(0===l){if("complex64"===n){return[Is(Ts(e)[0],0,n)]}return"bool"===n?[Ss(e[0])]:[e[0].toString()]}if(1===l){if(o>xs){let t=ws*i,r=Array.from(e.slice(0,t)),s=Array.from(e.slice((o-ws)*i,o*i));return"complex64"===n&&(r=Ts(r),s=Ts(s)),["["+r.map(((e,t)=>Is(e,a[t],n))).join(", ")+", ..., "+s.map(((e,t)=>Is(e,a[o-ws+t],n))).join(", ")+"]"]}return["["+("complex64"===n?Ts(e):Array.from(e)).map(((e,t)=>Is(e,a[t],n))).join(", ")+"]"]}let u=t.slice(1),d=r.slice(1),p=r[0]*i,h=[];if(o>xs){for(let t=0;t<ws;t++){let r=t*p,s=r+p;h.push(...Ns(e.slice(r,s),u,n,d,a,!1))}h.push("...");for(let t=o-ws;t<o;t++){let r=t*p,s=r+p;h.push(...Ns(e.slice(r,s),u,n,d,a,t===o-1))}}else for(let t=0;t<o;t++){let r=t*p,s=r+p;h.push(...Ns(e.slice(r,s),u,n,d,a,t===o-1))}let c=2===l?",":"";h[0]="["+(o>0?h[0]+c:"");for(let e=1;e<h.length-1;e++)h[e]=" "+h[e]+c;let f=",\n";for(let e=2;e<l;e++)f+="\n";return h[h.length-1]=" "+h[h.length-1]+"]"+(s?"":f),h}function Ts(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var _s=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Ie(e),null!=n){let e=n.length;we(e===this.size,(()=>`Length of values '${e}' does not match the size inferred by the shape '${this.size}'.`))}if("complex64"===t)throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Le(t,this.size),this.strides=Xe(e)}set(e,...t){0===t.length&&(t=[0]),we(t.length===this.rank,(()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`));let n=this.locToIndex(t);this.values[n]=e}get(...e){0===e.length&&(e=[0]);let t=0;for(let n of e){if(n<0||n>=this.shape[t]){let t=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(t)}t++}let n=e[e.length-1];for(let t=0;t<e.length-1;++t)n+=this.strides[t]*e[t];return this.values[n]}locToIndex(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(0===this.rank)return[];if(1===this.rank)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Cs().makeTensor(this.values,this.shape,this.dtype)}},Cs=null,Es=null;var $s=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Ie(e),this.strides=Xe(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Es.buffer(this.shape,this.dtype,e)}bufferSync(){return Es.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Ze(this.shape,e,"complex64"===this.dtype)}arraySync(){return Ze(this.shape,this.dataSync(),"complex64"===this.dtype)}async data(){this.throwIfDisposed();let e=Cs().read(this.dataId);if("string"===this.dtype){let t=await e;

2try{return t.map((e=>cs(e)))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataToGPU(e){return this.throwIfDisposed(),Cs().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=Cs().readSync(this.dataId);if("string"===this.dtype)try{return e.map((e=>cs(e)))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Cs().read(this.dataId);return"string"===this.dtype?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(this.kerasMask&&this.kerasMask.dispose(),Cs().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Es.print(this,e)}clone(){return this.throwIfDisposed(),Es.clone(this)}toString(e=!1){return ks(this.dataSync(),this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Es.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Cs().makeVariable(this,e,t,n)}};function As(){return ct("Tensor",(()=>$s))}Object.defineProperty($s,Symbol.hasInstance,{value:e=>!!e&&null!=e.data&&null!=e.dataSync&&null!=e.throwIfDisposed}),As();var Rs=class extends $s{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Te(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Cs().disposeTensor(this),this.dataId=e.dataId,Cs().incRef(this,null)}dispose(){Cs().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Rs,Symbol.hasInstance,{value:e=>e instanceof $s&&null!=e.assign&&e.assign instanceof Function});var Fs,Ds,Ms,Os,Ls,zs,Ps={};B(Ps,{assertTypesMatch:()=>js,getTensorsInContainer:()=>Ks,isTensorInList:()=>qs,makeTypesMatch:()=>Hs}),(Ds=Fs||(Fs={})).R0="R0",Ds.R1="R1",Ds.R2="R2",Ds.R3="R3",Ds.R4="R4",Ds.R5="R5",Ds.R6="R6",function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"}(Ms||(Ms={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"}(Os||(Os={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"}(Ls||(Ls={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"}(zs||(zs={}));var Bs={float32:Ls,int32:Ms,bool:Os,complex64:zs};function Ws(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Bs[e][t]}function Vs(e){return Ws(e,"int32")}function Us(e){return null!=e&&"object"==typeof e&&"texture"in e&&e.texture instanceof WebGLTexture}function Gs(e){return"undefined"!=typeof GPUBuffer&&null!=e&&"object"==typeof e&&"buffer"in e&&e.buffer instanceof GPUBuffer}function Hs(e,t){if(e.dtype===t.dtype)return[e,t];let n=Ws(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function js(e,t){we(e.dtype===t.dtype,(()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`))}function qs(e,t){return t.some((t=>t.id===e.id))}function Ks(e){let t=[];return Xs(e,t,new Set),t}function Xs(e,t,n){if(null==e)return;if(e instanceof $s)return void t.push(e);if(!function(e){return Array.isArray(e)||"object"==typeof e}(e))return;let r=e;for(let e in r){let a=r[e];n.has(a)||(n.add(a),Xs(a,t,n))}}function Ys(e){return null!=e.kernelName}var Zs=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map((e=>e.name))))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Js=class e{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Zs}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then((()=>{}));if(null!=this.backendInstance)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error("Could not initialize any backends, all backend initializations failed.")}

2get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(null==this.backendInstance){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{let{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(Fa(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(null==this.registryFactory[e])throw new Error(`Backend name '${e}' not found in registry`);

2if(this.backendName=e,null==this.registry[e]){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new gs(this.backendInstance),!0}setupRegisteredKernels(){Pa(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){Pa(e).forEach((t=>{null!=t.disposeFunc&&t.disposeFunc(this.registry[e])}))}initializeBackend(e){let t=this.registryFactory[e];if(null==t)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(!n||n instanceof de||"function"!=typeof n.then)return this.registry[e]=n,{success:!0,asyncInit:!1};{let t=++this.pendingBackendInitId,r=n.then((n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0))).catch((n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,Fa(`Initialization of backend ${e} failed`),Fa(n.stack||n.message)),!1)));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}}catch(t){return Fa(`Initialization of backend ${e} failed`),Fa(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&null!=this.pendingBackendInit&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(0===Object.keys(this.registryFactory).length)throw new 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:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n,r=null;if(null==t){if("function"!=typeof e)throw new Error("Please provide a function to tidy()");t=e}else{if("string"!=typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}return this.scopedRun((()=>this.startScope(r)),(()=>this.endScope(n)),(()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n)))}scopedRun(e,t,n){e();try{let e=n();return t(),e}catch(e){throw t(),e}}nextTensorId(){return e.nextTensorId++}nextVariableId(){return e.nextVariableId++}clone(e){let t=ei.runKernel(An,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{let t={x:e},n={dtype:"float32"};return ei.runKernel(zt,t,n)}})),[],{}),t}runKernel(e,t,n){if(null==this.backendName&&this.backend,null==La(e,this.backendName))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach((e=>{a+="complex64"===e.dtype?3:1}));let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n,r=[],a=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;let o,l=Ys(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(Ys(e)){let{kernelName:t,inputs:s,attrs:i}=e;null==this.backendName&&this.backend;let l=La(t,this.backendName);we(null!=l,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),n=()=>{let e=this.backend.numDataIds();

2o=l.kernelFunc({inputs:s,attrs:i,backend:this.backend});let n=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,n);let u=n.map((e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e)));if(a){let e=this.getTensorsForGradient(t,s,u);r=this.saveTensorsForBackwardMode(e)}return u}}else{let{forwardFunc:t}=e,s=e=>{a&&(r=e.map((e=>this.keep(this.clone(e)))))};n=()=>{let e=this.backend.numDataIds();o=this.tidy((()=>t(this.backend,s)));let n=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,e,n),n}}let u,{inputs:d,attrs:p}=e,h=Ys(e)?null:e.backwardsFunc;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(u=this.profiler.profileKernel(l,d,(()=>n())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(u),t=u.outputs):t=n()})),a&&this.addTapeNode(l,d,t,h,r,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(d).map((e=>null!=d[e]?d[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:u.timeMs,extraInfo:u.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map((e=>this.keep(this.clone(e))))}getTensorsForGradient(e,t,n){let r=za(e);if(null!=r){let e,a=r.inputsToSave||[],s=r.outputsToSave||[];r.saveAllInputs?(we(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),e=Object.keys(t).map((e=>t[e]))):e=a.map((e=>t[e]));let i=n.filter(((e,t)=>s[t]));return e.concat(i)}return[]}makeTensor(e,t,n,r){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;"string"===n&&Ue(e[0])&&(a=e.map((e=>hs(e))));let s=r.write(a,t,n),i=new $s(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),"string"===n){let e=this.state.tensorInfo.get(s),t=Ve(a);this.state.numBytes+=t-e.bytes,e.bytes=t}return i}makeTensorFromDataId(e,t,n,r){let a={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(a,r)}makeTensorFromTensorInfo(e,t){let{dataId:n,shape:r,dtype:a}=e,s=new $s(r,a,n,this.nextTensorId());return this.trackTensor(s,t),s}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));let a=new Rs(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[a.name])throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*We(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 Rs||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--,"string"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),"complex64"!==e.dtype&&"string"!==e.dtype){let t=e.size*We(e.dtype);this.state.numBytes-=t}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),null!=this.state.registeredVariables[e.name]&&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,null==e.reasons&&(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((e=>e.totalBytesSnapshot))),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let e of this.state.activeProfile.kernels)e.kernelTimeMs=await e.kernelTimeMs,e.extraInfo=await e.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>

20&&0===this.state.kernelDepth}addTapeNode(e,t,n,r,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},o=za(e);null!=o&&(r=o.gradFunc),null!=r&&(i.gradient=e=>(e=e.map(((e,t)=>{if(null==e){let e=n[t],r=et(e.size,e.dtype);return this.makeTensor(r,e.shape,e.dtype)}return e})),r(e.length>1?e:e[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){0===this.state.gradientDepth&&(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=Ks(e),n=new Set(t.map((e=>e.id)));for(let e=0;e<this.state.activeScope.track.length;e++){let t=this.state.activeScope.track[e];!t.kept&&!n.has(t.id)&&t.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach((e=>{!e.kept&&e.scopeId===r.id&&this.track(e)}))}gradients(e,t,n,r=!1){if(we(t.length>0,(()=>"gradients() received an empty list of xs.")),null!=n&&"float32"!==n.dtype)throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun((()=>this.startTape()),(()=>this.endTape()),(()=>this.tidy("forward",e)));we(a instanceof $s,(()=>"The result y returned by f() must be a tensor."));let s=function(e,t,n){let r={},a={};for(let e=0;e<t.length;e++)r[t[e].id]=!0;for(let n=0;n<e.length;n++){let s=e[n],i=s.inputs;for(let e in i){let n=i[e],o=!1;for(let e=0;e<t.length;e++)if(r[n.id]){s.outputs.forEach((e=>r[e.id]=!0)),o=!0,a[s.id]=!0;break}if(o)break}}let s={};s[n.id]=!0;let i={};for(let t=e.length-1;t>=0;t--){let n=e[t],r=n.inputs;for(let e=0;e<n.outputs.length;e++)if(s[n.outputs[e].id]){for(let e in r)s[r[e].id]=!0,i[n.id]=!0;break}}let o=[];for(let t=0;t<e.length;t++){let n=e[t];if(a[n.id]&&i[n.id]){let e={};for(let t in n.inputs){let a=n.inputs[t];r[a.id]&&(e[t]=a)}let t=Object.assign({},n);t.inputs=e,t.outputs=n.outputs,o.push(t)}}return o}(this.state.activeTape,t,a);if(!r&&0===s.length&&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 e={};e[a.id]=null==n?function(e){let t=Qe(Ie(e),"float32");return ei.makeTensor(t,e,"float32")}(a.shape):n,function(e,t,n,r){for(let a=t.length-1;a>=0;a--){let s=t[a],i=[];if(s.outputs.forEach((t=>{let n=e[t.id];null!=n?i.push(n):i.push(null)})),null==s.gradient)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let o=s.gradient(i);for(let t in s.inputs){if(!(t in o))throw new Error(`Cannot backprop through input ${t}. Available gradients found: ${Object.keys(o)}.`);let a=n((()=>o[t]()));if("float32"!==a.dtype)throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${t} must have 'float32' dtype, but has '${a.dtype}'`);let i=s.inputs[t];if(!Te(a.shape,i.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${t}' has shape '${a.shape}', which does not match the shape of the input '${i.shape}'`);if(null==e[i.id])e[i.id]=a;else{let t=e[i.id];e[i.id]=r(t,a),t.dispose()}}}}(e,s,(e=>this.tidy(e)),ti);let r=t.map((t=>e[t.id]));return 0===this.state.gradientDepth&&(this.state.activeTape.forEach((e=>{for(let t of e.saved)t.dispose()})),this.state.activeTape=null),{value:a,grads:r}}))}customGrad(e){return we(qe(e),(()=>"The f passed in customGrad(f) must be a function.")),(...t)=>{we(t.every((e=>e instanceof $s)),(()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors"));let n,r={};t.forEach(((e,t)=>{r[t]=e}));return this.runKernelFunc({forwardFunc:(r,a)=>(n=e(...t,a),we(n.value instanceof $s,(()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor")),we(qe(n.gradFunc),(()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.")),n.value),backwardsFunc:(e,r)=>{let a=n.gradFunc(e,r),s=Array.isArray(a)?a:[a];we(s.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(...).")),we(s.every((e=>e instanceof $s)),(()=>"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 i={};

2return s.forEach(((e,t)=>{i[t]=()=>e})),i},inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=ds(),n=await this.backend.time(e);return n.wallMs=ds()-t,n}track(e){return null!=this.state.activeScope&&(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 Zs;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}};function Qs(){let e=ht();if(null==e._tfengine){let t=new ot(e);e._tfengine=new Js(t)}return function(e){pt=e}(e._tfengine.ENV),function(e){Cs=e}((()=>e._tfengine)),e._tfengine}Js.nextTensorId=0,Js.nextVariableId=0;var ei=Qs();function ti(e,t){let n={a:e,b:t};return ei.runKernel(yt,n)}var ni,ri={};function ai(e){ni=e}function si(e){if(void 0!==ni)return ni;if(e||"undefined"!=typeof navigator&&null!=navigator){if(e||(e=navigator),"ReactNative"===e.product)return!0;let t=e.userAgent||e.vendor||("undefined"!=typeof window?window.opera:"");if(!t){let t=e;return t.userAgentData&&t.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 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2.`)),vl("avgPool",r,a);let l={x:i},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a},d=ei.runKernel(Ct,l,u);return d=Wo(d,s.dtype),o?kl(d,[d.shape[1],d.shape[2],d.shape[3]]):d}});var Sl=fi({avgPool3d_:function(e,t,n,r,a,s="NDHWC"){let i=pi(e,"x","avgPool3d","float32"),o=i,l=!1;4===i.rank&&(l=!0,o=kl(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),we(5===o.rank,(()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`)),we("NDHWC"===s,(()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`)),we("number"==typeof n&&n>0||Array.isArray(n)&&n[0]>0&&n[1]>0&&n[2]>0,(()=>`Error in avgPool3d: Stride must be > 0, but got '${n}'`)),vl("avgPool3d",r,a);let u={x:o},d={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},p=ei.runKernel($t,u,d);return p=Wo(p,o.dtype),l?kl(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}});var Nl=fi({concat_:function(e,t=0){we(e.length>=1,(()=>"Pass at least one tensor to concat"));let n=hi(e,"tensors","concat","string_or_numeric");if("complex64"===n[0].dtype&&n.forEac

2h((e=>{if("complex64"!==e.dtype)throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${e.dtype}. `)})),1===n.length)return Vo(n[0]);let r=n,a={axis:t};return ei.runKernel(Ut,r,a)}});var Tl=fi({matMul_:function(e,t,n=!1,r=!1){let a=pi(e,"a","matMul"),s=pi(t,"b","matMul");[a,s]=Hs(a,s);let i={a,b:s},o={transposeA:n,transposeB:r};return ei.runKernel(Rt,i,o)}});var _l=fi({sigmoid_:function(e){let t={x:pi(e,"x","sigmoid","float32")};return ei.runKernel(Yr,t)}});var Cl=fi({slice_:function(e,t,n){let r=pi(e,"x","slice","string_or_numeric");if(0===r.rank)throw new Error("Slicing scalar is not possible");let a={x:r},s={begin:t,size:n};return ei.runKernel(jr,a,s)}});var El=fi({tanh_:function(e){let t={x:pi(e,"x","tanh","float32")};return ei.runKernel(ya,t)}});var $l=fi({basicLSTMCell_:function(e,t,n,r,a,s){let i=pi(e,"forgetBias","basicLSTMCell"),o=pi(t,"lstmKernel","basicLSTMCell"),l=pi(n,"lstmBias","basicLSTMCell"),u=pi(r,"data","basicLSTMCell"),d=pi(a,"c","basicLSTMCell"),p=pi(s,"h","basicLSTMCell"),h=Nl([u,p],1),c=Tl(h,o),f=Go(c,l),m=f.shape[0],g=f.shape[1]/4,y=[m,g],b=Cl(f,[0,0],y),x=Cl(f,[0,g],y),w=Cl(f,[0,2*g],y),v=Cl(f,[0,3*g],y),k=Go(qo(_l(b),El(x)),qo(d,_l(Go(i,w))));return[k,qo(El(k),_l(v))]}});var Al=fi({batchToSpaceND_:function(e,t,n){let r=pi(e,"x","batchToSpaceND"),a=t.reduce(((e,t)=>e*t));we(r.rank>=1+t.length,(()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`)),we(n.length===t.length,(()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`)),we(r.shape[0]%a==0,(()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`));let s={x:r},i={blockShape:t,crops:n};return ei.runKernel(Ft,s,i)}});function Rl(e){let t;return t=0===e.rank||1===e.rank?kl(e,[1,1,1,e.size]):2===e.rank?kl(e,[1,1,e.shape[0],e.shape[1]]):3===e.rank?kl(e,[1,e.shape[0],e.shape[1],e.shape[2]]):e,t}var Fl=fi({batchNorm_:function(e,t,n,r,a,s){null==s&&(s=.001);let i,o,l=pi(e,"x","batchNorm"),u=pi(t,"mean","batchNorm"),d=pi(n,"variance","batchNorm");null!=a&&(i=pi(a,"scale","batchNorm")),null!=r&&(o=pi(r,"offset","batchNorm")),we(u.rank===d.rank,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),we(null==o||u.rank===o.rank,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),we(null==i||u.rank===i.rank,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));let p={x:Rl(l),scale:i,offset:o,mean:u,variance:d},h={varianceEpsilon:s},c=ei.runKernel(Tn,p,h);return kl(c,l.shape)}});var Dl=fi({batchNorm2d_:function(e,t,n,r,a,s){let i,o,l=pi(e,"x","batchNorm"),u=pi(t,"mean","batchNorm"),d=pi(n,"variance","batchNorm");return null!=a&&(i=pi(a,"scale","batchNorm")),null!=r&&(o=pi(r,"offset","batchNorm")),we(2===l.rank,(()=>`Error in batchNorm2D: x must be rank 2 but got rank ${l.rank}.`)),we(2===u.rank||1===u.rank,(()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${u.rank}.`)),we(2===d.rank||1===d.rank,(()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${d.rank}.`)),null!=i&&we(2===i.rank||1===i.rank,(()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${i.rank}.`)),null!=o&&we(2===o.rank||1===o.rank,(()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${o.rank}.`)),Fl(l,u,d,o,i,s)}});var Ml=fi({batchNorm3d_:function(e,t,n,r,a,s){let i,o,l=pi(e,"x","batchNorm"),u=pi(t,"mean","batchNorm"),d=pi(n,"variance","batchNorm");return null!=a&&(i=pi(a,"scale","batchNorm")),null!=r&&(o=pi(r,"offset","batchNorm")),we(3===l.rank,(()=>`Error in batchNorm3D: x must be rank 3 but got rank ${l.rank}.`)),we(3===u.rank||1===u.rank,(()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${u.rank}.`)),we(3===d.rank||1===d.rank,(()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${d.rank}.`)),null!=i&&we(3===i.rank||1===i.rank,(()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${i.rank}

2.`)),null!=o&&we(3===o.rank||1===o.rank,(()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${o.rank}.`)),Fl(l,u,d,o,i,s)}});var Ol=fi({batchNorm4d_:function(e,t,n,r,a,s){let i,o,l=pi(e,"x","batchNorm"),u=pi(t,"mean","batchNorm"),d=pi(n,"variance","batchNorm");return null!=a&&(i=pi(a,"scale","batchNorm")),null!=r&&(o=pi(r,"offset","batchNorm")),we(4===l.rank,(()=>`Error in batchNorm4D: x must be rank 4 but got rank ${l.rank}.`)),we(4===u.rank||1===u.rank,(()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${u.rank}.`)),we(4===d.rank||1===d.rank,(()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${d.rank}.`)),null!=i&&we(4===i.rank||1===i.rank,(()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${i.rank}.`)),null!=o&&we(4===o.rank||1===o.rank,(()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${o.rank}.`)),Fl(l,u,d,o,i,s)}});var Ll=fi({bincount_:function(e,t,n){let r=pi(e,"x","bincount"),a=pi(t,"weights","bincount");we("int32"===r.dtype,(()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`)),we(n>=0,(()=>`size must be non-negative, but got ${n}.`)),we(a.size===r.size||0===a.size,(()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`));let s={x:r,weights:a},i={size:n};return ei.runKernel(Dt,s,i)}});var zl=fi({bitwiseAnd_:function(e,t){let n=pi(e,"x","bitwiseAnd"),r=pi(t,"y","bitwiseAnd");if(!Te(n.shape,r.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${n.shape}, y: ${r.shape}`);if("int32"!==n.dtype||"int32"!==r.dtype)throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${n.dtype} and type of y: ${r.dtype}`);let a={a:n,b:r};return ei.runKernel(Mt,a)}});var Pl=fi({broadcastArgs_:function(e,t){let n=pi(e,"s0","broadcastArgs","int32"),r=pi(t,"s1","broadcastArgs","int32");if(1!==n.rank)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(1!==r.rank)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${r.rank}`);let a={s0:n,s1:r};return ei.runKernel(Lt,a)}});var Bl=fi({broadcastTo_:function(e,t){let n=pi(e,"broadcastTo","x"),r=n.shape;if(nt(t),t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let e=n.shape.slice();for(;e.length<t.length;)e.unshift(1);n=kl(n,e)}let a=n.shape,s=Array.from(t);for(let e=t.length-1;e>=0;e--)if(a[e]===t[e])s[e]=1;else if(1!==n.shape[e])throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(0===s.map(((e,t)=>e>1?t:-1)).filter((e=>e>=0)).length)return Vo(n);let i={x:n},o={reps:s};return ei.runKernel(ba,i,o)}});var Wl=fi({ceil_:function(e){let t={x:pi(e,"x","ceil","float32")};return ei.runKernel(Pt,t)}});function Vl(e,t,n){nt(e);let r={shape:e,value:t,dtype:n=n||je(t)};return ei.runKernel(kn,{},r)}var Ul=fi({clipByValue_:function(e,t,n){let r=pi(e,"x","clipByValue");if(we(t<=n,(()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`)),t===n)return Vl(r.shape,t,r.dtype);let a={x:r},s={clipValueMin:t,clipValueMax:n};return ei.runKernel(Bt,a,s)}});var Gl=fi({concat1d_:function(e){return Nl(e,0)}});var Hl=fi({concat2d_:function(e,t){return Nl(e,t)}});var jl=fi({concat3d_:function(e,t){return Nl(e,t)}});var ql=fi({concat4d_:function(e,t){return Nl(e,t)}});var Kl=fi({conv2d_:function(e,t,n,r,a="NHWC",s=[1,1],i){let o=pi(e,"x","conv2d","float32"),l=pi(t,"filter","conv2d","float32"),u=o,d=!1;3===o.rank&&(d=!0,u=kl(o,[1,o.shape[0],o.shape[1],o.shape[2]])),we(4===u.rank,(()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`)),we(4===l.rank,(()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`)),vl("conv2d",r,i);let p="NHWC"===a?u.shape[3]:u.shape[1];we(p===l.shape[2],(()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`)),we(bl(n,s),(()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`)),we(xl(s),(()=>"Error in conv2D: Dilated rates should be larger than 0.")),we(xl(n),(()=>"Error in conv2D: Strides should be larger than 0."));let h={x:u,filter:l},c={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i}

2,f=ei.runKernel(Gt,h,c);return d?kl(f,[f.shape[1],f.shape[2],f.shape[3]]):f}});var Xl=fi({conv1d_:function(e,t,n,r,a="NWC",s=1,i){let o=pi(e,"x","conv1d"),l=pi(t,"filter","conv1d"),u=o,d=!1;2===o.rank&&(d=!0,u=kl(o,[1,o.shape[0],o.shape[1]])),we(3===u.rank,(()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`)),we(3===l.rank,(()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`)),vl("conv1d",r,i),we(u.shape[2]===l.shape[1],(()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`)),we(bl(n,s),(()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`)),we(xl(s),(()=>"Error in conv1D: Dilated rates should be larger than 0.")),we(xl(n),(()=>"Error in conv1D: Stride should be larger than 0.")),we("NWC"===a,(()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`));let p=kl(l,[1,l.shape[0],l.shape[1],l.shape[2]]),h=kl(u,[u.shape[0],1,u.shape[1],u.shape[2]]),c=Kl(h,p,[1,n],r,"NHWC",[1,s],i);return kl(c,d?[c.shape[2],c.shape[3]]:[c.shape[0],c.shape[2],c.shape[3]])}});var Yl=fi({conv2DBackpropInput_:function(e,t,n,r,a,s="NHWC",i){we(e.length===t.rank,(()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`));let o=e,l=t,u=!1;3===t.rank&&(u=!0,l=kl(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),we(4===o.length,(()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`)),we(4===l.rank,(()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`)),we(4===n.rank,(()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`));let d="NHWC"===s?o[3]:o[1],p="NHWC"===s?l.shape[3]:l.shape[1];we(d===n.shape[2],(()=>`Error in conv2dDerInput: depth of input (${d}) must match input depth for filter ${n.shape[2]}.`)),we(p===n.shape[3],(()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`)),vl("conv2dDerInput",a,i);let h={dy:l,filter:n},c={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=ei.runKernel(jt,h,c);return u?kl(f,[f.shape[1],f.shape[2],f.shape[3]]):f}});var Zl=fi({conv2dTranspose_:function(e,t,n,r,a,s){let i=pi(e,"x","conv2dTranspose"),o=pi(t,"filter","conv2dTranspose");return Yl(n,i,o,r,a,"NHWC",s)}});var Jl=fi({conv3d_:function(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=pi(e,"x","conv3d"),o=pi(t,"filter","conv3d"),l=i,u=!1;4===i.rank&&(u=!0,l=kl(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),we(5===l.rank,(()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`)),we(5===o.rank,(()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`)),we(l.shape[4]===o.shape[3],(()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`)),we(bl(n,s),(()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`)),we("NDHWC"===a,(()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`)),we(xl(s),(()=>"Error in conv3D: Dilated rates should be larger than 0.")),we(xl(n),(()=>"Error in conv3D: Strides should be larger than 0."));let d={x:l,filter:o},p={strides:n,pad:r,dataFormat:a,dilations:s},h=ei.runKernel(qt,d,p);return u?kl(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}});var Ql=fi({conv3DBackpropInput_:function(e,t,n,r,a){we(e.length===t.rank,(()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`));let s=e,i=t,o=!1;4===t.rank&&(o=!0,i=kl(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],u=i.shape[4];we(5===s.length,(()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`)),we(5===i.rank,(()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`)),we(5===n.rank,(()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`)),we(l===n.shape[3],(()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`)),we(u===n.shape[4],(()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`));let d={dy:i,filter:n}

2,p={pad:a,strides:r,inputShape:s},h=ei.runKernel(Xt,d,p);return o?kl(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}});var eu=fi({conv3dTranspose_:function(e,t,n,r,a){let s=pi(e,"x","conv3dTranspose"),i=pi(t,"filter","conv3dTranspose");return Ql(n,s,i,r,a)}});var tu=fi({cos_:function(e){let t={x:pi(e,"x","cos","float32")};return ei.runKernel(Yt,t)}});var nu=fi({cosh_:function(e){let t={x:pi(e,"x","cosh","float32")};return ei.runKernel(Zt,t)}});var ru=fi({cumprod_:function(e,t=0,n=!1,r=!1){let a={x:pi(e,"x","cumprod")},s={axis:t,exclusive:n,reverse:r};return ei.runKernel(Jt,a,s)}});var au=fi({cumsum_:function(e,t=0,n=!1,r=!1){let a={x:pi(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return ei.runKernel(Qt,a,s)}});var su=fi({denseBincount_:function(e,t,n,r=!1){let a=pi(e,"x","denseBincount"),s=pi(t,"weights","denseBincount");we("int32"===a.dtype,(()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`)),we(a.rank<=2,(()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`)),we(n>=0,(()=>`size must be non-negative, but got ${n}.`)),we(s.size===a.size||0===s.size,(()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`));let i={x:a,weights:s},o={size:n,binaryOutput:r};return ei.runKernel(tn,i,o)}});var iu=fi({depthToSpace_:function(e,t,n="NHWC"){let r=pi(e,"x","depthToSpace","float32"),a="NHWC"===n?r.shape[1]:r.shape[2],s="NHWC"===n?r.shape[2]:r.shape[3],i="NHWC"===n?r.shape[3]:r.shape[1];we(t>1,(()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`)),we(a*t>=0,(()=>`Negative dimension size caused by overflow when multiplying\n    ${a} and ${t}  for depthToSpace with input shape\n    ${r.shape}`)),we(s*t>=0,(()=>`Negative dimension size caused by overflow when multiplying\n    ${s} and ${t} for depthToSpace with input shape\n        ${r.shape}`)),we(i%(t*t)==0,(()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} 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uu=fi({dilation2d_:function(e,t,n,r,a=[1,1],s="NHWC"){let i=pi(e,"x","dilation2d"),o=pi(t,"filter","dilation2d");we(3===i.rank||4===i.rank,(()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`)),we(3===o.rank,(()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`)),we("NHWC"===s,(()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`));let l=i,u=!1;3===i.rank&&(l=kl(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=!0),we(l.shape[3]===o.shape[2],(()=>`Error in dilation2d:  input and filter must have the same depth: ${l.shape[3]} vs ${o.shape[2]}`));let d={x:l,filter:o},p={strides:n,pad:r,dilations:a},h=ei.runKernel(ln,d,p);return u?kl(h,[h.shape[1],h.shape[2],h.shape[3]]):h}}),du={};function pu(e,t){let n=e.length,r=[];for(let a=0;a<n;a++){let s=n-1-a,i=e[s]||1;(t[t.length-1-a]||1)>1&&1===i&&r.unshift(s)}return r}function hu(e,t){let n=[];for(let r=0;r<t.length;r++){let 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id=fi({log1p_:function(e){let t={x:pi(e,"x","log1p")};return ei.runKernel(Vn,t)}});function od(e){return we(qe(e),(()=>"The f passed in grad(f) must be a function")),(t,n)=>{let r=pi(t,"x","tf.grad","string_or_numeric"),a=null!=n?pi(n,"dy","tf.grad"):null;return ei.tidy((()=>{let{value:t,grads:n}=ei.gradients((()=>e(r)),[r],a);return null!=a&&ve(t.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),cd(n),n[0]}))}}function ld(e){return we(qe(e),(()=>"The f passed in grads(f) must be a function")),(t,n)=>{we(Array.isArray(t),(()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s"));let r=hi(t,"args","tf.grads","string_or_numeric"),a=null!=n?pi(n,"dy","tf.grads"):null;return ei.tidy((()=>{let{value:t,grads:n}=ei.gradients((()=>e(...r)),r,a);return null!=a&&ve(t.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),cd(n),n}))}}function ud(e){return 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Make sure that\n    the f you passed encloses all operations that lead from x to y.")}var fd=fi({neg_:function(e){let t={x:pi(e,"x","neg")};return ei.runKernel(hr,t)}});var md=fi({softplus_:function(e){let t={x:pi(e,"x","softplus")};return ei.runKernel(Zr,t)}});var gd=fi({logSigmoid_:function(e){let t=pi(e,"x","logSigmoid");return hd((e=>({value:fd(md(fd(e))),gradFunc:t=>qo(t,_l(fd(e)))})))(t)}});var yd=fi({sub_:function(e,t){let n=pi(e,"a","sub"),r=pi(t,"b","sub");[n,r]=Hs(n,r);let a={a:n,b:r};return ei.runKernel(ma,a)}});var bd=fi({logSoftmax_:function(e,t=-1){let n=pi(e,"logits","logSoftmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank}

2 and axis was ${t}`);return hd(((e,n)=>{let r=Au(e,t,!0),a=yd(e,r),s=yd(Wo(a,"float32"),sd(Lu(Wu(a),t,!0)));return n([s]),{value:s,gradFunc:(e,n)=>{let[r]=n,a=Wu(r);return yd(e,qo(Lu(e,t,!0),a))}}}))(n)}});var xd=fi({logSumExp_:function(e,t=null,n=!1){let r=pi(e,"x","logSumExp"),a=De(t,r.shape),s=Au(r,a,!0),i=yd(r,s),o=Wu(i),l=Lu(o,a),u=sd(l),d=Go(kl(s,u.shape),u);if(n){let e=Tu(d.shape,a);return kl(d,e)}return d}});var wd=fi({logicalAnd_:function(e,t){let n=pi(e,"a","logicalAnd","bool"),r=pi(t,"b","logicalAnd","bool");cu(n.shape,r.shape);let a={a:n,b:r};return ei.runKernel(Un,a)}});var vd=fi({logicalNot_:function(e){let t={x:pi(e,"x","logicalNot","bool")};return ei.runKernel(Gn,t)}});var kd=fi({logicalOr_:function(e,t){let n=pi(e,"a","logicalOr","bool"),r=pi(t,"b","logicalOr","bool");cu(n.shape,r.shape);let a={a:n,b:r};return ei.runKernel(Hn,a)}});var Id=fi({logicalXor_:function(e,t){let n=pi(e,"a","logicalXor","bool"),r=pi(t,"b","logicalXor","bool");return cu(n.shape,r.shape),wd(kd(e,t),vd(wd(e,t)))}}),Sd=2147483648;var Nd=fi({searchSorted_:function(e,t,n="left"){let r=pi(e,"sortedSequence","searchSorted"),a=pi(t,"values","searchSorted"),s=r.shape[r.shape.length-1],i=a.shape[a.shape.length-1],o=kl(r,[-1,s]),l=kl(a,[-1,i]);if(o.rank<2)throw new Error("Sorted input argument must be at least 2-dimensional");if(o.shape[0]!==l.shape[0])throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");if(Ie(l.shape)>=Sd)throw new Error(`values tensor size must less than ${Sd}`);if(o.shape[1]>=Sd)throw new Error(`trailing dim_size must less than ${Sd} for int32 output type, was ${o.shape[1]}`);let u={sortedSequence:o,values:l},d={side:n};return ei.runKernel(Ur,u,d)}});function Td(e,t){return Nd(e,t,"left")}var _d=fi({maxPool_:function(e,t,n,r,a){let s=pi(e,"x","maxPool"),i=s,o=!1;3===s.rank&&(o=!0,i=kl(s,[1,s.shape[0],s.shape[1],s.shape[2]])),we(4===i.rank,(()=>`Error in maxPool: input must be rank 4 but got rank ${i.rank}.`)),we(bl(n,1),(()=>`Error in maxPool: Either strides or dilations must be 1. 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Mode must be either reflect or symmetric. Got ${n}.`));let r=pi(e,"x","mirrorPad");if(0===r.rank)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");we(t.length===r.rank,(()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`));let a="reflect"===n?1:0;for(let e=0;e<r.rank;e++)we(2===t[e].length,(()=>"Invalid number of paddings. 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2var Wd=fi({notEqual_:function(e,t){let n=pi(e,"a","notEqual","string_or_numeric"),r=pi(t,"b","notEqual","string_or_numeric");[n,r]=Hs(n,r),cu(n.shape,r.shape);let a={a:n,b:r};return ei.runKernel(cr,a)}});var Vd=fi({oneHot_:function(e,t,n=1,r=0,a="int32"){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let s={indices:pi(e,"indices","oneHot","int32")},i={dtype:a,depth:t,onValue:n,offValue:r};return ei.runKernel(br,s,i)}});var Ud=fi({onesLike_:function(e){let t={x:pi(e,"x","onesLike")};return ei.runKernel(yr,t)}});var Gd=fi({outerProduct_:function(e,t){let n=pi(e,"v1","outerProduct"),r=pi(t,"v2","outerProduct");we(1===n.rank&&1===r.rank,(()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`));let a=kl(n,[-1,1]),s=kl(r,[1,-1]);return Tl(a,s)}});var Hd=fi({pad_:function(e,t,n=0){let r=pi(e,"x","pad");if(0===r.rank)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let a={paddings:t,constantValue:n},s={x:r};return ei.runKernel(wr,s,a)}});var jd=fi({pad1d_:function(e,t,n=0){return we(2===t.length,(()=>"Invalid number of paddings. Must be length of 2.")),Hd(e,[t],n)}});var qd=fi({pad2d_:function(e,t,n=0){return we(2===t.length&&2===t[0].length&&2===t[1].length,(()=>"Invalid number of paddings. Must be length of 2 each.")),Hd(e,t,n)}});var Kd=fi({pad3d_:function(e,t,n=0){return we(3===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length,(()=>"Invalid number of paddings. Must be length of 2 each.")),Hd(e,t,n)}});var Xd=fi({pad4d_:function(e,t,n=0){return we(4===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length&&2===t[3].length,(()=>"Invalid number of paddings. Must be length of 2 each.")),Hd(e,t,n)}});var Yd=fi({spaceToBatchND_:function(e,t,n){let r=pi(e,"x","spaceToBatchND");we(r.rank>=1+t.length,(()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`)),we(n.length===t.length,(()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`)),we(r.shape.reduce(((e,r,a)=>a>0&&a<=t.length?e&&(r+n[a-1][0]+n[a-1][1])%t[a-1]==0:e),!0),(()=>`input spatial dimensions ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`));let a={x:r},s={blockShape:t,paddings:n};return ei.runKernel(ea,a,s)}});var Zd=fi({pool_:function(e,t,n,r,a,s,i){null==a&&(a=[1,1]),null==s&&(s=1),0===r&&(r="valid");let o=pi(e,"x","maxPool"),l=o,u=!1;3===o.rank&&(u=!0,l=kl(o,[1,o.shape[0],o.shape[1],o.shape[2]])),we(bl(s,a),(()=>`Error in pool: Either strides or dilations must be 1. 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20&&(n=Lu(n,r)),kl(n,e.shape)}function Vh(e,t,n,r){if("linear"===t)return e;if("relu"===t)return Ap(e);if("elu"===t)return wu(e);if("relu6"===t)return Rp(e);if("prelu"===t)return Jd(e,n);if("leakyrelu"===t)return ed(e,r);if("sigmoid"===t)return _l(e);throw new Error(`Unknown fused activation ${t}.`)}var Uh=(e,t)=>!(e>0)||"linear"===t;var Gh=fi({fusedConv2d_:function({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:d}){if(l=l||"linear",!1===Uh(ei.state.gradientDepth,l)){we("NHWC"===a,(()=>`Error in fused conv2d: got dataFormat of ${a} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`));let p=Kl(e,t,n,r,a,s,i);return null!=o&&(p=Go(p,o)),Vh(p,l,u,d)}let p=pi(e,"x","conv2d","float32"),h=pi(t,"filter","conv2d","float32"),c=p,f=!1;3===p.rank&&(f=!0,c=kl(p,[1,p.shape[0],p.shape[1],p.shape[2]])),we(4===c.rank,(()=>`Error in fused conv2d: input must be rank 4, but got rank ${c.rank}.`)),we(4===h.rank,(()=>`Error in fused conv2d: filter must be rank 4, but got rank ${h.rank}.`)),vl("fused conv2d",r,i);let m="NHWC"===a?c.shape[3]:c.shape[1];we(h.shape[2]===m,(()=>`Error in conv2d: depth of input (${m}) must match input depth for filter ${h.shape[2]}.`)),we(bl(n,s),(()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`));let g,y,b=dl(c.shape,h.shape,n,s,r,i);if(null!=o&&(g=pi(o,"bias","fused conv2d"),[g]=Hs(g,p),"NHWC"===a?cu(b.outShape,g.shape):(we(g.shape.length<=1,(()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${g.shape.length}.`)),we(0===g.shape.length||g.shape[0]===b.outChannels||1===g.shape[0],(()=>`Error in fused conv2d: bias shape (${g.shape}) is not compatible with the number of output channels (${b.outChannels})`)))),null!=u){let e=u.shape;if(we(e.length<=1||3===e.length,(()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${e.length}.`)),1===e.length)we(1===e[0]||e[0]===b.outChannels,(()=>`Error in fused conv2d: PReLU activation weights (${e}) is not compatible with the number of output channels (${b.outChannels}).`));else if(3===e.length)try{cu(e,b.outShape)}catch(t){let n=`Error in fused conv2d: PReLU activation weights (${e}) is not compatible with the output shape of the conv2d (${b.outShape}).`;throw Error(n)}y=pi(u,"prelu weights","fused conv2d")}let x=(e,t)=>{we("NHWC"===a,(()=>`Error in gradient of fused conv2D: got dataFormat of ${a} but only NHWC is currently supported.`));let[i,o,u,d]=t,p=Bh(e,u,l);we(yl(s),(()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`));let h=[Yl(o.shape,p,i,n,r),Ph(o,p,i.shape,n,r)];if(null!=d){let e=Wh(d,p);h.push(e)}return h},w={x:c,filter:h,bias:g,preluActivationWeights:y},v={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:d};return null==o?hd(((e,t,n)=>{let r=ei.runKernel(Aa,w,v);return n([t,e,r]),f&&(r=kl(r,[r.shape[1],r.shape[2],r.shape[3]])),{value:r,gradFunc:x}}))(c,h):hd(((e,t,n,r)=>{let a=ei.runKernel(Aa,w,v);return r([t,e,a,n]),f&&(a=kl(a,[a.shape[1],a.shape[2],a.shape[3]])),{value:a,gradFunc:x}}))(c,h,g)}});var Hh=fi({depthwiseConv2dNativeBackpropFilter_:function(e,t,n,r,a,s=[1,1],i){let o=e;3===e.rank&&(o=kl(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;3===l.rank&&(l=kl(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:o,dy:l},d={strides:r,pad:a,dimRoundingMode:i,dilations:s,filterShape:n};return ei.runKernel(an,u,d)}});var jh=fi({depthwiseConv2dNativeBackpropInput_:function(e,t,n,r,a,s=[1,1],i){let o=t,l=!1;3===t.rank&&(l=!0,o=kl(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:o,filter:n},d={strides:r,pad:a,dimRoundingMode:i,dilations:s,inputShape:e},p=ei.runKernel(sn,u,d);return l?kl(p,[p.shape[1],p.shape[2],p.shape[3]]):p}});var qh=fi({fusedDepthwiseConv2d_:function({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:d}){if(!1===Uh(ei.state.gradientDepth,l)){let p=ou(e,t,n,r,a,s,i);return null!=o&&(p=Go(p,o)),Vh(p,l,u,d)}let p=pi(e,"x","depthwiseConv2d","float32"),h=pi(t,"filter","depthwiseConv2d","float32"),c=p,f=!1;3===p.rank&&(f=!0,c=kl(p,[1,p.shape[0],p.shape[1],p.shape[2]])),we(4===c.rank,(()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`)),we(4===h.rank,(()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${h.rank}.`)),we(c.shape[3]===h.shape[2],(()=>`Error in fused depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${h.shape[2]}.`)),null==s&&(s=[1,1]),we(bl(n,s),(()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`)),vl("fused depthwiseConv2d",r,i);let m,g,y=dl(c.shape,h.shape,n,s,r,i,!0);null!=o&&(m=pi(o,"bias","fused conv2d"),[m]=Hs(m,p),cu(y.outShape,m.shape)),null!=u&&(g=pi(u,"prelu weights","fused depthwiseConv2d"));let b=(e,t)=>{we(yl(s),(()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`));let[a,o,u,d]=t,p=Bh(e,u,l),h=jh(o.shape,p,a,n,r,s,i),c=Hh(o,p,a.shape,n,r,s,i);if(null!=d){return[h,c,Wh(m,p)]}return[h,c]},x={x:c,filter:h,bias:m,preluActivationWeights:g},w={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:d};return null==o?hd(((e,t,n)=>{let r=ei.runKernel(Ra,x,w);return n([t,e,r]),f&&(r=kl(r,[r.shape[1],r.shape[2],r.shape[3]])),{value:r,gradFunc:b}}))(c,h):hd(((e,t,n,r)=>{let a=ei.runKernel(Ra,x,w);return r([t,e,a,n]),f&&(a=kl(a,[a.shape[1],a.shape[2],a.shape[3]])),{value:a,gradFunc:b}}))(c,h,m)}});var Kh=fi({fusedMatMul_:function({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:a,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o=.2}){if(!1===Uh(ei.state.gradientDepth,s)){let l=Tl(e,t,n,r);return null!=a&&(l=Go(l,a)),Vh(l,s,i,o)}let l=pi(e,"a","fused matMul"),u=pi(t,"b","fused matMul");[l,u]=Hs(l,u);let d=n?l.shape[l.rank-2]:l.shape[l.rank-1],p=r?u.shape[u.rank-1]:u.shape[u.rank-2],h=n?l.shape[l.rank-1]:l.shape[l.rank-2],c=r?u.shape[u.rank-2]:u.shape[u.rank-1],f=l.shape.slice(0,-2),m=u.shape.slice(0,-2),g=Ie(f),y=Ie(m);we(d===p,(()=>`Error in fused matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${r} must match.`));

2let b,x,w=cu(l.shape.slice(0,-2),u.shape.slice(0,-2)).concat([h,c]),v=kl(l,n?[g,d,h]:[g,h,d]),k=kl(u,r?[y,c,p]:[y,p,c]);null!=a&&(b=pi(a,"bias","fused matMul"),[b]=Hs(b,l),cu(w,b.shape)),null!=i&&(x=pi(i,"prelu weights","fused matMul"));let I=(e,t)=>{let i,o,[l,u,d,p]=t,h=Bh(kl(e,d.shape),d,s);if(n||r?!n&&r?(i=Tl(h,u,!1,!1),o=Tl(h,l,!0,!1)):n&&!r?(i=Tl(u,h,!1,!0),o=Tl(l,h,!1,!1)):(i=Tl(u,h,!0,!0),o=Tl(h,l,!0,!0)):(i=Tl(h,u,!1,!0),o=Tl(l,h,!0,!1)),null!=a){return[i,o,Wh(p,h)]}return[i,o]},S={a:v,b:k,bias:b,preluActivationWeights:x},N={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:o};return null==a?hd(((e,t,n)=>{let r=ei.runKernel($a,S,N);return n([e,t,r]),{value:kl(r,w),gradFunc:I}}))(v,k):hd(((e,t,n,r)=>{let a=ei.runKernel($a,S,N);return r([e,t,a,n]),{value:kl(a,w),gradFunc:I}}))(v,k,b)}});var Xh=fi({hammingWindow_:function(e){return Oh(e,.54,.46)}});var Yh=fi({hannWindow_:function(e){return Oh(e,.5,.5)}});var Zh=fi({frame_:function(e,t,n,r=!1,a=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Cl(e,s,t)),s+=n;if(r)for(;s<e.size;){let r=s+t-e.size,o=Nl([Cl(e,s,t-r),Vl([r],a)]);i.push(o),s+=n}return 0===i.length?uh([],[0,t]):kl(Nl(i),[i.length,t])}});var Jh=fi({stft_:function(e,t,n,r,a=Yh){null==r&&(r=Mh(t));let s=Zh(e,t,n),i=qo(s,a(t));return th(i,r)}});var Qh=fi({cropAndResize_:function(e,t,n,r,a="bilinear",s=0){let i=pi(e,"image","cropAndResize"),o=pi(t,"boxes","cropAndResize","float32"),l=pi(n,"boxInd","cropAndResize","int32"),u=o.shape[0];we(4===i.rank,(()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`)),we(2===o.rank&&4===o.shape[1],(()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`)),we(1===l.rank&&l.shape[0]===u,(()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`)),we(2===r.length,(()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`)),we(r[0]>=1&&r[1]>=1,(()=>`cropSize must be atleast [1,1], but was ${r}`)),we("bilinear"===a||"nearest"===a,(()=>`method must be bilinear or nearest, but was ${a}`));let d={image:i,boxes:o,boxInd:l},p={method:a,extrapolationValue:s,cropSize:r};return ei.runKernel(en,d,p)}});var ec=fi({flipLeftRight_:function(e){let t=pi(e,"image","flipLeftRight","float32");we(4===t.rank,(()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`));let n={image:t};return ei.runKernel(In,n,{})}});var tc=fi({grayscaleToRGB_:function(e){let t=pi(e,"image","grayscaleToRGB"),n=t.rank-1,r=t.shape[n];we(t.rank>=2,(()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`)),we(1===r,(()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${r}.`));let a=new Array(t.rank);return a.fill(1,0,n),a[n]=3,Gu(t,a)}});var nc=fi({rgbToGrayscale_:function(e){let t=pi(e,"image","RGBToGrayscale"),n=t.rank-1,r=t.shape[n];we(t.rank>=2,(()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`)),we(3===r,(()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${r}.`));let a,s=t.dtype,i=Wo(t,"float32"),o=lh([.2989,.587,.114]);switch(t.rank){case 2:a=xu("ij,j->i",i,o);break;case 3:a=xu("ijk,k->ij",i,o);break;case 4:a=xu("ijkl,l->ijk",i,o);break;case 5:a=xu("ijklm,m->ijkl",i,o);break;case 6:a=xu("ijklmn,n->ijklm",i,o);break;default:throw new Error("Not a valid tensor rank.")}return a=Vu(a,-1),Wo(a,s)}});var rc=fi({rotateWithOffset_:function(e,t,n=0,r=.5){let a=pi(e,"image","rotateWithOffset","float32");we(4===a.rank,(()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${a.rank}.`));let s={image:a},i={radians:t,fillValue:n,center:r};return ei.runKernel(Ea,s,i)}});function ac(e,t,n,r,a,s){null==r&&(r=.5),null==a&&(a=Number.NEGATIVE_INFINITY),null==s&&(s=0);let i=e.shape[0];return n=Math.min(n,i),we(0<=r&&r<=1,(()=>`iouThreshold must be in [0, 1], but was '${r}'`)),we(2===e.rank,(()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`)),we(4===e.shape[1],(()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`)),we(1===t.rank,(()=>"scores must be a 1D tensor")),we(t.shape[0]===i,(()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`)),we(0<=s&&s<=1,(()=>`softNmsSigma must be in [0, 1], but was '${s}'`)),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}var sc=fi({nonMaxSuppression_:function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=pi(e,"boxes","nonMaxSuppression","float32"),i=pi(t,"scores","nonMaxSuppression","float32"),o=ac(s,i,n,r,a),l={maxOutputSize:n=o.maxOutputSize,iouThreshold:r=o.iouThreshold,scoreThreshold:a=o.scoreThreshold};return ei.runKernel(fr,{boxes:s,scores:i},l)}});function ic(e,t,n){let r=function(e,t,n){return function(e,t,n){let r=0,a=e.length,s=0,i=!1;for(;r<a;){s=r+(a-r>>>1);let o=n(t,e[s]);o>0?r=s+1:(a=s,i=!o)}return i?r:-r-1}(e,t,n||oc)}

2(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function oc(e,t){return e>t?1:e<t?-1:0}function lc(e,t,n,r,a){return pc(e,t,n,r,a,0)}function uc(e,t,n,r,a,s){return pc(e,t,n,r,a,0,!1,s,!0)}function dc(e,t,n,r,a,s){return pc(e,t,n,r,a,s,!0)}function pc(e,t,n,r,a,s,i=!1,o=!1,l=!1){let u=[];for(let e=0;e<t.length;e++)t[e]>a&&u.push({score:t[e],boxIndex:e,suppressBeginIndex:0});u.sort(fc);let d=s>0?-.5/s:0,p=[],h=[];for(;p.length<n&&u.length>0;){let t=u.pop(),{score:n,boxIndex:s,suppressBeginIndex:i}=t;if(n<a)break;let o=!1;for(let n=p.length-1;n>=i;--n){let i=hc(e,s,p[n]);if(i>=r){o=!0;break}if(t.score=t.score*cc(r,d,i),t.score<=a)break}t.suppressBeginIndex=p.length,o||(t.score===n?(p.push(s),h.push(t.score)):t.score>a&&ic(u,t,fc))}let c=p.length,f=n-c;o&&f>0&&(p.push(...new Array(f).fill(0)),h.push(...new Array(f).fill(0)));let m={selectedIndices:p};return i&&(m.selectedScores=h),l&&(m.validOutputs=c),m}function hc(e,t,n){let r=e.subarray(4*t,4*t+4),a=e.subarray(4*n,4*n+4),s=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),o=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),u=Math.min(a[0],a[2]),d=Math.min(a[1],a[3]),p=Math.max(a[0],a[2]),h=Math.max(a[1],a[3]),c=(o-s)*(l-i),f=(p-u)*(h-d);if(c<=0||f<=0)return 0;let m=Math.max(s,u),g=Math.max(i,d),y=Math.min(o,p),b=Math.min(l,h),x=Math.max(y-m,0)*Math.max(b-g,0);return x/(c+f-x)}function cc(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function fc(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}var mc=async function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=pi(e,"boxes","nonMaxSuppressionAsync"),i=pi(t,"scores","nonMaxSuppressionAsync"),o=ac(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],d=l[1],{selectedIndices:p}=lc(u,d,n,r,a);return s!==e&&s.dispose(),i!==t&&i.dispose(),lh(p,"int32")};var gc=fi({nonMaxSuppressionWithScore_:function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=pi(e,"boxes","nonMaxSuppression"),o=pi(t,"scores","nonMaxSuppression"),l=ac(i,o,n,r,a,s),u={boxes:i,scores:o},d={maxOutputSize:n=l.maxOutputSize,iouThreshold:r=l.iouThreshold,scoreThreshold:a=l.scoreThreshold,softNmsSigma:s=l.softNmsSigma},p=ei.runKernel(gr,u,d);return{selectedIndices:p[0],selectedScores:p[1]}}});var yc=async function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=pi(e,"boxes","nonMaxSuppressionAsync"),o=pi(t,"scores","nonMaxSuppressionAsync"),l=ac(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),d=u[0],p=u[1],{selectedIndices:h,selectedScores:c}=dc(d,p,n,r,a,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:lh(h,"int32"),selectedScores:lh(c)}};var bc=fi({nonMaxSuppressionPadded_:function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=pi(e,"boxes","nonMaxSuppression"),o=pi(t,"scores","nonMaxSuppression"),l=ac(i,o,n,r,a,null),u={boxes:i,scores:o},d={maxOutputSize:l.maxOutputSize,iouThreshold:l.iouThreshold,scoreThreshold:l.scoreThreshold,padToMaxOutputSize:s},p=ei.runKernel(mr,u,d);return{selectedIndices:p[0],validOutputs:p[1]}}});var xc=async function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=pi(e,"boxes","nonMaxSuppressionAsync"),o=pi(t,"scores","nonMaxSuppressionAsync"),l=ac(i,o,n,r,a,null),u=l.maxOutputSize,d=l.iouThreshold,p=l.scoreThreshold,[h,c]=await Promise.all([i.data(),o.data()]),{selectedIndices:f,validOutputs:m}=uc(h,c,u,d,p,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:lh(f,"int32"),validOutputs:Du(m,"int32")}};var wc=fi({resizeBilinear_:function(e,t,n=!1,r=!1){let a=pi(e,"images","resizeBilinear");we(3===a.rank||4===a.rank,(()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`)),we(2===t.length,(()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`)),we(!1===r||!1===n,(()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false."));let s=a,i=!1;3===a.rank&&(i=!0,s=kl(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},u=ei.runKernel(Mr,o,l);return i?kl(u,[u.shape[1],u.shape[2],u.shape[3]]):u}});var vc=fi({resizeNearestNeighbor_:function(e,t,n=!1,r=!1){let a=pi(e,"images","resizeNearestNeighbor");we(3===a.rank||4===a.rank,(()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`)),we(2===t.length,(()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`)),we("float32"===a.dtype||"int32"===a.dtype,(()=>"`images` must have `int32` or `float32` as dtype")),we(!1===r||!1===n,(()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false."));let s=a,i=!1;3===a.rank&&(i=!0,s=kl(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},u=ei.runKernel(Fr,o,l);return i?kl(u,[u.shape[1],u.shape[2],u.shape[3]]):u}});var kc=fi({threshold_:function(e,t="binary",n=!1,r=.5){let a,s,i,o,l=pi(e,"image","threshold"),u=l.shape[0]*l.shape[1],d=qo(lh([r]),255);if(we(3===l.rank,(()=>`Error in threshold: image must be rank 3,but got rank ${l.rank}.`)),we(3===l.shape[2]||1===l.shape[2],(()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${l.shape[2]}.`)),we("int32"===l.dtype||"float32"===l.dtype,(()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${l.dtype}.`)),we("otsu"===t||"binary"===t,(()=>`Method must be binary or otsu, but was ${t}`)),3===l.shape[2]){[a,s,i]=eh(l,[1,1,1],-1);let e=qo(a,.2989),t=qo(s,.587),n=qo(i,.114);o=Go(Go(e,t),n)}else o=e;if("otsu"===t){d=function(e,t){let n,r,a,s,i,o,l=lh([-1]),u=lh([0]),d=lh([0]);for(let 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Oc=fi({sigmoidCrossEntropy_:function(e,t,n,r=0,a=_c.SUM_BY_NONZERO_WEIGHTS){let s=pi(e,"multiClassLabels","sigmoidCrossEntropy"),i=pi(t,"logits","sigmoidCrossEntropy"),o=null;if(null!=n&&(o=pi(n,"weights","sigmoidCrossEntropy")),ve(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let e=Du(r),t=Du(1),n=Du(.5);s=Go(qo(s,yd(t,e)),qo(n,e))}let l=function(e,t){let n=pi(e,"labels","sigmoidCrossEntropyWithLogits"),r=pi(t,"logits","sigmoidCrossEntropyWithLogits");ve(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=Ap(r),s=qo(r,n),i=id(Wu(fd(Ko(r))));return Go(yd(a,s),i)}(s,i);return Ec(l,o,a)}});var Lc=fi({softmaxCrossEntropy_:function(e,t,n,r=0,a=_c.SUM_BY_NONZERO_WEIGHTS){let s=pi(e,"onehotLabels","softmaxCrossEntropy"),i=pi(t,"logits","softmaxCrossEntropy"),o=null;if(null!=n&&(o=pi(n,"weights","softmaxCrossEntropy")),ve(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let e=Du(r),t=Du(1),n=Du(s.shape[1]);s=Go(qo(s,yd(t,e)),jo(e,n))}let l=function(e,t,n=-1){if(-1===n&&(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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2if(1!==a.rank)throw new Error(`Indices should be Tensor1D but received shape\n          ${a.shape}`);if(1!==s.rank)throw new Error(`Segment ids should be Tensor1D but received shape\n          ${s.shape}`);let i={data:r,indices:a,segmentIds:s};return ei.runKernel(sa,i)}});var Wc=fi({sparseSegmentSum_:function(e,t,n){let r=pi(e,"data","sparseSegmentSum"),a=pi(t,"indices","sparseSegmentSum","int32"),s=pi(n,"segmentIds","sparseSegmentSum","int32");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.rank)throw new Error(`Indices should be Tensor1D but received shape\n         ${a.shape}`);if(1!==s.rank)throw new Error(`Segment ids should be Tensor1D but received shape\n         ${s.shape}`);let i={data:r,indices:a,segmentIds:s};return ei.runKernel(ia,i)}});var Vc=fi({stringNGrams_:function(e,t,n,r,a,s,i,o){let l=pi(e,"data","stringNGrams","string");if("string"!==l.dtype)throw new Error("Data must be of datatype 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a={input:n};return ei.runKernel(fa,a,r)}});var Hc=fi({staticRegexReplace_:function(e,t,n,r=!0){let a=pi(e,"input","staticRegexReplace","string"),s={pattern:t,rewrite:n,replaceGlobal:r};return ei.runKernel(da,{x:a},s)}}),jc={fft:Zp,ifft:Jp,rfft:th,irfft:Qp},qc={hammingWindow:Xh,hannWindow:Yh,frame:Zh,stft:Jh},Kc={flipLeftRight:ec,grayscaleToRGB:tc,resizeNearestNeighbor:vc,resizeBilinear:wc,rgbToGrayscale:nc,rotateWithOffset:rc,cropAndResize:Qh,nonMaxSuppression:sc,nonMaxSuppressionAsync:mc,nonMaxSuppressionWithScore:gc,nonMaxSuppressionWithScoreAsync:yc,nonMaxSuppressionPadded:bc,nonMaxSuppressionPaddedAsync:xc,threshold:kc,transform:Ic},Xc={bandPart:Sc,gramSchmidt:Nc,qr:Cc},Yc={absoluteDifference:$c,computeWeightedLoss:Ec,cosineDistance:Ac,hingeLoss:Rc,huberLoss:Fc,logLoss:Dc,meanSquaredError:Mc,sigmoidCrossEntropy:Oc,softmaxCrossEntropy:Lc},Zc={sparseFillEmptyRows:zc,sparseReshape:Pc,sparseSegmentMean:Bc,sparseSegmentSum:Wc},Jc={stringNGrams:Vc,stringSplit:Uc,stringToHashBucketFast:Gc,staticRegexReplace:Hc},Qc={};B(Qc,{Serializable:()=>nf,SerializationMap:()=>rf,getRegisteredName:()=>sf,registerClass:()=>af});var ef=new Map,tf=new Map,nf=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},rf=class e{constructor(){this.classNameMap={}}static getMap(){return null==e.instance&&(e.instance=new e),e.instance}static register(t){e.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function af(e,t,n){we(null!=e.className,(()=>"Class being registered does not have the static className property defined.")),we("string"==typeof e.className,(()=>"className is required to be a string, but got type "+typeof e.className)),we(e.className.length>0,(()=>"Class being registered has an empty-string as its className, which is disallowed.")),void 0===t&&(t="Custom"),void 0===n&&(n=e.className);let r=t+">"+n;return rf.register(e),ef.set(r,e),tf.set(e,r),e}function sf(e){return tf.has(e)?tf.get(e):e.className}var of=class extends nf{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(null!=n){let e=n.map((e=>({name:e.name,tensor:a[e.name]})));this.applyGradients(e)}else this.applyGradients(a);return Ei(a),t?r:(r.dispose(),null)}get iterations(){return null==this.iterations_&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pd(e,t)}dispose(){null!=this.iterations_&&Ei(this.iterations_)}async saveIterations(){return null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:Du(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(of,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});var lf=class extends of{static get className(){return"Adadelta"}constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=ei.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map((e=>

2e.name)):Object.keys(e)).forEach(((t,n)=>{let r=ei.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:Ci((()=>gu(r).variable(false)))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:Ci((()=>gu(r).variable(false)))});let a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;let s=this.accumulatedGrads[n].variable,i=this.accumulatedUpdates[n].variable;Ci((()=>{let e=Go(qo(s,this.rho),qo(Ou(a),1-this.rho)),t=qo(jo(Mu(Go(i,this.epsilon)),Mu(Go(s,this.epsilon))),a),n=Go(qo(i,this.rho),qo(Ou(t),1-this.rho));s.assign(e),i.assign(n);let o=Go(qo(t,-this.learningRate),r);r.assign(o)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&(Ei(this.accumulatedGrads.map((e=>e.variable))),Ei(this.accumulatedUpdates.map((e=>e.variable))))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){let t=(e=await this.extractIterations(e)).length/2;this.accumulatedGrads=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)}))),this.accumulatedUpdates=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},uf=class extends of{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{let r=ei.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:Ci((()=>Vl(r.shape,this.initialAccumulatorValue).variable(!1)))});let a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;let s=this.accumulatedGrads[n].variable;Ci((()=>{let e=Go(s,Ou(a));s.assign(e);let t=Go(qo(jo(a,Mu(Go(e,ei.backend.epsilon()))),-this.learningRate),r);r.assign(t)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&Ei(this.accumulatedGrads.map((e=>e.variable)))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);this.accumulatedGrads=e.map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},df=class extends of{static get className(){return"Adam"}constructor(e,t,n,r=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ci((()=>{this.accBeta1=Du(t).variable(),this.accBeta2=Du(n).variable()})),null==r&&(this.epsilon=ei.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);Ci((()=>{let n=yd(1,this.accBeta1),r=yd(1,this.accBeta2);t.forEach(((t,a)=>{let s=ei.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:Ci((()=>gu(s).variable(false)))}),null==this.accumulatedSecondMoment[a]&&(this.accumulatedSecondMoment[a]={originalName:`${t}/v`,variable:Ci((()=>gu(s).variable(false)))});let i=Array.isArray(e)?e[a].tensor:e[t];if(null==i)return;let o=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,u=Go(qo(o,this.beta1),qo(i,1-this.beta1)),d=Go(qo(l,this.beta2),qo(Ou(i),1-this.beta2)),p=jo(u,n),h=jo(d,r);o.assign(u),l.assign(d);let c=Go(qo(jo(p,Go(Mu(h),this.epsilon)),-this.learningRate),s);s.assign(c)})),this.accBeta1.assign(qo(this.accBeta1,this.beta1)),this.accBeta2.assign(qo(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&Ei(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&Ei(this.accumulatedSecondMoment.map((e=>e.variable)))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}

2))))}async setWeights(e){e=await this.extractIterations(e),Ci((()=>{this.accBeta1.assign(Fu(this.beta1,this.iterations_+1)),this.accBeta2.assign(Fu(this.beta2,this.iterations_+1))}));let t=e.length/2;this.accumulatedFirstMoment=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)}))),this.accumulatedSecondMoment=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}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)}},pf=class extends of{static get className(){return"Adamax"}constructor(e,t,n,r=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ci((()=>{this.iteration=Du(0).variable(),this.accBeta1=Du(t).variable()})),null==r&&(this.epsilon=ei.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);Ci((()=>{let n=yd(1,this.accBeta1),r=jo(-this.learningRate,Go(qo(this.iteration,this.decay),1));t.forEach(((t,a)=>{let s=ei.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:gu(s).variable(false)}),null==this.accumulatedWeightedInfNorm[a]&&(this.accumulatedWeightedInfNorm[a]={originalName:`${t}/v`,variable:gu(s).variable(false)});let i=Array.isArray(e)?e[a].tensor:e[t];if(null==i)return;let o=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,u=Go(qo(o,this.beta1),qo(i,1-this.beta1)),d=qo(l,this.beta2),p=Ko(i),h=$d(d,p);o.assign(u),l.assign(h);let c=Go(qo(jo(r,n),jo(u,Go(h,this.epsilon))),s);s.assign(c)})),this.iteration.assign(Go(this.iteration,1)),this.accBeta1.assign(qo(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&Ei(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&Ei(this.accumulatedWeightedInfNorm.map((e=>e.variable)))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},hf=class extends of{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;let a=ei.registeredVariables[t];Ci((()=>{let e=Go(qo(this.c,r),a);a.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=$i(Du(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).length)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}},cf=class extends hf{static get className(){return"Momentum"}constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Du(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{let r=ei.registeredVariables[t];null==this.accumulations[n]&&(this.accumulations[n]={originalName:`${t}/momentum`,variable:Ci((()=>gu(r).variable(!1)))});let a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];null!=s&&Ci((()=>{let e,t=Go(qo(this.m,a),s);e=this.useNesterov?Go(qo(this.c,Go(s,qo(t,this.m))),r):Go(qo(this.c,t),r),a.assign(t),r.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&Ei(this.accumulations.map((e=>e.variable)))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map((e=>({name:e.originalName,tensor:e.variable}

2))))}async setWeights(e){e=await this.extractIterations(e);this.accumulations=e.map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}},ff=class extends of{static get className(){return"RMSProp"}constructor(e,t=.9,n=0,r=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,null==r&&(this.epsilon=ei.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{let r=ei.registeredVariables[t],a=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:Ci((()=>gu(r).variable(a)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:Ci((()=>gu(r).variable(a)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:Ci((()=>gu(r).variable(a)))});let s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;Ci((()=>{let e=Go(qo(i,this.decay),qo(Ou(s),1-this.decay));if(this.centered){let t=this.accumulatedMeanGrads[n].variable,a=Go(qo(t,this.decay),qo(s,1-this.decay)),l=jo(qo(s,this.learningRate),Mu(yd(e,Go(Ou(a),this.epsilon)))),u=Go(qo(o,this.momentum),l);i.assign(e),t.assign(a),o.assign(u);let d=yd(r,u);r.assign(d)}else{let e=Go(qo(i,this.decay),qo(Ou(s),1-this.decay)),t=Go(qo(o,this.momentum),jo(qo(s,this.learningRate),Mu(Go(e,this.epsilon))));i.assign(e),o.assign(t);let n=yd(r,t);r.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&Ei(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&Ei(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&Ei(this.accumulatedMoments.map((e=>e.variable)))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.accumulatedMoments=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.centered&&(this.accumulatedMeanGrads=e.slice(2*t,3*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}},mf=[lf,uf,df,pf,cf,ff,hf];var 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browser.");t.startsWith(e.URL_SCHEME)&&(t=t.slice(e.URL_SCHEME.length)),(null==t||0===t.length)&&(t="model"),this.modelJsonFileName=t+".json",this.weightDataFileName=t+".weights.bin"}async save(e){if("undefined"==typeof document)throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=xi.join(e.weightData),n=window.URL.createObjectURL(new Blob([t],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let t=eo(e,[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}]),r=window.URL.createObjectURL(new Blob([JSON.stringify(t)],{type:"application/json"})),a=null==this.modelJsonAnchor?document.createElement("a"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=r,await yf((()=>a.dispatchEvent(new MouseEvent("click")))),null!=e.weightData){let e=null==this.weightDataAnchor?document.createElement("a"):this.weightDataAnchor;e.download=this.weightDataFileName,e.href=n,await yf((()=>

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2{if(2!==e.length||e[0]!==e[1])throw new Ob("Identity matrix initializer can only be used for 2D square matrices.");return qo(this.gain,Hu(e[0]))}))}getConfig(){return{gain:this.gain}}};rw.className="Identity",Qc.registerClass(rw);var aw=class extends Yx{constructor(e){if(super(),e.scale<0)throw new Ob(`scale must be a positive float. Got: ${e.scale}`);this.scale=null==e.scale?1:e.scale,this.mode=null==e.mode?"fanIn":e.mode,function(e){ex(Kx,"FanMode",e)}(this.mode),this.distribution=null==e.distribution?"normal":e.distribution,function(e){ex(Xx,"Distribution",e)}(this.distribution),this.seed=e.seed}apply(e,t){let n=function(e,t="channelsLast"){let n,r;if(mx(t),2===e.length)n=e[0],r=e[1];else if(-1!==[3,4,5].indexOf(e.length)){if("channelsFirst"===t){let t=_x(e,2);n=e[1]*t,r=e[0]*t}else if("channelsLast"===t){let t=_x(e,0,e.length-2);n=e[e.length-2]*t,r=e[e.length-1]*t}}else{let t=_x(e);n=Math.sqrt(t),r=Math.sqrt(t)}return[n,r]}(e),r=n[0],a=n[1],s=this.scale;if("fanIn"===this.mode?s/=Math.max(1,r):"fanOut"===this.mode?s/=Math.max(1,a):s/=Math.max(1,(r+a)/2),"normal"===this.distribution){let n=Math.sqrt(s);if("float32"!==(t=t||"float32")&&"int32"!==t)throw new Lb(`${this.getClassName()} does not support dType ${t}.`);return wh(e,0,n,t,this.seed)}{let n=Math.sqrt(3*s);return Tp(e,-n,n,t,this.seed)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};aw.className="VarianceScaling",Qc.registerClass(aw);var sw=class extends aw{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return aw.className}};sw.className="GlorotUniform",Qc.registerClass(sw);var iw=class extends aw{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return aw.className}};iw.className="GlorotNormal",Qc.registerClass(iw);var ow=class extends aw{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return aw.className}};ow.className="HeNormal",Qc.registerClass(ow);var lw=class extends aw{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return aw.className}};lw.className="HeUniform",Qc.registerClass(lw);var uw=class extends aw{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return aw.className}};uw.className="LeCunNormal",Qc.registerClass(uw);var dw=class extends aw{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return aw.className}};dw.className="LeCunUniform",Qc.registerClass(dw);var pw=class extends Yx{constructor(e){super(),this.DEFAULT_GAIN=1,this.ELEMENTS_WARN_SLOW=2e3,this.gain=null==e.gain?this.DEFAULT_GAIN:e.gain,this.seed=e.seed}apply(e,t){return Ci((()=>{if(e.length<2)throw new Lb("Shape must be at least 2D.");if("int32"!==t&&"float32"!==t&&void 0!==t)throw new TypeError(`Unsupported data type ${t}.`);let n=ja.sizeFromShape(e.slice(0,-1)),r=e[e.length-1],a=n*r;a>this.ELEMENTS_WARN_SLOW&&console.warn(`Orthogonal initializer is being called on a matrix with more than ${this.ELEMENTS_WARN_SLOW} (${a}) elements: Slowness may result.`);let s=Bx([Math.max(r,n),Math.min(r,n)],0,1,t,this.seed),i=Xc.qr(s,!1),o=i[0],l=i[1].flatten().stridedSlice([0],[Math.min(r,n)*Math.min(r,n)],[Math.min(r,n)+1]);return o=qo(o,l.sign()),n<r&&(o=o.transpose()),qo(Du(this.gain),o.reshape(e))}))}getConfig(){return{gain:this.gain,seed:this.seed}}};pw.className="Orthogonal",Qc.registerClass(pw);var hw={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function cw(e,t={}){return Yb(e,Qc.SerializationMap.getMap().classNameMap,t,"initializer")}function fw(e){return Kb(e)}function mw(e){if("string"==typeof e){let t=e in hw?hw[e]:e;if("GlorotNormal"===t)return new iw;if("GlorotUniform"===t)return new sw;if("HeNormal"===t)return new ow;if("HeUniform"===t)return new lw;if("LeCunNormal"===t)return new uw;if("LeCunUniform"===t)return new dw;{let e={};return e.className=t,e.config={},cw(e)}}return e instanceof Yx?e:cw(e)}function gw(e){return Array.isArray(e)&&Array.isArray(e[0])}function yw(e){return 0===e.length?[]:Array.isArray(e[0])?e:[e]}function bw(e){let t;if(Array.isArray(e)){if(1!==e.length)throw new Ob(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function xw(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(1===e.length)return e[0];throw new Ob(`Expected exactly 1 Shape; got ${e.length}`)}return e}function ww(e){let t=0;for(let n of e)0===n.shape.length?t+=1:t+=n.shape.reduce(((e,t)=>e*t));return t}

2var vw="Variable",kw=class{constructor(e,t="float32",n=vw,r=!0,a=null){this.dtype=null==t?"float32":t,this.shape=e.shape,this.id=ix(),n=null==n?vw:n,this.originalName=vx(n),this.name=kx(this.originalName),this.trainable_=r,this.constraint=a,this.val=Nh(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),function(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}(this.val,e),this.val.id!==e.id&&(this.val.assign(e),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function Iw(e){return e.map((e=>e.read()))}function Sw(e){e.forEach((e=>{e[0].write(e[1])}))}var Nw=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,null!=e.shape?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Tw=class{constructor(e,t,n,r,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=i,this.id=ix(),null!=s&&(this.originalName=vx(s),this.name=kx(this.originalName)),this.rank=t.length}},_w=0,Cw=class{constructor(e,t){this.callArgs=t,this.id=_w++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let t of e.inboundLayers)null!=t&&t.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)null!=t?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},Ew=0,$w=class extends Qc.Serializable{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Ew++,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 e=this.getClassName();t=Hb(e)+"_"+lx(e)}if(this.name=t,this.trainable_=null==e.trainable||e.trainable,null!=e.inputShape||null!=e.batchInputShape){let t;if(null!=e.batchInputShape)t=e.batchInputShape;else if(null!=e.inputShape){let n=null;null!=e.batchSize&&(n=e.batchSize),t=[n].concat(e.inputShape)}this.batchInputShape=t;let n=e.dtype;null==n&&(n=e.inputDType),null==n&&(n="float32"),this.dtype=n}null!=e.weights?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(0===this.inboundNodes.length)throw new Mb(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new Ob(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Ub(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Ub(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Db(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(0===this.inboundNodes.length)throw new Db(`Layer ${this.name} is not connected, no input to return.`);return Ub(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(0===this.inboundNodes.length)throw new Db(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Db(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Ub(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_}

2set 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){let t=Gb(e);if(null==this.inputSpec||0===this.inputSpec.length)return;let n=Gb(this.inputSpec);if(t.length!==n.length)throw new Ob(`Layer ${this.name} expects ${n.length} inputs, but it received ${t.length} input tensors. Input received: ${e}`);for(let e=0;e<t.length;e++){let r=t[e],a=n[e];if(null==a)continue;let s=r.rank;if(null!=a.ndim&&s!==a.ndim)throw new Ob(`Input ${e} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(null!=a.maxNDim&&s>a.maxNDim)throw new Ob(`Input ${e} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(null!=a.minNDim&&s<a.minNDim)throw new Ob(`Input ${e} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(null!=a.dtype&&r.dtype!==a.dtype)throw new Ob(`Input ${e} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${r.dtype}.`);if(a.axes){let t=r.shape;for(let n in a.axes){let r=Number(n),s=a.axes[n],i=r>=0?t[r]:t[t.length+r];if(null!=s&&-1===[s,null].indexOf(i))throw new Ob(`Input ${e} is incompatible with layer ${this.name}: expected axis ${r} of input shape to have value ${s} but got shape ${t}.`)}}if(null!=a.shape)for(let t=0;t<a.shape.length;++t){let n=a.shape[t],s=r.shape[t];if(null!=n&&null!=s&&n!==s)throw new Ob(`Input ${e} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){null!=this._callHook&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=Gb(e),r=function(e){let t=!0;for(let n of Gb(e))if(!(n instanceof Tw)){t=!1;break}return t}(e),a=function(e){let t=!0;for(let n of Gb(e))if(n instanceof Tw){t=!1;break}return t}(e);if(r===a)throw new Ob("Arguments to apply() must be all SymbolicTensors or all Tensors");return wx(this.name,(()=>{if(!this.built){this.assertInputCompatibility(e);let t=[];for(let n of Gb(e))t.push(n.shape);this.build(Ub(t)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let r=this.call(e,t);this.supportsMasking&&this.setMaskMetadata(e,r);let a=Gb(r),s=[];for(let e of a)-1!==n.indexOf(e)&&(e=e.clone()),s.push(e);if(r=Ub(s),null!=this.activityRegularizer)throw new Lb("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}{let n,r=function(e){e=Gb(e);let t=[];for(let n of e)t.push(n.shape);return Ub(t)}(e),a=this.computeOutputShape(r),s="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(e)?r[0]:r),n=null!=a&&a.length>0&&Array.isArray(a[0])?a.map(((n,r)=>new Tw(s,n,this,Gb(e),t,this.name,r))):new Tw(s,a,this,Gb(e),t,this.name),this.addInboundNode(e,n,null,null,r,a,t),this._refCount++,null!=this.activityRegularizer)throw new Lb("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return n}}))}warnOnIncompatibleInputShape(e){if(null!=this.batchInputShape)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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach(((n,r)=>{null!=n&&null!=e[r]&&e[r]!==n&&(t=!0)})),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}

2get outputShape(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new Db(`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);-1===e.indexOf(n)&&e.push(n)}if(1===e.length){let e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&1===e.length?e[0]:e}throw new Db(`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 Mb(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return ww(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Iw(e?this.trainableWeights:this.weights)}setWeights(e){Ci((()=>{let t=this.weights;if(t.length!==e.length)throw new Ob(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(0===t.length)return;let n=[],r=Iw(t);for(let a=0;a<r.length;++a){let s=r[a],i=t[a],o=e[a];if(!ja.arraysEqual(s.shape,o.shape))throw new Ob(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Sw(n)}))}addWeight(e,t,n,r,a,s,i,o){if(-1!==this._addedWeightNames.indexOf(e))throw new Ob(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),null==n&&(n="float32"),this.fastWeightInitDuringBuild&&(r=null!=o?o():mw("zeros"));let l=r.apply(t,n),u=new kw(l,n,e,s,i);return l.dispose(),null!=a&&this.addLoss((()=>a.apply(u.read()))),null==s&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){null==e||Array.isArray(e)&&0===e.length||(e=Gb(e),void 0!==this._losses&&null!==this._losses&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(null!=t){if(!Array.isArray(t))throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);t.forEach((e=>{if(null!=e)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)}))}return null}return t}setMaskMetadata(e,t,n){if(!this.supportsMasking)return;let r=this.computeMask(e,n),a=Gb(t),s=Gb(r);if(a.length!==s.length)throw new Error(`${this.name} outputs ${a.length} tensors but ${a.length} masks for those tensors`);for(let e=0;e<a.length;e++)a[e].kerasMask=s[e]}addInboundNode(e,t,n,r,a,s,i=null){let o=Gb(e);t=Gb(t),n=Gb(n),r=Gb(r),a=yw(a),s=yw(s);let l=[],u=[],d=[];for(let e of o)l.push(e.sourceLayer),u.push(e.nodeIndex),d.push(e.tensorIndex);new Cw({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:s},i);for(let e=0;e<t.length;e++)t[e].sourceLayer=this,t[e].nodeIndex=this.inboundNodes.length-1,t[e].tensorIndex=e}getConfig(){let e={name:this.name,trainable:this.trainable};return null!=this.batchInputShape&&(e.batchInputShape=this.batchInputShape),null!=this.dtype&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach((e=>e.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error(`Layer '${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(null===this._refCount)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return 0==--this._refCount&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function Aw(e,t,n){if((null==t||null!=n&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),0===t.inboundNodes.length)return[e];{let e=t.inboundNodes[n];if(0===e.inboundLayers.length)return e.inputTensors;{let t=[];for(let n=0;n<e.inboundLayers.length;n++){let r=Aw(e.inputTensors[n],e.inboundLayers[n],e.nodeIndices[n]);for(let e of r)-1===t.indexOf(e)&&t.push(e)}return t}}}var Rw=class extends $w{constructor(e){if(super({dtype:e.dtype,name:null!=e.name?e.name:lx("input").toString()}),null==e.batchSize&&(e.batchSize=null),null==e.sparse&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,null!=e.inputShape&&null!=e.batchInputShape)throw new Ob("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(null==t){if(null==e.inputShape)throw new Ob("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(null!=e.batchSize)throw new Ob("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 r=new Tw(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new Cw({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new Ob(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};function Fw(e){if(null==e.batchShape&&null==e.shape)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");

2if(null!=e.batchShape&&null!=e.shape)throw new Ob("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;null!=e.shape&&null==t&&(t=[null].concat(e.shape));let n=e.dtype;return null==n&&(n="float32"),new Rw({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}Rw.className="InputLayer",Qc.registerClass(Rw);var Dw=class e{constructor(t){if(this.id2Value={},this.id2Mask={},this.name2Id={},t instanceof e)for(let e in t.id2Value)this.id2Value[e]=t.id2Value[e],e in t.id2Mask&&(this.id2Mask[e]=t.id2Mask[e]);else{if(null==t)return;for(let e of t)this.add(e.key,e.value)}}add(e,t,n){if(null!=this.id2Value[e.id])throw new Ob(`Duplicate key: name=${e.name}, id=${e.id}`);return this.id2Value[e.id]=function(e,t){if(null==e.dtype||e.dtype===t.dtype)return t;try{return Wo(t,e.dtype)}catch(n){throw new Ob(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}(e,t),this.name2Id[e.name]=e.id,null!=n&&(this.id2Mask[e.id]=n),this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return null!=this.id2Value[e.id]}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof Tw){if(null==this.id2Value[e.id])throw new Ob(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}{let t=this.name2Id[e];if(null==t)throw new Ob(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Tw){if(null==this.id2Value[e.id])throw new Ob(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}{let t=this.name2Id[e];if(null==t)throw new Ob(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){null!=this.id2Mask&&Ei(this.id2Mask)}},Mw=new Pb,Ow=new Pb;function Lw(e,t,n,r){let a=null!=n&&n.training,s=Array.isArray(e),i=s?e:[e],o=i.map((e=>e.name)),l=[],u=t.names();for(let e of o)-1!==u.indexOf(e)?l.push(t.getValue(e)):l.push(null);null!=r&&(r.maxNumTensors=-1/0,r.minNumTensors=1/0);let d,p=o.join(",")+"|"+t.names().sort().join(","),h=Mw.get(p);if(null==h){let e=function(e,t){ja.assert(null!=e&&e.length>0,(()=>"Expected at least one fetch, got none"));let n=[],r={};if(1===e.length){let a=Pw(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:e,recipientMap:i}=Pw(s,t);for(let t of e)a.has(t.name)||(n.push(t),a.add(t.name));for(let e in i)null==r[e]&&(r[e]=new Set),i[e].forEach((t=>r[e].add(t)))}}return{sorted:n,recipientCounts:zw(r)}}(i,t);h=e.sorted,d=e.recipientCounts,Mw.put(p,h),Ow.put(p,d)}d={},a||Object.assign(d,Ow.get(p));let c=new Dw(t);for(let e=0;e<h.length;++e){if(null!=r){let e=Ti().numTensors;e>r.maxNumTensors&&(r.maxNumTensors=e),e<r.minNumTensors&&(r.minNumTensors=e)}let s=h[e],i=s.sourceLayer;if(i instanceof Rw)continue;let u=[],p=[],f=[],m=!1;for(let e of s.inputs){let n=c.getValue(e),r=c.getMask(e);u.push(n),p.push(r),null!=r&&(m=!0),a||(d[e.name]--,0===d[e.name]&&!t.hasKey(e)&&-1===o.indexOf(e.name)&&!n.isDisposed&&!0!==e.sourceLayer.stateful&&f.push(n))}m&&((n=n||{}).mask=p[0]);let g=Gb(i.apply(u,n)),y=null;i.supportsMasking&&(y=i.computeMask(u,p));let b=Bw(s),x=Array.isArray(b)?b:[b];for(let e=0;e<x.length;++e){c.hasKey(x[e])||c.add(x[e],g[e],Array.isArray(y)?y[0]:y);let t=o.indexOf(x[e].name);-1!==t&&(l[t]=g[e])}a||Ei(f)}return c.disposeMasks(),s?l:l[0]}function zw(e){let t={};for(let n in e)t[n]=e[n].size;return t}function Pw(e,t){let n=new Set,r=[],a={};for(let e of t.names())n.add(e);let s=[],i=[];for(s.push(e);s.length>0;){let e=s[s.length-1];if(n.has(e.name)){s.pop();continue}let t=i[i.length-1]===s.length-1;if(0===e.inputs.length||t)s.pop(),r.push(e),n.add(e.name),t&&i.pop();else{i.push(s.length-1);for(let t of e.inputs)null==a[t.name]&&(a[t.name]=new Set),a[t.name].add(e.name),!n.has(t.name)&&s.push(t)}}return{sorted:r,recipientMap:a}}function Bw(e){let t;

2if(1===e.sourceLayer.inboundNodes.length)t=e.sourceLayer.output;else{let n=null;for(let t=0;t<e.sourceLayer.inboundNodes.length;++t)for(let r of e.sourceLayer.inboundNodes[t].outputTensors)if(r.id===e.id){n=t;break}t=e.sourceLayer.getOutputAt(n)}return t}ut().registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES",(()=>100),(function(e){null!=Mw&&Mw.setMaxEntries(e),null!=Ow&&Ow.setMaxEntries(e)}));var Ww={};function Vw(e,t){return Ci((()=>Mu(Lu(qo(e,e),t,!0))))}B(Ww,{maxNorm:()=>Jw,minMaxNorm:()=>tv,nonNeg:()=>ev,unitNorm:()=>Qw});var Uw=class extends Qc.Serializable{getConfig(){return{}}},Gw=class extends Uw{constructor(e){super(),this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=null!=e.maxValue?e.maxValue:this.defaultMaxValue,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return Ci((()=>{let t=Vw(e,this.axis),n=Ul(t,0,this.maxValue);return qo(e,jo(n,Go(Ax(),t)))}))}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};Gw.className="MaxNorm",Qc.registerClass(Gw);var Hw=class extends Uw{constructor(e){super(),this.defaultAxis=0,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return Ci((()=>jo(e,Go(Ax(),Vw(e,this.axis)))))}getConfig(){return{axis:this.axis}}};Hw.className="UnitNorm",Qc.registerClass(Hw);var jw=class extends Uw{apply(e){return Ap(e)}};jw.className="NonNeg",Qc.registerClass(jw);var qw=class extends Uw{constructor(e){super(),this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=null!=e.minValue?e.minValue:this.defaultMinValue,this.maxValue=null!=e.maxValue?e.maxValue:this.defaultMaxValue,this.rate=null!=e.rate?e.rate:this.defaultRate,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return Ci((()=>{let t=Vw(e,this.axis),n=Go(qo(this.rate,Ul(t,this.minValue,this.maxValue)),qo(1-this.rate,t));return qo(e,jo(n,Go(Ax(),t)))}))}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};qw.className="MinMaxNorm",Qc.registerClass(qw);var Kw={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Xw(e){return Kb(e)}function Yw(e,t={}){return Yb(e,Qc.SerializationMap.getMap().classNameMap,t,"constraint")}function Zw(e){if(null==e)return null;if("string"==typeof e){return Yw({className:e in Kw?Kw[e]:e,config:{}})}return e instanceof Uw?e:Yw(e)}function Jw(e){return new Gw(e)}function Qw(e){return new Hw(e)}function ev(){return new jw}function tv(e){return new qw(e)}var nv={};function rv(){return new Zx}function av(){return new Jx}function sv(e){return new Qx(e)}function iv(e){return new ew(e)}function ov(e){return new tw(e)}function lv(e){return new nw(e)}function uv(e){return new rw(e)}function dv(e){return new aw(e)}function pv(e){return new sw(e)}function hv(e){return new iw(e)}function cv(e){return new ow(e)}function fv(e){return new lw(e)}function mv(e){return new uw(e)}function gv(e){return new dw(e)}function yv(e){return new pw(e)}B(nv,{constant:()=>sv,glorotNormal:()=>hv,glorotUniform:()=>pv,heNormal:()=>cv,heUniform:()=>fv,identity:()=>uv,leCunNormal:()=>mv,leCunUniform:()=>gv,ones:()=>av,orthogonal:()=>yv,randomNormal:()=>ov,randomUniform:()=>iv,truncatedNormal:()=>lv,varianceScaling:()=>dv,zeros:()=>rv});var bv,xv={};async function wv(e){if(null==e)return;let t=[],n=[],r=[];for(let a in e){let s=e[a];if("number"!=typeof s){let e=s;t.push(e.data()),n.push(a),r.push(e)}}if(t.length>0){let a=await Promise.all(t);for(let t=0;t<a.length;++t)e[n[t]]=a[t][0];Ei(r)}}function vv(e){if(null!=e)for(let t in e){let n=e[t];"number"!=typeof n&&n.dispose()}}B(xv,{Layer:()=>$w,RNN:()=>MI,RNNCell:()=>OI,activation:()=>fN,add:()=>IN,alphaDropout:()=>cT,average:()=>SN,averagePooling1d:()=>FN,averagePooling2d:()=>ON,averagePooling3d:()=>PN,avgPool1d:()=>DN,avgPool2d:()=>LN,avgPool3d:()=>BN,avgPooling1d:()=>MN,avgPooling2d:()=>zN,avgPooling3d:()=>WN,batchNormalization:()=>$N,bidirectional:()=>sT,categoryEncoding:()=>bT,centerCrop:()=>gT,concatenate:()=>NN,conv1d:()=>sN,conv2d:()=>iN,conv2dTranspose:()=>oN,conv3d:()=>lN,conv3dTranspose:()=>uN,convLstm2d:()=>tT,convLstm2dCell:()=>nT,cropping2D:()=>pN,dense:()=>mN,depthwiseConv2d:()=>cN,dot:()=>EN,dropout:()=>gN,elu:()=>QS,embedding:()=>kN,flatten:()=>bN,gaussianDropout:()=>hT,gaussianNoise:()=>pT,globalAveragePooling1d:()=>VN,globalAveragePooling2d:()=>UN,globalMaxPool1d:()=>oT,globalMaxPool2d:()=>lT,globalMaxPooling1d:()=>GN,globalMaxPooling2d:()=>HN,gru:()=>XN,gruCell:()=>

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2s}. Input ${e} (0-based) originates from layer type ${n.getClassName()}.`);this.inputNames.push(n.name),this.feedInputShapes.push(n.batchInputShape),this.feedInputNames.push(n.name)}for(let e of this.outputLayers)this.outputNames.push(e.name);this.internalInputShapes=this.inputs.map((e=>e.shape)),this.internalOutputShapes=this.outputs.map((e=>e.shape));let n={},r={},a={},s={},i={},o=[],l=(t,n,r,a,s,u)=>{(null==a||null==s||null==u)&&(a=t.sourceLayer,s=t.nodeIndex,u=t.tensorIndex);let d=a.inboundNodes[s];if(-1!==r.indexOf(d))throw new Mb(`The tensor ${t.name} at layer "${a.name}" is part of a cycle.`);if(-1!==n.indexOf(d))return;this.containerNodes.add(e.nodeKey(a,s)),a.id in i||(i[a.id]=Object.keys(i).length),-1===r.indexOf(d)&&r.push(d);let p=d.inboundLayers.length;for(let e=0;e<p;e++){let t=d.inputTensors[e],a=d.inboundLayers[e],s=d.nodeIndices[e],i=d.tensorIndices[e];l(t,n,r,a,s,i)}for(n.push(d);r.indexOf(d)>=0;)r.splice(r.indexOf(d),1);o.push(d)},u=[],d=[];for(let e of this.outputs)l(e,u,d);let p=o.slice().reverse();for(let e of p){r[e.id]=e,e.id in n||(n[e.id]=0);let t=n[e.id],i=null==a[e.outboundLayer.id]?0:a[e.outboundLayer.id];t=Math.max(t,i),a[e.outboundLayer.id]=t,s[e.outboundLayer.id]=e.outboundLayer,n[e.id]=t;for(let a=0;a<e.inboundLayers.length;a++){let s=e.inboundLayers[a],i=e.nodeIndices[a],o=s.inboundNodes[i],l=null==n[o.id]?0:n[o.id];n[o.id]=Math.max(t+1,l),r[o.id]=o}}let h={};for(let e in n){let t=n[e];t in h||(h[t]=[]),h[t].push(r[e])}let c={};for(let e in a){let t=a[e];t in c||(c[t]=[]),c[t].push(s[e])}let f=Object.keys(c).map((e=>parseInt(e,10))).sort(Zb);this.layers=[];for(let t of f){let n=c[t];n.sort(((e,t)=>{let n=i[e.id],r=i[t.id];return n<r?-1:n>r?1:0}));for(let t of n)t instanceof e&&this.internalContainerRefs.push(t),this.layers.push(t)}this.layersByDepth=c,f=Object.keys(h).map((e=>parseInt(e,10))).sort(Zb);let m=this.inputs.slice(),g=[];for(let e of f)for(let t of h[e]){let e=t.outboundLayer;if(null!=e){for(let n of t.inputTensors)if(-1===m.indexOf(n))throw new Mb(`Graph disconnected: cannot obtain value for tensor ${n} at layer "${e.name}". The following previous layers were accessed without issue: ${g}`);for(let e of t.outputTensors)m.push(e);g.push(e.name)}}this.nodesByDepth=h;let y=this.layers.map((e=>e.name));for(let e of y){let t=y.filter((t=>t===e)).length;if(1!==t)throw new Mb(`The name "${e}" is used ${t} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(y))}this.outboundNodes=[],this.inboundNodes=[],new Cw({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map((e=>null)),outputMasks:this.outputs.map((e=>null)),inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs.map((e=>e.shape))}),this.built=!0,this._refCount=1}assertNotDisposed(){if(0===this._refCount)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(0==--this._refCount){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((t=>t.trainable=e))})),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new Ob("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 e of this.layers)t.push(...e.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0,a=(e=>{let t=Object.keys(e);if(0===t.length)return!1;let n=t[0].split("/");return!isNaN(parseInt(n[n.length-1],10))})(e);a&&this.parseWeights(e);for(let e of this.layers)for(let[t,s]of e.weights.entries()){let e=a?`${s.name.split("/").slice(0,-1).join("/")+"/"}${t}`:s.originalName;if(null!=n[e])throw new Ob(`Duplicate weight name: ${e}`);n[e]=s,r++}let s=[];for(let r in e){let a=r;if(null==n[r]){let e=r.split("/");a=e.slice(0,-2).concat([e[e.length-1]]).join("/")}if(null!=n[a])s.push([n[a],e[r]]);else if(t)throw new Ob(`Provided weight data has no target variable: ${r}`);delete n[a]}if(t){let e=[];for(let t in n)e.push(t);if(e.length>0)throw new Ob(`${e.length} of ${r}

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They will not be included in the serialized model (and thus will be missing at deserialization time).`),o={}}if(a.inboundLayers.length>0){let t=[];for(let r=0;r<a.inboundLayers.length;r++){let s=a.inboundLayers[r],i=a.nodeIndices[r],l=a.tensorIndices[r],u=n[e.nodeKey(s,i)];null==u&&(u=0),t.push([s.name,u,l,o])}i.push(t)}}}let o={};o.name=t.name,o.className=a,o.config=s,o.inboundNodes=i,r.push(o)}t.layers=r;let a=[];for(let t=0;t<this.inputLayers.length;t++){let r=this.inputLayers[t],s=this.inputLayersNodeIndices[t],i=e.nodeKey(r,s);if(!this.containerNodes.has(i))continue;let o=n[i];null==o&&(o=0);let l=this.inputLayersTensorIndices[t];a.push([r.name,o,l])}t.inputLayers=a;let s=[];for(let t=0;t<this.outputLayers.length;t++){let r=this.outputLayers[t],a=this.outputLayersNodeIndices[t],i=e.nodeKey(r,a);if(!this.containerNodes.has(i))continue;let o=n[i];null==o&&(o=0);let l=this.outputLayersTensorIndices[t];s.push([r.name,o,l])}return t.outputLayers=s,t}static fromConfig(e,t,n={},r=!1){let a={},s={};function i(e,t){e.name in s?s[e.name].push(t):s[e.name]=[t]}function o(e,t){let n,r=[];for(let s of t){let o=s[0],l=s[1],u=s[2];if(n=null==s[3]?{}

2:s[3],!(o in a))return void i(e,t);let d=a[o];if(d.inboundNodes.length<=l)return void i(e,t);let p=d.inboundNodes[l];r.push(p.outputTensors[u])}r.length>0&&e.apply(Ub(r),n)}function l(e){let n=e.name,s=$v(e,null!=t.customObjects?t.customObjects:{});s.setFastWeightInitDuringBuild(r),a[n]=s,e.inboundNodes.forEach((e=>{if(!(e instanceof Array))throw new Ob(`Corrupted configuration, expected array for nodeData: ${e}`);i(s,e)}))}let u=t.name,d=t.layers;for(let e of d)l(e);for(;!Qb(s);)for(let e of d){let t=a[e.name];if(t.name in s){let e=s[t.name];delete s[t.name];for(let n of e)o(t,n)}}let p=[],h=[],c=t.inputLayers;for(let e of c){let t=e[0],n=e[1],r=e[2];Wb(t in a);let s=a[t].inboundNodes[n].outputTensors;p.push(s[r])}let f=t.outputLayers;for(let e of f){let t=e[0],n=e[1],r=e[2];Wb(t in a);let s=a[t].inboundNodes[n].outputTensors;h.push(s[r])}return new e({inputs:p,outputs:h,name:u})}get stateful(){if(this._stateful)throw new Ob("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(){Ci((()=>{this.layers.forEach((e=>{e.stateful&&e.resetStates()}))}))}};function dk(e,t){return function(e,t,n){let r=t.length;if(null==e||Array.isArray(e)&&0===e.length)return t.map((e=>null));if(1===r)return Array.isArray(e)&&1===e.length?e:"object"==typeof e&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}if("object"==typeof e&&Object.keys(e).length>0&&"object"==typeof e[Object.keys(e)[0]]){let n=[];return t.forEach((t=>{t in e?n.push(e[t]):n.push(null)})),n}throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}(e,t,"classWeight")}async function pk(e,t,n,r){if(null!=t||null!=r)throw new Error("Support sampleWeight is not implemented yet");if(null!=n){let t=Ci((()=>{if(1===e.shape.length)return Vo(e);if(2===e.shape.length){if(e.shape[1]>1)return el(e,1);if(1===e.shape[1])return kl(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}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.`)})),r=Array.from(await t.data());Ei(t);let a=[];return r.forEach((e=>{if(null==n[e])throw new Error(`classWeight must contain all classes in the training data. The class ${e} exists in the data but not in classWeight`);a.push(n[e])})),lh(a,"float32")}return null}function hk(e,t){return qo(e,t)}function ck(e,t){let n,r,a=t;n=a.xs,r=a.ys,ja.assert(null!=n&&null!=r,(()=>`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=fk("input",e.inputNames,n),i=fk("output",e.outputNames,r),o=s[0].shape[0];ja.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)})`)),ja.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 t=0;t<s.length;t++)ja.assert(s[t].shape[0]===o,(()=>`Batch size mismatch: input ${e.inputNames[t]} has ${s[t].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`));for(let t=0;t<i.length;t++)ja.assert(i[t].shape[0]===o,(()=>`Batch size mismatch: output ${e.outputNames[t]} has ${i[t].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`));return{xs:s,ys:i}}function fk(e,t,n){if(n instanceof $s)return[n];if(Array.isArray(n))return ja.assert(n.length===t.length,(()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`)),n;{let r=[];for(let a of t){if(null==n[a])throw new Ob(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}async function mk(e,t,n){let r=null!=n.batchesPerEpoch;if(ja.assert(null!=e.optimizer,(()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).")),ja.assert(null!=n,

2(()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.")),ja.assert(null!=n.epochs&&n.epochs>0&&Number.isInteger(n.epochs),(()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`)),ja.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),(()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`)),ja.assert(null==n.validationSplit,(()=>"`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 a,s,i=null!=n.validationData;if(i)if(gk(n.validationData))ja.assert(null==n.validationBatches||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 e=function(e){if(3===e.length)throw new Lb("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}(n.validationData);a=e.xs,s=e.ys}let o,l=e.makeTrainFunction(),u=e.getDedupedMetricsNames();o=i?u.slice().concat(u.map((e=>"val_"+e))):u.slice();let d=_v(n.callbacks,n.yieldEvery),p=null==n.verbose?1:n.verbose,{callbackList:h,history:c}=Ev(d,p,n.epochs,null,null,function(e,t){let n=null;return null!=t.batchesPerEpoch?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}(t,n),null,i,o);h.setModel(e),e.history=c,await h.onTrainBegin(),e.stopTraining_=!1;let f=null==n.initialEpoch?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let o={};await h.onEpochBegin(f);let d=0,p=0;for(r||(m=await t.iterator());!r||d<n.batchesPerEpoch;){let t=await m.next();if(r&&t.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${d} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(null!=t.value){let{xs:r,ys:a}=ck(e,t.value),s={};s.batch=p,s.size=r[0].shape[0],await h.onBatchBegin(p,s);let i=[];if(null!=n.classWeight){let t=dk(n.classWeight,e.outputNames);for(let e=0;e<t.length;++e)i.push(await pk(a[e],null,t[e]))}let o=r.concat(a).concat(i),c=l(o);Ei(o);for(let e=0;e<u.length;++e){let t=u[e],n=c[e];s[t]=n,$i(n)}await h.onBatchEnd(p,s),vv(s),p++,d++}if(r?d>=n.batchesPerEpoch:t.done){if(i){let t;t=gk(n.validationData)?Gb(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):Gb(e.evaluate(a,s,{batchSize:null==n.validationBatchSize?32:n.validationBatchSize,verbose:0}));for(let n=0;n<e.metricsNames.length;++n)o[`val_${e.metricsNames[n]}`]=t[n]}break}if(e.stopTraining_)break}if(await h.onEpochEnd(f,o),f++,e.stopTraining_)break}return await h.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function gk(e){return"function"==typeof e.iterator}function yk(e){ja.assert(e>0&&Number.isInteger(e),(()=>`batchSize is required to be a positive integer, but got ${e}`))}function bk(e,t,n){return null==e?[null]:Array.isArray(e)?e.map((e=>Dx(e,t,n-t))):Dx(e,t,n-t)}function xk(e,t){return Ci((()=>null==e?null:Array.isArray(e)?e.map((e=>xk(e,t))):Vx(e,"int32"===t.dtype?t:Wo(t,"int32"))))}function wk(e,t){let n=[],r=0,a=null;for(;r<e;)a=r+t,a>=e&&(a=e),n.push([r,a]),r=a;return n}function vk(e){let t=[];e instanceof $s&&(e=[e]);for(let n=0;n<e.length;++n){let r=e[n];if(1===r.rank)t.push(Fx(r,1));else{if(0===r.rank)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(r)}}return t}function kk(e,t){if(null==e)return;let n=[];if(t instanceof $s)n.push(t.id);else if(Array.isArray(t))t.forEach((e=>n.push(e.id)));else if(null!=t)for(let e in t){let r=t[e];n.push(r.id)}let r=[];if(e instanceof $s)-1===n.indexOf(e.id)&&r.push(e);else if(Array.isArray(e))e.forEach((e=>{-1===n.indexOf(e.id)&&r.push(e)}));else if(null!=e)for(let t in e){let a=e[t];-1===n.indexOf(a.id)&&r.push(a)}r.forEach((e=>{e.isDisposed||e.dispose()}))}function Ik(e){return Array.isArray(e)}function Sk(e){return!function(e){return e instanceof $s}(e)&&!Ik(e)}function Nk(e,t,n,r=!0,a=""){if(null==t||0===t.length){if(null!=e){let t=!1;if(Ik(e)&&e.length>0)t=!0;else if(Sk(e)){for(let n in e)if(e.hasOwnProperty(n)){t=!0;break}}else t=!0;if(t)throw new Ob(`Error when checking model ${a} expected no data, but got ${e}`)}return[]}if(null==e)return t.map((e=>null));let s;if(Sk(e)){s=[];for(let n of t){if(null==e[n])throw new Ob(`No data provided for "${n}". Need data for each key in: ${t}`);s.push(e[n])}}else if(Ik(e)){if(e.length!==t.length)throw new Ob(`Error when checking model ${a}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);

2s=e}else{if(t.length>1)throw new Ob(`The model ${a} expects ${t.length} Tensor(s), but only received one Tensor. 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Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);s=e}else{if(t.length>1)throw new Ob(`The model expects ${t.length} ${a} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);s=[e]}if(null!=n)for(let e=0;e<t.length;++e){if(null==n[e])continue;let i=s[e];if(i.shape.length!==n[e].length)throw new Ob(`Error when checking ${a}: expected ${t[e]} to have ${n[e].length} dimension(s), but got array with shape ${JSON.stringify(i.shape)}`);for(let s=0;s<n[e].length;++s){if(0===s&&!r)continue;let o=i.shape[s],l=n[e][s];if(null!=l&&l!==o)throw new Ob(`Error when checking ${a}: expected ${t[e]} to have shape ${JSON.stringify(n[e])} but got array with shape ${JSON.stringify(i.shape)}.`)}}}var _k=class extends uk{constructor(e){super(e),this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new Ob("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");tk(this,e,t,n)}compile(e){if(null==e.loss&&(e.loss=[]),this.loss=e.loss,"string"==typeof e.optimizer)this.optimizer_=function(e){let t={Adagrad:()=>xm.adagrad(.01),Adadelta:()=>xm.adadelta(1,.95,Ax()),Adam:()=>xm.adam(.001,.9,.999,Ax()),Adamax:()=>xm.adamax(.002,.9,.999,Ax(),0),RMSProp:()=>xm.rmsprop(.001,.9,0,Ax()),SGD:()=>xm.sgd(.01)};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,e in t)return t[e]();throw new Ob(`Unknown Optimizer ${e}`)}(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof of))throw new Ob("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(Array.isArray(e.loss)||"string"==typeof e.loss||"function"==typeof e.loss)if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new Ob(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map((e=>Bv(e)))}else{let n=Bv(e.loss);this.outputs.forEach((e=>{t.push(n)}))}else{e.loss=e.loss;for(let t in e.loss)if(-1===this.outputNames.indexOf(t))throw new Ob(`Unknown entry in loss dictionary: "${t}". Only expected the following keys: ${this.outputNames}`);for(let n of this.outputNames)null==e.loss[n]&&console.warn(`Output "${n}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${n} during training`),t.push(Bv(e.loss[n]))}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let e=0;e<this.outputs.length;++e){let t=this.internalOutputShapes[e],n=this.outputNames[e];this.feedOutputNames.push(n),this.feedOutputShapes.push(t),this.feedLossFns.push(this.lossFunctions[e])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],wx("loss",(()=>{for(let e=0;e<this.outputs.length;++e){if(-1!==n.indexOf(e))continue;let t=this.lossFunctions[e];this.outputs.length>1&&(this.metricsTensors.push([t,e]),this.metricsNames.push(this.outputNames[e]+"_loss"))}}));let r=function(e,t){if(null==e||Array.isArray(e)&&0===e.length)return t.map((e=>[]));let n;if("string"==typeof e||"function"==typeof e)n=[e];else{if(!Array.isArray(e)&&"object"!=typeof e)throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);n=e}if(Array.isArray(n))return t.map((e=>n));{let e=[];for(let r of t){let t=n.hasOwnProperty(r)?n[r]:[];Array.isArray(t)||(t=[t]),e.push(t)}return e}}(e.metrics,this.outputNames),a=(e,t,n)=>{this.outputNames.length>1&&(t=this.outputNames[e]+"_"+t),this.metricsNames.push(t),this.metricsTensors.push([n,e])};wx("metric",(()=>{for(let e=0;e<this.outputs.length;++e){if(-1!==n.indexOf(e))continue;(t=>{let n,r,s;for(let i of t){if("string"==typeof i&&-1!==["accuracy","acc","crossentropy","ce"].indexOf(i)){let t,a=this.internalOutputShapes[e];1===a[a.length-1]||this.lossFunctions[e]===Lv?-1!==["accuracy","acc"].indexOf(i)?r=Wv:-1!==["crossentropy","ce"].indexOf(i)&&(r=jv):this.lossFunctions[e]===Ov?-1!==["accuracy","acc"].indexOf(i)?r=qv:-1!==["crossentropy","ce"].indexOf(i)&&(r=Xv):-1!==["accuracy","acc"].indexOf(i)?r=Vv:-1!==["crossentropy","ce"].indexOf(i)&&(r=Kv),-1!==["accuracy","acc"].indexOf(i)?t="acc":-1!==["crossentropy","ce"].indexOf(i)&&(t="ce"),s=r,n=""+t}else s=Zv(i),n=""+Jv(i);let t;wx(n,(()=>{t=s}

2)),a(e,n,t)}})(r[e])}})),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){null!=this.collectedTrainableWeights&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let r=null==n.batchSize?32:n.batchSize;yk(r);let a=this.standardizeUserDataXY(e,t,!0,r);try{let e=a[0].concat(a[1]);this.makeTestFunction();let t=this.testFunction;return Ub(this.testLoop(t,e,r,n.verbose,n.steps))}finally{kk(a[0],e),kk(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),async function(e,t,n){let r=null!=(n=n||{}).batches,a=e.testFunction,s=[];if(n.verbose>0)throw new Lb("Verbose mode is not implemented yet.");ja.assert(!r||n.batches>0&&Number.isInteger(n.batches),(()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`));let i=function(e){return"function"==typeof e.next}(t)?t:await t.iterator(),o=0,l=0;for(;!r||l<n.batches;){let t=await i.next();if(s=Ci((()=>{if(t.value){let{xs:n,ys:r}=ck(e,t.value),i=n.concat(r),u=Ci((()=>a(i)));if(Ei(i),0===l)for(let e=0;e<u.length;++e)s.push(Du(0));let d=i[0].shape[0];for(let e=0;e<u.length;++e){let t=u[e],n=s[e];s[e]=Ci((()=>Go(s[e],qo(d,t)))),l>0&&Ei(n)}Ei(u),o+=d,++l}return s})),t.done){r&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let e=0;e<s.length;++e){let t=s[e];s[e]=jo(s[e],o),Ei(t)}return Ub(s)}(this,e,t)}checkNumSamples(e,t,n,r="steps"){let a;if(null!=n){if(a=null,null!=t)throw new Ob(`If ${r} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else{if(null==e)throw new Ob(`Either the input data should have a defined shape, or ${r} shoud be specified.`);a=Array.isArray(e)?e[0].shape[0]:e.shape[0]}return a}execute(e,t){if(Array.isArray(t)&&0===t.length)throw new Ob("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),r=n?t:[t],a=this.retrieveSymbolicTensors(r),s=new Dw;if(e instanceof $s&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new Ob(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let t=0;t<this.inputs.length;++t)s.add(this.inputs[t],e[t])}else for(let t of this.inputs){let n=e[t.name];if(null==n)throw new Ob(`No value is provided for the model's input ${t.name}`);s.add(t,n)}let i=Lw(a,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=Bb(null,e.length),n=e.length;for(let r of this.layers){let a=Array.isArray(r.output)?r.output:[r.output],s=a.map((e=>e.name));for(let r=0;r<e.length;++r){let i=s.indexOf(e[r]);if(-1!==i&&(t[r]=a[i],n--),0===n)break}if(0===n)break}if(n>0){let n=[];throw t.forEach(((t,r)=>{null==t&&n.push(e[r])})),new Ob(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(n)}`)}return t}predictLoop(e,t=32,n=!1){return Ci((()=>{let r=this.checkNumSamples(e);if(n)throw new Lb("Verbose predictLoop() is not implemented yet.");let a=wk(r,t),s=this.outputs.map((e=>[]));for(let t=0;t<a.length;++t)Ci((()=>{let n=a[t][0],r=a[t][1],s=bk(e,n,r),i=[];if(Array.isArray(s))for(let e=0;e<s.length;++e)i.push({key:this.inputs[e],value:s[e]});else i.push({key:this.inputs[0],value:s});let o=new Dw(i);return Lw(this.outputs,o)})).forEach(((e,t)=>s[t].push(e)));return Ub(s.map((e=>Nl(e,0))))}))}predict(e,t={}){let n=vk(e);Tk(n,this.inputNames,this.feedInputShapes,!1);try{let e=null==t.batchSize?32:t.batchSize;return yk(e),this.predictLoop(n,e)}finally{kk(n,e)}}predictOnBatch(e){Tk(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,r){if(null==this.optimizer_)throw new Mb("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let a=[];for(let e=0;e<this.feedOutputShapes.length;++e){let t=this.feedOutputShapes[e];this.feedLossFns[e]===Ov?a.push(t.slice(0,t.length-1).concat([1])):a.push(t)}if(function(e,t,n){let r=Jb(e.map((e=>e.shape[0])));r.sort();let a=Jb(t.map((e=>e.shape[0])));if(a.sort(),r.length>1)throw new Ob(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map((e=>e.shape)))}`);if(a.length>1)throw new Ob(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map((e=>e.shape)))}`);if(r.length>0&&a.length>0&&!ja.arraysEqual(r,a))throw new Ob(`Input Tensors should have the same number of samples as target Tensors. Found ${r[0]} input sample(s) and ${a[0]} target sample(s).`)}(e=Nk(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Nk(t,this.feedOutputNames,a,!1,"target")),function(e,t,n){let r=[Rv,Lv,Mv];for(let a=0;a<e.length;++a){let s=e[a],i=t[a],o=n[a];if(null!=i){if(i===Mv&&1===s.shape[s.shape.length-1])throw new Ob(`You are passing a target array of shape ${s.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(-1!==r.indexOf(i)){let e=s.shape.slice(1),t=o.slice(1);for(let n=0;n<e.length;++n){let r=e[n],a=t[n];if(null!=a&&r!==a)throw new Ob(`A target Tensor with shape ${s.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&null!=r&&r>0&&e[0].shape[0]%r!=0)throw new Ob(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(null!=n)throw new Error("sample weight is not supported yet.");let l=null;if(null!=r){let e=dk(r,this.outputNames);l=[];for(let t=0;t<e.length;++t)l.push(await pk(o[t],null,e[t]))}return[i,o,l]}testLoop(e,t,n,r=0,a){return Ci((()=>{let s=this.checkNumSamples(t,n,a,"steps"),i=[];if(r>0)throw new Lb("Verbose mode is not implemented yet.");if(null!=a)throw new Lb("steps mode in testLoop() is not implemented yet");{let r=wk(s,n),a=lh($x(0,s));for(let n=0;n<r.length;++n){let s=r[n][0],o=r[n][1],l=Dx(a,s,o-s),u=xk(t,l),d=e(u);

2if(0===n)for(let e=0;e<d.length;++e)i.push(Du(0));for(let e=0;e<d.length;++e){let t=d[e];i[e]=Go(i[e],qo(o-s,t))}}for(let e=0;e<i.length;++e)i[e]=jo(i[e],s)}return i}))}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let r=e[n],a=r;if(Vb(e,r)>1){a+=`_${Vb(e.slice(0,n),r)}`}t.push(a)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+2*this.outputs.length),s=[],i=this.collectedTrainableWeights.map((e=>e.read()));return[this.optimizer_.minimize((()=>{let e=[];for(let t=0;t<this.inputs.length;++t)e.push({key:this.inputs[t],value:n[t]});let i,o=new Dw(e),l=Lw(this.outputs,o,{training:!0});for(let e=0;e<this.lossFunctions.length;++e){let n=(0,this.lossFunctions[e])(r[e],l[e]);null!=a[e]&&(n=hk(n,a[e]));let s=Ad(n);t.push(s),i=0===e?n:Go(i,n)}for(let e=0;e<this.metricsTensors.length;++e){let n;if(this.outputs.length>1&&e<this.outputs.length)n=t[e];else{let t=this.metricsTensors[e][0],a=this.metricsTensors[e][1];n=Ad(t(r[a],l[a]))}$i(n),s.push(n)}return i=Ad(i),this.calculateLosses().forEach((e=>{i=Go(i,e)})),i}),!0,i)].concat(s)}}makeTestFunction(){this.testFunction=e=>Ci((()=>{let t,n=[],r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let e=0;e<this.inputs.length;++e)s.push({key:this.inputs[e],value:r[e]});let i=new Dw(s),o=Lw(this.outputs,i);for(let e=0;e<this.lossFunctions.length;++e){let r=this.lossFunctions[e],s=Ad(r(a[e],o[e]));t=0===e?s:Go(t,s),n.push(t)}for(let e=0;e<this.metricsTensors.length;++e){let t=this.metricsTensors[e][0],r=this.metricsTensors[e][1],s=Ad(t(a[r],o[r]));n.push(s)}return n}))}async fit(e,t,n={}){if(this.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");let r,a,s,i,o,l,u,d,p;this.isTraining=!0;try{let h=null==n.batchSize?32:n.batchSize;yk(h);let c=await this.standardizeUserData(e,t,n.sampleWeight,n.classWeight,!1,h);r=c[0],a=c[1],p=c[2];let f,m=!1;if(null!=n.validationData&&n.validationData.length>0){if(m=!0,2!==n.validationData.length)throw 3===n.validationData.length?new Lb("validationData including sample weights is not supported yet."):new Ob(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${n.validationData} is invalid.`);o=n.validationData[0],l=n.validationData[1];let e=await this.standardizeUserData(o,l,null,null,!0,h);u=e[0],d=e[1],f=u.concat(d)}else if(null!=n.validationSplit&&n.validationSplit>0&&n.validationSplit<1){m=!0;let e=Math.floor(r[0].shape[0]*(1-n.validationSplit)),t=r[0].shape[0];

2u=bk(r,e,t),s=r,r=bk(r,0,e),d=bk(a,e,t),i=a,a=bk(a,0,e),f=u.concat(d)}else null!=n.validationSteps&&(m=!0);let g=r.concat(a).concat(p);this.checkTrainableWeightsConsistency();let y,b,x=this.makeTrainFunction(),w=this.getDedupedMetricsNames();m?(this.makeTestFunction(),y=this.testFunction,b=w.slice().concat(w.map((e=>"val_"+e)))):(y=null,f=[],b=w.slice());let v=_v(n.callbacks,n.yieldEvery);return await this.fitLoop(x,g,w,h,n.epochs,n.verbose,v,y,f,n.shuffle,b,n.initialEpoch,null,null)}finally{this.isTraining=!1,kk(r,e),kk(a,t),kk(s,e),kk(i,t),kk(u,o),kk(d,l),null!=p&&Ei(p)}}async fitLoop(e,t,n,r,a,s,i,o,l,u,d,p,h,c){null==r&&(r=32),null==a&&(a=1),null==u&&(u=!0),null==p&&(p=0);let f=!1;if(null!=o&&null!=l&&(f=!0),null!=c&&(f=!0,null==h))throw new Ob("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let m,g=this.checkNumSamples(t,r,h,"steps_per_epoch");null!=g&&(m=$x(0,g)),null==s&&(s=1);let{callbackList:y,history:b}=Ev(i,s,a,p,g,h,r,f,d);y.setModel(this),this.history=b,await y.onTrainBegin(),this.stopTraining_=!1;for(let s=p;s<a;++s){await y.onEpochBegin(s);let a={};if(null!=h)throw new Lb("stepsPerEpoch mode is not implemented yet.");{if("batch"===u)throw new Lb("batch shuffling is not implemneted yet");u&&ja.shuffle(m);let s=lh(m),i=wk(g,r);for(let u=0;u<i.length;++u){let d={};if(await y.onBatchBegin(u,d),Ci((()=>{let p=i[u][0],h=i[u][1],c=Dx(s,p,h-p);d.batch=u,d.size=h-p;let m=xk(t,c),g=e(m);for(let e=0;e<n.length;++e){let t=n[e],r=g[e];d[t]=r,$i(r)}if(u===i.length-1&&f){let e=this.testLoop(o,l,r);for(let t=0;t<n.length;++t){let r=n[t],s=e[t];$i(s),a["val_"+r]=s}}})),await y.onBatchEnd(u,d),vv(d),this.stopTraining_)break}s.dispose()}if(await y.onEpochEnd(s,a),this.stopTraining_)break}return await y.onTrainEnd(),await this.history.syncData(),this.history}async fitDataset(e,t){return mk(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let e of s){let t=await e.data();i.push(t[0])}return Ei(s),kk(n[0],e),kk(n[1],t),Ub(i)}getNamedWeights(e){let t=[],n=null!=e&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let e=0;e<r.length;++e)n&&!r[e].trainable||t.push({name:r[e].originalName,tensor:a[e]});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(0===e.refCountAfterDispose&&null!=this.optimizer&&this.isOptimizerOwned){let t=Ti().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Ti().numTensors}return e}getLossIdentifiers(){let e;if("string"==typeof this.loss)e=Hb(this.loss);else if(Array.isArray(this.loss)){for(let e of this.loss)if("string"!=typeof e)throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map((e=>Hb(e)))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t){if("string"!=typeof n[r])throw new Error("Serialization of non-string loss is not supported.");e[r]=Hb(n[r])}}return e}getMetricIdentifiers(){if("string"==typeof this.metrics||"function"==typeof this.metrics)return[Hb(Jv(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map((e=>Hb(Jv(e))));{let e={};for(let t in this.metrics)e[t]=Hb(Jv(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(null!=e.weighted_metrics)throw new Error("Loading weight_metrics is not supported yet.");if(null!=e.loss_weights)throw new Error("Loading loss_weights is not supported yet.");if(null!=e.sample_weight_mode)throw new Error("Loading sample_weight_mode is not supported yet.");let t,n,r=$v(ik(e.optimizer_config));if("string"==typeof e.loss)t=jb(e.loss);else if(Array.isArray(e.loss))t=e.loss.map((e=>jb(e)));else if(null!=e.loss){t={};for(let n in e.loss)t[n]=jb(e.loss[n])}if(Array.isArray(e.metrics))n=e.metrics.map((e=>jb(e)));else if(null!=e.metrics){n={};for(let t in e.metrics)n[t]=jb(e.metrics[t])}this.compile({loss:t,metrics:n,optimizer:r})}async save(e,t){if("string"==typeof e){let t=gf.getSaveHandlers(e);if(0===t.length)throw new Ob(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Ob(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)throw new Ob("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await gf.encodeWeights(this.getNamedWeights(t)),r={modelTopology:this.toJSON(null,!1),format:"layers-model",generatedBy:`TensorFlow.js tfjs-layers v${lk}`,convertedBy:null};if(null!=t&&t.includeOptimizer&&null!=this.optimizer){r.trainingConfig=this.getTrainingConfig();let e="optimizer",{data:t,specs:a}=await gf.encodeWeights(await this.optimizer.getWeights(),e);n.specs.push(...a),n.data=gf.concatenateArrayBuffers([n.data,t])}return null!=this.userDefinedMetadata&&(Qv(this.userDefinedMetadata,this.name,!0),r.userDefinedMetadata=this.userDefinedMetadata),r.weightData=n.data,r.weightSpecs=n.specs,e.save(r)}setUserDefinedMetadata(e){Qv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};_k.className="Model",Qc.registerClass(_k);var Ck=class extends _k{};async function Ek(e,t){"modelTopology"in e||(e={modelTopology:e});let n=e.modelTopology;null!=n.model_config&&(n=n.model_config);let r=$v(ik(n),t);

2if(null!=e.weightsManifest){let t=await gf.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map((e=>e.originalName))),n={};for(let e of r.weights)n[e.originalName]=t[e.originalName];r.loadWeights(n),Ei(t)}return r}async function $k(e,t){if(null==t&&(t={}),"string"==typeof e){let n=gf.getLoadHandlers(e,t);if(0===n.length)n.push(gf.browserHTTPRequest(e,t));else if(n.length>1)throw new Ob(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return async function(e,t,n){if(null==n&&(n={}),null==e.load)throw new Ob("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;null!=a.model_config&&(a=a.model_config);let s=null==n.strict||n.strict,i=null!=r.weightData&&null!=r.weightSpecs&&s,o=$v(ik(a),t,i),l=r.trainingConfig;if(null!=l&&o.loadTrainingConfig(l),null!=r.userDefinedMetadata&&o.setUserDefinedMetadata(r.userDefinedMetadata),null!=r.weightData){if(null==r.weightSpecs)throw new Ob("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:e,optimizerWeights:t}=function(e,t){let n=gf.decodeWeights(e,t),r={},a=[];return t.forEach((e=>{"optimizer"===e.group?a.push({name:e.name,tensor:n[e.name]}):r[e.name]=n[e.name]})),{modelWeights:r,optimizerWeights:a}}(r.weightData,r.weightSpecs);o.loadWeights(e,s),null!=o.optimizer&&t.length>0&&await o.optimizer.setWeights(t),Ei(e),Ei(t.map((e=>e.tensor)))}return o}(e,void 0,t)}Ck.className="Functional",Qc.registerClass(Ck);var Ak=class e extends _k{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=null!=e.name?e.name:lx("sequential_"),null!=e.layers)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some((e=>e<0)))throw new Ob(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(t){let n,r=t instanceof e||t instanceof _k;if(r){if(n=t,1!==n.outputs.length)throw new Ob("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(1!==n.inputs.length)throw new Ob("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(0===this.outputs.length){if(0===t.inboundNodes.length){if(null==t.batchInputShape)throw new Ob("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let e=Fw({batchShape:t.batchInputShape,dtype:t.dtype,name:t.name+"_input"});t.apply(e)}if(r)this.outputs=n.outputs,this.inputs=n.inputs;else{if(1!==t.inboundNodes.length)throw new Ob(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${t.name} which has ${t.inboundNodes.length} pre-existing inbound connections.`);if(1!==t.inboundNodes[0].outputTensors.length)throw new Ob("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[t.inboundNodes[0].outputTensors[0]],this.inputs=Aw(this.outputs[0])}this.inboundNodes=[],new Cw({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Bb(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs[0].shape})}else{let e=t.apply(this.outputs[0]);if(Array.isArray(e))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(t),this.outputs=[e],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(t),this.built=!1}pop(){if(0===this.layers.length)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),0===this.layers.length)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 null==this.model&&this.build(),this.model.call(e,t)}build(e){if(xw(e),0===this.inputs.length||0===this.outputs.length)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new _k({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supp

2ortsMasking,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){null==this.model&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Mb("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 Mb("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return null==this.model&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return null==this.model&&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 null==this.model?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Mb("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 Mb("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(t,n,r={},a=!1){let s,i={};if(n instanceof Array){if(null==n[0].className||"Merge"===n[0].className)throw new Ob("Legacy serialization format not supported yet.");s=n}else ja.assert(null!=n.layers,(()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.")),s=n.layers,delete n.layers,i=n;let o=new t(i);if(!(o instanceof e))throw new Lb(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let e of s){let t=$v(e,void 0,a);a&&t.setFastWeightInitDuringBuild(!0),o.add(t)}return o}set stopTraining(e){if(null==this.model)throw new Ob("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(null==this.model)throw new Ob("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}}};function Rk(e){return new _k(e)}function Fk(e){return new Ak(e)}function Dk(e){return Fw(e)}function Mk(e,t){Cv.registerCallbackConstructor(e,t)}Ak.className="Sequential",Qc.registerClass(Ak);var Ok=class extends Qc.Serializable{getConfig(){return{}}},Lk=class extends Ok{apply(e,t=1){return function(e,t=1){if(1!==t)throw new Lb(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return wu(e)}(e,t)}};Lk.className="elu",Qc.registerClass(Lk);var zk=class extends Ok{apply(e){return Bp(e)}};zk.className="selu",Qc.registerClass(zk);var Pk=class extends Ok{apply(e){return Ap(e)}};Pk.className="relu",Qc.registerClass(Pk);var Bk=class extends Ok{apply(e){return Ci((()=>Md(6,Ap(e))))}};Bk.className="relu6",Qc.registerClass(Bk);var Wk=class extends Ok{apply(e){return e}};Wk.className="linear",Qc.registerClass(Wk);var Vk=class extends Ok{apply(e){return _l(e)}};Vk.className="sigmoid",Qc.registerClass(Vk);var Uk=class extends Ok{apply(e){return function(e){return Ci((()=>{let t=Go(.5,qo(.2,e));return Ul(t,0,1)}))}(e)}};Uk.className="hardSigmoid",Qc.registerClass(Uk);var Gk=class extends Ok{apply(e){return md(e)}};Gk.className="softplus",Qc.registerClass(Gk);var Hk=class extends Ok{apply(e){return function(e){return Ci((()=>jo(e,Go(Ko(e),1))))}(e)}};Hk.className="softsign",Qc.registerClass(Hk);var jk=class extends Ok{apply(e){return El(e)}};jk.className="tanh",Qc.registerClass(jk);var qk=class extends Ok{apply(e,t=-1){return Yp(e,t)}};qk.className="softmax",Qc.registerClass(qk);var Kk=class extends Ok{apply(e,t=-1){return bd(e,t)}};Kk.className="logSoftmax",Qc.registerClass(Kk);var Xk=class extends Ok{apply(e){return Ci((()=>Ci((()=>{let t=Math.sqrt(2),n=qo(.5,Go(1,ku(jo(e,t))));return qo(e,n)}))))}};Xk.className="gelu",Qc.registerClass(Xk);var Yk=class extends Ok{apply(e){return Ci((()=>qo(.5,qo(e,Go(1,El(qo(Mu(jo(2,Math.PI)),Go(e,qo(.044715,Fu(e,3))))))))))}};

2Yk.className="gelu_new",Qc.registerClass(Yk);var Zk=class extends Ok{apply(e){return Ci((()=>qo(e,El(md(e)))))}};Zk.className="mish",Qc.registerClass(Zk);var Jk=class extends Ok{apply(e,t=1){return Ci((()=>qo(_l(qo(e,t)),e)))}};function Qk(e){return e.getClassName()}function eI(e,t={}){return Yb(e,Qc.SerializationMap.getMap().classNameMap,t,"activation")}function tI(e){if(null==e){let e={className:"linear",config:{}};return eI(e)}if("string"==typeof e){let t={};return t.className=e,t.config={},eI(t)}return e instanceof Ok?e:eI(e)}function nI(e){if(null!=e&&"object"!=typeof e)throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}Jk.className="swish",Qc.registerClass(Jk);var rI=class extends Qc.Serializable{},aI=class extends rI{constructor(e){super(),nI(e),this.l1=null==e||null==e.l1?.01:e.l1,this.l2=null==e||null==e.l2?.01:e.l2,this.hasL1=0!==this.l1,this.hasL2=0!==this.l2}apply(e){return Ci((()=>{let t=Rd([1]);return this.hasL1&&(t=Go(t,Lu(qo(this.l1,Ko(e))))),this.hasL2&&(t=Go(t,Lu(qo(this.l2,Ux(e))))),kl(t,[])}))}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};aI.className="L1L2",Qc.registerClass(aI);var sI={l1l2:"L1L2"};function iI(e){return Kb(e)}function oI(e,t={}){return Yb(e,Qc.SerializationMap.getMap().classNameMap,t,"regularizer")}function lI(e){if(null==e)return null;if("string"==typeof e){return oI({className:e in sI?sI[e]:e,config:{}})}return e instanceof rI?e:oI(e)}var uI=class extends $w{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,null!=e&&(this.maxValue=e.maxValue)}call(e,t){e=bw(e);let n=Ap(e);return null!=this.maxValue&&(n=Ul(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};uI.className="ReLU",Qc.registerClass(uI);var dI=class extends $w{constructor(e){super(null==e?{}:e),this.DEFAULT_ALPHA=.3,null==e&&(e={}),this.alpha=null==e.alpha?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=bw(e);return ed(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};dI.className="LeakyReLU",Qc.registerClass(dI);var pI=class extends $w{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",null==e&&(e={}),this.supportsMasking=!0,this.alphaInitializer=mw(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=lI(e.alphaRegularizer),this.alphaConstraint=Zw(e.alphaConstraint),null==e.sharedAxes)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else{if("number"!=typeof e.sharedAxes)throw new Ob(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`);this.sharedAxes=[e.sharedAxes]}}build(e){let t=(e=xw(e)).slice(1);if(null!=this.sharedAxes)for(let e of this.sharedAxes)t[e-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(null!=this.sharedAxes)for(let t=1;t<e.length;++t)n[t]=e[t];this.inputSpec=[new Nw({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=bw(e),Jd(e,this.alpha.read())}getConfig(){let e={alphaInitializer:fw(this.alphaInitializer),alphaRegularizer:iI(this.alphaRegularizer),alphaConstraint:Xw(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};pI.className="PReLU",Qc.registerClass(pI);var hI=class extends $w{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA=1,null==e&&(e={}),null!=e.alpha&&e.alpha!==this.DEFAULT_ALPHA)throw new Lb(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=null==e.alpha?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=bw(e);return wu(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};hI.className="ELU",Qc.registerClass(hI);var cI=class extends $w{constructor(e){super(null==e?{}:e),this.DEFAULT_THETA=1,null==e&&(e={}),this.theta=null==e.theta?this.DEFAULT_THETA:e.theta}call(e,t){let n=bw(e);return qo(n,Wo(Ku(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};cI.className="ThresholdedReLU",Qc.registerClass(cI);var fI=class extends $w{constructor(e){super(null==e?{}:e),this.DEFAULT_AXIS=1,null==e&&(e={}),this.softmax=(new qk).apply,this.axis=null==e.axis?this.DEFAULT_AXIS:e.axis}call(e,t){return Ci((()=>{let n=bw(e),r=t.mask;if(null!=r){let e=qo(yd(Fd(n.shape),Wo(r,n.dtype)),Du(-1e9));n=Go(n,e)}return this.axis instanceof Array?this.axis.length>1?Wu(yd(n,xd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)}))}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};function mI(e,t,n){if("number"==typeof e)return Bb(e,t);if(e.length!==t)throw new Ob(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!Tx(a))throw new Ob(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function gI(e,t,n,r,a=1){if(null==e)return e;let s;return s="same"===n?e:e-(t+(t-1)*(a-1))+1,Math.floor((s+r-1)/r)}function yI(e,t,n,r){if(null==e)return null;if("valid"===r)e=e*t+Ex([n-t,0]);else{if("same"!==r)throw new Ob(`Unsupport padding mode: ${r}.`);e*=t}return e}function bI(e,t){return Ci((()=>(mx(t),"channelsFirst"===t?Eh(e,[0,2,3,1]):e)))}function xI(e,t){return Ci((()=>(mx(t),"channelsFirst"===t?Eh(e,[0,2,3,4,1]):e)))}function wI(e,t,n,r=[1,1],a="valid",s,i,o=null){return Ci((()=>

2{if(null==s&&(s="channelsLast"),mx(s),3!==e.rank&&4!==e.rank)throw new Ob(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(3!==t.rank&&4!==t.rank)throw new Ob(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=bI(e,s);if("causal"===a)throw new Lb("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=zh.conv2d({x:l,filter:t,strides:r,pad:"same"===a?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),"channelsFirst"===s&&(l=Eh(l,[0,3,1,2])),l}))}fI.className="Softmax",Qc.registerClass(fI);var vI=class e extends $w{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.verifyArgs(n),this.rank=t,nx(this.rank,"rank"),1!==this.rank&&2!==this.rank&&3!==this.rank)throw new Lb(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=mI(n.kernelSize,t,"kernelSize"),this.strides=mI(null==n.strides?1:n.strides,t,"strides"),this.padding=null==n.padding?"valid":n.padding,gx(this.padding),this.dataFormat=null==n.dataFormat?"channelsLast":n.dataFormat,mx(this.dataFormat),this.activation=tI(n.activation),this.useBias=null==n.useBias||n.useBias,this.biasInitializer=mw(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Zw(n.biasConstraint),this.biasRegularizer=lI(n.biasRegularizer),this.activityRegularizer=lI(n.activityRegularizer),this.dilationRate=mI(null==n.dilationRate?1:n.dilationRate,t,"dilationRate"),1===this.rank&&Array.isArray(this.dilationRate)&&1!==this.dilationRate.length)throw new Ob(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(2===this.rank){if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate];else if(2!==this.dilationRate.length)throw new Ob(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(3===this.rank)if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(3!==this.dilationRate.length)throw new Ob(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}static verifyArgs(e){if(Wb("kernelSize"in e,"required key 'kernelSize' not in config"),"number"!=typeof e.kernelSize&&!tx(e.kernelSize,"number",1,3))throw new Ob(`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:Qk(this.activation),useBias:this.useBias,biasInitializer:fw(this.biasInitializer),biasRegularizer:iI(this.biasRegularizer),activityRegularizer:iI(this.activityRegularizer),biasConstraint:Xw(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},kI=class e extends vI{constructor(t,n){super(t,n),this.kernel=null,e.verifyArgs(n),this.filters=n.filters,nx(this.filters,"filters"),this.kernelInitializer=mw(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Zw(n.kernelConstraint),this.kernelRegularizer=lI(n.kernelRegularizer)}build(e){e=xw(e);let t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Ob(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return Ci((()=>{e=bw(e);let t,n=null==this.bias?null:this.bias.read(),r=ax(this.activation.getClassName());if(null!=r&&2===this.rank)t=wI(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(1===this.rank)t=function(e,t,n,r=1,a="valid",s,i=1){return Ci((()=>

2{if(null==s&&(s="channelsLast"),mx(s),3!==e.shape.length)throw new Ob(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(3!==t.shape.length)throw new Ob(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(null!=n&&1!==n.shape.length)throw new Ob(`The bias for a conv1dWithBias operation should be 1, but is ${n.shape.length} instead`);if("channelsFirst"===s&&(e=Eh(e,[0,2,1])),"causal"===a)throw new Lb("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Xl(e,t,r,"same"===a?"same":"valid","NWC",i);return null!=n&&(o=Hx(o,n)),o}))}(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(2===this.rank)t=wI(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else{if(3!==this.rank)throw new Lb("convolutions greater than 3D are not implemented yet.");t=function(e,t,n,r=[1,1,1],a="valid",s,i){return Ci((()=>{if(null==s&&(s="channelsLast"),mx(s),4!==e.rank&&5!==e.rank)throw new Ob(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(4!==t.rank&&5!==t.rank)throw new Ob(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=xI(e,s);if("causal"===a)throw new Lb("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Jl(o,t,r,"same"===a?"same":"valid","NDHWC",i),null!=n&&(o=Hx(o,n)),"channelsFirst"===s&&(o=Eh(o,[0,4,1,2,3])),o}))}(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate)}null!=this.activation&&(t=this.activation.apply(t))}return t}))}computeOutputShape(e){e=xw(e);let t=[],n="channelsLast"===this.dataFormat?e.slice(1,e.length-1):e.slice(2);for(let e=0;e<n.length;++e){let r=gI(n[e],this.kernelSize[e],this.padding,this.strides[e],"number"==typeof this.dilationRate?this.dilationRate:this.dilationRate[e]);t.push(r)}let r=[e[0]];return"channelsLast"===this.dataFormat?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:fw(this.kernelInitializer),kernelRegularizer:iI(this.kernelRegularizer),kernelConstraint:Xw(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||"number"!=typeof e.filters||e.filters<1)throw new Ob(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},II=class e extends kI{constructor(t){super(2,t),e.verifyArgs(t)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!tx(e.kernelSize,"number",1,2))throw new Ob(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};II.className="Conv2D",Qc.registerClass(II);var SI=class e extends kI{constructor(t){super(3,t),e.verifyArgs(t)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&(!Array.isArray(e.kernelSize)||1!==e.kernelSize.length&&3!==e.kernelSize.length))throw new Ob(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};SI.className="Conv3D",Qc.registerClass(SI);var NI=class extends II{constructor(e){if(super(e),this.inputSpec=[new Nw({ndim:4})],"same"!==this.padding&&"valid"!==this.padding)throw new Ob(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(4!==(e=xw(e)).length)throw new Ob("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Ob("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Nw({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return Ci((()=>{let t=bw(e);if(4!==t.shape.length)throw new Ob(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);let n,r,a=t.shape,s=a[0];"channelsFirst"===this.dataFormat?(n=2,r=3):(n=1,r=2);let i=a[n],o=a[r],l=this.kernelSize[0],u=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=[s,yI(i,d,l,this.padding),yI(o,p,u,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=Eh(t,[0,2,3,1]));let c=Zl(t,this.kernel.read(),h,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(c=Eh(c,[0,3,1,2])),null!=this.bias&&(c=Hx(c,this.bias.read(),this.dataFormat)),null!=this.activation&&(c=this.activation.apply(c)),c}))}computeOutputShape(e){let t,n,r,a=(e=xw(e)).slice();"channelsFirst"===this.dataFormat?(t=1,n=2,r=3):(t=3,n=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return a[t]=this.filters,a[n]=yI(a[n],o,s,this.padding),a[r]=yI(a[r],l,i,this.padding),a}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};NI.className="Conv2DTranspose",Qc.registerClass(NI);var TI=class extends SI{constructor(e){if(super(e),this.inputSpec=[new Nw({ndim:5})],"same"!==this.padding&&"valid"!==this.padding)throw new Ob(`Conv3DTranspose currently supports only padding modes '

2same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(5!==(e=xw(e)).length)throw new Ob("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Ob("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Nw({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return Ci((()=>{let t=bw(e);if(5!==t.shape.length)throw new Ob(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);let n,r,a,s=t.shape,i=s[0];"channelsFirst"===this.dataFormat?(a=2,n=3,r=4):(a=1,n=2,r=3);let o=s[a],l=s[n],u=s[r],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],c=this.strides[0],f=this.strides[1],m=this.strides[2],g=[i,yI(o,c,d,this.padding),yI(l,f,p,this.padding),yI(u,m,h,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=Eh(t,[0,2,3,4,1]));let y=eu(t,this.kernel.read(),g,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(y=Eh(y,[0,4,1,2,3])),null!==this.bias&&(y=Hx(y,this.bias.read(),this.dataFormat)),null!==this.activation&&(y=this.activation.apply(y)),y}))}computeOutputShape(e){let t,n,r,a,s=(e=xw(e)).slice();"channelsFirst"===this.dataFormat?(t=1,n=2,r=3,a=4):(t=4,n=1,r=2,a=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 s[t]=this.filters,s[n]=yI(s[n],u,i,this.padding),s[r]=yI(s[r],d,o,this.padding),s[a]=yI(s[a],p,l,this.padding),s}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};TI.className="Conv3DTranspose",Qc.registerClass(TI);var _I=class extends kI{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,null==t.filters)throw new Ob("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=t.kernelInitializer||null!=t.kernelRegularizer||null!=t.kernelConstraint)throw new Ob("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=t.padding&&"same"!==t.padding&&"valid"!==t.padding)throw new Ob(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,this.depthwiseInitializer=mw(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=lI(t.depthwiseRegularizer),this.depthwiseConstraint=Zw(t.depthwiseConstraint),this.pointwiseInitializer=mw(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=lI(t.pointwiseRegularizer),this.pointwiseConstraint=Zw(t.pointwiseConstraint)}build(e){if((e=xw(e)).length<this.rank+2)throw new Ob(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t]||e[t]<0)throw new Ob(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let e=0;e<this.rank;++e)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"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 Nw({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return Ci((()=>{let t;if(e=bw(e),1===this.rank)throw new Lb("1D separable convolution is not implemented yet.");return 2===this.rank&&("channelsFirst"===this.dataFormat&&(e=Eh(e,[0,2,3,1])),t=Wp(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(t=Hx(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),"channelsFirst"===this.dataFormat&&(t=Eh(t,[0,3,1,2])),t}))}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=fw(this.depthwiseInitializer),e.pointwiseInitializer=fw(this.pointwiseInitializer),e.depthwiseRegularizer=iI(this.depthwiseRegularizer),e.pointwiseRegularizer=iI(this.pointwiseRegularizer),e.depthwiseConstraint=Xw(this.depthwiseConstraint),e.pointwiseConstraint=Xw(this.pointwiseConstraint),e}};_I.className="SeparableConv";var CI=class extends _I{constructor(e){super(2,e)}};CI.className="SeparableConv2D",Qc.registerClass(CI);var EI=class e extends kI{constructor(t){super(1,t),e.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!tx(e.kernelSize,"number",1,1))throw new Ob(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};EI.className="Conv1D",Qc.registerClass(EI);var $I=class extends $w{constructor(e){super(e),"number"==typeof e.cropping?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:"number"==typeof e.cropping[0]?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=void 0===e.dataFormat?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return"channelsFirst"===this.dataFormat?[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 Ci((()=>

2{if(e=bw(e),"channelsLast"===this.dataFormat){let t=Ox(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ox(t,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}{let t=Ox(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ox(t,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}};$I.className="Cropping2D",Qc.registerClass($I);var AI=class extends $w{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=null==e.size?this.DEFAULT_SIZE:e.size,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,mx(this.dataFormat),this.interpolation=null==e.interpolation?"nearest":e.interpolation,function(e){ex(dx,"InterpolationFormat",e)}(this.interpolation)}computeOutputShape(e){if("channelsFirst"===this.dataFormat){let t=null==e[2]?null:this.size[0]*e[2],n=null==e[3]?null:this.size[1]*e[3];return[e[0],e[1],t,n]}{let t=null==e[1]?null:this.size[0]*e[1],n=null==e[2]?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return Ci((()=>{let t=bw(e),n=t.shape;if("channelsFirst"===this.dataFormat){t=Eh(t,[0,2,3,1]);let e=this.size[0]*n[2],r=this.size[1]*n[3],a="nearest"===this.interpolation?Kc.resizeNearestNeighbor(t,[e,r]):Kc.resizeBilinear(t,[e,r]);return Eh(a,[0,3,1,2])}{let e=this.size[0]*n[1],r=this.size[1]*n[2];return"nearest"===this.interpolation?Kc.resizeNearestNeighbor(t,[e,r]):Kc.resizeBilinear(t,[e,r])}}))}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};AI.className="UpSampling2D",Qc.registerClass(AI);var RI=class extends vI{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=mw(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Zw(e.depthwiseConstraint),this.depthwiseRegularizer=lI(e.depthwiseRegularizer)}build(e){if((e=xw(e)).length<4)throw new Ob(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t="channelsFirst"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new Ob(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Ci((()=>{let t=function(e,t,n=[1,1],r="valid",a,s){return Ci((()=>{null==a&&(a="channelsLast"),mx(a);let i=bI(e,a);if(4!==e.rank)throw new Ob(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(4!==t.rank)throw new Ob(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ou(i,t,n,"same"===r?"same":"valid","NHWC",s),"channelsFirst"===a&&(i=Eh(i,[0,3,1,2])),i}))}(e=bw(e),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(t=Hx(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),t}))}computeOutputShape(e){e=xw(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=gI(t,this.kernelSize[0],this.padding,this.strides[0]),s=gI(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=fw(this.depthwiseInitializer),e.depthwiseRegularizer=iI(this.depthwiseRegularizer),e.depthwiseConstraint=Xw(this.depthwiseRegularizer),e}};function FI(e,t,n,r){if(Array.isArray(e)){if(null!=t||null!=n)throw new Ob("When input

2s is an array, neither initialState or constants should be provided");null!=r&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(e){return null==e||Array.isArray(e)?e:[e]}return{inputs:e,initialState:t=a(t),constants:n=a(n)}}function DI(e,t,n,r=!1,a,s,i=!1,o=!1){return Ci((()=>{let l=t.shape.length;if(l<3)throw new Ob(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat($x(2,l));if(t=Eh(t,u),null!=s)throw new Lb("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."),null!=a&&((a=Wo(Wo(a,"bool"),"float32")).rank===l-1&&(a=Vu(a,-1)),a=Eh(a,u)),r&&(t=Fp(t,0),null!=a&&(a=Fp(a,0)));let d,p,h,c=[],f=n,m=t.shape[0],g=Ih(t);null!=a&&(p=Ih(a));for(let t=0;t<m;++t){let n=g[t],r=Ci((()=>e(n,f)));if(null==a)d=r[0],f=r[1];else{let e=Ci((()=>{let e=p[t],n=yd(Ud(e),e);return{output:Go(qo(r[0],e),qo(f[0],n)),newStates:f.map(((t,a)=>Go(qo(r[1][a],e),qo(t,n))))}}));d=e.output,f=e.newStates}o&&c.push(d)}return o&&(h=ah(c,1)),[d,h,f]}))}RI.className="DepthwiseConv2D",Qc.registerClass(RI);var MI=class e extends $w{constructor(e){let t;if(super(e),null==e.cell)throw new Ob("cell property is missing for the constructor of RNN.");if(t=Array.isArray(e.cell)?new UI({cells:e.cell}):e.cell,null==t.stateSize)throw new Ob("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=null!=e.returnSequences&&e.returnSequences,this.returnState=null!=e.returnState&&e.returnState,this.goBackwards=null!=e.goBackwards&&e.goBackwards,this._stateful=null!=e.stateful&&e.stateful,this.unroll=null!=e.unroll&&e.unroll,this.supportsMasking=!0,this.inputSpec=[new Nw({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(null==this.states_){return $x(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map((e=>null))}return this.states_}setStates(e){this.states_=e}computeOutputShape(e){gw(e)&&(e=e[0]);let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n,r=t[0];if(n=this.returnSequences?[e[0],e[1],r]:[e[0],r],this.returnState){let r=[];for(let n of t)r.push([e[0],n]);return[n].concat(r)}return n}computeMask(e,t){return Ci((()=>{Array.isArray(t)&&(t=t[0]);let e=this.returnSequences?t:null;if(this.returnState){let t=this.states.map((e=>null));return[e].concat(t)}return e}))}get states(){if(null==this.states_){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}return this.states_}set states(e){this.states_=e}build(e){if(null!=this.numConstants)throw new Lb("Constants support is not implemented in RNN yet.");gw(e)&&(e=e[0]);let t=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new Nw({shape:[t,null,...n]});let r,a=[e[0]].concat(e.slice(2));if(this.cell.build(a),r=Array.isArray(this.cell.stateSize)?this.cell.stateSize:[this.cell.stateSize],null!=this.stateSpec){if(!ja.arraysEqual(this.stateSpec.map((e=>e.shape[e.shape.length-1])),r))throw new Ob(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=r.map((e=>new Nw({shape:[null,e]})));this.stateful&&this.resetStates()}resetStates(e,t=!1){Ci((()=>{if(!this.stateful)throw new Db("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(null==n)throw new Ob("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(null==this.states_)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>Rd([n,e]))):this.states_=[Rd([n,this.cell.stateSize])];else if(null==e)Ei(this.states_),null!=this.keptStates&&(Ei(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>Rd([n,e]))):this.states_[0]=Rd([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Ob(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);!0===t?this.keptStates.push(this.states_.slice()):Ei(this.states_);for(let t=0;t<this.states_.length;++t){let r=e[t],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[t]:this.cell.stateSize,s=[n,a];if(!ja.arraysEqual(r.shape,s))throw new Ob(`State ${t} is incompatible with layer ${this.name}: expected shape=${s}, received shape=${r.shape}`);this.states_[t]=r}}this.states_=this.states_.map((e=>$i(e.clone())))}

2))}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});let a=FI(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(null!=n){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let e of n)this.stateSpec.push(new Nw({shape:e.shape}));i=i.concat(this.stateSpec)}if(null!=r&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Tw){let n=[e].concat(s),r=this.inputSpec.concat(i),a=this.inputSpec;this.inputSpec=r;let o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return Ci((()=>{let n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;e=bw(e),null==a&&(a=this.stateful?this.states_:this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new Ob(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=DI(((e,t)=>{let n=this.cell.call([e].concat(t),i);return[n[0],n.slice(1)]}),e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,r);let p=this.returnSequences?u:l;return this.returnState?[p].concat(d):p}))}getInitialState(e){return Ci((()=>{let t=Rd(e.shape);return t=Lu(t,[1,2]),t=Fx(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map((e=>e>1?Px(t,[1,e]):t)):this.cell.stateSize>1?[Px(t,[1,this.cell.stateSize])]:[t]}))}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(n.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===e.className&&(n.cell={className:this.cell.getClassName(),config:r}),Object.assign(Object.assign(Object.assign({},r),t),n)}static fromConfig(e,t,n={}){let r=$v(t.cell,n);return new e(Object.assign(t,{cell:r}))}};MI.className="RNN",Qc.registerClass(MI);var OI=class extends $w{},LI=class extends OI{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,nx(this.units,"units"),this.activation=tI(null==e.activation?this.DEFAULT_ACTIVATION:e.activation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=mw(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mw(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mw(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=lI(e.kernelRegularizer),this.recurrentRegularizer=lI(e.recurrentRegularizer),this.biasRegularizer=lI(e.biasRegularizer),this.kernelConstraint=Zw(e.kernelConstraint),this.recurrentConstraint=Zw(e.recurrentConstraint),this.biasConstraint=Zw(e.biasConstraint),this.dropout=Cx([1,Ex([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=Cx([1,Ex([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=xw(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 Ci((()=>{if(2!==e.length)throw new Ob(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=null!=t.training&&t.training;0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=GI({ones:()=>Ud(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=GI({ones:()=>Ud(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;a=Wx(null!=s?qo(e,s):e,this.kernel.read()),null!=this.bias&&(a=Hx(a,this.bias.read())),null!=i&&(n=qo(n,i));let o=Go(a,Wx(n,this.recurrentKernel.read()));return null!=this.activation&&(o=this.activation.apply(o)),[o,o]}))}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qk(this.activation),useBias:this.useBias,kernelInitializer:fw(this.kernelInitializer),recurrentInitializer:fw(this.recurrentInitializer),biasInitializer:fw(this.biasInitializer),kernelRegularizer:iI(this.kernelRegularizer),recurrentRegularizer:iI(this.recurrentRegularizer),biasRegularizer:iI(this.biasRegularizer),activityRegularizer:iI(this.activityRegularizer),kernelConstraint:Xw(this.kernelConstraint),recurrentConstraint:Xw(this.recurrentConstraint),biasConstraint:Xw(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};LI.className="SimpleRNNCell",Qc.registerClass(LI);var zI=class extends MI{constructor(e){e.cell=new LI(e),super(e)}call(e,t){return Ci((()=>{null!=this.cell.dropoutMask&&(Ei(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Ei(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return new e(t)}};zI.className="SimpleRNN",Qc.registerClass(zI);var PI=class extends OI{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new Ob("GRUCell does not support reset_after parameter set to true.");this.units=e.units,nx(this.units,"units"),this.activation=tI(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=tI(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=mw(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mw(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mw(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=lI(e.kernelRegularizer),this.recurrentRegularizer=lI(e.recurrentRegularizer),this.biasRegularizer=lI(e.biasRegularizer),this.kernelConstraint=Zw(e.kernelConstraint),this.recurrentConstraint=Zw(e.recurrentConstraint),this.biasConstraint=Zw(e.biasConstraint),this.dropout=Cx([1,Ex([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=Cx([1,Ex([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){let t=(e=xw(e))[e.length-1];this.kernel=this.addWeight("kernel",[t,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Ci((()=>

2{if(2!==e.length)throw new Ob(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=null!=t.training&&t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=GI({ones:()=>Ud(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=GI({ones:()=>Ud(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let a,s,i,o=this.dropoutMask,l=this.recurrentDropoutMask;0<this.dropout&&this.dropout<1&&(e=qo(e,o[0]));let u=Wx(e,this.kernel.read());this.useBias&&(u=Hx(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=qo(r,l[0]));let d=this.recurrentKernel.read(),[p,h]=eh(d,[2*this.units,this.units],d.rank-1),c=Wx(r,p),[f,m,g]=eh(u,3,u.rank-1),[y,b]=eh(c,2,c.rank-1);a=this.recurrentActivation.apply(Go(f,y)),s=this.recurrentActivation.apply(Go(m,b));let x=Wx(qo(s,r),h);i=this.activation.apply(Go(g,x));let w=Go(qo(a,r),qo(Go(1,fd(a)),i));return[w,w]}))}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qk(this.activation),recurrentActivation:Qk(this.recurrentActivation),useBias:this.useBias,kernelInitializer:fw(this.kernelInitializer),recurrentInitializer:fw(this.recurrentInitializer),biasInitializer:fw(this.biasInitializer),kernelRegularizer:iI(this.kernelRegularizer),recurrentRegularizer:iI(this.recurrentRegularizer),biasRegularizer:iI(this.biasRegularizer),activityRegularizer:iI(this.activityRegularizer),kernelConstraint:Xw(this.kernelConstraint),recurrentConstraint:Xw(this.recurrentConstraint),biasConstraint:Xw(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},e),t)}};PI.className="GRUCell",Qc.registerClass(PI);var BI=class extends MI{constructor(e){0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new PI(e),super(e)}call(e,t){return Ci((()=>{null!=this.cell.dropoutMask&&(Ei(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Ei(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}};BI.className="GRU",Qc.registerClass(BI);var WI=class extends OI{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,nx(this.units,"units"),this.activation=tI(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=tI(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=mw(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mw(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mw(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=lI(e.kernelRegularizer),this.recurrentRegularizer=lI(e.recurrentRegularizer),this.biasRegularizer=lI(e.biasRegularizer),this.kernelConstraint=Zw(e.kernelConstraint),this.recurrentConstraint=Zw(e.recurrentConstraint),this.biasConstraint=Zw(e.biasConstraint),this.dropout=Cx([1,Ex([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=Cx([1,Ex([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;let n,r=(e=xw(e))[e.length-1];if(this.kernel=this.addWeight("kernel",[r,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){let e=this.biasInitializer,r=this.units;n=new((t=class extends Yx{apply(t,n){let a=e.apply([r]),s=(new Jx).apply([r]),i=e.apply([2*r]);return zx(zx(a,s),i)}}).className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return Ci((()=>{let n=null!=t.training&&t.training;if(3!==e.length)throw new Ob(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=GI({ones:()=>Ud(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=GI({ones:()=>Ud(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s,i,o,l,u=this.dropoutMask,d=this.recurrentDropoutMask;0<this.dropout&&this.dropout<1&&(e=qo(e,u[0]));let p=Wx(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=qo(r,d[0])),p=Go(p,Wx(r,this.recurrentKernel.read())),this.useBias&&(p=Hx(p,this.bias.read()));let[h,c,f,m]=eh(p,4,p.rank-1);s=this.recurrentActivation.apply(h),i=this.recurrentActivation.apply(c),o=Go(qo(i,a),qo(s,this.activation.apply(f))),l=this.recurrentActivation.apply(m);let g=qo(l,this.activation.apply(o));return[g,g,o]}))}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qk(this.activation),recurrentActivation:Qk(this.recurrentActivation),useBias:this.useBias,kernelInitializer:fw(this.kernelInitializer),recurrentInitializer:fw(this.recurrentInitializer),biasInitializer:fw(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:iI(this.kernelRegularizer),recurrentRegularizer:iI(this.recurrentRegularizer),biasRegularizer:iI(this.biasRegularizer),activityRegularizer:iI(this.activityRegularizer),kernelConstraint:Xw(this.kernelConstraint),recurrentConstraint:Xw(this.recurrentConstraint),biasConstraint:Xw(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},e),t)}};WI.className="LSTMCell",Qc.registerClass(WI);var VI=class extends MI{constructor(e){0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new WI(e),super(e)}call(e,t){return Ci((()=>{null!=this.cell.dropoutMask&&(Ei(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Ei(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}};VI.className="LSTM",Qc.registerClass(VI);var UI=class extends OI{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 Ci((()=>{let n=e.slice(1),r=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?r.push(n.splice(0,e.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a,s=[];for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],a=0===i?[e[0]].concat(n):[a[0]].concat(n),a=o.call(a,t),s.push(a.slice(1))}n=[];for(let e of s.slice().reverse())n.push(...e);return[a[0]].concat(n)}))}build(e){let t;

2gw(e)&&(e=e[0]),this.cells.forEach(((n,r)=>{wx(`RNNCell_${r}`,(()=>{n.build(e),t=Array.isArray(n.stateSize)?n.stateSize[0]:n.stateSize,e=[e[0],t]}))})),this.built=!0}getConfig(){let e=super.getConfig(),t={cells:this.cells.map((e=>({className:e.getClassName(),config:e.getConfig()})))};return Object.assign(Object.assign({},e),t)}static fromConfig(e,t,n={}){let r=[];for(let e of t.cells)r.push($v(e,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Iw(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let e=0;e<n.weights.length;++e)t.push([n.weights[e],a[e]])}Sw(t)}};function GI(e){let{ones:t,rate:n,training:r=!1,count:a=1,dropoutFunc:s}=e,i=()=>null!=s?s(t(),n):jx(t(),n),o=()=>qx(i,t,r);return!a||a<=1?$i(o().clone()):Array(a).fill(void 0).map(o).map((e=>$i(e.clone())))}UI.className="StackedRNNCells",Qc.registerClass(UI);var HI=class extends MI{constructor(e){if(e.unroll)throw new Lb("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Lb("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Nw({ndim:5})]}call(e,t){return Ci((()=>{if(null!=this.cell.dropoutMask&&(Ei(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Ei(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new Ob("ConvRNN2D cell does not support constants");let n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return Ci((()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=Rd([r[0],...r.slice(2)]);return Array.isArray(t)?Array(t.length).fill(a):[a]}))}resetStates(e,t=!1){Ci((()=>{if(!this.stateful)throw new Db("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(null==n[0])throw new Ob("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(null==this.getStates())Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>Rd(a))):this.states_=[Rd(a)];else if(null==e)Ei(this.states_),null!=this.keptStates&&(Ei(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>Rd(a))):this.states_[0]=Rd(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Ob(`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()):Ei(this.states_);for(let t=0;t<this.states_.length;++t){let n=e[t],r=a;if(!ja.arraysEqual(n.shape,r))throw new Ob(`State ${t} is incompatible with layer ${this.name}: expected shape=${r}, received shape=${n.shape}`);this.states_[t]=n}}this.states_=this.states_.map((e=>$i(e.clone())))}))}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o="channelsFirst"===t,l=e[o?3:2],u=e[o?4:3],d=gI(l,r[0],a,s[0],i[0]),p=gI(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,d,p]:[d,p,n]]}};HI.className="ConvRNN2D";var jI=class extends WI{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,nx(this.filters,"filters"),this.kernelSize=mI(n,2,"kernelSize"),this.kernelSize.forEach((e=>nx(e,"kernelSize"))),this.strides=mI(r||1,2,"strides"),this.strides.forEac

2h((e=>nx(e,"strides"))),this.padding=a||"valid",gx(this.padding),this.dataFormat=s||"channelsLast",mx(this.dataFormat),this.dilationRate=mI(i||1,2,"dilationRate"),this.dilationRate.forEach((e=>nx(e,"dilationRate")))}build(e){var t;e=xw(e);let n="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[n])throw new Ob(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=this.kernelSize.concat([r,4*this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let s=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",s,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let e;if(this.unitForgetBias){let n=this.biasInitializer,r=this.filters;e=new((t=class extends Yx{apply(e,t){return Lx([n.apply([r]),Fd([r]),n.apply([2*r])])}}).className="CustomInit",t)}else e=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.filters],null,e,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return Ci((()=>{if(3!==e.length)throw new Ob(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2];0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=GI({ones:()=>Ud(r),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,o=(e,t,n)=>t&&t[n]?qo(t[n],e):e,l=o(r,i,0),u=o(r,i,1),d=o(r,i,2),p=o(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=GI({ones:()=>Ud(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,c=o(a,h,0),f=o(a,h,1),m=o(a,h,2),g=o(a,h,3),[y,b,x,w]=eh(this.kernel.read(),4,3),[v,k,I,S]=this.useBias?eh(this.bias.read(),4):[null,null,null,null];l=this.inputConv(l,y,v,this.padding),u=this.inputConv(u,b,k,this.padding),d=this.inputConv(d,x,I,this.padding),p=this.inputConv(p,w,S,this.padding);let[N,T,_,C]=eh(this.recurrentKernel.read(),4,3);c=this.recurrentConv(c,N),f=this.recurrentConv(f,T),m=this.recurrentConv(m,_),g=this.recurrentConv(g,C);let E=this.recurrentActivation.apply(Go(l,c)),$=this.recurrentActivation.apply(Go(u,f)),A=Go(qo($,s),qo(E,this.activation.apply(Go(d,m)))),R=qo(this.recurrentActivation.apply(Go(p,g)),this.activation.apply(A));return[R,R,A]}))}getConfig(){let e=super.getConfig(),{units:t}=e,n=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols){var a=0;for(r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]])}return n}(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),r)}inputConv(e,t,n,r){let a=Kl(e,t,this.strides,r||"valid","channelsFirst"===this.dataFormat?"NCHW":"NHWC",this.dilationRate);return n?Hx(a,n,this.dataFormat):a}recurrentConv(e,t){return Kl(e,t,1,"same","channelsFirst"===this.dataFormat?"NCHW":"NHWC")}};jI.className="ConvLSTM2DCell",Qc.registerClass(jI);var qI=class extends HI{constructor(e){let t=new jI(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};qI.className="ConvLSTM2D",Qc.registerClass(qI);var KI=class extends $w{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(null==this.noiseShape)return this.noiseShape;let t=e.shape,n=[];for(let e=0;e<this.noiseShape.length;++e)n.push(null==this.noiseShape[e]?t[e]:this.noiseShape[e]);return n}call(e,t){return Ci((()=>{this.invokeCallHook(e,t);let n=bw(e);if(0<this.rate&&this.rate<1){let e=null!=t.training&&t.training,r=this.getNoiseShape(n);return qx((()=>jx(n,this.rate,r,this.seed)),(()=>n),e)}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()}};KI.className="Dropout",Qc.registerClass(KI);var XI=class extends KI{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};XI.className="SpatialDropout1D",Qc.registerClass(XI);var YI=class extends $w{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",null==e.batchInputShape&&null==e.inputShape&&null!=e.inputDim){let t=null;null!=e.batchSize&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,nx(this.units,"units"),this.activation=tI(e.activation),null!=e.useBias&&(this.useBias=e.useBias),this.kernelInitializer=mw(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=mw(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Zw(e.kernelConstraint),this.biasConstraint=Zw(e.biasConstraint),this.kernelRegularizer=lI(e.kernelRegularizer),this.biasRegularizer=lI(e.biasRegularizer),this.activityRegularizer=lI(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){let t=(e=xw(e))[e.length-1];null==this.kernel&&(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){let t=(e=xw(e)).slice();return t[t.length-1]=this.units,t}call(e,t){return Ci((()=>{this.invokeCallHook(e,t);let n,r=bw(e),a=ax(this.activation.getClassName());return null!=a?n=Wx(r,this.kernel.read(),a,this.bias?this.bias.read():null):(n=Wx(r,this.kernel.read()),null!=this.bias&&(n=Hx(n,this.bias.read())),null!=this.activation&&(n=this.activation.apply(n))),n}))}getConfig(){let e={units:this.units,activation:Qk(this.activation),useBias:this.useBias,kernelInitializer:fw(this.kernelInitializer),biasInitializer:fw(this.biasInitializer),kernelRegularizer:iI(this.kernelRegularizer),biasRegularizer:iI(this.biasRegularizer),activityRegularizer:iI(this.activityRegularizer),kernelConstraint:Xw(this.kernelConstraint),biasConstraint:Xw(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};YI.className="Dense",Qc.registerClass(YI);var ZI=class extends $w{constructor(e){super(e=e||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=xw(e);for(let t of e.slice(1))if(null==t)throw new Ob(`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],_x(e,1)]}call(e,t){return Ci((()=>{this.invokeCallHook(e,t);let n=bw(e);if("channelsFirst"===this.dataFormat&&n.rank>1){let e=[0];for(let t=2;t<n.rank;++t)e.push(t);e.push(1),n=Eh(n,e)}return function(e){if(e.rank<=1)throw new Ob(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],_x(e.shape,1)];return kl(e,t)}(n)}))}getConfig(){let e={};null!=this.dataFormat&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};ZI.className="Flatten",Qc.registerClass(ZI);var JI=class extends $w{constructor(e){super(e),this.supportsMasking=!0,this.activation=tI(e.activation)}call(e,t){return Ci((()=>{this.invokeCallHook(e,t);let n=bw(e);return this.activation.apply(n)}))}getConfig(){let e={activation:Qk(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};JI.className="Activation",Qc.registerClass(JI);var QI=class extends $w{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 Ci((()=>function(e,t){return Ci((()=>

2{if(2!==e.shape.length)throw new Ob(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);return Px(Fx(e,1),[1,t,1])}))}(e=bw(e),this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};QI.className="RepeatVector",Qc.registerClass(QI);var eS=class extends $w{constructor(e){super(e),this.targetShape=e.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(e){return e<0||null==e}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let e=0;e<r.length;++e){let t=r[e];if(this.isUnknown(t)){if(null!==s)throw new Ob("Can only specifiy one unknown dimension.");s=e}else a*=t}let i=_x(e);if(null!==s){if(0===a||i%a!=0)throw new Ob(n);r[s]=i/a}else if(i!==a)throw new Ob(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return Ci((()=>{this.invokeCallHook(e,t);let n=bw(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return kl(n,a)}))}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};eS.className="Reshape",Qc.registerClass(eS);var tS=class extends $w{constructor(e){if(super(e),null==e.dims)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 ${e.dims} instead.`);let t=$x(1,e.dims.length+1);if(!ja.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 Nw({ndim:this.dims.length+1})]}computeOutputShape(e){let t=(e=xw(e)).slice();return this.dims.forEach(((n,r)=>{t[r+1]=e[n]})),t}call(e,t){return Eh(bw(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};tS.className="Permute",Qc.registerClass(tS);var nS=class extends $w{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,this.maskValue=null!=e?null==e.maskValue?0:e.maskValue:0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=bw(e);return Qo(Wd(n,this.maskValue),-1)}call(e,t){return Ci((()=>{this.invokeCallHook(e,t);let n=bw(e),r=Qo(Wd(n,this.maskValue),-1,!0);return qo(n,Wo(r,n.dtype))}))}};nS.className="Masking",Qc.registerClass(nS);var rS=class extends $w{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==e.batchInputShape&&null==e.inputShape){let t=null;null!=e.batchSize&&(t=e.batchSize),null==e.inputLength?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Gb(e.inputLength))}this.inputDim=e.inputDim,nx(this.inputDim,"inputDim"),this.outputDim=e.outputDim,nx(this.outputDim,"outputDim"),this.embeddingsInitializer=mw(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=lI(e.embeddingsRegularizer),this.activityRegularizer=lI(e.activityRegularizer),this.embeddingsConstraint=Zw(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return Ci((()=>this.maskZero?(e=bw(e),Wd(e,gu(e))):null))}computeOutputShape(e){if(e=xw(e),null==this.inputLength)return[...e,this.outputDim];let t=Gb(this.inputLength);if(t.length!==e.length-1)throw new Ob(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];

2if(null!=a&&null!=s&&a!==s)throw new Ob(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);null==a&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return Ci((()=>{this.invokeCallHook(e,t);let n=bw(e);"int32"!==n.dtype&&(n=Rx(n,"int32"));let r=Vx(this.embeddings.read(),kl(n,[n.size]));return kl(r,xw(this.computeOutputShape(n.shape)))}))}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:fw(this.embeddingsInitializer),embeddingsRegularizer:iI(this.embeddingsRegularizer),activityRegularizer:iI(this.activityRegularizer),embeddingsConstraint:Xw(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};rS.className="Embedding",Qc.registerClass(rS);var aS=class extends $w{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Lb}computeElementwiseOpOutputShape(e,t){if(null==e||null==t)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(0===t.length)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(null==a||null==s||a<0||s<0)n.push(null);else if(1===a)n.push(s);else if(1===s)n.push(a);else{if(a!==s)throw new Ob("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[xw(e)]),e.length<2)throw new Ob(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let n of e)null!=n&&null!==n[0]&&t.push(n[0]);if(t=Jb(t),t.length>1)throw new Ob(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=null==e[0]?null:e[0].slice(1);for(let t=1;t<e.length;++t){let r=null==e[t]?null:e[t].slice(1);n=this.computeElementwiseOpOutputShape(n,r)}let r=e.map((e=>e.length));-1===e.indexOf(null)&&1===Jb(r).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return Ci((()=>{if(this.reshapeRequired){let t=[],n=e.map((e=>e.rank));if(-1===n.indexOf(null)){let r=Ex(n);for(let n of e){let e=n.rank;for(let t=0;t<r-e;++t)n=Fx(n,1);t.push(n)}return this.mergeFunction(t)}{let n=!1;for(let r of e){let e=r.rank;if(null==e){let e=r.shape,a=e[0],s=e.slice(1).concat([a]),i=kl(r,[a].concat(_x(e.slice(1))));i=Eh(i,[1,0]),i=kl(i,s),t.push(i),n=!0}else if(e>1){let a=$x(1,e).concat([0]);t.push(Eh(r,a)),n=!0}else t.push(r)}let r=this.mergeFunction(t),a=r.rank;if(n)if(null==a){let e=r.shape,t=e[e.length-1],n=[t].concat(e.slice(0,e.length-1));r=kl(Eh(kl(r,[-1,t]),[1,0]),n)}else if(a>1){let e=[a-1].concat($x(0,a-1));r=Eh(r,e)}return r}}return this.mergeFunction(e)}))}computeOutputShape(e){let t;t=null==e[0]?null:e[0].slice(1);for(let n=1;n<e.length;++n){let r=null==e[n]?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let t of e)null!=t&&null!==t[0]&&n.push(t[0]);return n=Jb(n),t=1===n.length?n.concat(t):[null].concat(t),t}computeMask(e,t){return Ci((()=>{if(null==t)return null;if(!Array.isArray(t))throw new Ob("`mask` should be an Array");if(!Array.isArray(e))throw new Ob("`inputs` should be an Array");if(t.length!==e.length)throw new Ob(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every((e=>null==e)))return null;let n=(t=t.map((e=>null==e?e:Vu(e,0))))[0];for(let e=1;e<t.length-1;++e)n=wd(n,t[e]);return n}))}},sS=class extends aS{constructor(e){super(e)}mergeFunction(e){return Ci((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Go(t,e[n]);return t}))}};sS.className="Add",Qc.registerClass(sS);var iS=class extends aS{constructor(e){super(e)}mergeFunction(e){return Ci((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=qo(t,e[n]);return t}))}};iS.className="Multiply",Qc.registerClass(iS);var oS=class extends aS{constructor(e){super(e)}mergeFunction(e){return Ci((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Go(t,e[n]);return qo(1/e.length,t)}))}};oS.className="Average",Qc.registerClass(oS);var lS=class extends aS{constructor(e){super(e)}mergeFunction(e){return Ci((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$d(t,e[n]);return t}))}};lS.className="Maximum",Qc.registerClass(lS);var uS=class extends aS{constructor(e){super(e)}mergeFunction(e){return Ci((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Md(t,e[n]);return t}))}};uS.className="Minimum",Qc.registerClass(uS);var dS=class extends aS{constructor(e){super(e),this.DEFAULT_AXIS=-1,null==e&&(e={}),this.axis=null==e.axis?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!Array.isArray(e)||!Array.isArray(e[0])||1===e.length)throw new Ob("A `Concatenate` layer should be called on a list of at least 2 inputs");let t=!0;for(let n of e)if(null!=n){t=!1;break}if(t)return;let n=[];for(let t=0;t<e.length;++t){let r=e[t].slice();r.splice(this.axis,1);let a=!1;for(let e of n)if(ja.arraysEqual(e,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new Ob("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return Ci((()=>

2Lx(e,this.axis)))}computeOutputShape(e){if(!Array.isArray(e)||!Array.isArray(e[0]))throw new Ob("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let e of t.slice(1)){if(null==n[r]||null==e[r]){n[r]=null;break}n[r]+=e[r]}return n}computeMask(e,t){if(null==t)return null;if(!Array.isArray(t))throw new Ob("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new Ob("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new Ob(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return Ci((()=>{let n=!0;if(t.forEach((e=>{null==e||(n=!1)})),n)return null;let r=[];for(let n=0;n<e.length;++n)null==t[n]?r.push(Wo(Ud(e[n]),"bool")):t[n].rank<e[n].rank?r.push(Vu(t[n],-1)):r.push(t[n]);let a=Nl(r,this.axis);return Jo(a,-1,!1)}))}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};function pS(e,t){for(;e<0;)e+=t;return e}dS.className="Concatenate",Qc.registerClass(dS);var hS=class extends aS{constructor(e){super(e),this.axes=e.axes,this.normalize=null!=e.normalize&&e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){ja.assert(Array.isArray(e)&&2===e.length&&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 Lb("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new Ob(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(2!==e.length)throw new Ob(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t,n=e[0],r=e[1];return t=Array.isArray(this.axes)?this.axes.map(((t,n)=>pS(t,e[n].shape.length))):[pS(this.axes,n.shape.length),pS(this.axes,r.shape.length)],this.normalize&&(n=Av(n,t[0]),r=Av(r,t[1])),function(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Lb("batchDot is not implemented for tensors of 4D or higher rank yet");if(ja.assert(e.shape.length>=2,(()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`)),ja.assert(e.shape.length>=2,(()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`)),"number"==typeof n&&(n=[n,n]),"complex64"===e.dtype||"complex64"===t.dtype)throw new Lb("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;null==n&&(n=[r-1,a-2]);let s=n;return Ci((()=>{let n,i;if(r>a){n=r-a;let e=[];for(let t=0;t<n;++t)e.push(1);t=kl(t,t.shape.concat(e))}else if(a>r){n=a-r;let t=[];for(let e=0;e<n;++e)t.push(1);e=kl(e,e.shape.concat(t))}else n=0;if(2===e.shape.length&&2===t.shape.length)i=s[0]===s[1]?Lu(qo(e,t),s[0]):Lu(qo(Eh(e,[1,0]),t),s[1]);else{let n=s[0]!==e.shape.length-1,r=s[1]===t.shape.length-1;i=Tl(e,t,n,r)}if(n>0){let e;e=r>a?r+a-3:r-1;let t=[];for(let r=e;r<e+n;++r)t.push(r);i=rh(i,t)}return 1===i.shape.length&&(i=Vu(i,1)),i}))}(n,r,t)}interpretAxes(e,t){let n;return n=Array.isArray(this.axes)?this.axes:[pS(this.axes,e.length),pS(this.axes,t.length)],n}computeOutputShape(e){ja.assert(Array.isArray(e)&&2===e.length&&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 Lb("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return 1===a.length&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};hS.className="Dot",Qc.registerClass(hS);var cS=class extends $w{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 Ci((()=>{this.invokeCallHook(e,t);let n=bw(e);return qx((()=>Go(Bx(n.shape,0,this.stddev),n)),(()=>n),t.training||!1)}))}};cS.className="GaussianNoise",Qc.registerClass(cS);var fS=class extends $w{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 Ci((()=>{this.invokeCallHook(e,t);let n=bw(e);return this.rate>0&&this.rate<1?qx((()=>{let e=Math.sqrt(this.rate/(1-this.rate));return qo(n,Bx(n.shape,1,e))}),(()=>n),t.training||!1):n}))}};fS.className="GaussianDropout",Qc.registerClass(fS);var mS=class extends $w{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||bw(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 Ci((()=>

2{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return qx((()=>{let t=bw(e),r=-1.7580993408473766,a=Xu(Tp(n),this.rate);a=Rx(a,"float32");let s=((1-this.rate)*(1+this.rate*r**2))**-.5,i=-s*r*this.rate,o=Go(qo(t,a),qo(Go(a,-1),r));return Go(qo(o,s),i)}),(()=>bw(e)),t.training||!1)}return e}))}};function gS(e,t,n,r,a,s=.001){let i;if(2===e.rank)i=Dl(e,t,n,r,a,s);else if(3===e.rank)i=Ml(e,t,n,r,a,s);else{if(4!==e.rank)throw new Lb(`batchNormalization is not implemented for array of rank ${e.rank} yet`);i=Ol(e,t,n,r,a,s)}return i}function yS(e,t,n,r,a=.001){return ja.arraysEqual(r.slice().sort(),$x(0,e.rank-1))?function(e,t,n,r,a=.001){return Ci((()=>{let s=zd(e,r),i=s.mean,o=s.variance;return[gS(e,i,o,n,t,a),i,o]}))}(e,t,n,r,a):function(e,t,n,r,a=.001){return Ci((()=>{let s=zd(e,r),i=s.mean,o=s.variance,l=[];for(let t of $x(0,e.rank))-1!==r.indexOf(t)?l.push(1):l.push(e.shape[t]);let u=kl(i,l),d=kl(o,l),p=null==t?null:kl(t,l),h=null==n?null:kl(n,l);return[gS(e,u,d,h,p,a),i,o]}))}(e,t,n,r,a)}mS.className="AlphaDropout",Qc.registerClass(mS);var bS=class extends $w{constructor(e){null==e&&(e={}),super(e),this.supportsMasking=!0,this.axis=null==e.axis?-1:e.axis,this.momentum=null==e.momentum?.99:e.momentum,this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=mw(e.betaInitializer||"zeros"),this.gammaInitializer=mw(e.gammaInitializer||"ones"),this.movingMeanInitializer=mw(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=mw(e.movingVarianceInitializer||"ones"),this.betaConstraint=Zw(e.betaConstraint),this.gammaConstraint=Zw(e.gammaConstraint),this.betaRegularizer=lI(e.betaRegularizer),this.gammaRegularizer=lI(e.gammaRegularizer)}build(e){e=xw(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(null==n)throw new Ob(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Nw({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return Ci((()=>{let n=null!=t.training&&t.training,r=bw(e),a=r.shape,s=a.length,i=$x(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Bb(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!ja.arraysEqual(u,$x(0,s).slice(0,s-1));if(!n)return(()=>{if(d){let e=kl(this.movingMean.read(),l),t=kl(this.movingVariance.read(),l),n=this.center?kl(this.beta.read(),l):null,a=this.scale?kl(this.gamma.read(),l):null;return gS(r,e,t,n,a,this.epsilon)}return gS(r,this.movingMean.read(),this.movingVariance.read(),null==this.beta?null:this.beta.read(),null==this.gamma?null:this.gamma.read(),this.epsilon)})();let[p,h,c]=yS(r,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(e,t,n)=>{Ci((()=>{let r=1-n,a=e.read(),s=qo(yd(a,t),r);e.write(yd(a,s))}))};return f(this.movingMean,h,this.momentum),f(this.movingVariance,c,this.momentum),p}))}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:fw(this.betaInitializer),gammaInitializer:fw(this.gammaInitializer),movingMeanInitializer:fw(this.movingMeanInitializer),movingVarianceInitializer:fw(this.movingVarianceInitializer),betaRegularizer:iI(this.betaRegularizer),gammaRegularizer:iI(this.gammaRegularizer),betaConstraint:Xw(this.betaConstraint),gammaConstraint:Xw(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};bS.className="BatchNormalization",Qc.registerClass(bS);var xS=class extends $w{constructor(e){if(null==e&&(e={}),super(e),this.axis=null==e.axis?-1:e.axis,"number"==typeof this.axis){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else{if(!Array.isArray(this.axis))throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=mw(e.betaInitializer||"zeros"),this.gammaInitializer=mw(e.gammaInitializer||"ones"),this.betaRegularizer=lI(e.betaRegularizer),this.gammaRegularizer=lI(e.gammaRegularizer),this.supportsMasking=!0}build(e){let t=(e=xw(e)).length;"number"==typeof this.axis&&(this.axis=[this.axis]);for(let e=0;e<this.axis.length;++e)this.axis[e]<0&&(this.axis[e]+=t);for(let e of this.axis)if(e<0||e>=t)throw new Error(`Invalid axis: ${e}`);if(this.axis.length!==Jb(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map((t=>e[t]));this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,true):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,true):this.beta=null,this.built=!0}call(e,t){let n=bw(e),r=n.shape,a=r.length;return Ci((()=>{let{mean:e,variance:t}=zd(n,this.axis,!0),s=Bb(1,a);for(let e of this.axis)s[e]=r[e];let i=e=>null!=e&&e.shape.length!==a?kl(e,s):e,o=this.scale?i(this.gamma.read()):null,l=this.center?i(this.beta.read()):null,u=[],d=[];for(let e=0;e<a;++e)-1!==this.axis.indexOf(e)?(u.push(r[e]),d.push(1)):(u.push(1),d.push(r[e]));return e=Gu(e,u),t=Gu(t,u),null!=o&&(o=Gu(o,d)),null!=l&&(l=Gu(l,d)),gS(n,e,t,l,o,this.epsilon)}))}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:fw(this.betaInitializer),gammaInitializer:fw(this.gammaInitializer),betaRegularizer:iI(this.betaRegularizer),gammaRegularizer:iI(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};xS.className="LayerNormalization",Qc.registerClass(xS);var wS=class extends $w{constructor(e){if(null==e&&(e={}),super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,null==e.padding)this.padding=[[1,1],[1,1]];else if("number"==typeof e.padding)this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,2!==e.padding.length)throw new Ob(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if("number"==typeof e.padding[0])t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,2!==e.padding[0].length)throw new Ob(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],2!==e.padding[1].length)throw new Ob(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Nw({ndim:4})]}computeOutputShape(e){let t,n;return e=xw(e),"channelsFirst"===this.dataFormat?(t=null!=e[2]&&e[2]>=0?e[2]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[3]&&e[3]>=0?e[3]+this.padding[1][0]+this.padding[1][1]:null,[e[0],e[1],t,n]):(t=null!=e[1]&&e[1]>=0?e[1]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[2]&&e[2]>=0?e[2]+this.padding[1][0]+this.padding[1][1]:null,[e[0],t,n,e[3]])}call(e,t){return Ci((()=>function(e,t,n){return Ci((()=>

2{if(4!==e.rank)throw new Ob(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(null==t&&(t=[[1,1],[1,1]]),2!==t.length||2!==t[0].length||2!==t[1].length)throw new Ob("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(null==n&&(n="channelsLast"),"channelsLast"!==n&&"channelsFirst"!==n)throw new Ob(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return r="channelsFirst"===n?[[0,0],[0,0],t[0],t[1]]:[[0,0],t[0],t[1],[0,0]],Hd(e,r)}))}(bw(e),this.padding,this.dataFormat)))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};function vS(e,t,n,r,a,s){return Ci((()=>{mx(a),yx(s),gx(r),null==n&&(n=[1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==s&&(s="max"),e=bI(e,a);let i,o="same"===r?"same":"valid";return i="max"===s?_d(e,t,n,o):Il(e,t,n,o),"channelsFirst"===a&&(i=Eh(i,[0,3,1,2])),i}))}function kS(e,t,n,r,a,s){return Ci((()=>{mx(a),yx(s),gx(r),null==n&&(n=[1,1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==s&&(s="max"),e=xI(e,a);let i,o="same"===r?"same":"valid";return i="max"===s?Cd(e,t,n,o):Sl(e,t,n,o),"channelsFirst"===a&&(i=Eh(i,[0,4,1,2,3])),i}))}wS.className="ZeroPadding2D",Qc.registerClass(wS);var IS=class extends $w{constructor(e){if(null==e.poolSize&&(e.poolSize=2),super(e),"number"==typeof e.poolSize)this.poolSize=[e.poolSize];else{if(!Array.isArray(e.poolSize)||1!==e.poolSize.length||"number"!=typeof e.poolSize[0])throw new Ob(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);this.poolSize=e.poolSize}if(nx(this.poolSize,"poolSize"),null==e.strides)this.strides=this.poolSize;else if("number"==typeof e.strides)this.strides=[e.strides];else{if(!Array.isArray(e.strides)||1!==e.strides.length||"number"!=typeof e.strides[0])throw new Ob(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);this.strides=e.strides}nx(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,gx(this.padding),this.inputSpec=[new Nw({ndim:3})]}computeOutputShape(e){let t=gI((e=xw(e))[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return Ci((()=>{this.invokeCallHook(e,t),e=Fx(bw(e),2);let n=this.poolingFunction(bw(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return rh(n,[2])}))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},SS=class extends IS{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return mx(a),gx(r),vS(e,t,n,r,a,"max")}};SS.className="MaxPooling1D",Qc.registerClass(SS);var NS=class extends IS{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return mx(a),gx(r),vS(e,t,n,r,a,"avg")}};NS.className="AveragePooling1D",Qc.registerClass(NS);var TS=class extends $w{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(2!==e.strides.length)throw new Ob(`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];nx(this.poolSize,"poolSize"),nx(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,mx(this.dataFormat),gx(this.padding),this.inputSpec=[new Nw({ndim:4})]}computeOutputShape(e){e=xw(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2];return t=gI(t,this.poolSize[0],this.padding,this.strides[0]),n=gI(n,this.poolSize[1],this.padding,this.strides[1]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return Ci((()=>(this.invokeCallHook(e,t),this.poolingFunction(bw(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}},_S=class extends TS{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return mx(a),gx(r),vS(e,t,n,r,a,"max")}};_S.className="MaxPooling2D",Qc.registerClass(_S);var CS=class extends TS{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return mx(a),gx(r),vS(e,t,n,r,a,"avg")}};CS.className="AveragePooling2D",Qc.registerClass(CS);var ES=class extends $w{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(3!==e.strides.length)throw new Ob(`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];nx(this.poolSize,"poolSize"),nx(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,mx(this.dataFormat),gx(this.padding),this.inputSpec=[new Nw({ndim:5})]}computeOutputShape(e){e=xw(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[4]:e[3];return t=gI(t,this.poolSize[0],this.padding,this.strides[0]),n=gI(n,this.poolSize[1],this.padding,this.strides[1]),r

2=gI(r,this.poolSize[2],this.padding,this.strides[2]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return Ci((()=>(this.invokeCallHook(e,t),this.poolingFunction(bw(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}},$S=class extends ES{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return mx(a),gx(r),kS(e,t,n,r,a,"max")}};$S.className="MaxPooling3D",Qc.registerClass($S);var AS=class extends ES{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return mx(a),gx(r),kS(e,t,n,r,a,"avg")}};AS.className="AveragePooling3D",Qc.registerClass(AS);var RS=class extends $w{constructor(e){super(e),this.inputSpec=[new Nw({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Lb}},FS=class extends RS{constructor(e){super(e||{})}call(e,t){return Ci((()=>{let t=bw(e);return Ad(t,1)}))}};FS.className="GlobalAveragePooling1D",Qc.registerClass(FS);var DS=class extends RS{constructor(e){super(e||{})}call(e,t){return Ci((()=>{let t=bw(e);return Au(t,1)}))}};DS.className="GlobalMaxPooling1D",Qc.registerClass(DS);var MS=class extends $w{constructor(e){super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,mx(this.dataFormat),this.inputSpec=[new Nw({ndim:4})]}computeOutputShape(e){return"channelsLast"===this.dataFormat?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Lb}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},OS=class extends MS{call(e,t){return Ci((()=>{let t=bw(e);return"channelsLast"===this.dataFormat?Ad(t,[1,2]):Ad(t,[2,3])}))}};

2OS.className="GlobalAveragePooling2D",Qc.registerClass(OS);var LS=class extends MS{call(e,t){return Ci((()=>{let t=bw(e);return"channelsLast"===this.dataFormat?Au(t,[1,2]):Au(t,[2,3])}))}};LS.className="GlobalMaxPooling2D",Qc.registerClass(LS);var zS=class extends $w{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return null!=this.layer&&this.layer.trainable}set trainable(e){null!=this.layer&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.layer&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=$v(t.layer,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},PS=class extends zS{constructor(e){super(e),this.supportsMasking=!0}build(e){if((e=xw(e)).length<3)throw new Ob(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){let t=[(e=xw(e))[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return Ci((()=>DI(((e,n)=>[bw(this.layer.call(e,t)),[]]),e=bw(e),[],!1,null,null,!1,!0)[1]))}};PS.className="TimeDistributed",Qc.registerClass(PS);var BS=class extends zS{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=$v(n),t.goBackwards=!0!==t.goBackwards;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=$v(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=void 0===e.mergeMode?"concat":e.mergeMode,function(e){ex(cx,"BidirectionalMergeMode",e)}(this.mergeMode),e.weights)throw new Lb("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,null!=this.forwardLayer&&(this.forwardLayer.trainable=e),null!=this.backwardLayer&&(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,n,r,a=this.forwardLayer.computeOutputShape(e);return Array.isArray(a)&&Array.isArray(a[0])||(a=[a]),this.returnState&&(r=a.slice(1)),t=a[0],"concat"===this.mergeMode?(t[t.length-1]*=2,n=[t]):n=null==this.mergeMode?[t,t.slice()]:[t],this.returnState?null==this.mergeMode?n.concat(r).concat(r.slice()):[t].concat(r).concat(r.slice()):Ub(n)}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});let a=FI(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(null==n||0===n.length)&&null==r)return super.apply(e,t);let s=[],i=[];if(null!=n){let e=n.length;if(e%2>0)throw new Ob("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 r=n.map((e=>new Nw({shape:e.shape})));this.forwardLayer.stateSpec=r.slice(0,e/2),this.backwardLayer.stateSpec=r.slice(e/2),i.push(...r)}if(null!=r)throw new Lb("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Tw;for(let e of s)if(e instanceof Tw!==o)throw new Ob("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let n=[e].concat(s),r=this.inputSpec.concat(i),a=this.inputSpec;this.inputSpec=r;let o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return Ci((()=>{let n,r,a,s,i=t.initialState;if(null==i)n=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let a=i.slice(0,i.length/2),s=i.slice(i.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:a})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:s}))}return this.returnState&&(Array.isArray(n)&&(a=n.slice(1).concat(r.slice(1))),n=n[0],r=r[0]),this.returnSequences&&(r=Fp(r,1)),"concat"===this.mergeMode?s=Lx([n,r]):"sum"===this.mergeMode?s=Go(n,r):"ave"===this.mergeMode?s=qo(.5,Go(n,r)):"mul"===this.mergeMode?s=qo(n,r):null==this.mergeMode&&(s=[n,r]),this.returnState?null==this.mergeMode?s.c

2oncat(a):[s].concat(a):s}))}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){wx(this.forwardLayer.name,(()=>{this.forwardLayer.build(e)})),wx(this.backwardLayer.name,(()=>{this.backwardLayer.build(e)})),this.built=!0}computeMask(e,t){let n;if(Array.isArray(t)&&(t=t[0]),n=this.returnSequences?null==this.mergeMode?[t,t]:t:null==this.mergeMode?[null,null]:null,this.returnState){let e=this.forwardLayer.states.map((e=>null));return Array.isArray(n)?n.concat(e).concat(e):[n].concat(e).concat(e)}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),null!=this.forwardLayer&&this.forwardLayer.setFastWeightInitDuringBuild(e),null!=this.backwardLayer&&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=$v(t.layer);if(delete t.layer,null!=t.numConstants)throw new Lb("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let r=t;return r.layer=n,new e(r)}};BS.className="Bidirectional",Qc.registerClass(BS);var WS=class extends $w{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return Ci((()=>("float32"!==(e=bw(e)).dtype&&(e=Rx(e,"float32")),Go(qo(e,this.scale),this.offset))))}};WS.className="Rescaling",Qc.registerClass(WS);var{resizeBilinear:VS,cropAndResize:US}=Kc,GS=class extends $w{constructor(e){super(e),this.height=e.height,this.width=e.width}centerCrop(e,t,n,r,a,s,i,o){return Ci((()=>{let l,u=!1,d=[t/s,n/i,(r+t)/s,(a+n)/i],p=[];3===e.rank?(u=!0,l=ah([e])):l=e;for(let e=0;e<l.shape[0];e++)p.push(d);let h=yi(p,[p.length,4]),c=Cp(0,p.length,1,"int32"),f=US(l,h,c,[r,a],"nearest");return Rx(u?bw(Ih(f)):f,o)}))}upsize(e,t,n,r){return Ci((()=>Rx(VS(e,[t,n]),r)))}call(e,t){return Ci((()=>{let t=bw(e),n=t.dtype,r=t.shape,a=r[r.length-3],s=r[r.length-2],i=0;a!==this.height&&(i=Math.floor((a-this.height)/2));let o=0;return s!==this.width&&(o=Math.floor((s-this.width)/2),0===o&&(o=1)),i>=0&&o>=0?this.centerCrop(t,i,o,this.height,this.width,a,s,n):this.upsize(e,this.height,this.width,n)}))}getConfig(){let e={height:this.height,width:this.width},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){let t=(e=xw(e)).length-3,n=e.length-2;return e[t]=this.height,e[n]=this.width,e}};GS.className="CenterCrop",Qc.registerClass(GS);var HS=class extends $w{constructor(e){super(e),this.numTokens=e.numTokens,e.outputMode?this.outputMode=e.outputMode:this.outputMode="multiHot"}getConfig(){let e={numTokens:this.numTokens,outputMode:this.outputMode},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){return null==(e=xw(e))?[this.numTokens]:"oneHot"===this.outputMode&&1!==e[e.length-1]?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return Ci((()=>{let n;if("int32"!==(e=bw(e)).dtype&&(e=Rx(e,"int32")),void 0!==t.countWeights){if("count"!==this.outputMode)throw new Ob(`countWeights is not used when outputMode !== count.\n              Received countWeights=${t.countWeights}`);n=bw(t.countWeights)}let r=Au(e),a=Ru(e),s=Ku(this.numTokens,r).bufferSync().get(0),i=Xu(a,0).bufferSync().get(0);if(!s||!i)throw new Ob(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return function(e,t,n,r){let a=bw(e);if("int32"!==a.dtype&&(a=Rx(a,"int32")),"int"===t)return a;let s=a.shape;if(0===a.rank&&(a=Vu(a,-1)),"oneHot"===t&&1!==a.shape[a.shape.length-1]&&(a=Vu(a,-1)),a.rank>2)throw new Ob(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${a.rank}.`);let i,o=["multiHot","oneHot"].includes(t),l=a;if(i=su(l,void 0!==r&&"count"===t?r:[],n,o),"tfIdf"!==t)return i;if(r)return qo(i,r);throw new Ob("When outputMode is 'tfIdf', weights must be provided.")}(e,this.outputMode,this.numTokens,n)}))}};

2HS.className="CategoryEncoding",Qc.registerClass(HS);var jS=new Set(["bilinear","nearest"]),qS=class extends $w{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation){if(!jS.has(e.interpolation))throw new Ob(`Invalid interpolation parameter: ${e.interpolation} is not implemented`);this.interpolation=e.interpolation}else this.interpolation="bilinear";this.cropToAspectRatio=!!e.cropToAspectRatio}computeOutputShape(e){let t=(e=xw(e))[2];return[this.height,this.width,t]}getConfig(){let e={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return Ci((()=>{let t=[this.height,this.width];if("bilinear"===this.interpolation)return Kc.resizeBilinear(e,t,!this.cropToAspectRatio);if("nearest"===this.interpolation)return Kc.resizeNearestNeighbor(e,t,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...jS]} are supported`)}))}};qS.className="Resizing",Qc.registerClass(qS);var KS=class{constructor(e){this.seed=e}next(){if(void 0!==this.seed)return this.seed++}};KS.className="RandomSeed";var XS=class extends $w{constructor(e){super(e),this.randomGenerator=new KS(e.seed)}getConfig(){let e={seed:this.randomGenerator.seed},t=super.getConfig();return Object.assign(e,t),e}};XS.className="BaseRandomLayer";var YS=new Set(["bilinear","nearest"]),ZS=class extends XS{constructor(e){super(e);let{factor:t,interpolation:n="bilinear"}=e;if(this.factor=t,Array.isArray(this.factor)&&2===this.factor.length)this.widthLower=this.factor[0],this.widthUpper=this.factor[1];else{if(Array.isArray(this.factor)||!(this.factor>0))throw new Ob(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`);this.widthLower=-this.factor,this.widthUpper=this.factor}if(this.widthLower<-1||this.widthUpper<-1)throw new Ob(`factor must have values larger than -1. Got: ${this.factor}`);if(this.widthUpper<this.widthLower)throw new Ob(`factor cannot have upper bound less than lower bound.\n        Got upper bound: ${this.widthUpper}.\n        Got lower bound: ${this.widthLower}\n      `);if(n){if(!YS.has(n))throw new Ob(`Invalid interpolation parameter: ${n} is not implemented`);this.interpolation=n}}getConfig(){let e={factor:this.factor,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){let t=(e=xw(e))[2];return[this.imgHeight,-1,t]}call(e,t){return Ci((()=>{let t=bw(e);this.imgHeight=t.shape[t.shape.length-3];let n=t.shape[t.shape.length-2];this.widthFactor=Tp([1],1+this.widthLower,1+this.widthUpper,"float32",this.randomGenerator.next());let r=this.widthFactor.dataSync()[0]*n;r=Math.round(r);let a=[this.imgHeight,r];switch(this.interpolation){case"bilinear":return Kc.resizeBilinear(e,a);case"nearest":return Kc.resizeNearestNeighbor(e,a);default:throw new Error(`Interpolation is ${this.interpolation}\n          but only ${[...YS]} are supported`)}}))}};function JS(e){return new Rw(e)}function QS(e){return new hI(e)}function eN(e){return new uI(e)}function tN(e){return new dI(e)}function nN(e){return new pI(e)}function rN(e){return new fI(e)}function aN(e){return new cI(e)}function sN(e){return new EI(e)}function iN(e){return new II(e)}function oN(e){return new NI(e)}function lN(e){return new SI(e)}function uN(e){return new TI(e)}function dN(e){return new CI(e)}function pN(e){return new $I(e)}function hN(e){return new AI(e)}function cN(e){return new RI(e)}function fN(e){return new JI(e)}function mN(e){return new YI(e)}function gN(e){return new KI(e)}function yN(e){return new XI(e)}function bN(e){return new ZI(e)}function xN(e){return new QI(e)}function wN(e){return new eS(e)}function vN(e){return new tS(e)}function kN(e){return new rS(e)}function IN(e){return new sS(e)}function SN(e){return new oS(e)}function NN(e){return new dS(e)}function TN(e){return new lS(e)}function _N(e){return new uS(e)}function CN(e){return new iS(e)}function EN(e){return new hS(e)}function $N(e){return new bS(e)}function AN(e){return new xS(e)}function RN(e){return new wS(e)}function FN(e){return new NS(e)}function DN(e){return FN(e)}function MN(e){return FN(e)}function ON(e){return new CS(e)}function LN(e){return ON(e)}function zN(e){return ON(e)}function PN(e){return new AS(e)}function BN(e){return PN(e)}function WN(e){return PN(e)}function VN(e){return new FS(e)}function UN(e){return new OS(e)}function GN(e){return new DS(e)}function HN(e){return new LS(e)}function jN(e){return new SS(e)}function qN(e){return new _S(e)}function KN(e){return new $S(e)}function XN(e){return new BI(e)}function YN(e){return new PI(e)}function ZN(e){return new VI(e)}function JN(e){return new WI(e)}function QN(e){return new zI(e)}function eT(e){return new LI(e)}function tT(e){return new qI(e)}function nT(e){return new jI(e)}function rT(e){return new MI(e)}function aT(e){return new UI(e)}function sT(e){return new BS(e)}function iT(e){return new PS(e)}

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e=0;e<this.size();e++)t.push(e);let n=this.readMany(t);return lC(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),Nl(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n}  vs. ${this.maxSize})`);this.writeMany(e,Ih(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,r=e.map((e=>(n+=e,n)));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let a=0===n?0:t.size/n,s=[];Ci((()=>{t=kl(t,[1,n,a]);for(let n=0;n<e.length;++n){let i=[0,0===n?0:r[n-1],0],o=[1,e[n],a];s[n]=kl(Cl(t,i,o),this.elementShape)}return s}));let i=[];for(let t=0;t<e.length;t++)i[t]=t;this.writeMany(i,s)}},cC=class e{get id(){return this.idTensor.id}constructor(e,t,n,r=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,null!=e&&e.forEach((e=>{if(n!==e.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${e.dtype}`);lC(t,e.shape,"TensorList shape mismatch: "),$i(e)})),this.idTensor=Du(0),this.maxNumElements=r,$i(this.idTensor)}copy(){return new e([...this.tensors],this.elementShape,this.elementDtype)}

2clearAndClose(e){this.tensors.forEach((t=>{(null==e||!e.has(t.id))&&t.dispose()})),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(-1!==n&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);lC(e,this.elementShape,"TensorList shape mismatch: ");let r=dC(this.elementShape,this.tensors,e);return Ci((()=>{let e=this.tensors.map((e=>kl(e,r)));return ah(e,0)}))}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(0===this.size())throw new Error("Trying to pop from an empty list.");let n=dC(this.elementShape,this.tensors,e),r=this.tensors.pop();return r.kept=!1,lC(r.shape,e,"TensorList shape mismatch: "),kl(r,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(lC(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");$i(e),this.tensors.push(e)}resize(t){if(t<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${t}`);if(-1!==this.maxNumElements&&t>this.maxNumElements)throw new Error(`TensorListResize input size ${t} is greater maxNumElement ${this.maxNumElements}.`);let n=new e([],this.elementShape,this.elementDtype,this.maxNumElements);n.tensors.length=t;for(let e=0;e<Math.min(this.tensors.length,t);++e)n.tensors[e]=this.tensors[e];return n}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(null==this.tensors[e])throw new Error(`element at index ${e} is null.`);lC(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=dC(this.elementShape,this.tensors,t);return kl(this.tensors[e],r)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||-1!==this.maxNumElements&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);lC(this.elementShape,t.shape,"TensorList shape mismatch: "),$i(t),null!=this.tensors[e]&&(this.tensors[e].kept=!1),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);lC(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let r=dC(this.elementShape,this.tensors,n);return 0===e.length?yi([],[0].concat(r)):Ci((()=>{let t=e.map((e=>kl(this.tensors[e],r)));return ah(t,0)}))}concat(e,t){if(e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);lC(this.elementShape,t,"TensorList shape mismatch: ");let n=dC(this.elementShape,this.tensors,t);return 0===this.size()?yi([],[0].concat(n)):Ci((()=>{let e=this.tensors.map((e=>kl(e,n)));return Nl(e,0)}))}};var fC=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=QT("thenBranch",e,t,n),a=QT("elseBranch",e,t,n),s=QT("cond",e,t,n),i=QT("args",e,t,n);return(await s.data())[0]?n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let r=QT("body",e,t,n),a=QT("cond",e,t,n),s=QT("args",e,t,n),i=await n.functionMap[a].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map((e=>e.id)),l=await i[0].data();i.forEach((e=>{!e.kept&&-1===o.indexOf(e.id)&&e.dispose()}));let u=s;for(;l[0];){let e=u;u=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let t=u.map((e=>e.id));e.forEach((e=>{!e.kept&&-1===o.indexOf(e.id)&&-1===t.indexOf(e.id)&&e.dispose()}));let s=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);

2l=await s[0].data(),s.forEach((e=>{!e.kept&&-1===o.indexOf(e.id)&&-1===t.indexOf(e.id)&&e.dispose()}))}return u}case"LoopCond":return[i_(QT("pred",e,t,n))];case"Switch":{let r=QT("pred",e,t,n),a=QT("data",e,t,n);return a.kept||(a=i_(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let r=e.inputNames.find((e=>void 0!==e_(e,t,n)));if(r){return[i_(e_(r,t,n))]}return}case"Enter":{let r=QT("frameName",e,t,n),a=QT("tensor",e,t,n);return n.enterFrame(r),[i_(a)]}case"Exit":{let r=QT("tensor",e,t,n);return n.exitFrame(),[i_(r)]}case"NextIteration":{let r=QT("tensor",e,t,n);return n.nextIteration(),[i_(r)]}case"TensorArrayV3":{let r=QT("size",e,t,n),a=QT("dtype",e,t,n),s=QT("elementShape",e,t,n),i=QT("dynamicSize",e,t,n),o=QT("clearAfterRead",e,t,n),l=QT("identicalElementShapes",e,t,n),u=QT("name",e,t,n),d=new hC(u,a,r,s,l,i,o);return n.addTensorArray(d),[d.idTensor,Du(1)]}case"TensorArrayWriteV3":{let r=QT("tensorArrayId",e,t,n),a=QT("index",e,t,n),s=QT("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=QT("tensorArrayId",e,t,n),a=QT("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=QT("tensorArrayId",e,t,n),a=QT("indices",e,t,n),s=QT("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=QT("tensorArrayId",e,t,n),a=QT("indices",e,t,n),s=QT("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=QT("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=QT("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=QT("tensorArrayId",e,t,n),a=QT("tensor",e,t,n),s=QT("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=QT("tensorArrayId",e,t,n);return[Du(n.getTensorArray(r.id).size(),"int32")]}case"TensorArrayCloseV3":{let r=QT("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=QT("tensorListId",e,t,n),a=QT("index",e,t,n),s=QT("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=QT("tensorListId",e,t,n),a=QT("index",e,t,n),s=QT("elementShape",e,t,n),i=QT("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=QT("indices",e,t,n),a=function(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let a=Math.max(...t);if(null!=r&&-1!==r&&a>=r)throw new Error(`Max index must be < array size (${a}  vs. ${r})`);let s=new cC([],n,e.dtype,r),i=Ih(e,0);return t.forEach(((e,t)=>{s.setItem(e,i[t])})),s}(QT("tensor",e,t,n),r,QT("elementShape",e,t,n),QT("numElements",e,t,n));return n.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r,a=QT("elementShape",e,t,n),s=QT("elementDType",e,t,n);r="TensorListReserve"===e.op?"numElements":"maxNumElements";let i=QT(r,e,t,n),o=function(e,t,n,r){return new cC([],e,t,r)}(a,s,0,"TensorListReserve"===e.op?-1:i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=QT("tensorListId",e,t,n),a=QT("indices",e,t,n),s=QT("elementShape",e,t,n),i=QT("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=QT("tensorListId",e,t,n),a=QT("elementShape",e,t,n),s=QT("elementDType",e,t,n),i=QT("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=function(e,t,n){let r=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);lC(e.shape.slice(1),t,"TensorList shape mismatch: ");let a=Ih(e);return new cC(a,t,r)}(QT("tensor",e,t,n),QT("elementShape",e,t,n),QT("elementDType",e,t,n));return n.addTensorList(r),[r.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let r=QT("tensorListId",e,t,n),a=n.getTensorList(r.id),s=QT("dtype",e,t,n),i=QT("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=QT("tensorListId",e,t,n),a=QT("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let r=QT("tensorListId",e,t,n),a=QT("elementShape",e,t,n),s=QT("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(a,s)]}case"TensorListSplit":{let r=QT("tensor",e,t,n),a=QT("elementShape",e,t,n),s=function(e,t,n){let r=0,a=t.map((e=>(r+=e,r)));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${r}, and tensor's shape is: ${e.shape}`);let s=pC(e.shape.slice(1),n),i=0===r?0:e.size/r,o=Ci((()=>{let n=[];e=kl(e,[1,r,i]);for(let r=0;r<t.length;++r){let o=[0,0===r?0:a[r-1],0],l=[1,t[r],i];n[r]=kl(Cl(e,o,l),s)}return e.dispose(),n})),l=new cC([],n,e.dtype,t.length);for(let e=0;e<o.length;e++)l.setItem(e,o[e]);return l}(r,QT("lengths",e,t,n),a);return n.addTensorList(s),[s.idTensor]}case"TensorListLength":{let r=QT("tensorListId",e,t,n);return[Du(n.getTensorList(r.id).size(),"int32")]}case"TensorListResize":{let r=QT("tensorListId",e,t,n),a=QT("size",e,t,n),s=n.getTensorList(r.id).resize(a);return n.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function mC(e,t,n){let[r,a]=QT("fusedOps",e,t,n),s="biasadd"===r,i=!s,o="prelu"===a,l="fusedbatchnorm"===r,u=QT("numArgs",e,t,n);if(s){if(o&&2!==u)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&1!==u)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let d=QT("strides",e,t,n),p=s_(e,t,n),h=QT("dataFormat",e,t,n).toUpperCase(),c=QT("dilations",e,t,n),[f,m]=QT("args",e,t,n);return i&&(m=f,f=void 0),{stride:d,pad:p,dataFormat:h,dilations:c,biasArg:f,preluArg:m,activationFunc:a,leakyreluAlpha:QT("leakyreluAlpha",e,t,n)}}

2function gC(e,t,n){return{boxes:QT("boxes",e,t,n),scores:QT("scores",e,t,n),maxOutputSize:QT("maxOutputSize",e,t,n),iouThreshold:QT("iouThreshold",e,t,n),scoreThreshold:QT("scoreThreshold",e,t,n),softNmsSigma:QT("softNmsSigma",e,t,n)}}var yC=class{get id(){return this.handle.id}constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Du(0),this.tensorMap=new Map,$i(this.handle)}clearAndClose(){this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Du(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),Ci((()=>{let e=Ih(t),r=n.length,a=e.length;ja.assert(r===a,(()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`));for(let t=0;t<r;t++){let r=n[t],a=e[t];$i(a),this.tensorMap.set(r,a)}return this.handle}))}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return Ci((()=>{let e=[];for(let r=0;r<n.length;r++){let a=n[r],s=this.findWithDefault(a,t);e.push(s)}return ah(e)}))}findWithDefault(e,t){let n=this.tensorMap.get(e);return null!=n?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}};function bC(e,t,n,r,a=Ci){let s=((e,t,n)=>{switch(e.category){case"arithmetic":return a((()=>((e,t,n,r=oC)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[r.add(QT("a",e,t,n),QT("b",e,t,n))];case"AddN":return[r.addN(QT("tensors",e,t,n))];case"FloorMod":case"Mod":return[r.mod(QT("a",e,t,n),QT("b",e,t,n))];case"Mul":return[r.mul(QT("a",e,t,n),QT("b",e,t,n))];case"RealDiv":case"Div":return[r.div(QT("a",e,t,n),QT("b",e,t,n))];case"DivNoNan":return[r.divNoNan(QT("a",e,t,n),QT("b",e,t,n))];case"FloorDiv":return[r.floorDiv(QT("a",e,t,n),QT("b",e,t,n))];case"Sub":return[r.sub(QT("a",e,t,n),QT("b",e,t,n))];case"Minimum":return[r.minimum(QT("a",e,t,n),QT("b",e,t,n))];case"Maximum":return[r.maximum(QT("a",e,t,n),QT("b",e,t,n))];case"Pow":return[r.pow(QT("a",e,t,n),QT("b",e,t,n))];case"SquaredDifference":return[r.squaredDifference(QT("a",e,t,n),QT("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"basic_math":return a((()=>((e,t,n,r=oC)=>{switch(e.op){case"Abs":case"ComplexAbs":return[r.abs(QT("x",e,t,n))];case"Acos":return[r.acos(QT("x",e,t,n))];case"Acosh":return[r.acosh(QT("x",e,t,n))];case"Asin":return[r.asin(QT("x",e,t,n))];case"Asinh":return[r.asinh(QT("x",e,t,n))];case"Atan":return[r.atan(QT("x",e,t,n))];case"Atan2":return[r.atan2(QT("x",e,t,n),QT("y",e,t,n))];case"Atanh":return[r.atanh(QT("x",e,t,n))];case"Ceil":return[r.ceil(QT("x",e,t,n))];case"Complex":return[r.complex(QT("real",e,t,n),QT("imag",e,t,n))];case"Cos":return[r.cos(QT("x",e,t,n))];case"Cosh":return[r.cosh(QT("x",e,t,n))];case"Elu":return[r.elu(QT("x",e,t,n))];case"Erf":return[r.erf(QT("x",e,t,n))];case"Exp":return[r.exp(QT("x",e,t,n))];case"Expm1":return[r.expm1(QT("x",e,t,n))];case"Floor":return[r.floor(QT("x",e,t,n))];case"Log":return[r.log(QT("x",e,t,n))];case"Log1p":return[r.log1p(QT("x",e,t,n))];case"Imag":return[r.imag(QT("x",e,t,n))];case"Neg":return[r.neg(QT("x",e,t,n))];case"Reciprocal":return[r.reciprocal(QT("x",e,t,n))];case"Real":return[r.real(QT("x",e,t,n))];case"Relu":return[r.relu(QT("x",e,t,n))];case"Round":return[r.round(QT("x",e,t,n))];case"Selu":return[r.selu(QT("x",e,t,n))];case"Sigmoid":return[r.sigmoid(QT("x",e,t,n))];case"Sin":return[r.sin(QT("x",e,t,n))];case"Sign":return[r.sign(QT("x",e,t,n))];case"Sinh":return[r.sinh(QT("x",e,t,n))];case"Softplus":return[r.softplus(QT("x",e,t,n))];case"Sqrt":return[r.sqrt(QT("x",e,t,n))];case"Square":return[r.square(QT("x",e,t,n))];case"Tanh":return[r.tanh(QT("x",e,t,n))];case"Tan":return[r.tan(QT("x",e,t,n))];case"ClipByValue":return[r.clipByValue(QT("x",e,t,n),QT("clipValueMin",e,t,n),QT("clipValueMax",e,t,n))];case"Relu6":return[r.relu6(QT("x",e,t,n))];case"Rsqrt":return[r.rsqrt(e_(e.inputNames[0],t,n))];case"LeakyRelu":return[r.leakyRelu(QT("x",e,t,n),QT("alpha",e,t,n))];case"Prelu":return[r.prelu(QT("x",e,t,n),QT("alpha",e,t,n))];case"IsNan":return[r.isNaN(e_(e.inputNames[0],t,n))];case"IsInf":return[r.isInf(e_(e.inputNames[0],t,n))];case"IsFinite":return[r.isFinite(e_(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));

2case"control":return fC(e,t,n);case"convolution":return a((()=>((e,t,n,r=oC)=>{switch(e.op){case"Conv1D":{let a=QT("stride",e,t,n),s=QT("pad",e,t,n),i=QT("dataFormat",e,t,n).toUpperCase(),o=QT("dilation",e,t,n);return[r.conv1d(QT("x",e,t,n),QT("filter",e,t,n),a,s,i,o)]}case"Conv2D":{let a=QT("strides",e,t,n),s=s_(e,t,n),i=QT("dataFormat",e,t,n).toUpperCase(),o=QT("dilations",e,t,n);return[r.conv2d(QT("x",e,t,n),QT("filter",e,t,n),[a[1],a[2]],s,i,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:a,pad:s,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:d,leakyreluAlpha:p}=mC(e,t,n);return[r.fused.conv2d({x:QT("x",e,t,n),filter:QT("filter",e,t,n),strides:[a[1],a[2]],pad:s,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:d,preluActivationWeights:u,leakyreluAlpha:p})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:s,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:d,leakyreluAlpha:p}=mC(e,t,n);return[r.fused.depthwiseConv2d({x:QT("x",e,t,n),filter:QT("filter",e,t,n),strides:[a[1],a[2]],pad:s,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:d,preluActivationWeights:u,leakyreluAlpha:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"normalization":return a((()=>((e,t,n,r=oC)=>{switch(e.op){case"EuclideanNorm":return[r.euclideanNorm(QT("x",e,t,n),QT("axis",e,t,n),QT("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[r.batchNorm(QT("x",e,t,n),QT("mean",e,t,n),QT("variance",e,t,n),QT("offset",e,t,n),QT("scale",e,t,n),QT("epsilon",e,t,n))];case"LRN":return[r.localResponseNormalization(QT("x",e,t,n),QT("radius",e,t,n),QT("bias",e,t,n),QT("alpha",e,t,n),QT("beta",e,t,n))];case"Softmax":return[r.softmax(QT("x",e,t,n))];case"LogSoftmax":return[r.logSoftmax(QT("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"ragged":return a((()=>((e,t,n,r=oC)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:a,outputDenseValues:s}=r.raggedGather(QT("paramsNestedSplits",e,t,n),QT("paramsDenseValues",e,t,n),QT("indices",e,t,n),QT("outputRaggedRank",e,t,n));return 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a((()=>((e,t,n,r=oC)=>{switch(e.op){case"FFT":return[r.fft(QT("x",e,t,n))];case"IFFT":return[r.ifft(QT("x",e,t,n))];case"RFFT":return[r.rfft(QT("x",e,t,n))];case"IRFFT":return[r.irfft(QT("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"string":return a((()=>((e,t,n,r=oC)=>{switch(e.op){case"StaticRegexReplace":return[r.string.staticRegexReplace(QT("input",e,t,n),QT("pattern",e,t,n),QT("rewrite",e,t,n),QT("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:a,nGramsSplits:s}=r.string.stringNGrams(QT("data",e,t,n),QT("dataSplits",e,t,n),QT("separator",e,t,n),QT("nGramWidths",e,t,n),QT("leftPad",e,t,n),QT("rightPad",e,t,n),QT("padWidth",e,t,n),QT("preserveShortSequences",e,t,n));return[a,s]}case"StringSplit":{let{indices:a,values:s,shape:i}=r.string.stringSplit(QT("input",e,t,n),QT("delimiter",e,t,n),QT("skipEmpty",e,t,n));return[a,s,i]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(QT("input",e,t,n),QT("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"transformation":return a((()=>((e,t,n,r=oC)=>{switch(e.op){case"Cast":return[r.cast(QT("x",e,t,n),QT("dtype",e,t,n))];case"ExpandDims":{let a=QT("axis",e,t,n);return[r.expandDims(QT("x",e,t,n),a)]}case"Squeeze":{let a=QT("axis",e,t,n);return[r.squeeze(QT("x",e,t,n),a)]}case"Reshape":return[r.reshape(QT("x",e,t,n),QT("shape",e,t,n))];case"EnsureShape":return[r.ensureShape(QT("x",e,t,n),QT("shape",e,t,n))];case"MirrorPad":return[r.mirrorPad(QT("x",e,t,n),QT("padding",e,t,n),QT("mode",e,t,n))];case"PadV2":case"Pad":return[r.pad(QT("x",e,t,n),QT("padding",e,t,n),QT("constantValue",e,t,n))];case"SpaceToBatchND":{let a=QT("blockShape",e,t,n),s=QT("paddings",e,t,n);return[r.spaceToBatchND(QT("x",e,t,n),a,s)]}case"BatchToSpaceND":{let a=QT("blockShape",e,t,n),s=QT("crops",e,t,n);return[r.batchToSpaceND(QT("x",e,t,n),a,s)]}case"DepthToSpace":{let a=QT("blockSize",e,t,n),s=QT("dataFormat",e,t,n).toUpperCase();return[r.depthToSpace(QT("x",e,t,n),a,s)]}case"BroadcastTo":return[r.broadcastTo(QT("x",e,t,n),QT("shape",e,t,n))];case"BroadcastArgs":return[r.broadcastArgs(QT("s0",e,t,n),QT("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"hash_table":return(async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=r.getHashTableHandleByName(e.name);if(null!=a)return[a];{let a=QT("keyDType",e,t,n),s=QT("valueDType",e,t,n),i=new yC(a,s);return r.addHashTable(e.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let a=QT("tableHandle",e,t,n,r),s=QT("keys",e,t,n),i=QT("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=QT("tableHandle",e,t,n,r),s=QT("keys",e,t,n),i=QT("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let a=QT("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n,r);

2case"custom":let s=ZT(e.op);if(s&&s.customExecutor)return s.customExecutor(new iC(e,t,n));throw TypeError(`Custom op ${e.op} is not registered.`);default:throw TypeError(`Unknown op '${e.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 ja.isPromise(s)?s.then((e=>[].concat(e))):[].concat(s)}var xC=class{constructor(e={},t={},n={},r={},a){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.parseNodeNameCache=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((e=>0===e.id&&0===e.iterationId?"":`${e.frameName}-${e.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))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()}nextIteration(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");{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))}}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 wC(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map((e=>a_(e)[0])));r=r||[];let d=new Set(r.map((e=>a_(e.name)[0]))),p=[...t];for(;p.length>0;){let e=p.pop();if((TC(e)||_C(e)||CC(e))&&null==i&&(i=e,o=i.children.map((e=>e.name)).filter((e=>a.has(e)))),a.add(e.name),null==n[e.name]&&!u.has(e.name)&&!d.has(e.name)){if(0===e.inputs.length){s.push(e.name);continue}e.inputs.forEach((e=>{l.has(e.name)||(l.add(e.name),p.push(e))}))}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function vC(e,t){let{usedNodes:n,inputs:r}=t,a=Object.keys(r).map((e=>a_(e)[0])).map((t=>e.nodes[t])),s=e.initNodes||[],i=e=>n.has("string"==typeof e?e:e.name);function o(e){return[...new Map(e.map((e=>[e.name,e]))).values()]}let l=o([...a,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),d=new Map(u.map((e=>[e.name,e]))),p={};for(let e of u){p[e.name]=p[e.name]||0;for(let t of e.children)i(t)||(p[t.name]=Number.POSITIVE_INFINITY),p[t.name]=(p[t.name]||0)+1}let h=Object.entries(p).filter((([,e])=>0===e)).map((([e])=>e)),c=[...h];for(;h.length>0;){let e=h.pop(),t=d.get(e);for(let e of t.children.filter(i))0==--p[e.name]&&(c.push(e.name),h.push(e.name))}let f=function(e,t){let n=new Map(e.map((e=>[e.name,e]))),r=t.map((e=>e.name)),a=new Set(r);for(;r.length>0;){let e=r.pop(),t=n.get(e);for(let e of t.children)!n.has(e.name)||a.has(e.name)||(a.add(e.name),r.push(e.name))}return e.filter((e=>a.has(e.name)))}(c.map((e=>d.get(e))),l);return function(e,t){let n=new Map(e.map(((e,t)=>[e.name,t]))),r=new Set(t.map((e=>e.name))),a=e=>r.has("string"==typeof e?e:e.name),s=new Set(e.map((e=>e.name))),i=e=>s.has("string"==typeof e?e:e.name);for(let t of e){for(let e of t.children.filter(i)){if(!n.has(e.name))throw new kC(`Child ${e.name} of node ${t.name} is unreachable.`);if(n.get(t.name)>n.get(e.name))throw new kC(`Node ${t.name} is scheduled to run after its child ${e.name}.`)}

2if(!a(t))for(let e of t.inputs){if(!n.has(e.name))throw new kC(`Input ${e.name} of node ${t.name} is unreachable.`);if(n.get(e.name)>n.get(t.name))throw new kC(`Node ${t.name} is scheduled to run before its input ${e.name}.`)}}}(f,l),f}var kC=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};var IC=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),SC=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),NC=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function TC(e){return IC.has(e.op)}function _C(e){return SC.has(e.op)}function CC(e){return NC.has(e.op)}var EC=class e{get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map((t=>e[t].map((e=>e.id))));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get outputs(){return this._outputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get inputNodes(){return this._inputs.map((e=>e.signatureKey||e.name))}get outputNodes(){return this._outputs.map((e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t}))}get functions(){return Object.keys(this._functions).reduce(((e,t)=>(e[t]=this._functions[t].signature,e)),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,null!=t.functions&&Object.keys(t.functions).forEach((n=>{this._functionExecutorMap[n]=new e(t.functions[n],this)}))}getCompilationKey(e,t){let n=e.map((e=>e.name)).sort(),r=t.map((e=>e.name)).sort();return n.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(e,t){let n=wC(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(null!=a)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let n=t.map((e=>e.name)),a=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${a}]. Missing the following inputs: [${r}]`)}let i=vC(this.graph,n),o=function(e){let t=new Map(e.map(((e,t)=>[e.name,t]))),n=Number.MAX_SAFE_INTEGER,r=e.map(((e,t)=>TC(e)?n:t)),a=e=>{let n=r[t.get(e.name)];return null==n?-1:n},s=e.map(((e,t)=>e.children.map(a).reduce(((e,t)=>Math.max(e,t)),r[t]))),i=new Map;for(let t=0;t<e.length;++t){let r=s[t];if(r===n)continue;let a=e[t],o=e[r];i.has(o.name)||i.set(o.name,[]),i.get(o.name).push(a)}return i}(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(null==e)return null;let t=e.clone();return $i(t),t}cloneTensorList(e){return e?e.map((e=>this.cloneAndKeepTensor(e))):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map((([e,t])=>[e,this.cloneTensorList(t)])))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map((e=>this.graph.nodes[a_(e)[0]])),a=t.map((e=>a_(e)[0])),s=new Set(a),i=a.map((e=>this.graph.nodes[e]));0===i.length&&(i=this._outputs);let o=this.getCompilationKey(r,i),l=this.compiledMap.get(o);null==l&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=ut().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){this.keepIntermediateTensors=!1,console.warn(e.message)}let u={},d={};return Ci((()=>{let n=new xC(this.weightMap,u,d,this.functionExecutorMap,this.parseNodeNameCache),r=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach((t=>{let[a,s]=a_(t,n),i=[];i[s]=e[t],r[a]=i,this.keepIntermediateTensors&&(this.clonedTensorsMap[a]=this.cloneTensorList(i))}));let a=this.getFrozenTensorIds(r),{orderedNodes:i,nodeLiveUntilMap:o}=l;for(let e of i){if(r[e.name])continue;let t=bC(e,r,n,this._resourceManager);if(ja.isPromise(t))throw new Error(`The execution of the op '${e.op}' returned a promise. Please use model.executeAsync() instead.`);r[e.name]=t,this.keepIntermediateTensors&&(this.clonedTensorsMap[e.name]=this.cloneTensorList(t)),this.checkTensorForDisposalWithNodeLiveUntilInfo(e,r,n,a,s,o.get(e.name))}return null==this.parent&&n.dispose(a),t.map((e=>e_(e,r,n)))}

2))}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map((t=>e[t])).map((e=>e.map((e=>e.id)))));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){if(!TC(t)&&!s.has(e)){for(let r of n[e])null!=r&&(i[r.id]=(i[r.id]||0)+t.children.length);for(let e of t.inputs){if(TC(e))continue;let t=t_(e.name,n,r);if(null!=t)for(let e of t){if(!e||e.kept||a.has(e.id))continue;let t=i[e.id];1===t?(e.dispose(),delete i[e.id]):null!=t&&i[e.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,n,r,a,s){function i(e){return TC(e)||a.has(e.name)}if(!TC(e)&&null!=s)for(let e of s){if(i(e))continue;let a=t_(e.name,t,n);for(let e of a)!e||e.kept||r.has(e.id)||e.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach((e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()})),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,r={},a={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=ut().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){this.keepIntermediateTensors=!1,console.warn(e.message)}let s=new xC(this.weightMap,r,a,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,n),o=t.map((e=>e_(e,i,s))),l=o.map((e=>e.id)),u=Object.keys(e).map((t=>e[t].id)),d=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach((e=>{e.forEach((e=>{e&&!e.isDisposed&&!d.has(e.id)&&e.dispose()}))})),null==this.parent&&s.dispose(d),o}async executeFunctionAsync(e,t,n){let r=e.reduce(((e,t,n)=>(e[this.inputs[n].name]=t,e)),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map((e=>this.graph.nodes[a_(e)[0]])),i=n.map((e=>a_(e)[0])),o=new Set(i),l=i.map((e=>this.graph.nodes[e]));0===l.length&&(l=this._outputs);let{usedNodes:u,missingInputs:d,dynamicNode:p,syncInputs:h}=wC(e,l,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),f=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{let[n,r]=a_(t),a=[];a[r]=e[t],f[n]=a}));let m={},g=this.getFrozenTensorIds(f),y={};for(;c.length>0;){let e=this.processStack(s,c,t,f,y,g,o,m,u);await Promise.all(e)}null==p&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=l.filter((e=>!TC(e)&&!e_(e.name,f,t))).map((e=>e.name));if(b.length>0){let e="";throw null!=p&&(e=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${a}]. Consider providing the following inputs: [${d}]. ${e}`)}return f}processStack(e,t,n,r,a,s,i,o,l){let u=[];for(;t.length>0;){let e=t.pop();n.currentContext=e.contexts;let d="";if("Enter"===e.node.op&&QT("isConstant",e.node,r,n)&&([d]=n_(e.node.name,n)),null==r[e.node.name]){let p=bC(e.node,r,n,this._resourceManager);d||([d]=n_(e.node.name,n));let h=n.currentContext;ja.isPromise(p)?u.push(p.then((u=>(r[d]=u,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(u)),n.currentContext=h,this.checkTensorForDisposal(d,e.node,r,n,s,i,o),this.processChildNodes(e.node,t,n,r,a,l),u)))):(r[d]=p,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(p)),this.checkTensorForDisposal(d,e.node,r,n,s,i,o),this.processChildNodes(e.node,t,n,r,a,l))}else this.processChildNodes(e.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach((e=>{let[i]=n_(e.name,n);a[i]||!s.has(e.name)||("Merge"===e.op?e.inputNames.some((e=>!!e_(e,r,n)))&&(a[i]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!e_(e,r,n)))&&(a[i]=!0,t.push({contexts:n.currentContext,node:e})))}))}

2dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((t=>{let n=e[t],[r]=a_(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let e=a.attrParams.shape.value,t=e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t));ja.assert(t,(()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`))}a.attrParams.dtype&&a.attrParams.dtype.value&&ja.assert(n.dtype===a.attrParams.dtype.value,(()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`))}))}mapInputs(e){var t,n;let r={};for(let a in e){let s=null===(n=null===(t=this._signature)||void 0===t?void 0:t.inputs)||void 0===n?void 0:n[a];null!=s?r[s.name]=e[a]:r[a]=e[a]}return r}checkInputs(e){let t=Object.keys(e).filter((e=>{let[t]=a_(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>{var t,n;let r=null===(n=null===(t=this._signature)||void 0===t?void 0:t.outputs)||void 0===n?void 0:n[e];return null!=r?r.name:e}),{})}checkOutputs(e){e.forEach((e=>{let[t]=a_(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}},$C=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]}},AC="?tfjs-format=file",RC="model.json",FC=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=gf){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,null==t&&(this.loadOptions={}),this.resourceManager=new $C}findIOHandler(){let e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return ja.isPromise(e)?e.then((e=>null==e.getWeightStream?this.loadSync(e):this.loadStreaming(e))):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(null==e.getWeightStream)throw new Error("Model artifacts missing streamWeights function");let t=await Ki(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let n=this.artifacts.modelTopology,r=this.artifacts.signature;if(null!=this.artifacts.userDefinedMetadata){let e=this.artifacts.userDefinedMetadata;null!=e.signature&&(r=e.signature),null!=e.structuredOutputKeys&&(this.structuredOutputKeys=e.structuredOutputKeys)}if(this.signature=r,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new EC(H_.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){let t=H_.Instance.transformGraph(e.modelInitializer);this.initializer=new EC(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if("string"==typeof e){let t=this.io.getSaveHandlers(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Error(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t={};return(e instanceof $s?[e]:e).forEach(((e,n)=>t[this.structuredOutputKeys[n]]=e)),t}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof $s||Array.isArray(e))){let n=null===(t=this.signature)||void 0===t?void 0:t.inputs;if(null!=n)for(let t in n){let r=n[t];null!=r.resourceId&&(e[t]=this.resourceIdToCapturedInput[r.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let r=0;return this.inputNodes.reduce(((t,n)=>{var a,s,i;let o=null===(i=null===(s=null===(a=this.signature)||void 0===a?void 0:a.inputs)||void 0===s?void 0:s[n])||void 0===i?void 0:i.resourceId;return t[n]=null!=o?this.resourceIdToCapturedInput[o]:e[r++],t}),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let r=0;r<n.length;r++){let a=t[n[r]];this.resourceIdToCapturedInput[a.resourceId]=e[r]}}}execute(e,t){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ei(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function DC(e,t={},n=gf){if(null==e)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");null==t&&(t={}),t.fromTFHub&&"string"==typeof e&&(e=function(e){return e.endsWith("/")||(e+="/"),`${e}${RC}${AC}`}(e));let r=new FC(e,t,n);return await r.load(),r}function MC(e){if(null==e)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[n,r]=e;if(!n)throw new Error("modelJSON must be the first element of the array");if(!(r&&r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");

2if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");let a=gf.getWeightSpecs(n.weightsManifest),s=gf.getModelArtifactsForJSONSync(n,a,r);t=gf.fromMemorySync(s)}else if("load"in e)t=e;else{if(!("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e))throw new Error("Unknown model format");t=gf.fromMemorySync(e)}let n=new FC(t);return n.load(),n}var OC="4.21.0",LC={};B(LC,{CSVDataset:()=>EE,Dataset:()=>gE,FileDataSource:()=>VE,TextLineDataset:()=>kE,URLDataSource:()=>UE,array:()=>bE,csv:()=>GE,func:()=>HE,generator:()=>jE,microphone:()=>KE,version_data:()=>XE,webcam:()=>qE,zip:()=>xE});var zC=W(Q()),PC=W(Q());function BC(e,t,n=new Map,r=new Set){if(null==e)return null;if("function"==typeof Blob&&e instanceof Blob)return e.slice();if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse){if(HC(e)){let a=Array.isArray(e)?[]:{};r.add(e);for(let s in e){let i=BC(e[s],t,n,r);a[s]=i}return r.delete(e),e.__proto__&&(a.__proto__=e.__proto__),a}throw new Error(`Can't recurse into non-iterable type: ${e}`)}return n.set(e,a.value),a.value}function WC(e,t=UC){return VC(e,t)}function VC(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse){if(HC(r)){let a=Array.isArray(r)?[]:{};n.add(r);for(let s in r){let r=VC(e.map((e=>e[s])),t,n);a[s]=r}return n.delete(r),a}throw new Error(`Can't recurse into non-iterable type: ${r}`)}return a.value}function UC(e){return null===e?null:HC(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function GC(e,t){let n=new Map;BC(e,t,n);for(let e of Array.from(n.keys())){let t=n.get(e);if(ja.isPromise(t)){let r=await t;n.set(e,r)}}return BC(e,t,n)}function HC(e){let t=!1;if(ut().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=ee();t=e instanceof n}return null!=e&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||"object"==typeof e&&!(e instanceof $s)&&!(e instanceof Promise)&&!t)}function jC(e){return function(e,t){return BC(e,t)}(e,qC)}function qC(e){return e instanceof $s?{value:e.clone(),recurse:!1}:HC(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var KC=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,null==e)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return 0===this.length()}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},XC=class e extends KC{constructor(){super(e.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=2*this.capacity,t=new Array(e),n=this.length();for(let e=0;e<n;e++)t[e]=this.get(this.wrap(this.begin+e));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};function YC(e){return new eE(e)}function ZC(e){return new tE(e)}XC.INITIAL_CAPACITY=32;var JC,QC=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new lE(this,e)}filter(e){return new iE(this,e)}map(e){return new oE(this,e)}mapAsync(e){return new uE(this,e)}serialMapAsync(e){return new uE(this,e).serial()}flatmap(e){return new pE(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile((e=>!0===e))}rowMajorBatch(e,t=!0){return new sE(this,e,t)}columnMajorBatch(e,t=!0,n=UC){return this.rowMajorBatch(e,t).map((e=>WC(e,n)))}concatenate(e,t){return new hE(YC([this,e]),t)}take(e){return e<0||null==e?this:new aE(this,e)}skip(e){return e<0||null==e?this:new rE(this,e)}prefetch(e){return new fE(this,e)}shuffle(e,t){return new mE(this,e,t)}serial(){return new nE(this)}},eE=class extends QC{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:jC(e),done:!1}}},tE=class extends QC{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},nE=class extends QC{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){return this.upstream.next()}},rE=class extends QC{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ei(e.value)}return this.upstream.next()}},aE=class extends QC{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},sE=class extends QC{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},iE=class extends QC{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ei(e.value)}}},oE=class extends QC{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ps.getTensorsInContainer(e.value),n=this.transform(e.value),r=Ps.getTensorsInContainer(n);for(let e of t)Ps.isTensorInList(e,r)||e.dispose();return{value:n,done:!1}}},lE=class extends QC{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},uE=class extends QC{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ps.getTensorsInContainer(e.value),n=await this.transform(e.value),r=Ps.getTensorsInContainer(n);for(let e of t)Ps.isTensorInList(e,r)||e.dispose();return{value:n,done:!1}}},dE=class extends QC{constructor(){super(),this.outputQueue=new XC,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;0===this.outputQueue.length();)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},pE=class extends dE{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ps.getTensorsInContainer(e.value),n=this.transform(e.value),r=Ps.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let e of t)Ps.isTensorInList(e,r)||e.dispose();return!0}},hE=class extends QC{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,null==this.iterator){let e=await this.moreIterators.next();if(e.done)return{value:null,done:!0};this.iterator=e.value,null!=this.baseErrorHandler&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}};

2!function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"}(JC||(JC={}));var cE=class extends QC{constructor(e,t=JC.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;let r=await GC(this.iterators,(function(e){return e instanceof QC?{value:e.next().then((e=>(t++,e.done&&n++,e.value))),recurse:!1}:{value:null,recurse:!0}}));if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case JC.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case JC.SHORTEST:return{value:null,done:!0};case JC.LONGEST:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},fE=class extends QC{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new KC(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},mE=class extends fE{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=PC.alea(n||ja.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(!t.done)return this.refill(),t;this.upstreamExhausted=!0}return{value:null,done:!0}}},gE=class{constructor(){this.size=null}batch(e,t=!0){let n,r=this;return ja.assert(e>0,(()=>`batchSize needs to be positive, but it is\n      ${e}`)),n=this.size===1/0||null==this.size?this.size:t?Math.ceil(this.size/e):Math.floor(this.size/e),yE((async()=>(await r.iterator()).columnMajorBatch(e,t,wE)),n)}concatenate(e){let t,n=this;return t=this.size===1/0||e.size===1/0?1/0:null!=this.size&&null!=e.size?this.size+e.size:null,yE((async()=>(await n.iterator()).concatenate(await e.iterator())),t)}filter(e){let t,n=this;return t=this.size===1/0?1/0:null,yE((async()=>(await n.iterator()).filter((t=>Ci((()=>e(t)))))),t)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return yE((async()=>(await t.iterator()).map((t=>Ci((()=>e(t)))))),this.size)}mapAsync(e){let t=this;return yE((async()=>(await t.iterator()).mapAsync(e)),this.size)}prefetch(e){if(null==e)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return yE((async()=>(await t.iterator()).prefetch(e)),this.size)}repeat(e){let t,n=this;return t=null!=this.size&&e>0?this.size*e:0===e?0:null!=this.size&&(void 0===e||e<0)?1/0:null,yE((async()=>function(e,t){return new hE(e,t)}(ZC((async()=>({value:await n.iterator(),done:!1}))).take(e))),t)}skip(e){let t,n=this;return t=null!=this.size&&e>=0&&this.size>=e?this.size-e:null!=this.size&&(this.size<e||void 0===e||e<0)?0:null,yE((async()=>(await n.iterator()).skip(e)),t)}shuffle(e,t,n=!0){if(null==e||e<0)throw null==this.size?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=zC.alea(t||ja.now().toString());return yE((async()=>{let t=a.int32();return n&&(t+=a.int32()),(await r.iterator()).shuffle(e,t.toString())}),this.size)}take(e){let t,n=this;return t=null!=this.size&&this.size>e?e:null!=this.size&&this.size<=e?this.size:null,yE((async()=>(await n.iterator()).take(e)),t)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};function yE(e,t=null){return new class extends gE{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function bE(e){return yE((async()=>YC(e)),e.length)}function xE(e){if(!HC(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=null==t?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=null==t?e[n].size:Math.min(t,e[n].size);return yE((async()=>function(e,t=JC.FAIL){return new cE(e,t)}(await GC(e,(e=>{if(e instanceof gE)return{value:e.iterator(),recurse:!1};if(HC(e))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")})),JC.SHORTEST)),t)}function wE(e){if(null===e)return null;return function(e){return null==e||function(e){return null===e||"object"!=typeof e&&"function"!=typeof e}(e)||Array.isArray(e)||"object"==typeof e&&e instanceof $s||ja.isTypedArray(e)}(e[0])?{value:vE(e),recurse:!1}:{value:null,recurse:!0}}function vE(e){if(0===e.length)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof $s?ah(e):yi(e)}gE.MAX_BUFFER_SIZE=1e4;var kE=class extends gE{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split("\n").map((e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e)))}},IE='"',SE=Symbol("out"),NE=Symbol("field"),TE=Symbol("quote"),_E=Symbol("quoteafterquote"),CE=Symbol("quoteinquote"),EE=class extends gE{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&ja.assert(e.length===this.fullColumnNames.length,(()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+").")),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce(((e,t)=>(e[t]=e[t]+1||1,e)),{}),n=Object.keys(t).filter((e=>t[e]>1));if(ja.assert(0===n.length,(()=>"Duplicate column names found: "+n.toString())),this.columnConfigs)for(let e of Object.keys(this.columnConfigs))if(-1===this.fullColumnNames.indexOf(e))throw new Error('The key "'+e+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").");this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}return null}constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new kE(e),t||(t={}),this.hasHeader=!1!==t.hasHeader,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(ja.assert(null==t.delimiter,(()=>"Delimiter should not be provided when delimWhitespace is true.")),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map((e=>this.makeDataElement(e)))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!this.configuredColumnsOnly||i){let o=t[a],l=null;if(""===o)if(i&&void 0!==i.default)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let e=Number(o);if(isNaN(e))l=i&&"bool"===i.dtype?this.getBoolean(o):o;else if(i&&i.dtype)switch(i.dtype){case"float32":default:l=e;break;case"int32":l=Math.floor(e);break;case"bool":l=this.getBoolean(o)}else l=e}i&&i.isLabel?r[s]=l:n[s]=l}}return 0===Object.keys(r).length?n:{xs:n,ys:r}}getBoolean(e){return"1"===e||"true"===e.toLowerCase()?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=SE;for(let t=0;t<a;t++)switch(s){case SE:switch(e.charAt(t)){case IE:r=t+1,s=TE;break;case this.delimiter:if(r=t+1," "===this.delimiter&&this.delimWhitespace)break;n.push(""),s=SE;break;default:s=NE,r=t}break;case NE:if(e.charAt(t)===this.delimiter)n.push(e.substring(r,t)),s=SE,r=t+1;break;case TE:if(e.charAt(t)===IE)s=_E;break;case _E:switch(e.charAt(t)){case this.delimiter:n.push(e.substring(r,t-1)),s=SE,r=t+1;break;case IE:s=TE;break;default:s=CE}break;case CE:if(e.charAt(t)===IE)s=TE}if(s===_E?n.push(e.substring(r,a-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},$E=class e extends QC{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);

2if(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=!1!==e.includeSpectrogram,this.includeWaveform=!0===e.includeWaveform,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!ut().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new e(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:null==this.audioTrackConstraints||this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.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){if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`)}else this.sampleRateHz=this.audioContext.sampleRate;let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=2*this.fftSize,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 t=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(t,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let e=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(e,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise((r=>{let a=setInterval((()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))}),this.fftSize/this.sampleRateHz*1e3)}))}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),null!=this.stream&&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(((e,r)=>n.set(e,r*t))),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(ja.sizeFromShape(t));return n.set(e,n.length-e.length),yi(n,t)}},AE=class e extends QC{constructor(e,t){if(super(),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=lh([0],"int32"),this.webcamConfig.centerCrop){let e=1*this.webcamConfig.resizeWidth/this.webcamVideoElement.width,t=1*this.webcamConfig.resizeHeight/this.webcamVideoElement.height,n=(1-e)/2,r=(1-t)/2,a=n+e,s=t+r;this.cropBox=uh([r,n,s,a],[1,4])}else this.cropBox=uh([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!ut().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let r=new e(t,n);return await r.start(),r}async start(){this.webcamConfig.facingMode&&ja.assert("user"===this.webcamConfig.facingMode||"environment"===this.webcamConfig.facingMode,(()=>`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

2: ${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=Pf.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(!this.resize)return{value:e,done:!1};try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{e.dispose()}}needToResize(){return!(!this.webcamConfig.resizeWidth||!this.webcamConfig.resizeHeight||this.webcamVideoElement.width===this.webcamConfig.resizeWidth&&this.webcamVideoElement.height===this.webcamConfig.resizeHeight)}cropAndResizeFrame(e){return Ci((()=>{let t,n=Vu(Wo(e,"float32"),0);t=Kc.cropAndResize(n,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=t.shape;return kl(t,r.slice(1))}))}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach((e=>e.stop()));try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},RE=class{},FE=class extends QC{split(e){return new DE(this,e)}},DE=class extends FE{constructor(e,t){super(),this.upstream=e,this.impl=new ME(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ME=class extends dE{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return""!==this.carryover&&(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let e of t.slice(0,-1))this.outputQueue.push(e);return this.carryover=t[t.length-1],!0}},OE=class extends QC{decodeUTF8(){return new LE(this)}},LE=class extends FE{constructor(e){super(),this.upstream=e,this.impl=new zE(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zE=class extends dE{constructor(e){if(super(),this.upstream=e,ut().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=ee();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e,t,n=await this.upstream.next();return!n.done&&(e=n.value,t=ut().get("IS_BROWSER")?this.decoder.decode(e,{stream:!0}):this.decoder.write(E.from(e.buffer)),this.outputQueue.push(t),!0)}},PE=class extends OE{constructor(e,t={}){super(),this.file=e,this.options=t,ja.assert(e instanceof Uint8Array||!!ut().get("IS_BROWSER")&&(e instanceof File||e instanceof Blob),(()=>"FileChunkIterator only supports File, Blob and Uint8Array right now.")),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1048576}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise(((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=n=>{let a=r.result;if(a instanceof ArrayBuffer&&(a=new Uint8Array(a)),!(a instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(a)},r.onabort=e=>t(new Error("Aborted")),r.onerror=e=>t(new Error(e.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n})),done:!1}}};var BE=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function WE(e){return"string"==typeof e&&"file://"===e.slice(0,7)}var VE=class extends RE{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(WE(this.input)&&ut().get("IS_NODE")){let e=te();this.input=e.readFileSync(this.input.slice(7))}return new PE(this.input,this.options)}},UE=class extends RE{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return WE(this.url)?new VE(this.url,this.fileOptions).iterator():async function(e,t={},n){let r,a;"string"==typeof e?r=e:(r=e.url,a=BE(e));let s=await(n||ja.fetch)(r,a);if(s.ok){let e=new Uint8Array(await s.arrayBuffer());return new PE(e,t)}throw new Error(s.statusText)}(this.url,this.fileOptions)}};function GE(e,t={}){return new EE(new UE(e),t)}function HE(e){let t=ZC(e);return yE((async()=>t))}function jE(e){return yE((async()=>{let t=await e();return ZC((()=>t.next()))}

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2++t){let r=t+i+d*(a+u*(n+l*e));m[g++]=f[r]}}}return n.makeTensorInfo([o,p,h,c],a.dtype,m)}};function GF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r;YE([a,s],"depthwiseConv2DNative");let d=ja.computeStrides(a.shape),p=ja.computeStrides(s.shape),h=l;null==h&&(h=[1,1]),ja.assert(Im.eitherStridesOrDilationsAreOne(i,h),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${h}'`));let c=Im.computeConv2DInfo(a.shape,s.shape,i,h,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:y,padInfo:b}=c,x=b.left,w=b.top,v=c.outChannels/c.inChannels,k=new _s(c.outShape,a.dtype),I=n.data.get(a.dataId).values,S=n.data.get(s.dataId).values,N=k.values;for(let e=0;e<c.batchSize;++e){let t=e*d[0],n=e*k.strides[0];for(let e=0;e<c.outHeight;++e){let r=n+e*k.strides[1],a=e*c.strideHeight-w;for(let e=0;e<f;++e){let n=a+e*g;if(n<0||n>=c.inHeight)continue;let s=e*p[0],i=t+n*d[1];for(let e=0;e<c.outWidth;++e){let t=r+e*k.strides[2],n=e*c.strideWidth-x;for(let e=0;e<m;++e){let r=n+e*y;if(r<0||r>=c.inWidth)continue;let a=s+e*p[1],o=i+r*c.inChannels,l=t,u=a;for(let e=0;e<c.inChannels;++e){let t=I[o+e];for(let e=0;e<v;++e)N[l+e]+=t*S[u+e];l+=v,u+=v}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var HF={kernelName:rn,backendName:"cpu",kernelFunc:GF};var jF={kernelName:an,backendName:"cpu",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=r;YE([a,s],"depthwiseConv2dNativeBackpropFilter");let p=Im.computeConv2DInfo(a.shape,d,i,o,l,u,!0),{strideHeight:h,strideWidth:c,filterHeight:f,filterWidth:m}=p,g=new _s(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,x=p.outChannels/p.inChannels,w=n.data.get(a.dataId).values,v=new _s(a.shape,a.dtype,w),k=n.data.get(s.dataId).values,I=new _s(s.shape,s.dtype,k);for(let e=0;e<f;++e){let t=Math.max(0,Math.ceil((b-e)/h)),n=Math.min(p.outHeight,(p.inHeight+b-e)/h);for(let r=0;r<m;++r){let a=Math.max(0,Math.ceil((y-r)/c)),s=Math.min(p.outWidth,(p.inWidth+y-r)/c);for(let i=0;i<p.outChannels;++i){let o=Math.trunc(i/x),l=i%x,u=0;for(let l=0;l<p.batchSize;++l)for(let d=t;d<n;++d){let t=e+d*h-b;for(let e=a;e<s;++e){let n=r+e*c-y;u+=v.get(l,t,n,o)*I.get(l,d,e,i)}}g.set(u,e,r,o,l)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}};var qF={kernelName:sn,backendName:"cpu",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=r;YE([a,s],"depthwiseConv2DNativeBackpropInput");let p=ja.computeStrides(a.shape),h=ja.computeStrides(s.shape),c=Im.computeConv2DInfo(d,s.shape,i,o,l,u,!0),f=new _s(c.inShape,"float32"),m=f.values,[g,y,b]=f.strides,x=n.data.get(a.dataId).values,[w,v,k]=p,I=n.data.get(s.dataId).values,[S,N,T]=h,{batchSize:_,filterHeight:C,filterWidth:E,inChannels:$,inHeight:A,inWidth:R,outChannels:F,outHeight:D,outWidth:M,strideHeight:O,strideWidth:L}=c,z=C-1-c.padInfo.top,P=E-1-c.padInfo.left,B=F/$;for(let e=0;e<_;++e)for(let t=0;t<$;++t)for(let n=0;n<A;++n){let r=n-z,a=Math.max(0,Math.ceil(r/O)),s=Math.min(D,(C+r)/O);for(let i=0;i<R;++i){let o=i-P,l=Math.max(0,Math.ceil(o/L)),u=Math.min(M,(E+o)/L),d=0;for(let n=a;n<s;++n){let a=n*O-r;for(let r=l;r<u;++r){let s=w*e+v*n+k*r,i=S*(C-1-a)+N*(E-1-(r*L-o))+T*t;for(let e=0;e<B;++e){d+=x[s+(t*B+e)]*I[i+e]}}}m[g*e+y*n+b*i+t]=d}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}};var KF={kernelName:on,backendName:"cpu",kernelFunc:function(e){let{inputs:t,backend:n}=e,{x:r}=t,a=ja.sizeFromShape(r.shape),s=n.data.get(r.dataId).values,i=Bo([a,a],r.dtype),o=i.values;for(let e=0;e<s.length;e++)o[e*a+e]=s[e];let l=[...r.shape,...r.shape];return n.makeTensorInfo(l,i.dtype,i.values)}},XF={kernelName:ln,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(r.dataId).values,d=r.shape.length,p=l.data.get(a.dataId).values,h=a.shape.length,{batchSize:c,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:w,strideWidth:v,filterHeight:k,filterWidth:I,dilationHeight:S,dilationWidth:N,outShape:T}=Im.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),_=ja.sizeFromShape(T),C=T.length,E=ja.getArrayFromDType(r.dtype,_);for(let e=0;e<c;++e)for(let t=0;t<y;++t){let n=t*w-x.top;for(let s=0;s<b;++s){let i=s*v-x.left;for(let o=0;o<g;++o){let l=Number.MIN_SAFE_INTEGER;for(let t=0;t<k;++t){let s=n+t*S;if(s>=0&&s<f)for(let n=0;n<I;++n){let c=i+n*N;if(c>=0&&c<m){let i=ja.locToIndex([e,s,c,o],d,ja.computeStrides(r.shape)),f=ja.locToIndex([t,n,o],h,ja.computeStrides(a.shape)),m=u[i]+p[f];m>l&&(l=m)}}}E[ja.locToIndex([e,t,s,o],C,ja.computeStrides(T))]=l}}}return{dataId:l.write(ja.toTypedArray(E,r.dtype),T,r.dtype),shape:T,dtype:r.dtype}}}

2,YF={kernelName:dn,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,d=ja.toNestedArray(r.shape,u.data.get(r.dataId).values),p=ja.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:h,inHeight:c,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:v,filterWidth:k,dilationHeight:I,dilationWidth:S,outShape:N}=Im.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);ja.assert(s.rank===N.length,(()=>`Error in ${dn}, dy must have the same rank as output ${N.length}, but got ${s.rank}`));let T=ja.toNestedArray(N,u.data.get(s.dataId).values),_=ja.makeZerosNestedTypedArray(a.shape,a.dtype);for(let e=0;e<h;++e)for(let t=0;t<g;++t){let n=t*x-b.top;for(let r=0;r<y;++r){let a=r*w-b.left;for(let s=0;s<m;++s){let i=Number.MIN_SAFE_INTEGER,o=0,l=0;for(let t=0;t<v;++t){let r=n+t*I;if(r>=0&&r<c)for(let n=0;n<k;++n){let u=a+n*S;if(u>=0&&u<f){let a=d[e][r][u][s]+p[t][n][s];a>i&&(i=a,o=t,l=n)}}}_[o][l][s]+=T[e][t][r][s]}}}return{dataId:u.write(ja.toTypedArray(_,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},ZF={kernelName:un,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,d=ja.toNestedArray(r.shape,u.data.get(r.dataId).values),p=ja.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:h,inHeight:c,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:v,filterWidth:k,dilationHeight:I,dilationWidth:S,outShape:N}=Im.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);ja.assert(s.rank===N.length,(()=>`Error in ${un}, dy must have the same rank as output ${N.length}, but got ${s.rank}`));let T=ja.toNestedArray(N,u.data.get(s.dataId).values),_=ja.makeZerosNestedTypedArray(r.shape,r.dtype);for(let e=0;e<h;++e)for(let t=0;t<g;++t){let n=t*x-b.top;for(let r=0;r<y;++r){let a=r*w-b.left;for(let s=0;s<m;++s){let i=Number.MIN_SAFE_INTEGER,o=n<0?0:n,l=a<0?0:a;for(let t=0;t<v;++t){let r=n+t*I;if(r>=0&&r<c)for(let n=0;n<k;++n){let u=a+n*S;if(u>=0&&u<f){let a=d[e][r][u][s]+p[t][n][s];a>i&&(i=a,o=r,l=u)}}}_[e][o][l][s]+=T[e][t][r][s]}}}return{dataId:u.write(ja.toTypedArray(_,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};var JF={kernelName:pn,backendName:"cpu",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{image:a}=t,{canvas:s,options:i}=r,{contextOptions:o,imageOptions:l}=i||{},u=(null==l?void 0:l.alpha)||1,d=(null==o?void 0:o.contextType)||"2d";if("2d"!==d)throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let p=s.getContext(d,(null==o?void 0:o.contextAttributes)||{});if(null==p)throw new Error(`Could not get the context with ${d} type.`);let[h,c]=a.shape.slice(0,2),f=2===a.shape.length?1:a.shape[2],m=n.data.get(a.dataId).values,g="float32"===a.dtype?255:1,y=new Uint8ClampedArray(c*h*4);for(let e=0;e<h*c;++e){let t=[0,0,0,255*u];for(let n=0;n<f;n++){let r=m[e*f+n];if("float32"===a.dtype){if(r<0||r>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${r}.`)}else if("int32"===a.dtype&&(r<0||r>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${r}.`);1===f?(t[0]=r*g,t[1]=r*g,t[2]=r*g):t[n]=r*g}let n=4*e;y[n+0]=Math.round(t[0]),y[n+1]=Math.round(t[1]),y[n+2]=Math.round(t[2]),y[n+3]=Math.round(t[3])}s.width=c,s.height=h;let b=new ImageData(y,c,h);return p.putImageData(b,0,0),a}};function QF(e){let t,{inputs:n,backend:r,attrs:a}=e,{x:s}=n,{axis:i,keepDims:o}=a;YE(s,"sum"),t="bool"===s.dtype?p$({inputs:{x:s},backend:r,attrs:{dtype:"int32"}}):i$({inputs:{x:s},backend:r});let l=t.shape.length,u=ja.parseAxisParam(i,t.shape),d=Im.getAxesPermutation(u,l),p=u,h=t;null!=d&&(h=_A({inputs:{x:t},backend:r,attrs:{perm:d}}),p=Im.getInnerMostAxes(p.length,l)),Im.assertAxesAreInnerMostDims("sum",p,h.shape.length);let[c,f]=Im.computeOutAndReduceShapes(h.shape,p),m=s$(r,c,Im.upcastType(h.dtype,"int32")),g=ja.sizeFromShape(f),y=r.data.get(m.dataId).values,b=r.data.get(h.dataId).values;for(let e=0;e<y.length;++e){let t=e*g,n=0;for(let e=0;e<g;++e)n+=b[t+e];y[e]=n}if(o){let e=m;

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2\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  ":`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${r[0]}, ${r[1]}));\n      return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);\n    }\n  `}(0,t,n);case 2:return function(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(ja.arraysEqual(e,t))return n?"\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    ":`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${r[0]}, ${r[1]}));\n      }\n    `;let a=Math.ceil(e[1]/2);return n?"\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  ":`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${r[0]}, ${r[1]}));\n\n      int index = resTexRC.x * ${r[1]} + resTexRC.y;\n      int r = 2 * (index / ${a});\n      int c = imod(index, ${a}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}(e,t,n);case 3:return function(e,t,n){if(n)return"\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),s=a*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${r[0]}, ${r[1]}));\n      int index = resTexRC.x * ${r[1]} + resTexRC.y;\n\n      int b = index / ${s};\n      index -= b * ${s};\n\n      int r = 2 * (index / ${a});\n      int c = imod(index, ${a}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}(e,t,n);default:return function(e,t,n){if(n)return"\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  ";let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),s=a*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let t=2;t<e.length-1;t++)i*=e[e.length-t-1],o=`\n      int b${t} = index / ${i};\n      index -= b${t} * ${i};\n    `+o,l=`b${t}, `+l;return`\n    ivec${e.length}

2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${r[0]}, ${r[1]}));\n      int index = resTexRC.x * ${r[1]} + resTexRC.y;\n\n      ${o}\n\n      int b = index / ${s};\n      index -= b * ${s};\n\n      int r = 2 * (index / ${a});\n      int c = imod(index, ${a}) * 2;\n\n      return ivec${e.length}(${l});\n    }\n  `}(e,t,n)}}(t.logicalShape,l,n.enableShapeUniforms),s=function(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}(u)):(a=function(e,t,n){switch(e.length){case 0:return az();case 1:return function(e,t,n){return 1===t[0]?n?"\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    ":`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `:1===t[1]?n?"\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    ":`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `:n?"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  ":`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}(0,t,n);case 2:return function(e,t,n){return ja.arraysEqual(e,t)?n?"\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    ":`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `:1===e[1]?n?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ":`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:1===e[0]?n?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ":`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:n?"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  ":`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}(e,t,n);case 3:return function(e,t,n){if(n)return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${jL(["r","c","d"],e)}\n    return ivec3(r, c, d);\n  }\n`;let r=HL(["r","c","d"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${r}\n      return ivec3(r, c, d);\n    }\n  `}(e,t,n);case 4:return function(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${jL(["r","c","d","d2"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let r=HL(["r","c","d","d2"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ive

2c2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${r}\n      return ivec4(r, c, d, d2);\n    }\n  `}(e,t,n);case 5:return function(e,t){let n=HL(["r","c","d","d2","d3"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}(e,t);case 6:return function(e,t){let n=HL(["r","c","d","d2","d3","d4"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}(t.logicalShape,l,n.enableShapeUniforms),s=function(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}(u)),n.packedInputs&&(p+=rz),[p,d,s,i,a,o,n.userCode].join("\n")}function JL(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return function(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${r}() {return ${n};}`;let[a,s]=e.shapeInfo.texShape;if(1===a&&1===s)return`\n      float ${r}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let i=sz(n);if(t)return`\n    float ${r}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;let[o,l]=e.shapeInfo.texShape;return`\n    float ${r}() {\n      vec2 uv = uvFromFlat(${o}, ${l}, ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}(e,t);case 1:return function(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${r}(int index) {\n        ${iz(e)}\n      }\n    `;let a=e.shapeInfo.texShape,s=a[0],i=a[1];if(1===i&&1===s)return`\n      float ${r}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let o=sz(n);return 1===i?t?`\n      float ${r}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${r}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `:1===s?t?`\n      float ${r}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${r}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:t?`\n    float ${r}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n    float ${r}(int index) {\n      vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}(e,t);case 2:return function(e,t){let n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape;if(null!=s&&ja.arraysEqual(n,s)){if(t)return`\n      float ${a}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `;let e=s[0];return`\n    float ${a}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${s[1]}.0, ${e}.0);\n      return sampleTexture(${r}, uv);\n    }\n  `}let{newShape:i,keptDims:o}=ja.squeezeShape(n),l=i;if(l.length<n.length){let n=["row","col"];return`\n      ${JL(uz(e,l),t)}\n      float ${a}(int row, int col) {\n        return ${a}(${dz(n,o)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${a}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${iz(e)}\n      }\n    `;let u=s[0],d=s[1],p=sz(r);

2return 1===d?t?`\n      float ${a}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${r}TexShape[0]));\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n    float ${a}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);\n      return sampleTexture(${r}, uv);\n    }\n  `:1===u?t?`\n      float ${a}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${r}TexShape[1]), 0.5);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n    float ${a}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${d}.0, 0.5);\n      return sampleTexture(${r}, uv);\n    }\n  `:t?`\n      float ${a}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${r}Shape[1] + col + ${p};\n        vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n  float ${a}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${p};\n    vec2 uv = uvFromFlat(${u}, ${d}, index);\n    return sampleTexture(${r}, uv);\n  }\n`}(e,t);case 3:return function(e,t){let n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=ja.squeezeShape(n),u=o;if(u.length<n.length){let n=["row","col","depth"];return`\n        ${JL(uz(e,u),t)}\n        float ${a}(int row, int col, int depth) {\n          return ${a}(${dz(n,l)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${a}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${s}, ${i}, 1)));\n        ${iz(e)}\n      }\n    `;let d=e.shapeInfo.texShape,p=d[0],h=d[1],c=e.shapeInfo.flatOffset;if(h===s&&null==c)return t?`\n      float ${a}(int row, int col, int depth) {\n        int stride1 = ${r}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n        float ${a}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${i}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${h}.0, ${p}.0);\n          return sampleTexture(${r}, uv);\n        }\n      `;if(h===i&&null==c)return t?`\n      float ${a}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${r}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n    float ${a}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${p}.0);\n      return sampleTexture(${r}, uv);\n    }\n  `;let f=sz(r);return t?`\n    float ${a}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${r}Shape[1] * ${r}Shape[2];\n      int stride1 = ${r}Shape[2];\n      int index = row * stride0 + col * stride1 + depth + ${f};\n      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);\n      return sampleTexture(${r}, uv);\n    }\n    `:`\n      float ${a}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s} + col * ${i} + depth + ${f};\n        vec2 uv = uvFromFlat(${p}, ${h}, index);\n        return sampleTexture(${r}, uv);\n      }\n  `}(e,t);case 4:return function(e,t){let n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=n[3],i=n[2]*s,o=n[1]*i,{newShape:l,keptDims:u}=ja.squeezeShape(n);if(l.length<n.length){let n=["row","col","depth","depth2"];return`\n      ${JL(uz(e,l),t)}\n      float ${a}(int row, int col, int depth, int depth2) {\n        return ${a}(${dz(n,u)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${a}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${o}, ${i}, ${s}, 1)));\n        ${iz(e)}\n      }\n    `;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],c=p[1],f=`int stride2 = ${r}Shape[3];`,m=`int stride1 = ${r}Shape[2] * stride2;`,g=`int stride0 = ${r}Shape[1] * stride1;`;if(c===o&&null==d)return t?`\n      float ${a}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, str

2ide2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n      float ${a}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${i}, ${s}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${c}.0, ${h}.0);\n        return sampleTexture(${r}, uv);\n      }\n    `;if(c===s&&null==d)return t?`\n      float ${a}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${r}Shape[1] * ${r}Shape[2], ${r}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n      float ${a}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${c}.0, ${h}.0);\n        return sampleTexture(${r}, uv);\n      }\n    `;let y=sz(r);return t?`\n    float ${a}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${y});\n      return sampleTexture(${r}, uv);\n    }\n  `:`\n    float ${a}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} +\n          depth * ${s} + depth2;\n      vec2 uv = uvFromFlat(${h}, ${c}, index + ${y});\n      return sampleTexture(${r}, uv);\n    }\n  `}(e,t);case 5:return function(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=ja.squeezeShape(t);if(l.length<t.length){let t=["row","col","depth","depth2","depth3"];return`\n      ${JL(uz(e,l))}\n      float ${r}(int row, int col, int depth, int depth2, int depth3) {\n        return ${r}(${dz(t,u)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${o}, ${i}, ${s}, ${a})) +\n          depth3;\n        ${iz(e)}\n      }\n    `;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],c=p[1];if(c===o&&null==d)return`\n      float ${r}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${i}, ${s}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${c}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(c===a&&null==d)return`\n      float ${r}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${c}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let f=sz(n);return`\n    float ${r}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} + depth * ${s} +\n          depth2 * ${a} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${h}, ${c}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}(e);case 6:return function(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=ja.squeezeShape(t);if(a.length<t.length){let t=["row","col","depth","depth2","depth3","depth4"];return`\n      ${JL(uz(e,a))}\n      float ${r}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${r}(${dz(t,s)});\n      }\n    `}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`\n      float ${r}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${d}, ${u}, ${l}, ${o})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${i}, 1)));\n        ${iz(e)}\n      }\n    `;let p=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,c=h[0],f=h[1];if(f===d&&null==p)return`\n      float ${r}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${u}, ${l}, ${o}, ${i})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${c}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===i&&null==p)return`\n      float ${r}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot

2(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${c}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let m=sz(n);return`\n    float ${r}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${d} + col * ${u} + depth * ${l} +\n          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${c}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function QL(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return function(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=GL();return`\n    vec4 ${n}() {\n      return ${r.texture2D}(${t}, halfCR);\n    }\n  `}(e);case 1:return function(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=GL();if(t)return`\n    vec4 ${r}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${s.texture2D}(${n}, uv);\n    }\n  `;let i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];return`\n    vec4 ${r}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${i[0]}, ${i[1]}, index);\n      return ${s.texture2D}(${n}, uv);\n    }\n  `}(e,t);case 2:return function(e,t){let n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=GL();if(null!=s&&ja.arraysEqual(n,s))return t?`\n      vec4 ${a}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);\n\n        return ${l.texture2D}(${r}, uv);\n      }\n    `:`\n      vec4 ${a}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);\n\n        return ${l.texture2D}(${r}, uv);\n      }\n    `;if(t)return`\n    vec4 ${a}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${r}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${l.texture2D}(${r}, uv);\n    }\n  `;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],d=Math.ceil(n[1]/2);return`\n    vec4 ${a}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${d}, ${u[0]}, ${u[1]}, row, col);\n      return ${l.texture2D}(${r}, uv);\n    }\n  `}(e,t);case 3:return function(e,t){let n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(1===n[0]){let r=[1,2],s=["b","row","col"];return`\n        ${QL(uz(e,n.slice(1)),t)}\n        vec4 ${a}(int b, int row, int col) {\n          return ${a}(${dz(s,r)});\n        }\n      `}let o=GL();if(t)return`\n    vec4 ${a}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${r}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${o.texture2D}(${r}, uv);\n    }\n  `;let l=i[0],u=i[1],d=Math.ceil(n[2]/2),p=d*Math.ceil(n[1]/2);return`\n    vec4 ${a}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${l}, ${u}, ${p}, ${d}, b, row, col);\n      return ${o.texture2D}(${r}, uv);\n    }\n  `}(e,t);default:return function(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=GL();if(t)return`\n    vec4 ${r}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${a.texture2D}(${n}, uv);\n    }\n  `;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),h=p*Math.ceil(s[i-2]/2),c="int b, int row, int col",f=`b * ${h} + (row / 2) * ${p} + (col / 2)`;for(let e=2;e<i-1;e++)c=`int b${e}, `+c,h*=s[i-e-1],f=`b${e} * ${h} + `+f;return`\n    vec4 ${r}(${c}) {\n      int index = ${f};\n      int texR = index / ${d};\n      int texC = index - texR * ${d};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}(e,t)}}var ez="\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",tz="\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",nz="\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",rz="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function az(){return"\n    int getOutputCoords() {\n      return 0;\n    }\n  "}function sz(e){return`offset${e}`}function iz(e){let t=e.name,n=ja.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n};

2 i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function oz(e){if(e<=1)return"int";if(2===e)return"ivec2";if(3===e)return"ivec3";if(4===e)return"ivec4";if(5===e)return"ivec5";if(6===e)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function lz(e,t,n){let{newShape:r,keptDims:a}=ja.squeezeShape(t),s=t.length,i=e&&3===s&&1===t[0],o=i?t.slice(1):r,l=!e&&s>1&&!ja.arraysEqual(t,n)&&r.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function uz(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function dz(e,t){return t.map((t=>e[t])).join(", ")}function pz(e,t,n){let r,a,s,i=[],o=[],l=null,u=null;u=e.getUniformLocation(n,"NAN",!1),1===ut().getNumber("WEBGL_VERSION")&&(l=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let r of t.variableNames){let a={name:r,uniform:e.getUniformLocation(n,r,d),offset:e.getUniformLocation(n,`offset${r}`,d)};t.enableShapeUniforms&&(a.shape=e.getUniformLocation(n,`${r}Shape`,d),a.texShape=e.getUniformLocation(n,`${r}TexShape`,d)),i.push(a)}if(t.enableShapeUniforms&&(r=e.getUniformLocation(n,"outShape",d),s=e.getUniformLocation(n,"outShapeStrides",d),a=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms)for(let r of t.customUniforms)o.push(e.getUniformLocation(n,r.name,d));return{variablesLocations:i,customUniformLocations:o,infLoc:l,nanLoc:u,outShapeLocation:r,outShapeStridesLocation:s,outTexShapeLocation:a}}function hz(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(((e,n)=>{let r=e.logicalShape,a=t[n],s=a.shape;if(!ja.arraysEqual(r,s))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${s} must match`);if(e.isUniform&&a.isUniform)return;let i=e.texShape,o=a.isUniform?null:a.texData.texShape;if(!ja.arraysEqual(i,o))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${o} must match`)}))}function cz(e){return ut().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var fz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=DO.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=GL();this.outputShape=e,this.enableShapeUniforms=cz(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?jL(["r","c","d"],e):HL(["r","c","d"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${t.output} = result;\n      }\n    `}},mz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=DO.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=GL();this.outputShape=e,this.enableShapeUniforms=cz(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?jL(["r","c","d"],e):HL(["r","c","d"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${t.output} = result;\n      }\n    `}},gz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=MO.DOWNLOAD;let t=GL();this.outputShape=e,this.userCode=`\n      ${XL}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}},yz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=MO.DOWNLOAD;let t=GL();this.outputShape=e,this.userCode=`\n      ${XL}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${t.output} = encode_float(x);\n      }\n    `}},bz={R:0,G:1,B:2,A:3},xz=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=GL();this.outputShape=e,this.enableShapeUniforms=cz(this.outputShape.length);let a="result";t&&(a="floor(result * 255. + 0.5)");let s="";for(let e=0;e<n.length;e++){let t=n[e];s+=`\n          if(offset == ${e}) {\n            result = values[${bz[t]}];\n          }`}this.userCode=`\n      ${this.enableShapeUniforms?"\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n":KL(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int flatIndex = getFlatIndex(coords);\n        float result = 0.;\n        int offset = imod(flatIndex, ${n.length});\n\n        flatIndex = idiv(flatIndex, ${n.length}, 1.);\n\n        int r = flatIndex / texShape[1];

2\n        if (r < texShape[0]) {\n          int c = imod(flatIndex, texShape[1]);\n          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n          vec4 values = ${r.texture2D}(A, uv);\n          ${s}\n        }\n        ${r.output} = vec4(${a}, 0., 0., 0.);\n      }\n    `}},wz=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=GL();this.outputShape=e,this.enableShapeUniforms=cz(this.outputShape.length);let r="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let t=0;t<=1;t++)for(let a=0;a<=1;a++){let s=2*t+a;r+=`\n          localCoords = coords;\n          if(localCoords[2] + ${a} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {\n          localCoords[2] += ${a};\n          if (localCoords[1] + ${t} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {\n            localCoords[1] += ${t};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${s}] = values[0];\n            } else if (offset == 1) {\n              result[${s}] = values[1];\n            } else if (offset == 2) {\n              result[${s}] = values[2];\n            } else {\n              result[${s}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?"\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n":KL(e)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${r}\n\n          ${n.output} = ${a};\n        }\n    `}},vz={};function kz(e){let t=GL();return ZO(e,`${t.version}\n    precision highp float;\n    ${t.attribute} vec3 clipSpacePos;\n    ${t.attribute} vec2 uv;\n    ${t.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`)}function Iz(e){return iL(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function Sz(e){return oL(e,new Uint16Array([0,1,2,2,1,3]))}function Nz(e,t,n,r,a,s){dL(t,n);let i=uL(e),o=e.TEXTURE_2D;return HO(e,(()=>e.bindTexture(o,i))),HO(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE))),HO(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE))),HO(e,(()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST))),HO(e,(()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST))),1===ut().getNumber("WEBGL_VERSION")?HO(e,(()=>e.texImage2D(o,0,r,t,n,0,a,s,null))):HO(e,(()=>e.texStorage2D(o,1,r,t,n))),HO(e,(()=>e.bindTexture(e.TEXTURE_2D,null))),{texture:i,texShape:[n,t]}}function Tz(e){return e.internalFormatFloat}function _z(e,t,n,r){let[a,s]=WO(t,n);return Nz(e,a,s,Tz(r),r.textureFormatFloat,e.FLOAT)}function Cz(e){return e.internalFormatHalfFloat}function Ez(e,t,n,r){let[a,s]=WO(t,n);return Nz(e,a,s,Cz(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function $z(e){return e.downloadTextureFormat}function Az(e,t,n,r){let[a,s]=WO(t,n);return Nz(e,a,s,$z(r),e.RGBA,e.UNSIGNED_BYTE)}function Rz(e){return e.internalFormatPackedFloat}function Fz(e,t,n,r){let[a,s]=UO(t,n);return 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Gz=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=ut().getNumber("WEBGL_VERSION");if(null!=e?(this.gl=e,PO(t,e)):this.gl=BO(t),e=this.gl,2===ut().getNumber("WEBGL_VERSION")){let t=e;this.createVertexArray=()=>HO(t,(()=>t.createVertexArray())),this.bindVertexArray=e=>HO(t,(()=>t.bindVertexArray(e))),this.deleteVertexArray=e=>HO(t,(()=>t.deleteVertexArray(e))),this.getVertexArray=()=>HO(t,(()=>t.getParameter(t.VERTEX_ARRAY_BINDING)))}else if(null!=e){let t=e.getExtension("OES_vertex_array_object");

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this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),yL(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=UO(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){null!=this.program&&sL(this.gl,this.program),vL(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and 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ut().platform&&(n=ut().platform.setTimeoutCustom.bind(ut().platform)),ja.repeatedTry((()=>(this.pollItems(),0===this.itemsToPoll.length)),(()=>0),null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),xL(this.gl,e,this.framebuffer),this.debug&&vL(this.gl)}unbindTextureToFrameBuffer(){null!=this.outputTexture?(xL(this.gl,this.outputTexture,this.framebuffer),this.debug&&vL(this.gl)):wL(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;xL(r,e,this.framebuffer),this.debug&&vL(r),this.outputTexture=e,HO(r,(()=>r.viewport(0,0,t,n))),HO(r,(()=>r.scissor(0,0,t,n)))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),HO(this.gl,(()=>this.gl.scissor(e,t,n,r)))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(null==this.program)throw new Error("No GPU program is currently set.")}};var{addImpl:Hz,bincountImpl:jz,bincountReduceImpl:qz,bitwiseAndImpl:Kz,castImpl:Xz,ceilImpl:Yz,concatImpl:Zz,equalImpl:Jz,expImpl:Qz,expm1Impl:eP,floorImpl:tP,gatherNdImpl:nP,gatherV2Impl:rP,greaterImpl:aP,greaterEqualImpl:sP,lessImpl:iP,lessEqualImpl:oP,linSpaceImpl:lP,logImpl:uP,maxImpl:dP,maximumImpl:pP,minimumImpl:hP,multiplyImpl:cP,negImpl:fP,notEqualImpl:mP,prodImpl:gP,raggedGatherImpl:yP,raggedRangeImpl:bP,raggedTensorToTensorImpl:xP,rangeImpl:wP,rsqrtImpl:vP,scatterImpl:kP,sigmoid

2Impl:IP,simpleAbsImpl:SP,sliceImpl:NP,sparseFillEmptyRowsImpl:TP,sparseReshapeImpl:_P,sparseSegmentReductionImpl:CP,sqrtImpl:EP,staticRegexReplaceImpl:$P,stridedSliceImpl:AP,stringNGramsImpl:RP,stringSplitImpl:FP,stringToHashBucketFastImpl:DP,subImpl:MP,tileImpl:OP,topKImpl:LP,transposeImpl:zP,uniqueImpl:PP}=QE;function BP(e,t){return["x","y","z","w","u","v"].slice(0,t).map((t=>`${e}.${t}`))}function WP(e,t){return 1===t?[e]:BP(e,t)}var VP=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=cz(this.outputShape.length),0===this.rank)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{let e=WP("rc",this.rank),t=oz(this.rank),n=this.getOutOfBoundsCondition(e),r=this.getSetup(e),a=this.getOutput(e);this.userCode=`\n        void main() {\n          ${t} rc = getOutputCoords();\n\n          if(${n}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let r=0;r<=1;r++){let a=`${0===n?"r":"rp1"}, ${0===r?"c":"cp1"}`;for(let t=2;t<this.rank;t++)a=`${e[e.length-1-t]},`+a;t.push(a)}return t}getOutOfBoundsCondition(e){if(1===this.rank)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(1===this.rank)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],r=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${r};\n    `}getOutput(e){let t=this.getSourceCoordsArr(e);return 1===this.rank?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}},UP=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=cz(this.outputShape.length);let n="";for(let e=0;e<4;e++){let t="thisRC = rc;";e%2==1&&(t+="thisRC.z += 1;"),e>1&&(t+="thisRC.y += 1;"),n+=`\n        ${t}\n        ${e>0?"if(thisRC.y < rows && thisRC.z < cols){":""}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${e}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${e>0?"}":""}\n      `}this.userCode=`\n      ${function(e,t){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t?qL(["r","c","d"],"inputShape"):HL(["r","c","d"],e)}\n      return ivec3(r, c, d);\n    }\n  `}(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?"\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n":KL(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};\n        int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}};var GP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let r=jP(t,n),a=qP(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s,i=HP(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log(

2);let e=this.freeTextures[a].pop();return this.usedTextures[a].push(e),e}return r===OO.PACKED_2X2_FLOAT32?s=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===OO.PACKED_2X2_FLOAT16?s=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===OO.UNPACKED_FLOAT32?s=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===OO.UNPACKED_FLOAT16?s=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===OO.PACKED_4X1_UNSIGNED_BYTE&&(s=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(s),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),s}releaseTexture(e,t,n,r){if(null==this.freeTextures)return;let a=jP(n,r),s=qP(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=HP(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=ut().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");-1!==o&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(null==u||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(null!=this.freeTextures){for(let e in this.freeTextures)this.freeTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));for(let e in this.usedTextures)this.usedTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function HP(e,t,n,r,a){let s,i=function(e,t){switch(e){case OO.PACKED_2X2_FLOAT32:return Rz(t);case OO.PACKED_2X2_FLOAT16:return Dz(t);case OO.UNPACKED_FLOAT32:return Tz(t);case OO.UNPACKED_FLOAT16:return Cz(t);case OO.PACKED_4X1_UNSIGNED_BYTE:return $z(t);default:throw new Error(`Unknown physical texture type ${e}`)}}(t,r);if(a){let[t,n]=UO(e[0],e[1]);s=t*n}else{let[t,n]=WO(e[0],e[1]);s=t*n}let o=function(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;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}(n,i);return s*o}function jP(e,t){if(e===MO.UPLOAD)return OO.PACKED_2X2_FLOAT32;if(e===MO.RENDER||null==e)return function(e){return ut().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?OO.PACKED_2X2_FLOAT32:OO.UNPACKED_FLOAT32:e?OO.PACKED_2X2_FLOAT16:OO.UNPACKED_FLOAT16}(t);if(e===MO.DOWNLOAD||e===MO.PIXELS)return OO.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function qP(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var KP=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=cz(this.outputShape.length),this.userCode=`\n      float unaryOperation(float x) {\n        ${t}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},XP="if (isnan(x)) return x;",YP="return x;",ZP="return abs(x);",JP="return (x >= 0.0) ? x : (exp(x) - 1.0);",QP=XP+"\n  return (x < 0.0) ? 0.0 : x;\n",eB=XP+"\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n",tB="return x;",nB="return 1.0 / (1.0 + exp(-1.0 * x));",rB="return x;",aB="\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n",sB="\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;

2\n",iB="\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n",oB="return 1.0 / (1.0 + exp(-1.0 * x));",lB=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=cz(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},uB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=cz(this.outputShape.length);let t=e.length,n=WP("rc",t),r=oz(t),a=function(e,t){if(1===e)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`\n      void main() {\n        ${r} rc = getOutputCoords();\n        vec4 packedInput = getA(${a});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}},dB=Eg.whereImpl,pB={};var hB=ut().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");var cB=class e extends de{nextDataId(){return e.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!ut().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(null!=e){if(e instanceof Gz)t=e;else{let n=BO(ut().getNumber("WEBGL_VERSION"),e);t=new Gz(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let e=BO(ut().getNumber("WEBGL_VERSION"));t=new Gz(e),this.binaryCache=function(e){return e in pB||(pB[e]={}),pB[e]}(ut().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new GP(this.gpgpu),this.numMBBeforeWarning=null==ut().global.screen?1024:ut().global.screen.height*ut().global.screen.width*window.devicePixelRatio*600/1024/1024,this.texData=new ue(this,Ni())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,r,a,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[r,a]},o.texShape=[r,a];let l=_L(t),u=new xz(l,!1,s),d=this.runWebGLProgram(u,[i],n,[[r,a]]);return d.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),d.dataId}write(e,t,n){if((ut().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ut().getBool("DEBUG"))&&this.checkNumericalProblems(e),"complex64"===n&&null!=e)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:MO.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){this.texData.get(e).refCount++}decRef(e){if(this.texData.has(e)){this.texData.get(e).refCount--}}move(e,t,n,r,a){if(ut().getBool("DEBUG")&&this.checkNumericalProblems(t),"complex64"===r)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:MO.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(null!=s){let t;t=o?new lB(i,tB):new KP(i,tB);let n=this.runWebGLProgram(t,[{dataId:e,shape:i,dtype:r}],r),a=this.readSync(n.dataId);return this.disposeIntermediateTensorInfo(n),a}if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===r)return n;let l,u,d=null!=this.activeTimers;if(d&&(l=ja.now()),"complex64"===r){let e=this.readSync(a.real.dataId),t=this.readSync(a.imag.dataId);u=Im.mergeRealAndImagArrays(e,t)}else u=this.getValuesFromTexture(e);return d&&(this.downloadWaitMs+=ja.now()-l),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let t=this.pendingRead.get(e);return new Promise((e=>t.push(e)))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(null!=a){let t;t=o?new lB(r,tB):new KP(r,tB);let n=this.runWebGLProgram(t,[{dataId:e,shape:r,dtype:s}],s),a=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),a}if(null!=n)return this.convertAndCacheOnCPU(e);if(ut().getBool("DEBUG")&&!ut().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===ut().getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l,u,d=null;if("complex64"!==s&&ut().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let t=this.texData.get(l.dataId);d=this.gpgpu.createBufferFromTexture(t.texture.texture,...VO(r))}if(this.pendingRead.set(e,[]),"complex64"!==s&&await this.gpgpu.createAndWaitForFence(),"complex64"===s){let e=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),t=e[0],n=e[1];u=Im.mergeRealAndImagArrays(t,n)}else if(null==d)u=this.getValuesFromTexture(e);else{let e=ja.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(d,e)}if(null!=l&&this.disposeIntermediateTensorInfo(l),null!=d){let e=this.gpgpu.gl;

2HO(e,(()=>e.deleteBuffer(d)))}let p=this.convertAndCacheOnCPU(e,u),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach((e=>e(p))),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ni().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:r,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=n;if("complex64"===i)throw new Error("Does not support reading texture for complex64 dtype.");if(null!=s){let n;n=o?new lB(a,tB):new KP(a,tB);let r=this.runWebGLProgram(n,[{dataId:e,shape:a,dtype:i}],i),s=this.readToGPU(r,t);return this.disposeIntermediateTensorInfo(r),s}if(null==l)throw null!=r?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=Ni().makeTensorFromTensorInfo(u),p=this.texData.get(u.dataId);return Object.assign({tensorRef:d},p.texture)}bufferSync(e){let t=this.readSync(e.dataId);if("string"===e.dtype)try{let n=t.map((e=>ja.decodeString(e)));return Bo(e.shape,e.dtype,n)}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return Bo(e.shape,e.dtype,t)}checkNumericalProblems(e){if(null!=e)for(let t=0;t<e.length;t++){let n=e[t];if(!KO(n))throw ut().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=ja.sizeFromShape(t);if(ut().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let n=this.decode(e),r=this.texData.get(n.dataId),s=this.gpgpu.downloadMatrixFromPackedTexture(r.texture.texture,...VO(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(n),s}let s=ut().getBool("WEBGL_PACK")&&!0===r,i=s?_L(t):t,o=s?new yz(i):new gz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return ut().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],r=!1;null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=ja.flatten(this.activeTimers.map((e=>e.query))).filter((e=>null!=e)),s=ja.flatten(this.activeTimers.map((e=>e.name))).filter((e=>null!=e));this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(ut().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let e=await Promise.all(a);i.kernelMs=ja.sum(e),i.getExtraProfileInfo=()=>e.map(((e,t)=>({name:s[t],ms:e}))).map((e=>`${e.name}: ${e.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 ut().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:ja.now(),endMs:null}}endTimer(e){return ut().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=ja.now(),e)}async getQueryTime(e){if(ut().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 null!=n&&(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:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),null!=t&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,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.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=hB){return ut().getBool("WEBGL_CPU_FORWARD")&&e.every((e=>null==this.texData.get(e.dataId).texture&&ja.sizeFromShape(e.shape)<t))}getGPGPUContext(){return this.gpgpu}where(e){Im.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return dB(e.shape,t)}packedUnaryOp(e,t,n){let r=new lB(e.shape,t),a=this.compileAndRun(r,[e],n);return Ni().makeTensorFromTensorInfo(a)}abs(e){if(this.shouldExecuteOnCPU([e])&&"complex64"!==e.dtype){let t=SP(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,t)}if(ut().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,ZP,e.dtype);let t=new KP(e.shape,ZP),n=this.compileAndRun(t,[e]);return Ni().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let r;if("string"===t&&null!=n&&n.length>0&&ja.isString(n[0])){let a=n.map((e=>ja.encodeString(e)));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){return Ni().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new uB(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new VP(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){let n=[NL(e.shape),...TL(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[NL(t),...TL(t)],s=new UP(a,n),i=[n],o=this.runWebGLProgram(s,[r],e.dtype,i,!0);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:r,shape:a,dtype:s}=n;if(null!=t){let e=ja.sizeFromShape(a),n=t[0]*t[1]*4;ja.assert(e<=n,(()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."))}let i,o=_L(a);i=r?new mz(o):new fz(o);let l=[null!=t?t:VO(o)];return{dtype:s,shape:a,dataId:this.runWebGLProgram(i,[{shape:o,dtype:s,dataId:e}],s,l,!0,t).dataId}}runWebGLProgram(e,t,n,r,a=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===DO.DENSE){let t=null!=s?s:VO(e.outputShape);o.texShape=t.map((e=>2*e))}if(null!=e.outTexUsage&&(o.usage=e.outTexUsage),0===ja.sizeFromShape(i.shape))return o.values=ja.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map((t=>{if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let n=this.texData.get(t.dataId);

2if(null==n.texture){if(!e.packedInputs&&ja.sizeFromShape(t.shape)<=ut().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:n.values};e.packedInputs&&(n.isPacked=!0,n.shape=t.shape)}if(this.uploadToGPU(t.dataId),!!n.isPacked!=!!e.packedInputs)t=n.isPacked?this.unpackTensor(t):this.packTensor(t),l.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!$L(n.shape,t.shape)){let e=t,r=t.shape;t.shape=n.shape,t=this.packedReshape(t,r),l.push(t),n=this.texData.get(t.dataId),e.shape=r}return{shape:t.shape,texData:n,isUniform:!1}}));this.uploadToGPU(i.dataId);let d,p={shape:i.shape,texData:o,isUniform:!1},h=function(e,t,n){let r="";t.concat(n).forEach((t=>{let a=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!t.isUniform){let s=t.texData.texShape,{useSqueezeShape:i,uniformShape:o,keptDims:l}=lz(e.packedInputs,t.shape,s),u="",d="",p="";if(1===o.length&&e.packedInputs){let e=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];u=`${e[0]>1}_${e[1]>1}`}else if(2!==o.length||e.packedInputs){if(o.length>2&&!e.packedInputs){let e=ja.computeStrides(o);p=`${e[0]===s[1]}_${e[e.length-1]===s[1]}`}}else d=`${o[0]>1}_${o[1]>1}`;let h=t.shape.length,c=2===o.length&&ja.arraysEqual(t.shape,s),f=1===ja.sizeFromShape(t.shape),m=Im.getBroadcastDims(t.shape,n.shape),g=!e.packedInputs&&h===n.shape.length&&ja.arraysEqual(s,n.texData.texShape),y=e.packedInputs||o.length>2?"":`${s[0]>1}_${s[1]>1}`;r+=`${h}_${g}_${i?l:""}_${o.length}_${f}_${m}_${c}_${u}_${d}_${p}_${y}_${a}`}else{let e=t.isUniform?"uniform":t.texData.texShape;r+=`${t.shape}_${e}_${a}`}}));let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a+`${ut().getNumber("WEBGL_VERSION")}`,s}(e,u,p),c=this.getAndSaveBinary(h,(()=>function(e,t,n,r){let a=n.map(((e,n)=>{let r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked,flatOffset:null};return null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0&&(r.flatOffset=e.texData.slice.flatOffset),{name:t.variableNames[n],shapeInfo:r}})),s=a.map((e=>e.shapeInfo)),i={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},o=ZL(a,i,t),l=JO(e.gl,o),u=e.createProgram(l);return ut().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},pz(e,t,u)))}(this.gpgpu,e,u,p))),f=null!=this.activeTimers;f&&(d=this.startTimer()),ut().get("ENGINE_COMPILE_ONLY")||function(e,t,n,r,a){t.program.enableShapeUniforms||(hz(t.inShapeInfos,n),hz([t.outShapeInfo],[r]));let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),1===ut().getNumber("WEBGL_VERSION")&&null!==t.infLoc&&e.gl.uniform1f(t.infLoc,1/0),null!==t.nanLoc&&e.gl.uniform1f(t.nanLoc,NaN);for(let r=0;r<n.length;++r){let a=n[r],{uniform:s,offset:i,shape:o,texShape:l}=t.variablesLocations[r];if(o){let{uniformShape:n}=lz(t.program.packedInputs,a.shape,a.texData.texShape);switch(n.length){case 1:e.gl.uniform1iv(o,new Int32Array(n));break;case 2:e.gl.uniform2iv(o,new Int32Array(n));break;case 3:e.gl.uniform3iv(o,new Int32Array(n));break;case 4:e.gl.uniform4iv(o,new Int32Array(n))}}if(l&&e.gl.uniform2i(l,a.texData.texShape[0],a.texData.texShape[1]),null!=s){if(a.isUniform){if(ja.sizeFromShape(a.shape)<2)e.gl.uniform1f(s,a.uniformValues[0]);else{let t=a.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(s,t)}continue}null!=a.texData.slice&&null!=i&&e.gl.uniform1i(i,a.texData.slice.flatOffset),e.setInputMatrixTexture(a.texData.texture.texture,s,r)}}let o=t.outShapeLocation;if(o)switch(r.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(r.shape))}if(t.outShapeStridesLocation){let n=ja.computeStrides(r.shape);switch(r.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(n));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(n));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(n))}}if(t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&a)for(let n=0;n<t.program.customUniforms.length;++n){let r=t.program.customUniforms[n],s=t.customUniformLocations[n],i=a[n];if("float"===r.type)e.gl.uniform1fv(s,i);else if("vec2"===r.type)e.gl.uniform2fv(s,i);else if("vec3"===r.type)e.gl.uniform3fv(s,i);else if("vec4"===r.type)e.gl.uniform4fv(s,i);else if("int"===r.type)e.gl.uniform1iv(s,i);else if("ivec2"===r.type)e.gl.uniform2iv(s,i);else if("ivec3"===r.type)e.gl.uniform3iv(s,i);else{if("ivec4"!==r.type)throw Error(`uniform type ${r.type} is not supported yet.`);e.gl.uniform4iv(s,i)}}e.executeProgram()}(this.gpgpu,c,u,p,r),l.forEach((e=>this.disposeIntermediateTensorInfo(e))),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));let m=ut().getNumber("WEBGL_FLUSH_THRESHOLD");if(m>0){let e=ja.now();e-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!ut().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&!1===a){let e=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),e}return i}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ut().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach((e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}

2)),this.textureManager.dispose(),null!=this.canvas&&"undefined"!=typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return null==this.floatPrecisionValue&&(this.floatPrecisionValue=Ci((()=>{if(!ut().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ut().getBool("DEBUG");ut().set("DEBUG",!1);let t=this.abs(Du(1e-8)).dataSync()[0];if(ut().set("DEBUG",e),t>0)return 32}return 16}))),this.floatPrecisionValue}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(null!=s)return;let l,u=null!=this.activeTimers;u&&(l=ja.now());let d=t.texShape;if(null==d&&(d=CL(n,o),t.texShape=d),null!=a){let e,s=_L(n),i=d[1],p=d[0],h=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!h)&&([i,p]=UO(d[0],d[1])),e=o?new wz(s,h):new xz(s,h);let c=h?[p,i]:d,f=this.makeTensorInfo(c,r),m=this.texData.get(f.dataId);m.usage=h?MO.PIXELS:MO.UPLOAD,m.texShape=c,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(f.dataId),i,p,a);let g=[[p,i]],y=this.runWebGLProgram(e,[f],r,g,!0),b=this.texData.get(y.dataId);t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,ut().get("ENGINE_COMPILE_ONLY")?this.disposeData(y.dataId):(t.texture=b.texture,t.values=null,this.texData.delete(y.dataId)),this.disposeIntermediateTensorInfo(f),u&&(this.uploadWaitMs+=ja.now()-l)}else{let e=this.acquireTexture(d,i,r,o);t.texture=e}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return null!=t&&(n.values=function(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){let n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length);for(let t=0;t<n.length;++t)n[t]=Math.round(e[t]);return n}throw new Error(`Unknown dtype ${t}`)}(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>1024*this.numMBBeforeWarning*1024){let e=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${e} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*ja.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];

2if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise((e=>{try{this.checkCompletion_(t),e(!0)}catch(e){throw e}}));e.push(n)}return Promise.all(e)}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await vm(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(!1===this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS))throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),!1===this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)?(nL(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:n,infLoc:r,nanLoc:a,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:o}=pz(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=n,e.infLoc=r,e.nanLoc=a,e.outShapeLocation=s,e.outShapeStridesLocation=i,e.outTexShapeLocation=o}}createTensorFromGPUData(e,t,n){e.channels=e.channels||"RGBA";let{texture:r,height:a,width:s,channels:i}=e,o=Ni().backend;if(!o.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(r,t,n,a,s,i);return Ni().makeTensorFromDataId(l,t,n,o)}};cB.nextDataId=0;var fB="4.21.0";function mB(){ut().set("WEBGL_FORCE_F16_TEXTURES",!0)}ri.isBrowser()&&zi("webgl",(()=>new cB),2);var gB={forceHalfFloat:mB},yB="\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n",bB=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=Im.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=cz(this.outputShape.length),this.userCode=`\n      float binaryOperation(float a, float b) {\n        ${e}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `}},xB="\n  result.r = isNaN.r ? NAN : result.r;\n  result.g = isNaN.g ? NAN : result.g;\n  result.b = isNaN.b ? NAN : result.b;\n  result.a = isNaN.a ? NAN : result.a;\n",wB=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=Im.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length;this.enableShapeUniforms=cz(a);let s="";if(r)if(0===a||1===ja.sizeFromShape(this.outputShape))s="\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        ";else if(s=`\n          ${oz(a)} coords = getOutputCoords();\n        `,1===a)this.enableShapeUniforms?s+="\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          ":s+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let e=WP("coords",a);this.enableShapeUniforms?s+=`\n            bool nextRowOutOfBounds =\n              (${e[a-2]} + 1) >= outShape[${a} - 2];\n            bool nextColOutOfBounds =\n              (${e[a-1]} + 1) >= outShape[${a} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:s+=`\n            bool nextRowOutOfBounds =\n              (${e[a-2]} + 1) >= ${this.outputShape[a-2]};\n            bool nextColOutOfBounds =\n              (${e[a-1]} + 1) >= ${this.outputShape[a-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${s}\n\n        setOutput(result);\n      }\n    `}};function vB(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var kB={kernelName:An,backendName:"webgl",kernelFunc:vB};function IB(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=vB({input

2s:{x:r},backend:n}),l=vB({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var SB={kernelName:Wt,backendName:"webgl",kernelFunc:IB},NB="return (a < 0.) ? b * a : a;",TB="\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";var _B={kernelName:Ln,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",ja.createScalarValue(s,"float32")),o=ut().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wB(TB,a.shape,i.shape):new bB(NB,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],"float32");return n.disposeIntermediateTensorInfo(i),l}},CB="return (a < 0.) ? b * a : a;",EB="\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";var $B={kernelName:Ir,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=ut().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wB(EB,r.shape,a.shape):new bB(CB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],"float32")}},AB="if (isnan(x)) return x;";function RB({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let i,{x:o}=a,l=s,u=r||o.dtype;if(l.shouldExecuteOnCPU([o])&&null!=n){let e=l.texData.get(o.dataId),t=n(e.values,u);return l.makeTensorInfo(o.shape,u,t)}return i=ut().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&null!=t?new lB(o.shape,t):new KP(o.shape,e),l.runWebGLProgram(i,[o],u)}}function FB({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(r&&"complex64"===l.dtype){let t=d.texData.get(l.dataId),n=d.texData.get(u.dataId),[r,a]=[[t.complexTensorInfos.real,n.complexTensorInfos.real],[t.complexTensorInfos.imag,n.complexTensorInfos.imag]].map((t=>{let[n,r]=t,a={dataId:n.dataId,dtype:n.dtype,shape:l.shape},s={dataId:r.dataId,dtype:r.dtype,shape:u.shape},i=new bB(e,l.shape,u.shape);return d.runWebGLProgram(i,[a,s],Ws(n.dtype,r.dtype))})),s=IB({inputs:{real:r,imag:a},backend:d});return d.disposeIntermediateTensorInfo(r),d.disposeIntermediateTensorInfo(a),s}let p,h=s||Ws(l.dtype,u.dtype);if(("string"===l.dtype||"string"===u.dtype||d.shouldExecuteOnCPU([l,u]))&&null!=a){let e=d.texData.get(l.dataId).values,t=d.texData.get(u.dataId).values,n="string"===l.dtype?Im.fromUint8ToStringArray(e):e,r="string"===l.dtype?Im.fromUint8ToStringArray(t):t,[s,i]=a(l.shape,u.shape,n,r,h),o=d.makeTensorInfo(i,h);return d.texData.get(o.dataId).values=s,o}return p=ut().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&null!=t?new wB(t,l.shape,u.shape,n):new bB(e,l.shape,u.shape),d.runWebGLProgram(p,[l,u],h)}}function DB(e,t=!1){if("linear"===e)return t?rB:YP;if("relu"===e)return t?sB:QP;if("elu"===e)return t?aB:JP;if("relu6"===e)return t?iB:eB;if("prelu"===e)return t?EB:CB;if("leakyrelu"===e)return t?TB:NB;if("sigmoid"===e)return t?oB:nB;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var MB=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=cz(this.outputShape.length);let u=r?e[1]:e[2],d=Math.ceil(u/2),p=r?"i * 2, rc.y":"rc.y, i * 2",h=a?"rc.z, i * 2":"i * 2, rc.z",c=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(m=o?`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:l?`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:`vec4 activation(vec4 x) {\n          ${i}\n        }`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]<t[0]?b=`imod(rc.x, ${e[0]})`:t[0]<e[0]&&(x=`imod(rc.x, ${t[0]})`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${d}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        int batchA = ${b};\n        int batchB = ${x};\n        for (int i = 0; i < ${d};

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The new shape and old shape must have the same number of elements.`));let d=i.texData.get(a.dataId);return!d.isPacked||$L(a.shape,l)||null!==d.texture&&$L(d.shape,l)?(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype}):function(e,t,n){let r=[NL(e.shape),...TL(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[NL(t),...TL(t)],i=new UP(s,r),o=[r],l=n.runWebGLProgram(i,[a],e.dtype,o,!0);return{dataId:l.dataId,shape:t,dtype:l.dtype}}(a,l,i)}var VB={kernelName:Rr,backendName:"webgl",kernelFunc:WB},UB=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=4*Math.floor(n/4),o=n%4,l="sumValue += dot(values, ones);";if(null!=t){let e=1/t;l=`sumValue += dot(values * ${ja.isInt(e)?e.toPrecision(2):e}, ones);`}let u="";a%n>0&&(u=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${u}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${l}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${1===o}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${l}\n        } else if (${2===o}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${l}\n        } else if (${3===o}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${l}\n        }\n        setOutput(sumValue);\n      }\n    `}},GB=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";"prod"===t?i="1.0":"min"===t?(i="1.0 / 1e-20",o="min"):"max"===t&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"sum"===t?l="sumValue":"prod"===t?l="prodValue":"all"===t?l="allValue":"any"===t&&(l="anyValue");let u=4*Math.floor(n/4),d=n%4,p=`\n      if (${"sum"===t}) {\n        sumValue += dot(values, ones);\n      } else if (${"prod"===t}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${o}(values, minMaxValue);\n        if (${"min"===t} || ${"max"===t}) {\n          minMaxValue = ${o}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,h="vec4";"all"===t?(i="1.0",p="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",h="bvec4"):"any"===t&&(i="0.0",p="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",h="bvec4");let c="";a%n>0&&(c=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${c}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${i});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${u}; i += 4) {\n          int inIdx = inOffset + i;\n          ${h} values = ${h}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${u};\n        if (${1===d}) {\n          ${h} values = ${h}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${2===d}) {\n          ${h} values = ${h}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${3===d}) {\n          ${h} values = ${h}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${p}\n        }\n        setOutput(${l});\n      }\n    `}};function HB(e,t,n,r){let a=function(e){let t=[];for(;0===t.length||1!==t[t.length-1].outSize;){let n=t.length?t[t.length-1].outSize:e[1],r=Im.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}(e.shape),s=e;for(let i=0;i<a.length;i++){let o,l,{inSize:u,windowSize:d,outSize:p}=a[i];o="mean"===n?0===i?new UB({windowSize:d,inSize:u,batchSize:e.shape[0],outSize:p},u):new UB({windowSize:d,inSize:u,batchSize:e.shape[0],outSize:p}):new GB({windowSize:d,inSize:u,batchSize:e.shape[0],outSize:p},n),l=s,s=r.runWebGLProgram(o,[s],t),l.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(l)}return s}var jB=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.rank=n.length;

2let r=oz(this.rank),a=function(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let t=0;t<e.length;t++)r[e[t]]=n[t];return r.join()}(t);this.userCode=`\n    void main() {\n      ${r} resRC = getOutputCoords();\n      setOutput(getA(${a}));\n    }\n    `}};var qB=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=oz(this.rank),a=BP("rc",this.rank),s=new Array(this.rank);for(let e=0;e<t.length;e++)s[t[e]]=a[e];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${r} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${l};\n      if(${o}) {\n        result[1] = ${l};\n      }\n      --${a[this.rank-1]};\n      if(++${a[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${l};\n        if(${o}) {\n          result[3] = ${l};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function KB(e,t,n){let r=ut().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qB(e.shape,t):new jB(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function XB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return function(e,t,n,r){let a=t,s=e.shape.length,i=ja.parseAxisParam(a,e.shape),o=i,l=Im.getAxesPermutation(o,s),u=null!=l,d=e;u&&(d=KB(e,l,r),o=Im.getInnerMostAxes(o.length,s)),Im.assertAxesAreInnerMostDims("sum",o,s);let[p,h]=Im.computeOutAndReduceShapes(d.shape,o),c=p;n&&(c=Im.expandShapeToKeepDim(p,i));let f=ja.sizeFromShape(h),m=WB({inputs:{x:d},attrs:{shape:[ja.sizeFromShape(e.shape)/f,f]},backend:r}),g=HB(m,Vs(e.dtype),"sum",r),y=WB({inputs:{x:g},attrs:{shape:c},backend:r});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),u&&r.disposeIntermediateTensorInfo(d),y}(a,s,i,n)}var YB={kernelName:Qr,backendName:"webgl",kernelFunc:XB};function ZB(e){let t,{inputs:n,backend:r,attrs:a}=e,{x:s}=n,{perm:i}=a,o=r,l=s.shape.length,u=new Array(l);for(let e=0;e<u.length;e++)u[e]=s.shape[i[e]];if(o.shouldExecuteOnCPU([s])){let e=o.texData.get(s.dataId).values,n=zP(e,s.shape,s.dtype,i,u);t=o.makeTensorInfo(u,s.dtype),o.texData.get(t.dataId).values=n}else t=KB(s,i,o);return t}var JB={kernelName:va,backendName:"webgl",kernelFunc:ZB},QB=1e3;function eW({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],h=r?t.shape[d-1]:t.shape[d-2],c=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=ja.sizeFromShape(m),b=ja.sizeFromShape(g),x=du.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);ja.assert(p===h,(()=>`Error in matMul: inner shapes (${p}) and (${h}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`));let w,v=n?[y,p,c]:[y,c,p],k=r?[b,f,h]:[b,h,f],I=WB({inputs:{x:e},backend:a,attrs:{shape:v}}),S=WB({inputs:{x:t},backend:a,attrs:{shape:k}}),N=[I,S],T=Math.max(y,b),_=n?I.shape[1]:I.shape[2],C=null!=s,E=null!=i,$="leakyrelu"===l,A=null!=l?DB(l,!0):null;if((1===c||1===f)&&_>QB&&!1===(C||E||$||null!=A)){let e=I,t=S;n&&(e=ZB({inputs:{x:I},backend:a,attrs:{perm:[0,2,1]}}),N.push(e)),r&&(t=ZB({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),N.push(t));let s=1===f,i=e;1!==f&&(i=WB({inputs:{x:e},backend:a,attrs:{shape:[T,_,1]}}),N.push(i));let o=1===f?2:1,l=t;s&&(l=WB({inputs:{x:t},backend:a,attrs:{shape:[T,1,_]}}),N.push(l));let u=PB({inputs:{a:i,b:l},backend:a});w=XB({inputs:{x:u},backend:a,attrs:{axis:o,keepDims:!0}}),N.push(u)}else{let l=Ws(e.dtype,t.dtype),u=new MB(v,k,[T,c,f],n,r,C,A,E,$),d=[I,S];if(null!=s&&d.push(s),E&&d.push(i),$){let e=a.makeTensorInfo([],"float32",ja.createScalarValue(o,"float32"));d.push(e),N.push(e)}w=a.runWebGLProgram(u,d,l)}

2let R=WB({inputs:{x:w},backend:a,attrs:{shape:x}});N.push(w);for(let e of N)a.disposeIntermediateTensorInfo(e);return R}var tW={kernelName:$a,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:p}=r;return eW({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:d})}},nW="return abs(x);";var rW={kernelName:ft,backendName:"webgl",kernelFunc:function(e){let t,{inputs:n,backend:r}=e,{x:a}=n;if(r.shouldExecuteOnCPU([a])&&"complex64"!==a.dtype){let e=r.texData.get(a.dataId),t=SP(e.values);return r.makeTensorInfo(a.shape,a.dtype,t)}return t=ut().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new lB(a.shape,nW):new KP(a.shape,nW),r.runWebGLProgram(t,[a],a.dtype)}},aW=RB({opSnippet:XP+"\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n"}),sW={kernelName:mt,backendName:"webgl",kernelFunc:aW},iW=RB({opSnippet:XP+"\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));"}),oW={kernelName:gt,backendName:"webgl",kernelFunc:iW},lW="return a + b;",uW=FB({opSnippet:lW,packedOpSnippet:lW,supportsComplex:!0,cpuKernelImpl:Hz}),dW={kernelName:yt,backendName:"webgl",kernelFunc:uW},pW=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));let n=[];this.variableNames.forEach((e=>{n.push(`float v${e} = get${e}AtOutCoords();`)}));let r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n      void main() {\n        ${n.join("\n        ")}\n\n        float result = ${r};\n        setOutput(result);\n      }\n    `}},hW=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));let n=[];this.variableNames.forEach((e=>{n.push(`vec4 v${e} = get${e}AtOutCoords();`)}));let r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n      void main() {\n        ${n.join("\n        ")}\n\n        vec4 result = ${r};\n        setOutput(result);\n      }\n    `}};var cW={kernelName:bt,backendName:"webgl",kernelFunc:function e(t){let{inputs:n,backend:r}=t,a=n;if(1===a.length)return vB({inputs:{x:a[0]},backend:r});if(a.length>ut().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let t=Math.floor(a.length/2),n=e({

2inputs:a.slice(0,t),backend:r}),s=e({inputs:a.slice(t),backend:r});return e({inputs:[n,s],backend:r})}let s=a.map((e=>e.dtype)).reduce(((e,t)=>Ws(e,t))),i=a.map((e=>e.shape)),o=ut().getBool("WEBGL_PACK")?new hW(a[0].shape,i):new pW(a[0].shape,i);return r.runWebGLProgram(o,a,s)}};var fW={kernelName:xt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=ja.parseAxisParam(s,a.shape),u=l,d=Im.getAxesPermutation(u,o),p=a;null!=d&&(p=ZB({inputs:{x:a},backend:n,attrs:{perm:d}}),u=Im.getInnerMostAxes(u.length,o)),Im.assertAxesAreInnerMostDims("all",u,o);let h,[c,f]=Im.computeOutAndReduceShapes(p.shape,u),m=WB({inputs:{x:p},backend:n,attrs:{shape:[-1,ja.sizeFromShape(f)]}}),g=HB(m,m.dtype,"all",n);if(i){h=WB({inputs:{x:g},backend:n,attrs:{shape:Im.expandShapeToKeepDim(c,l)}})}else h=WB({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=d&&n.disposeIntermediateTensorInfo(p),h}};var mW={kernelName:wt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=ja.parseAxisParam(s,a.shape),u=l,d=Im.getAxesPermutation(u,o),p=a;null!=d&&(p=ZB({inputs:{x:a},backend:n,attrs:{perm:d}}),u=Im.getInnerMostAxes(u.length,o)),Im.assertAxesAreInnerMostDims("any",u,o);let h,[c,f]=Im.computeOutAndReduceShapes(p.shape,u),m=WB({inputs:{x:p},backend:n,attrs:{shape:[-1,ja.sizeFromShape(f)]}}),g=HB(m,m.dtype,"any",n);if(i){h=WB({inputs:{x:g},backend:n,attrs:{shape:Im.expandShapeToKeepDim(c,l)}})}else h=WB({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=d&&n.disposeIntermediateTensorInfo(p),h}},gW=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i="max"===t?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${r};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${r}; i++) {\n          int inIdx = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}},yW=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,ja.assert(e.length>2,(()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`));let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i,o,l=this.outputShape,u=l.length,d=oz(u),p=WP("coords",u);if(1===s){o=u+1;let e=oz(o);i=`\n        ${e} sourceLocR = ${e}(${p.join()}, 0);\n        ++${p[u-1]};\n        ${e} sourceLocG = ${e}(${p.join()}, 0);\n        ++${p[u-2]};\n        ${e} sourceLocA = ${e}(${p.join()}, 0);\n        --${p[u-1]};\n        ${e} sourceLocB = ${e}(${p.join()}, 0);\n        --${p[u-2]};`}else o=u,i=`\n        ${d} sourceLocR = coords;\n        ++${p[u-1]};\n        ${d} sourceLocG = coords;\n        ++${p[u-2]};\n        ${d} sourceLocA = coords;\n        --${p[u-1]};\n        ${d} sourceLocB = coords;\n        --${p[u-2]};`;let h=["x","y","z","w","u","v"].slice(0,o),c="."+h[o-1],f=h.map((e=>"int "+e)),m=WP("sourceLocR",o-1).concat("inIdx.r"),g=WP("sourceLocG",o-1).concat("inIdx.g"),y=WP("sourceLocB",o-1).concat("inIdx.b"),b=WP("sourceLocA",o-1).concat("inIdx.a"),x="max"===n?"greaterThan":"lessThan",w=r?"":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${y.join()}),\n                             getBestIndicesAChannel(${b.join()})));`,v=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${y.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,k=r?"":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${h.join()}),\n                                          vec2(${h.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${h.join()}),\n                               vec2(${h.slice(-2).join()}));\n      }\n      ${k}\n      void main() {\n        ${d} coords = getOutputCoords();\n        bool hasNextCol = ${p[u-1]} < ${l[u-1]-1};\n        bool hasNextRow = ${p[u-2]} < ${l[u-2]-1};\n        ${i}\n        ivec4 srcIdx = ivec4(sourceLocR${c}, sourceLocG${c},\n          sourceLocB${c}, sourceLocA${c}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${v};\n\n        for (int i = 0; i < ${t};

2 i++) {\n          inIdx = srcIdx;\n          ${w}\n          vec4 candidate = ${v};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}};function bW(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];null!=r&&(a=r.shape[0],s=r.shape[1]);let i=Im.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new gW(o,n,null==r),u=[t];null!=r&&u.push(r);let d=e.runWebGLProgram(l,u,"int32");if(1===d.shape[1])return d;let p=bW(e,t,n,d);return e.disposeIntermediateTensorInfo(d),p}function xW(e,t,n,r=null){let a=null!=r?r.shape:t.shape,s=a[a.length-1],i=Im.computeOptimalWindowSize(s),o=new yW(a,i,n,null==r),l=null==r?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let r=xW(e,t,n,u);return e.disposeIntermediateTensorInfo(u),r}return u}function wW(e,t,n,r){let a=[n];if(Im.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!ut().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let n=[],s=e.texData.get(t.dataId),i=t;null!==s&&s.isPacked&&(i=e.unpackTensor(t),n.push(i));let[o,l]=Im.computeOutAndReduceShapes(i.shape,a),u=ja.sizeFromShape(l),d=WB({inputs:{x:i},backend:e,attrs:{shape:[-1,u]}});n.push(d);let p=bW(e,d,r);n.push(p);let h=WB({inputs:{x:p},backend:e,attrs:{shape:o}});return n.forEach((t=>e.disposeIntermediateTensorInfo(t))),h}return xW(e,t,r)}var vW={kernelName:vt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=ja.parseAxisParam(s,a.shape),o=Im.getAxesPermutation(i,a.shape.length),l=a,u=[];null!=o&&(l=ZB({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=Im.getInnerMostAxes(i.length,l.shape.length)),Im.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=wW(n,l,i[0],"max");return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),d}};var kW={kernelName:kt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=ja.parseAxisParam(s,a.shape),o=Im.getAxesPermutation(i,a.shape.length),l=a,u=[];null!=o&&(l=ZB({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=Im.getInnerMostAxes(i.length,l.shape.length)),Im.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=wW(n,l,i[0],"min");return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),d}},IW=RB({opSnippet:XP+"\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n"}),SW={kernelName:It,backendName:"webgl",kernelFunc:IW},NW=RB({opSnippet:XP+"return log(x + sqrt(x * x + 1.0));"}),TW={kernelName:St,backendName:"webgl",kernelFunc:NW},_W=RB({opSnippet:XP+"\n  return atan(x);\n"}),CW={kernelName:Nt,backendName:"webgl",kernelFunc:_W},EW=FB({opSnippet:yB+"\n  return atan(a, b);\n",packedOpSnippet:"\n  vec4 result = atan(a, b);\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  "+xB+"\n  return result;\n"}),$W={kernelName:_t,backendName:"webgl",kernelFunc:EW},AW=RB({opSnippet:XP+"\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;"}),RW={kernelName:Tt,backendName:"webgl",kernelFunc:AW},FW=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&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,h=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let f="avg"===t,m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let t=">=";return void(this.userCode=`\n        const ivec2 strides = ivec2(${i}, ${o});\n        const ivec2 pads = ivec2(${h}, ${c});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${d};\n              wR += ${l}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p};\n                wC += ${u}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${t} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${r?a?m:g:`wR * ${p} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `)}let b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(b="avgValue / max(count, 1.0)");let x=4*Math.floor(s/4),w=s%4,v=`\n      if (${f}

2) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${o});\n      const ivec2 pads = ivec2(${h}, ${c});\n      const float initializationValue = ${y};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${y});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${d};\n            wR += ${l}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${u};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${u}, d),\n              getValue(batch, xR, xC + 2 * ${u}, d),\n              getValue(batch, xR, xC + 3 * ${u}, d)\n            );\n\n            ${v}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${1===w}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${v}\n          } else if (${2===w}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${u}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${v}\n          } else if (${3===w}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${u}, d),\n              getValue(batch, xR, xC + 2 * ${u}, d),\n              initializationValue\n            );\n\n            ${v}\n          }\n        }\n        setOutput(${b});\n      }\n    `}},DW=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&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,h=e.effectiveFilterDepth,c=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b="avg"===t,x="0.0";if(b||(x="-1.0 / 1e-20"),n){let t=">=";return void(this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${o}, ${l});\n        const ivec3 pads = ivec3(${m}, ${g}, ${y});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${h};\n              wD += ${u}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${c};\n                wR += ${d}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${p}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${t} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;

2\n                  minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${c} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `)}let w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(w="avgValue / max(count, 1.0)");let v=4*Math.floor(s/4),k=s%4,I=`\n      if (${b}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${o}, ${l});\n      const ivec3 pads = ivec3(${m}, ${g}, ${y});\n      const float initializationValue = ${x};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${x});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${h};\n            wD += ${u}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${c};\n            wR += ${d}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${v}; wC += 4) {\n              int xC = xCCorner + wC * ${p};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n              );\n\n              ${I}\n            }\n\n            int xC = xCCorner + ${v};\n            if (${1===k}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${I}\n            } else if (${2===k}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${I}\n            } else if (${3===k}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                initializationValue\n              );\n\n              ${I}\n            }\n          }\n        }\n        setOutput(${w});\n      }\n    `}};var MW={kernelName:Ct,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;VL(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r;ja.assert(Im.eitherStridesOrDilationsAreOne(i,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));let u=Im.computePool2DInfo(a.shape,s,i,1,o,l);if(1===u.filterWidth&&1===u.filterHeight&&ja.arraysEqual(u.inShape,u.outShape))return vB({inputs:{x:a},backend:n});let d=new FW(u,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}};var OW={kernelName:$t,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,d=Im.computePool3DInfo(a.shape,s,i,[1,1,1],o,l,u),p=new DW(d,"avg",!1);return n.runWebGLProgram(p,[a],"float32")}},LW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${u}, ${d});\n      const float avgMultiplier = float(${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${o};\n            wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${r}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${l};\n            wC+= ${i}) {\n            float dyC = float(dyCCorner + wC) / ${a}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},zW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,h=e.effectiveFilterWidth,c=d-1-e.padInfo.front,f=p-1-e.padInfo.top,m=h-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(x

2D, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${o}) {\n          float dyD = float(dyDCorner + wD) / ${a}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${p};\n              wR += ${l}) {\n            float dyR = float(dyRCorner + wR) / ${s}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${h};\n                wC += ${u}) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};var PW={kernelName:At,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=r,p=Im.computePool3DInfo(i.shape,o,l,[1,1,1],u,d),h=new zW(p);return n.runWebGLProgram(h,[a],i.dtype)}};var BW={kernelName:Et,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;VL([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,d=Im.computePool2DInfo(i.shape,o,l,1,u),p=new LW(d);return n.runWebGLProgram(p,[a],i.dtype)}};var WW={kernelName:Rt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return eW({a,b:s,transposeA:i,transposeB:o,backend:n})}},VW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],Im.assertAndGetBroadcastShape(e,t),Im.assertAndGetBroadcastShape(e,n);let i="0.0";null!=r&&(Im.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";null!=a&&(Im.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${i};\n        float scale = ${o};\n        float inv = scale * inversesqrt(variance + float(${s}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `}},UW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],Im.assertAndGetBroadcastShape(e,t),Im.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";null!=r&&(Im.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";null!=a&&(Im.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 offset = ${i};\n        vec4 scale = ${o};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${s}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `}},GW={kernelName:Tn,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;ja.assert(a.shape.length===s.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),ja.assert(null==i||a.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),ja.assert(null==o||a.shape.length===o.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));let{varianceEpsilon:l}=n;null==l&&(l=.001);let u=[r,a,s],d=null;null!=i&&(d=i.shape,u.push(i));let p=null;null!=o&&(p=o.shape,u.push(o));let h=ut().getBool("WEBGL_PACK_NORMALIZATION")?new UW(r.shape,a.shape,s.shape,d,p,l):new VW(r.shape,a.shape,s.shape,d,p,l);return t.runWebGLProgram(h,u,u[0].dtype)}},HW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;

2let t=oz(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n,r=function(e){if(1===e)return"sourceLoc";if(e<=6)return jW.slice(0,e).map((e=>"sourceLoc."+e)).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}(this.rank);n=`\n        ${t} sourceLoc;\n        ${t} coords = getOutputCoords();\n        ${e.map(((e,t)=>`sourceLoc.${jW[t]} = start[${t}] + coords.${jW[t]};`)).join("\n")}\n      `,this.userCode=`\n      void main() {\n        ${n}\n        setOutput(getSource(${r}));\n      }\n    `}},jW=["x","y","z","w","u","v"];var qW=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=oz(this.rank),n=WP("coords",this.rank),r=WP("sourceLoc",this.rank),a=1===this.rank?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`\n      result.x = ${s};\n      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n        ++${r[this.rank-1]};\n        result.y = ${s};\n        --${r[this.rank-1]};\n      }\n    `,o=1===this.rank?"":`\n      --${n[this.rank-1]};\n      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {\n        ++${r[this.rank-2]};\n        result.z = ${s};\n        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n          ++${r[this.rank-1]};\n          result.w = ${s};\n        }\n      }\n    `,l=this.rank<=4?`sourceLoc = coords +\n            ${t}(${e.map(((e,t)=>`start[${t}]`)).join()});`:e.map(((e,t)=>`${r[t]} = ${n[t]} + start[${t}];`)).join("\n");this.userCode=`\n      void main() {\n        ${t} coords = getOutputCoords();\n        ${t} sourceLoc;\n        ${l}\n        vec4 result = vec4(0.);\n        ${i}\n        ${o}\n        setOutput(result);\n      }\n    `}};function KW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=Zf.parseSliceParams(a,s,i);if(Zf.assertParamsValid(a,o,l),0===ja.sizeFromShape(l))return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||"string"===a.dtype){let e=n.texData.get(a.dataId),t=NP(e.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,t)}let{isPacked:u}=n.texData.get(a.dataId),d=Zf.isSliceContinous(a.shape,o,l);if(u||!d){let e=ut().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qW(l):new HW(l),t=[o];return n.runWebGLProgram(e,[a],a.dtype,t)}return n.uploadToGPU(a.dataId),function(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=Zf.computeFlatOffset(t,ja.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}

2(a,o,l,n)}var XW={kernelName:jr,backendName:"webgl",kernelFunc:KW},YW={kernelName:Ft,backendName:"webgl",kernelFunc:e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ja.assert(a.shape.length<=4,(()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"));let o=s.reduce(((e,t)=>e*t)),l=Im.getReshaped(a.shape,s,o),u=Im.getPermuted(l.length,s.length),d=Im.getReshapedPermuted(a.shape,s,o),p=Im.getSliceBeginCoords(i,s.length),h=Im.getSliceSize(d,i,s.length),c=[],f=WB({inputs:{x:a},backend:n,attrs:{shape:l}}),m=ZB({inputs:{x:f},backend:n,attrs:{perm:u}}),g=WB({inputs:{x:m},backend:n,attrs:{shape:d}}),y=KW({inputs:{x:g},backend:n,attrs:{begin:p,size:h}});return c.push(f),c.push(m),c.push(g),c.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}};var ZW={kernelName:Dt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=jz(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}};var JW={kernelName:Mt,backendName:"webgl",kernelFunc:function(e){let t,{inputs:n,backend:r}=e,{a,b:s}=n,i=ut().getBool("WEBGL_PACK_BINARY_OPERATIONS"),o=ut().getNumber("WEBGL_VERSION");if(r.shouldExecuteOnCPU([a,s])||1===o){let e=r.texData.get(a.dataId).values,t=r.texData.get(s.dataId).values,[n,i]=Kz(a.shape,s.shape,e,t,a.dtype),o=r.makeTensorInfo(i,a.dtype);return r.texData.get(o.dataId).values=n,o}return t=i?new wB("\n  int r = int(a.r) & int(b.r);\n  int g = int(a.g) & int(b.g);\n  int rb = int(a.b) & int(b.b);\n  int ra = int(a.a) & int(b.a);\n  return vec4(r, g, rb, ra);\n",a.shape,s.shape,!1):new bB("\n  return float(int(a.r) & int(b.r));\n",a.shape,s.shape),r.runWebGLProgram(t,[a,s],a.dtype)}};var QW={kernelName:Lt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{s0:r,s1:a}=t,s=n.readSync(r.dataId),i=n.readSync(a.dataId),o=Im.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}},eV=FB({opSnippet:"return float(a != b);",cpuKernelImpl:mP,dtype:"bool"}),tV={kernelName:cr,backendName:"webgl",kernelFunc:eV};function nV(e){let{inputs:t,backend:n}=e,{input:r}=t;return vB({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.real},backend:n})}var rV={kernelName:Er,backendName:"webgl",kernelFunc:nV},aV="return float(int(x));";var sV={kernelName:zt,backendName:"webgl",kernelFunc:function e(t){let{inputs:n,backend:r,attrs:a}=t,{x:s}=n,{dtype:i}=a;if("complex64"===i){if("complex64"===s.dtype)return vB({inputs:{x:s},backend:r});let t=Rd(s.shape),n=e({inputs:{x:s},backend:r,attrs:{dtype:"float32"}}),a=IB({inputs:{real:n,imag:t},backend:r});return t.dispose(),r.disposeIntermediateTensorInfo(n),a}if("complex64"===s.dtype){let t=nV({inputs:{input:s},backend:r}),n=e({inputs:{x:t},backend:r,attrs:{dtype:i}});return r.disposeIntermediateTensorInfo(t),n}if(!ja.hasEncodingLoss(s.dtype,i)){let e=vB({inputs:{x:s},backend:r});return{dataId:e.dataId,shape:e.shape,dtype:i}}if(r.shouldExecuteOnCPU([s])){let e=r.texData.get(s.dataId).values,[t,n,a]=Xz(e,s.shape,s.dtype,i);return r.makeTensorInfo(t,n,a)}if("int32"===i)return function(e,t){let n=new KP(e.shape,aV),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}(s,r);if("bool"===i){let e=r.makeTensorInfo([],"bool",ja.getTypedArrayFromDType("bool",1)),t=eV({inputs:{a:s,b:e},backend:r});return r.disposeIntermediateTensorInfo(e),t}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${i}`)}},iV="return ceil(x);",oV=RB({opSnippet:iV,packedOpSnippet:iV,cpuKernelImpl:Yz}),lV={kernelName:Pt,backendName:"webgl",kernelFunc:oV},uV=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    "}},dV=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="\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    "}};var pV={kernelName:Bt,backendName:"webgl",kernelFunc:function(e){let t,{inputs:n,backend:r,attrs:a}=e,{x:s}=n,{clipValueMin:i,clipValueMax:o}=a;t=ut().getBool("WEBGL_PACK_CLIP")?new dV(s.shape):new uV(s.shape);let l=[[i],[o]];return r.runWebGLProgram(t,[s],s.dtype,l)}},hV=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    "}};function cV(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}var fV={kernelName:Vt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new hV(r.shape),i=[cV(r,a.complexTensorInfos.real),cV(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}},mV=class{constructor(e){this.outputShape=[],this.outputShape=Im.computeOutShape(e,1),this.variableNames=e.map(((e,t)=>`T${t}`));let t=new Array(e.length-1);t[0]=e[0][1];for(let n=1;n<t.length;n++)t[n]=t[n-1]+e[n][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let e=1;e<t.length;e++){let r=t[e-1];n.push(`else if (yC < ${t[e]}

2) setOutput(getT${e}(yR, yC-${r}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join("\n        ")}\n      }\n    `}},gV=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=Im.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=oz(r),s=WP("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map(((e,t)=>`T${t}`));let o=new Array(e.length-1);o[0]=e[0][t];for(let n=1;n<o.length;n++)o[n]=o[n-1]+e[n][t];let l=i[t],u=i.slice(-2),d=i.join(),p=`if (${l} < ${o[0]}) {\n        return getChannel(\n            getT0(${d}), vec2(${u.join()}));\n        }`;for(let e=1;e<o.length;e++){let t=o[e-1];p+=`\n        if (${l} < ${o[e]}  && ${l} >= ${o[e-1]}) {\n          return getChannel(\n            getT${e}(${yV(i,l,t)}),\n            vec2(${yV(u,l,t)}));\n        }`}let h=o.length,c=o[o.length-1];p+=`\n        return getChannel(\n          getT${h}(${yV(i,l,c)}),\n          vec2(${yV(u,l,c)}));`,this.userCode=`\n      float getValue(${i.map((e=>"int "+e))}) {\n        ${p}\n      }\n\n      void main() {\n        ${a} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${s}), 0., 0., 0.);\n\n        ${s[r-1]} = ${s[r-1]} + 1;\n        if (${s[r-1]} < ${n[r-1]}) {\n          result.g = getValue(${s});\n        }\n\n        ${s[r-2]} = ${s[r-2]} + 1;\n        if (${s[r-2]} < ${n[r-2]}) {\n          result.a = getValue(${s});\n        }\n\n        ${s[r-1]} = ${s[r-1]} - 1;\n        if (${s[r-2]} < ${n[r-2]} &&\n            ${s[r-1]} < ${n[r-1]}) {\n          result.b = getValue(${s});\n        }\n        setOutput(result);\n      }\n    `}};function yV(e,t,n){let r=e.indexOf(t);return e.map(((e,t)=>t===r?`${e} - ${n}`:e)).join()}function bV(e){let{inputs:t,backend:n}=e,{input:r}=t;return vB({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.imag},backend:n})}var xV={kernelName:Fn,backendName:"webgl",kernelFunc:bV};function wV(e,t,n){let r=e[0].dtype;if("complex64"===r){let r=e.map((e=>nV({inputs:{input:e},backend:n}))),a=e.map((e=>bV({inputs:{input:e},backend:n}))),s=wV(r,t,n),i=wV(a,t,n),o=IB({inputs:{real:s,imag:i},backend:n});return r.forEach((e=>n.disposeIntermediateTensorInfo(e))),a.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),o}let a=n.shouldExecuteOnCPU(e);if("string"===r&&(a=!0),a){let a=e.map((e=>{let r=[-1,ja.sizeFromShape(e.shape.slice(t))];return WB({inputs:{x:e},backend:n,attrs:{shape:r}})})),s=a.map((e=>({vals:n.readSync(e.dataId),shape:e.shape}))),i=Im.computeOutShape(a.map((e=>e.shape)),1),o=1===a[0].shape[0],l=Zz(s,i,r,o),u=Im.computeOutShape(e.map((e=>e.shape)),t),d=n.makeTensorInfo(u,r,l);return a.forEach((e=>n.disposeIntermediateTensorInfo(e))),d}let s=e.filter((e=>ja.sizeFromShape(e.shape)>0)),i=ut().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(1===s.length){let t=i?new KP(e[0].shape,tB):new lB(e[0].shape,tB);return n.runWebGLProgram(t,e,r)}let o=ut().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let e=[];for(let r=0;r<s.length;r+=o){let a=s.slice(r,r+o);e.push(wV(a,t,n))}let r=wV(e,t,n);for(let t of e)n.disposeIntermediateTensorInfo(t);return r}if(i){let e=new gV(s.map((e=>e.shape)),t);return n.runWebGLProgram(e,s,r)}let{tensors2D:l,outShape:u}=function(e,t,n){let r=Im.computeOutShape(e.map((e=>e.shape)),t);return{tensors2D:e.map((e=>WB({inputs:{x:e},attrs:{shape:[-1,ja.sizeFromShape(e.shape.slice(t))]},backend:n}))),outShape:r}}(s,t,n),d=new mV(l.map((e=>e.shape))),p=n.runWebGLProgram(d,l,r);l.forEach((e=>n.disposeIntermediateTensorInfo(e)));let h=WB({inputs:{x:p},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(p),h}function vV(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=ja.parseAxisParam(a,t[0].shape)[0],i=t.map((e=>e.shape));Im.assertParamsConsistent(i,s);let o=Im.computeOutShape(t.map((e=>e.shape)),s);if(0===ja.sizeFromShape(o))return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter((e=>ja.sizeFromShape(e.shape)>0));

2return 1===l.length?vB({inputs:{x:l[0]},backend:n}):wV(l,s,n)}var kV={kernelName:Ut,backendName:"webgl",kernelFunc:vV},IV=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,h=e.filterWidth,c=4*Math.floor(e.inChannels/4),f=e.inChannels%4,m="channelsLast"===e.dataFormat,g=m?1:2,y=m?2:3,b=m?3:1,x="",w="";n&&(x=r?`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:a?`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`\n          float activation(float x) {\n            ${n}\n          }\n        `,w="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${x}\n\n      const ivec2 strides = ivec2(${o}, ${l});\n      const ivec2 pads = ivec2(${s}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${b}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${g}], coords[${y}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${p}; wR++) {\n          int xR = xRCorner + wR * ${u};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${h}; wC++) {\n            int xC = xCCorner + wC * ${d};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${c}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${1===f}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${c}) *\n                    getW(wR, wC, ${c}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${c}, xR, xC) *\n                    getW(wR, wC, ${c}, d2);\n              }\n\n            } else if (${2===f}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${c}, d2),\n                getW(wR, wC, ${c} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${c}),\n                  getX(batch, xR, xC, ${c} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${c}, xR, xC),\n                  getX(batch, ${c} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${3===f}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${c}, d2),\n                getW(wR, wC, ${c} + 1, d2),\n                getW(wR, wC, ${c} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${c}),\n                  getX(batch, xR, xC, ${c} + 1),\n                  getX(batch, xR, xC, ${c} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${c}, xR, xC),\n                  getX(batch, ${c} + 1, xR, xC),\n                  getX(batch, ${c} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${v}\n        ${w}\n        setOutput(result);\n      }\n    `}},SV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,p=e.filterHeight,h=e.filterWidth,c=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${a}, ${s}, ${i});\n      const ivec3 pads = ivec3(${t}, ${n}, ${r});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${d};

2 wF++) {\n          int xF = xFCorner + wF * ${o};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${p}; wR++) {\n            int xR = xRCorner + wR * ${l};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${h}; wC++) {\n              int xC = xCCorner + wC * ${u};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${c}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${1===f}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${c}) *\n                  getW(wF, wR, wC, ${c}, d2);\n              } else if (${2===f}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${c}),\n                  getX(batch, xF, xR, xC, ${c} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${c}, d2),\n                  getW(wF, wR, wC, ${c} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${3===f}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${c}),\n                  getX(batch, xF, xR, xC, ${c} + 1),\n                  getX(batch, xF, xR, xC, ${c} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${c}, d2),\n                  getW(wF, wR, wC, ${c} + 1, d2),\n                  getW(wF, wR, wC, ${c} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},NV=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=cz(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,d=u,p="\n       int xR; int xC; int xCOffset;\n       vec4 wTexel; vec4 previous; vec4 final;";for(let e=0;e<u;e++)p+=`\n           vec4 xTexelC${2*e};\n           int xTexelC${2*e}Ready;\n           vec4 xTexelC${2*e+1};\n           int xTexelC${2*e+1}Ready;\n           vec4 xC${e};`;p+=`\n     for (int r = 0; r < ${l}; r++) {\n      for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {\n       `;for(let e=0;e<u;e++)p+=`\n           xTexelC${2*e} = vec4(0.0);\n           xTexelC${2*e}Ready = 0;\n           xTexelC${2*e+1} = vec4(0.0);\n           xTexelC${2*e+1}Ready = 0;\n           xC${e} = vec4(0.0);`;p+="\n         xR = xRCorner + r * dilations[0];\n         if (xR >=0 && xR < inDims[0]) {\n       ";for(let t=0;t<(d+1)/2;t++){let n=2*t;if(p+=`\n           xC = xCCorner + ${n*o};\n           `,1===i){if(n<u&&(s%2==1?(p+=`\n                 xCOffset = xC + 1;\n                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n}Ready == 0) {\n                   xTexelC${n} = getX(batch, xR, xCOffset, d1);\n\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${n}.zw = vec2(0.0);\n                   }\n                   xTexelC${n}Ready = 1;\n                 }\n               `,p+=1===o&&n>0?`\n                 xC${n} = vec4(xTexelC${n-2}.zw, xTexelC${n}.xy);\n                 `:`\n                   xCOffset = xC + 1 - 2;\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       previous.zw = vec2(0.0);\n                     }\n\n                     xC${n} = vec4(previous.zw, xTexelC${n}.xy);\n                   } else {\n                     xC${n} = vec4(0.0, 0.0, xTexelC${n}.xy);\n                   }\n                   `):p+=`\n                 if (xC >= 0 && xC < inDims[1] && xTexelC${n}Ready == 0) {\n                   xTexelC${n} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${n}.zw = vec2(0.0);\n                   }\n                   xTexelC${n}Ready = 1;\n                 }\n\n                 xC${n} = xTexelC${n};\n                 `,n+1<u)){let e=s%2==0?ja.nearestLargerEven(o):o;o%2==0&&s%2==1||o%2!=0&&s%2!=1?(p+=`\n                   xCOffset = xC + imod(pads[1], 2) + ${e};\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n+1}Ready == 0) {\n                     xTexelC${n+1} = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       xTexelC${n+1}.zw = vec2(0.0);\n                     }\n                     xTexelC${n+1}Ready = 1;\n                   }\n                   `,p+=o>1?`\n                     xCOffset -= 2;\n                     if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                      previous = getX(batch, xR, xCOffset, d1);\n                      xC${n+1} = vec4(previous.zw, xTexelC${n+1}.xy);\n                     } else {\n                      xC${n+1} = vec4(0.0, 0.0, xTexelC${n+1}.xy);\n                     }\n                     `:`\n                     xC${n+1} = vec4(xTexelC${n}.zw, xTexelC${n+1}.xy);

2\n                     `):p+=1===e?`\n                     xC${n+1} = xTexelC${n};\n                     `:`\n                     xCOffset = xC + ${e};\n\n                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n+1}Ready == 0) {\n                       xTexelC${n+1} = getX(batch, xR, xCOffset, d1);\n                       if (xCOffset + 1 >= inDims[1]) {\n                         xTexelC${n+1}.zw = vec2(0.0);\n                       }\n                       xTexelC${n+1}Ready = 1;\n                     }\n\n                     xC${n+1} = xTexelC${n+1};\n                     `}}else n<u&&(s%2==1?(p+=`\n                 xCOffset = xC + 1 - strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n}Ready == 0) {\n                   xTexelC${n} = getX(batch, xR, xCOffset, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${n}.zw = vec2(0.0);\n                   }\n                   xTexelC${n}Ready = 1;\n                 }\n\n                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${n+1}Ready == 0) {\n                   xTexelC${n+1} = getX(batch, xR, xC + 1, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xC + 2 >= inDims[1]) {\n                     xTexelC${n+1}.zw = vec2(0.0);\n                   }\n                   xTexelC${n+1}Ready = 1;\n                 }\n\n                 xC${n} = vec4(xTexelC${n}.zw, xTexelC${n+1}.zw);\n               `,n+1<u&&(p+=`\n                   final = vec4(0.0);\n                   xCOffset = xC + 1 + strides[1];\n                   if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                     final = getX(batch, xR, xCOffset, d1);\n                   }\n                   xC${n+1} = vec4(xTexelC${n+1}.xy, final.xy);\n                 `)):(p+=`\n                 if(xC >= 0 && xC < inDims[1] && xTexelC${n}Ready == 0) {\n                   xTexelC${n} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${n}.zw = vec2(0.0);\n                   }\n                   xTexelC${n}Ready = 1;\n                 }\n\n                 xCOffset = xC + strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n+1}Ready == 0) {\n                   xTexelC${n+1} = getX(batch, xR, xCOffset, d1);\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${n+1}.zw = vec2(0.);\n                   }\n                   xTexelC${n+1}Ready = 1;\n                 }\n\n                 xC${n} = vec4(\n                   xTexelC${n}.xy, xTexelC${n+1}.xy);\n               `,n+1<u&&(p+=`\n                   xC${n+1} = vec4(xTexelC${n}.zw, xTexelC${n+1}.zw);\n                 `)));n<u&&(p+=`\n             wTexel = getW(r, ${n}, d1, d2);\n             dotProd += xC${n}.xxzz * vec4(wTexel.xy, wTexel.xy);\n             if(d1 + 1 < ${e.inChannels}) {\n               dotProd += xC${n}.yyww * vec4(wTexel.zw, wTexel.zw);\n             }\n           `,n+1<u&&(p+=`\n               wTexel = getW(r, ${n+1}, d1, d2);\n               dotProd += xC${n+1}.xxzz * vec4(wTexel.xy, wTexel.xy);\n               if(d1 + 1 < ${e.inChannels}) {\n                 dotProd += xC${n+1}.yyww * vec4(wTexel.zw, wTexel.zw);\n               }\n             `))}p+="\n     }\n   ",p+="\n     }\n   ",p+="\n     }\n   ";let h="",c="";n&&(h=r?`vec4 activation(vec4 a) {\n           vec4 b = getPreluActivationWeightsAtOutCoords();\n           ${n}\n         }`:a?`vec4 activation(vec4 a) {\n           vec4 b = getLeakyreluAlphaAtOutCoords();\n           ${n}\n         }`:`vec4 activation(vec4 x) {\n           ${n}\n         }`,c="result = activation(result);");let f=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n       ${h}\n\n       void main() {\n         ivec4 coords = getOutputCoords();\n         int batch = coords.x;\n         ivec2 xRCCorner = coords.yz * strides - pads;\n         int d2 = coords.w;\n         int xRCorner = xRCCorner.x;\n         int xCCorner = xRCCorner.y;\n\n         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n         vec4 dotProd = vec4(0.000000000000001);\n\n         ${p}\n\n         vec4 result = dotProd - vec4(0.000000000000001);\n         ${f}\n         ${c}\n         setOutput(result);\n       }\n     `}},TV=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=cz(this.outputShape.length);let{dataFormat:n}=t,r=GL(),a="channelsLast"===n,s=a?1:2,i=a?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,l="";for(let e=0;e<=1;e++)for(let t=0;t<=1;t++)l+=`\n          blockIndex = rc.z + ${t};\n          pos = rc.y + ${e};\n\n          ${o}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${s}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[

21];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${a}) {\n                  innerDims = vec2(d1, ch);\n                  result[${2*e+t}] = getChannel(\n                    getA(rc.x, d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${2*e+t}] = getChannel(\n                    getA(rc.x, ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${l}\n\n        ${r.output} = result;\n      }\n    `}};function _V(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&1===n&&e[0]>1?[e[0],1]:null}function CV({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l,u=e.shape,d=r.texData.get(e.dataId),p=n.inChannels,h=u[0]*u[1]*u[2],c=n.outChannels,f="channelsLast"===n.dataFormat,m=[];if(null!=s){let e=_V(s.shape,f);null!=e&&(s=WB({inputs:{x:s},backend:r,attrs:{shape:e}}),m.push(s))}if(null!=a){let e=_V(a.shape,f);null!=e&&(a=WB({inputs:{x:a},backend:r,attrs:{shape:e}}),m.push(a))}if((1!==h&&1!==c||!(p>QB))&&d.isPacked&&f&&null!=d.texture&&u[2]%2!=0&&ja.arraysEqual(d.shape.slice(-3),u.slice(-3))){let p=u[0]*u[1]*(u[2]+1),h={dataId:e.dataId,shape:[1,p,n.inChannels],dtype:e.dtype},c=d.shape;d.shape=d.shape.slice(),d.shape[d.shape.length-2]++,ja.assert($L(d.shape,h.shape),(()=>`packed reshape ${d.shape} to ${h.shape} isn't free`));let f=WB({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});m.push(f);let g=eW({a:h,b:f,backend:r,transposeA:false,transposeB:false,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),y=r.texData.get(g.dataId);ja.assert(y.isPacked,(()=>"batchMatMul result is expected to be packed")),d.shape=c,y.shape=n.outShape,l=vB({input

2s:{x:g},backend:r}),l.shape=n.outShape,m.push(g)}else{let u=n.outHeight*n.outWidth,d=WB({inputs:{x:e},backend:r,attrs:{shape:f?[n.batchSize,u,n.inChannels]:[n.batchSize,n.inChannels,u]}}),p=WB({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),h=eW({a:f?d:p,b:f?p:d,transposeA:!f,transposeB:false,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});l=WB({inputs:{x:h},backend:r,attrs:{shape:n.outShape}}),m.push(d),m.push(p),m.push(h)}for(let e of m)r.disposeIntermediateTensorInfo(e);return l}function EV({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:p,outHeight:h,dataFormat:c}=n,f="channelsLast"===c,m=l*u*d,g=h*p,y=[n.batchSize,m,g],b=[];if(null!=s){let e=_V(s.shape,f);null!=e&&(s=WB({inputs:{x:s},backend:r,attrs:{shape:e}}),b.push(s))}if(null!=a){let e=_V(a.shape,f);null!=e&&(a=WB({inputs:{x:a},backend:r,attrs:{shape:e}}),b.push(a))}let x=WB({inputs:{x:t},backend:r,attrs:{shape:[1,m,ja.sizeFromShape(t.shape)/m]}});b.push(x);let w=new TV(y,n),v=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],k=r.runWebGLProgram(w,[e],"float32",v),I=WB({inputs:{x:k},backend:r,attrs:{shape:y}});b.push(k),b.push(I);let S=null!=a,N=null!=s,T="leakyrelu"===o,_=o?DB(o,!0):null,C=new MB(f?I.shape:x.shape,f?x.shape:I.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],!0,!1,S,_,N,T),E=f?[I,x]:[x,I];if(a&&E.push(a),N&&E.push(s),T){let e=r.makeTensorInfo([],"float32",ja.createScalarValue(i,"float32"));E.push(e),b.push(e)}let $=r.runWebGLProgram(C,E,"float32"),A=WB({inputs:{x:$},backend:r,attrs:{shape:n.outShape}});b.push($);for(let e of b)r.disposeIntermediateTensorInfo(e);return A}var $V={kernelName:Gt,backendName:"webgl",kernelFunc:function(e){let t,{inputs:n,backend:r,attrs:a}=e,{x:s,filter:i}=n,{strides:o,pad:l,dataFormat:u,dilations:d,dimRoundingMode:p}=a,h=Im.convertConv2DDataFormat(u),c=Im.computeConv2DInfo(s.shape,i.shape,o,d,l,p,!1,h);if(1!==c.filterHeight||1!==c.filterWidth||1!==c.dilationHeight||1!==c.dilationWidth||1!==c.strideHeight||1!==c.strideWidth||"SAME"!==c.padInfo.type&&"VALID"!==c.padInfo.type)if(c.strideWidth<=2&&"channelsLast"===h&&ut().getBool("WEBGL_EXP_CONV")){let e=new NV(c),n=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];t=r.runWebGLProgram(e,[s,i],"float32",n)}else if(ut().getBool("WEBGL_CONV_IM2COL"))t=EV({x:s,filter:i,convInfo:c,backend:r});else{let e=new IV(c);t=r.runWebGLProgram(e,[s,i],"float32")}else t=CV({x:s,filter:i,convInfo:c,backend:r});let f=WB({inputs:{x:t},backend:r,attrs:{shape:c.outShape}});return r.disposeIntermediateTensorInfo(t),f}},AV=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s="channelsLast"===e.dataFormat;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${r};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${a};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              ${s?"float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);":"float dyValue = getDy(b, d2, yR, yC);\n              float xValue = getX(b, d1, xR, xC);\n              dotProd += (xValue * dyValue);"}\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},RV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s="channelsLast"===e.dataFormat,i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${d}];\n\n        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${r}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${a}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${s}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},FV=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth};

2 yF++) {\n            int xF = wF + yF * ${t} - ${a};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${s};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${r} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},DV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${o}, ${l}, ${u});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${a}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${s}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${r}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${r} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};var MV={kernelName:Ht,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=r,p=Im.convertConv2DDataFormat(l),h=Im.computeConv2DInfo(a.shape,d,i,1,o,u,!1,p),c=new AV(h);return n.runWebGLProgram(c,[a,s],"float32")}},OV=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=cz(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,r=t-1-e.padInfo.top,a=n-1-e.padInfo.left;this.userCode=`\n      const ivec2 pads = ivec2(${r}, ${a});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        vec4 result = vec4(0.);\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / strides[0];\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            int wCPerm = ${n} - 1 - wC;\n\n            float dyC = float(dyCCorner + wC) / strides[1];\n            bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)\n              && (fract(dyC) == 0.0);\n            int idyC = int(dyC);\n\n            float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n            bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)\n              && (fract(dyC2) == 0.0);\n            int idyC2 = int(dyC2);\n\n            if (idyCVal && idyCVal2) {\n              for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n                vec4 dySample = getDy(batch, idyR, idyC, d2);\n                vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n                  dySample : getDy(batch, idyR, idyC2, d2);\n\n                vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n                  dySample.xy : dySample.zw;\n                result.xy += vec2(dot(dyValue, wValue.xy),\n                  dot(dyValue, wValue.zw));\n\n                dyValue = mod(float(idyC2), 2.) == 0. ?\n                  dySample2.xy : dySample2.zw;\n                result.zw += vec2(dot(dyValue, wValue.xy),\n                  dot(dyValue, wValue.zw));\n              }\n            } else if (idyCVal) {\n              for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n                vec4 dySample = getDy(batch, idyR, idyC, d2);\n                vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n                  dySample.xy : dySample.zw;\n                result.xy += vec2(dot(dyValue, wValue.xy),\n                  dot(dyValue, wValue.zw));\n              }\n            } else if (idyCVal2) {\n              for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n                vec4 dySample = getDy(batch, idyR, idyC2, d2);\n                vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n                  dySample.xy : dySample.zw;\n                result.zw += vec2(dot(dyValue, wValue.xy),\n                  dot(dyValue, wValue.zw));\n              }\n            }\n          }\n        }\n        setOutput(result);\n      }\n    `}};var LV={kernelName:jt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}

2=r,p=Im.convertConv2DDataFormat(u),h=Im.computeConv2DInfo(i,s.shape,o,1,l,d,!1,p);if(ut().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&"channelsLast"===p){let e=[[h.strideHeight,h.strideWidth]],t=new OV(h);return n.runWebGLProgram(t,[a,s],"float32",e)}{let e=new RV(h);return n.runWebGLProgram(e,[a,s],"float32")}}};var zV={kernelName:qt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=Im.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new SV(u);return n.runWebGLProgram(d,[a,s],"float32")}};var PV={kernelName:Kt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=Im.computeConv3DInfo(a.shape,l,i,1,o),d=new FV(u);return n.runWebGLProgram(d,[a,s],"float32")}};var BV,WV={kernelName:Xt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=Im.computeConv3DInfo(l,s.shape,o,1,i),d=new DV(u);return n.runWebGLProgram(d,[a,s],"float32")}}

2,VV=RB({opSnippet:AB+"\n  return cos(x);\n",packedOpSnippet:`\n  vec4 result = cos(x);\n  bvec4 isNaN = isnan(x);\n  ${xB}\n  return result;\n`}),UV={kernelName:Yt,backendName:"webgl",kernelFunc:VV},GV=RB({opSnippet:"\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n"}),HV={kernelName:Zt,backendName:"webgl",kernelFunc:GV},jV=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,p]=n;this.outputShape=[u,d,p,l];let h="bilinear"===r?1:0,[c,f]=[i-1+".0",o-1+".0"],[m,g,y]=d>1?[""+(i-1)/(d-1),"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[b,x,w]=p>1?[""+(o-1)/(p-1),"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${b});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${s}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${x};\n\n        float in_y = ${y};\n        if( in_y < 0.0 || in_y > ${c} ) {\n          setOutput(float(${a}));\n          return;\n        }\n        float in_x = ${w};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${a}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${h} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}},qV={kernelName:en,backendName:"webgl",kernelFunc:e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,d=new jV(a.shape,s.shape,o,l,u);return n.runWebGLProgram(d,[a,s,i],"float32")}};!function(e){e.Prod="*",e.Sum="+"}(BV||(BV={}));var KV=class{constructor(e,t,n,r){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let a=this.outputShape.length,s=this.op===BV.Prod?"1.0":"0.0",i=n?s:`getX(${XV(a,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=r?"end != "+(o-1):"end != 0",u=r?"end + 1":"end - 1"):(l=r?`end + pow2 < ${o}`:"end >= pow2",u=r?"end + pow2":"end - pow2"),this.userCode=`\n      void main() {\n        ${oz(a)} coords = getOutputCoords();\n        int end = ${YV(a,"coords",this.op)};\n        float val = ${i};\n        int pow2 = int(pow(2.0, index));\n        if (${l}) {\n          int idx = ${u};\n          ${YV(a,"coords",this.op)} = idx;\n          val ${this.op}= getX(${XV(a,"coords",this.op)});\n        }\n        setOutput(val);\n      }\n    `}};function XV(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.x, ${t}.y`;if(3===e)return`${t}.x, ${t}.y, ${t}.z`;if(4===e)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function YV(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.y`;if(3===e)return`${t}.z`;if(4===e)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function ZV(e,t,n,r,a,s){let i=t.shape.length,o=Im.getAxesPermutation([r],i),l=t;null!=o&&(l=ZB({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=Im.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${r}`);

2let d=l.shape[u],p=vB({inputs:{x:l},backend:n});for(let t=0;t<=Math.ceil(Math.log2(d))-1;t++){let r=new KV(e,l.shape,!1,s),a=[[t]],i=p;p=n.runWebGLProgram(r,[p],p.dtype,a),n.disposeIntermediateTensorInfo(i)}if(a){let t=new KV(e,l.shape,a,s),r=p;p=n.runWebGLProgram(t,[p],p.dtype),n.disposeIntermediateTensorInfo(r)}if(null!=o){let e=ZB({inputs:{x:p},backend:n,attrs:{perm:Im.getUndoAxesPermutation(o)}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(l),e}return p}var JV={kernelName:Jt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;return ZV(BV.Prod,a,n,s,i,o)}};var QV={kernelName:Qt,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;return ZV(BV.Sum,a,n,s,i,o)}};var eU={kernelName:tn,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(1===a.shape.length){let e=n.readSync(a.dataId),t=n.readSync(s.dataId),r=jz(e,t,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,r)}if(2===a.shape.length){let e=n.bufferSync(a),t=n.bufferSync(s),r=qz(e,t,i,o);return n.makeTensorInfo(r.shape,s.dtype,r.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}},tU=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}

2getHeightCoordString(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"}getWidthCoordString(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"}getDepthCoordString(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"}getOutputDepthSize(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};var nU={kernelName:nn,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r,o=a.shape[0],l=("NHWC"===i?a.shape[1]:a.shape[2])*s,u=("NHWC"===i?a.shape[2]:a.shape[3])*s,d=("NHWC"===i?a.shape[3]:a.shape[1])/(s*s),p=new tU("NHWC"===i?[o,l,u,d]:[o,d,l,u],s,i);return n.runWebGLProgram(p,[a],a.dtype)}},rU=class{constructor(e,t=!1,n=null,r=!1,a=!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=cz(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(l=r?`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:a?`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`\n          float activation(float x) {\n            ${n}\n          }\n        `,u="result = activation(result);");let d=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${l}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${o};\n        int q = d2 - d1 * ${o};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${s}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${i}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${d}\n        ${u}\n        setOutput(result);\n      }\n    `}},aU=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=cz(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,p=d,h="\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;";for(let e=0;e<d;e++)h+=`\n          vec4 xTexelC${2*e};\n          int xTexelC${2*e}Ready;\n          vec4 xTexelC${2*e+1};\n          int xTexelC${2*e+1}Ready;\n          vec4 xC${e};`;h+=`\n    for (int r = 0; r < ${u}; r++) {\n      `;for(let e=0;e<d;e++)h+=`\n          xTexelC${2*e} = vec4(0.0);\n          xTexelC${2*e}Ready = 0;\n          xTexelC${2*e+1} = vec4(0.0);\n          xTexelC${2*e+1}Ready = 0;\n          xC${e} = vec4(0.0);`;h+="\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      ";for(let e=0;e<(p+1)/2;e++){let t=2*e;if(h+=`\n          xC = xCCorner + ${t*l};\n          `,1===o){if(t<d&&(i%2==1?(h+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n                  xTexelC${t} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${t}.zw = vec2(0.0);\n                  }\n                  xTexelC${t}Ready = 1;\n                }\n              `,h+=1===l&&t>0?`\n                xC${t} = vec4(xTexelC${t-2}.zw, xTexelC${t}.xy);\n                `:`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${t} = vec4(previous.zw, xTexelC${t}.xy);\n                  } else {\n                    xC${t} = vec4(0.0, 0.0, xTexelC${t}.xy);\n                  }\n                  `):h+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n                  xTexelC${t} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${t}.zw = vec2(0.0);\n                  }\n                  xTexelC${t}Ready = 1;\n                }\n\n                xC${t} = xTexelC${t};\n                `,t+1<d)){let e=i%2==0?ja.nearestLargerEven(l):l;l%2==0&&i%2==1||l%2!=0&&i%2!=1?(h+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${e};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n                    xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${t+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${t+1}Ready = 1;\n                  }\n                  `,h+=l>1?`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n                     xC${t+1} = vec4(previous.zw, xTexelC${t+1}.xy);\n                    } else {\n                     xC${t+1} = vec4(0.0, 0.0, xTexelC${t+1}.xy);\n                    }\n                    `:`\n                    xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.xy);

2\n                    `):h+=1===e?`\n                    xC${t+1} = xTexelC${t};\n                    `:`\n                    xCOffset = xC + ${e};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n                      xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${t+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${t+1}Ready = 1;\n                    }\n\n                    xC${t+1} = xTexelC${t+1};\n                    `}}else t<d&&(i%2==1?(h+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n                  xTexelC${t} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${t}.zw = vec2(0.0);\n                  }\n                  xTexelC${t}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${t+1}Ready == 0) {\n                  xTexelC${t+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${t+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${t+1}Ready = 1;\n                }\n\n                xC${t} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n              `,t+1<d&&(h+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${t+1} = vec4(xTexelC${t+1}.xy, final.xy);\n                `)):(h+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n                  xTexelC${t} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${t}.zw = vec2(0.0);\n                  }\n                  xTexelC${t}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n                  xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${t+1}.zw = vec2(0.);\n                  }\n                  xTexelC${t+1}Ready = 1;\n                }\n\n                xC${t} = vec4(\n                  xTexelC${t}.xy, xTexelC${t+1}.xy);\n              `,t+1<d&&(h+=`\n                  xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n                `)));t<d&&(h+=`\n            wTexel = getW(r, ${t}, d1, q);\n            dotProd += xC${t} * vec4(wTexel.xz, wTexel.xz);\n          `,t+1<d&&(h+=`\n              wTexel = getW(r, ${t+1}, d1, q);\n              dotProd += xC${t+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}h+="\n    }\n  ",h+="\n      }\n    ";let c="",f="";n&&(c=r?`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:a?`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`vec4 activation(vec4 x) {\n          ${n}\n        }`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${c}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${s};\n        int q = d2 - d1 * ${s};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${h}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${m}\n        ${f}\n        setOutput(result);\n      }\n    `}};var sU={kernelName:rn,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r,d=l;null==d&&(d=[1,1]),ja.assert(Im.eitherStridesOrDilationsAreOne(i,d),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`));let p,h=Im.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0);p=ut().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?new aU(h):new rU(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth

2]];return n.runWebGLProgram(p,[a,s],"float32",c)}},iU=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${s} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${r};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${a};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},oU=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${s}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${r}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${a}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};var lU={kernelName:an,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=r,p=Im.computeConv2DInfo(a.shape,d,i,o,l,u,!0),h=new iU(p);return n.runWebGLProgram(h,[a,s],"float32")}};var uU={kernelName:sn,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=r,p=Im.computeConv2DInfo(d,s.shape,i,o,l,u,!0),h=new oU(p);return n.runWebGLProgram(h,[a,s],"float32")}},dU=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode="\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    "}};var pU={kernelName:on,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=ja.sizeFromShape(r.shape),i=WB({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new dU(s),l=n.runWebGLProgram(o,[i],i.dtype),u=WB({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}},hU=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:p}=r;this.userCode=`\n      const ivec2 strides = ivec2(${a}, ${s});\n      const ivec2 pads = ivec2(${d}, ${p});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${i}; h++) {\n          int hIn = hBeg + h * ${l};\n\n          if (hIn >= 0 && hIn < ${t}

2) {\n            for (int w = 0; w < ${o}; w++) {\n              int wIn = wBeg + w * ${u};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}};var cU={kernelName:ln,backendName:"webgl",kernelFunc:function(e){let t,{inputs:n,backend:r,attrs:a}=e,{x:s,filter:i}=n,{strides:o,pad:l,dilations:u}=a,d=Im.computeDilation2DInfo(s.shape,i.shape,o,l,"NHWC",u),p=new hU(d);t=r.runWebGLProgram(p,[s,i],"float32");let h=WB({inputs:{x:t},backend:r,attrs:{shape:d.outShape}});return r.disposeIntermediateTensorInfo(t),h}};var fU={kernelName:cn,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=Im.decodeEinsumEquation(a,s.length);Im.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=Im.getEinsumComputePath(o,l),p=d.length,h=null,c=i.length,f=[];for(let e=0;e<p;++e){for(let t of d[e]){let e,{permutationIndices:r,expandDims:a}=Im.getEinsumPermutation(c,l[t]);Im.isIdentityPermutation(r)?e=s[t]:(e=ZB({inputs:{x:s[t]},backend:n,attrs:{perm:r}}),f.push(e));let i=e.shape.slice();for(let e=0;e<a.length;++e)i.splice(a[e],0,1);ja.arraysEqual(e.shape,i)||(e=WB({inputs:{x:e},backend:n,attrs:{shape:i}}),f.push(e)),null===h?h=e:(h=PB({inputs:{a:e,b:h},backend:n}),f.push(h))}e<p-1&&(u[e]>=0&&(h=XB({inputs:{x:h},backend:n,attrs:{axis:u[e]-(i.length-c),keepDims:!1}}),f.push(h)),c--)}for(let e of f)e!==h&&n.disposeIntermediateTensorInfo(e);return h}},mU=RB({opSnippet:"return (x >= 0.0) ? x : (exp(x) - 1.0);",packedOpSnippet:"\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n"}),gU={kernelName:fn,backendName:"webgl",kernelFunc:mU},yU={kernelName:mn,backendName:"webgl",kernelFunc:e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=ut().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wB("\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));

2\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",r.shape,a.shape):new bB("return (b >= 0.0) ? a : a * (b + 1.0);",r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}},bU=FB({opSnippet:"return float(a == b);",packedOpSnippet:"\n  return vec4(equal(a, b));\n",dtype:"bool",cpuKernelImpl:Jz}),xU={kernelName:yn,backendName:"webgl",kernelFunc:bU},wU=RB({opSnippet:`\n  // Error function is calculated approximately with elementary function.\n  // See "Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n  float p = ${Im.ERF_P};\n  float a1 = ${Im.ERF_A1};\n  float a2 = ${Im.ERF_A2};\n  float a3 = ${Im.ERF_A3};\n  float a4 = ${Im.ERF_A4};\n  float a5 = ${Im.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`}),vU={kernelName:gn,backendName:"webgl",kernelFunc:wU},kU=RB({opSnippet:AB+"\n  return exp(x);\n",packedOpSnippet:"\n  vec4 result = exp(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n",cpuKernelImpl:Qz,dtype:"float32"}),IU={kernelName:bn,backendName:"webgl",kernelFunc:kU};function SU(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(ja.assert(-(i+1)<=a,(()=>`Axis must be in the interval [${-(i+1)}, ${i}

2]`)),l=i+a+1),o.splice(l,0,1),WB({inputs:{x:s},backend:r,attrs:{shape:o}})}var NU={kernelName:xn,backendName:"webgl",kernelFunc:SU},TU="return exp(x) - 1.0;",_U=RB({opSnippet:TU,packedOpSnippet:TU,cpuKernelImpl:eP}),CU={kernelName:wn,backendName:"webgl",kernelFunc:_U},EU=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a,s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${r}.0`:"1.0";if("real"===e)a="return real * expR - imag * expI;";else{if("imag"!==e)throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);a="return real * expI + imag * expR;"}this.userCode=`\n      const float exponentMultiplier = ${s};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${a}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${r});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${r}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${i};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `}};function $U(e,t,n){let r=n.texData.get(e.dataId),a=ja.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=WB({inputs:{x:e},backend:n,attrs:{shape:[a/s,s]}}),o=i.shape,l=new EU("real",o,t),u=new EU("imag",o,t),d=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:o},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:o}],p=n.runWebGLProgram(l,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),c=IB({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let f=WB({inputs:{x:c},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),f}var AU={kernelName:vn,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{input:r}=t;return $U(r,!1,n)}},RU=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode="\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    "}};function FU(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||ja.inferDtype(a),"string"===s){let e=ja.getArrayFromDType(s,ja.sizeFromShape(r));return e.fill(a),t.makeTensorInfo(r,s,e)}{let e=new RU(r,a),n=[[a]];return t.runWebGLProgram(e,[],s,n)}}var DU,MU={kernelName:kn,backendName:"webgl",kernelFunc:FU},OU=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${t} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${t}

2) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},LU={kernelName:In,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new OU(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},zU="return floor(x);",PU=RB({opSnippet:zU,packedOpSnippet:zU,cpuKernelImpl:tP}),BU={kernelName:Sn,backendName:"webgl",kernelFunc:PU},WU=FB({opSnippet:"\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n",packedOpSnippet:"\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n",dtype:"int32"}),VU={kernelName:Nn,backendName:"webgl",kernelFunc:WU},UU=class{constructor(e){this.variableNames=["A"];let t=GL(),[n,r]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);\n\n        vec4 values = ${t.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `}},GU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=GL(),[n,r]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${r}.0, ${n}.0);\n            vec4 values = ${t.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${t.output} = result;\n      }\n    `}},HU={kernelName:Ca,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i="undefined"!=typeof HTMLVideoElement&&a instanceof HTMLVideoElement,o="undefined"!=typeof HTMLImageElement&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],p=[u,l,s];if(o||i){let e=ut().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(null==DU||e!==jU)&&(jU=e,DU=document.createElement("canvas").getContext("2d",{willReadFrequently:jU})),DU.canvas.width=l,DU.canvas.height=u,DU.drawImage(a,0,0,l,u),a=DU.canvas}let h=n.makeTensorInfo(d,"int32");n.texData.get(h.dataId).usage=MO.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),a);let c=ut().getBool("WEBGL_PACK")?new GU(p):new UU(p),f=n.runWebGLProgram(c,[h],"int32");return n.disposeData(h.dataId),f}},jU=ut().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");var qU={kernelName:Aa,backendName:"webgl",kernelFunc:function(e){let t,{inputs:n,backend:r,attrs:a}=e,{x:s,filter:i,bias:o,preluActivationWeights:l}=n,{strides:u,pad:d,dataFormat:p,dilations:h,dimRoundingMode:c,activation:f,leakyreluAlpha:m}

2=a,g=Im.convertConv2DDataFormat(p),y=Im.computeConv2DInfo(s.shape,i.shape,u,h,d,c,!1,g),b=[],x=null!=o,w=null!=l,v="leakyrelu"===f,k=()=>{let e=[s,i],t=(e,t)=>{if("NCHW"===t&&1===e.shape.length&&1!==e.shape[0]){let t=WB({inputs:{x:e},backend:r,attrs:{shape:[e.shape[0],1,1]}});return b.push(t),t}return e};if(x&&e.push(t(o,p)),w&&e.push(t(l,p)),v){let t=r.makeTensorInfo([],"float32",ja.createScalarValue(m,"float32"));e.push(t),b.push(t)}return e};if(1!==y.filterHeight||1!==y.filterWidth||1!==y.dilationHeight||1!==y.dilationWidth||1!==y.strideHeight||1!==y.strideWidth||"SAME"!==y.padInfo.type&&"VALID"!==y.padInfo.type)if(y.strideWidth<=2&&"channelsLast"===g&&ut().getBool("WEBGL_EXP_CONV")){let e=f?DB(f,!0):null,n=new NV(y,x,e,w,v),a=[[y.padInfo.top,y.padInfo.left],[y.strideHeight,y.strideWidth],[y.dilationHeight,y.dilationWidth],[y.inHeight,y.inWidth]],s=k();t=r.runWebGLProgram(n,s,"float32",a)}else if(ut().getBool("WEBGL_CONV_IM2COL"))t=EV({x:s,filter:i,convInfo:y,backend:r,bias:o,activation:f,preluActivationWeights:l,leakyreluAlpha:m});else{let e=f?DB(f,!1):null,n=new IV(y,x,e,w,v),a=k();t=r.runWebGLProgram(n,a,"float32")}else t=CV({x:s,filter:i,convInfo:y,backend:r,bias:o,activation:f,preluActivationWeights:l,leakyreluAlpha:m});let I=WB({inputs:{x:t},backend:r,attrs:{shape:y.outShape}});return b.push(t),b.forEach((e=>r.disposeIntermediateTensorInfo(e))),I}};var KU={kernelName:Ra,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:c}=r,f=[],m=d;null==m&&(m=[1,1]),ja.assert(Im.eitherStridesOrDilationsAreOne(l,m),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`));let g,y=Im.computeConv2DInfo(a.shape,s.shape,l,m,u,p,!0),b=ut().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels==1,x=h?DB(h,b):null,w=[a,s],v=null!=i,k=null!=o,I="leakyrelu"===h;if(v&&w.push(i),k&&w.push(o),I){let e=n.makeTensorInfo([],"float32",ja.createScalarValue(c,"float32"));w.push(e),f.push(e)}g=b?new aU(y,v,x,k,I):new rU(y,v,x,k,I);let S=[[y.padInfo.top,y.padInfo.left],[y.strideHeight,y.strideWidth],[y.dilationHeight,y.dilationWidth],[y.inHeight,y.inWidth]],N=n.runWebGLProgram(g,w,"float32",S);return f.forEach((e=>n.disposeIntermediateTensorInfo(e))),N}},XU=class{constructor(e,t,n,r){this.sliceDim=e,this.strides=t,this.paramsShape=r,this.variableNames=["x","indices"],this.outputShape=n;let a=oz(n.length),s="\n    int index;";for(let e=0;e<this.sliceDim;e++)s+=`\n          index = round(getIndices(coords[0], ${e}));\n          out_of_bounds = out_of_bounds || index < 0;\n          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[e]};\n          flattenIndex += index * ${this.strides[e]};`;this.userCode=`\n         void main() {\n          ${a} coords = getOutputCoords();\n          int flattenIndex = 0;\n          bool out_of_bounds = false;\n\n          ${s}\n\n          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n        }\n      `}};var YU={kernelName:Cn,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],o=ja.sizeFromShape(r.shape),[l,u,d,p]=Im.prepareAndValidate(r,a),h=WB({inputs:{x:a},backend:n,attrs:{shape:[u,i]}}),c=WB({inputs:{x:r},backend:n,attrs:{shape:[ja.sizeFromShape(r.shape)/d,d]}});if(n.shouldExecuteOnCPU([r,a])||"string"===r.dtype){let e=n.readSync(a.dataId),t=n.bufferSync(r),s=nP(e,t,r.dtype,u,i,d,p,r.shape,o);return n.makeTensorInfo(l,r.dtype,s.values)}let f=new XU(i,p,[u,d],r.shape),m=n.runWebGLProgram(f,[c,h],c.dtype),g=WB({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),g}},ZU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=oz(this.rank),r=function(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let t=0;t<e.length;t++)2===t?r.push("index"):r.push(`${n[t]}`);return r.join()}(e);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${r}));\n      }\n    `}};function JU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=ja.parseAxisParam(i,a.shape)[0];if(ut().get("DEBUG")){let e=n.readSync(s.dataId),t=a.shape[l];for(let n=0;n<e.length;++n){let r=e[n];ja.assert(r<=t-1&&r>=0,(()=>`GatherV2: the index value ${r} is not in [0, ${t-1}]`))}}let u=Im.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=ja.sizeFromShape(s.shape),p=[],h=WB({input

2s:{x:a},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=WB({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,d/u.batchSize]}});p.push(h),p.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([a,s])||"string"===a.dtype){let e=n.bufferSync(c),t=n.bufferSync(h),r=rP(t,e,f);return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(u.outputShape,r.dtype,r.values)}let m=new ZU(h.shape,f),g=n.runWebGLProgram(m,[h,c],h.dtype);p.push(g);let y=WB({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}var QU={kernelName:_n,backendName:"webgl",kernelFunc:JU},eG=FB({opSnippet:"return float(a > b);",packedOpSnippet:"\n  return vec4(greaterThan(a, b));\n",cpuKernelImpl:aP,dtype:"bool"}),tG={kernelName:En,backendName:"webgl",kernelFunc:eG},nG=FB({opSnippet:"return float(a >= b);",packedOpSnippet:"\n  return vec4(greaterThanEqual(a, b));\n",dtype:"bool",cpuKernelImpl:sP}),rG={kernelName:$n,backendName:"webgl",kernelFunc:nG};var aG={kernelName:Rn,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{input:r}=t;return $U(r,!0,n)}},sG=RB({opSnippet:"return float(!isnan(x) && !isinf(x));",dtype:"bool"}),iG={kernelName:Dn,backendName:"webgl",kernelFunc:sG},oG=RB({opSnippet:"return float(isinf(x));",dtype:"bool"}),lG={kernelName:Mn,backendName:"webgl",kernelFunc:oG},uG=RB({opSnippet:"return float(isnan(x));",dtype:"bool"}),dG={kernelName:On,backendName:"webgl",kernelFunc:uG},pG=FB({opSnippet:"return float(a < b);",packedOpSnippet:"\n  return vec4(lessThan(a, b));\n",cpuKernelImpl:iP,dtype:"bool"}),hG={kernelName:zn,backendName:"webgl",kernelFunc:pG},cG=FB({opSnippet:"return float(a <= b);",packedOpSnippet:"\n  return vec4(lessThanEqual(a, b));\n",cpuKernelImpl:oP,dtype:"bool"}),fG={kernelName:Pn,backendName:"webgl",kernelFunc:cG};var mG={kernelName:Bn,backendName:"webgl",kernelFunc:function(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=lP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}},gG=RB({opSnippet:AB+"\n  return x < 0.0 ? 0./0. : log(x);\n",packedOpSnippet:"\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n",cpuKernelImpl:uP}),yG={kernelName:Wn,backendName:"webgl",kernelFunc:gG},bG=RB({opSnippet:AB+"\n  return log(1.0 + x);\n"}),xG={kernelName:Vn,backendName:"webgl",kernelFunc:bG},wG=FB({opSnippet:"return float(a >= 1.0 && b >= 1.0);",packedOpSnippet:"\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n",dtype:"bool"}),vG={kernelName:Un,backendName:"webgl",kernelFunc:wG},kG=RB({opSnippet:"return float(!(x >= 1.0));"}),IG={kernelName:Gn,backendName:"webgl",kernelFunc:kG},SG=FB({opSnippet:"return float(a >= 1.0 || b >= 1.0);",packedOpSnippet:"\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n",dtype:"bool"}),NG={kernelName:Hn,backendName:"webgl",kernelFunc:SG},TG=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;o=.5===a?`inversesqrt(${l})`:1===a?`1.0/(${l})`:`exp(log(${l}) * float(-${a}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${s}; j <= ${s}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${i}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${o};\n        setOutput(val);\n      }\n    `}},_G=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;o=.5===a?`inversesqrt(${l})`:1===a?`1.0/(${l})`:`exp(log(${l}) * float(-${a}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${s};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }

2\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${s}; j <= ${s}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${o};\n        setOutput(result);\n      }\n    `}},CG={kernelName:Xn,backendName:"webgl",kernelFunc:e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=ut().getBool("WEBGL_PACK_NORMALIZATION")?new _G(a.shape,s,i,o,l):new TG(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)}},EG=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${r}) * norm + float(${n});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${r})\n                * float(${a})\n                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${a});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}},$G={kernelName:Yn,backendName:"webgl",kernelFunc:e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=r,p=new EG(a.shape,o,l,u,d);return n.runWebGLProgram(p,[a,s,i],a.dtype)}};function AG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=ja.parseAxisParam(s,a.shape),u=l,d=Im.getAxesPermutation(u,o),p=null!=d,h=n.shouldExecuteOnCPU([a]),c=a;if(p){if(h){let e=n.texData.get(c.dataId).values,t=new Array(o);for(let e=0;e<t.length;e++)t[e]=a.shape[d[e]];let r=zP(e,a.shape,a.dtype,d,t);c=n.makeTensorInfo(t,a.dtype),n.texData.get(c.dataId).values=r}else c=KB(a,d,n);u=Im.getInnerMostAxes(u.length,o)}Im.assertAxesAreInnerMostDims("max",u,o);let f,[m,g]=Im.computeOutAndReduceShapes(c.shape,u),y=m;if(i&&(y=Im.expandShapeToKeepDim(m,l)),h){let e=n.texData.get(c.dataId).values,t=dP(e,ja.sizeFromShape(g),y,a.dtype);f=n.makeTensorInfo(y,a.dtype),n.texData.get(f.dataId).values=t}

2else f=function(e,t,n,r){let a=ja.sizeFromShape(t),s=WB({inputs:{x:e},attrs:{shape:[ja.sizeFromShape(e.shape)/a,a]},backend:r}),i=HB(s,e.dtype,"max",r),o=WB({inputs:{x:i},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),o}(c,g,y,n);return p&&n.disposeIntermediateTensorInfo(c),f}var RG={kernelName:Jn,backendName:"webgl",kernelFunc:AG},FG=FB({opSnippet:yB+"\n  return max(a, b);\n",packedOpSnippet:"\n  vec4 result = vec4(max(a, b));\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  "+xB+"\n  return result;\n",cpuKernelImpl:pP}),DG={kernelName:Qn,backendName:"webgl",kernelFunc:FG};var MG={kernelName:er,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;VL(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r;ja.assert(Im.eitherStridesOrDilationsAreOne(i,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));let u=Im.computePool2DInfo(a.shape,s,i,1,o,l);if(1===u.filterWidth&&1===u.filterHeight&&ja.arraysEqual(u.inShape,u.outShape))return vB({inputs:{x:a},backend:n});let d=new FW(u,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}};var OG={kernelName:nr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,d=Im.computePool3DInfo(a.shape,s,i,[1,1,1],o,u,l),p=new DW(d,"max",!1);return n.runWebGLProgram(p,[a],a.dtype)}},LG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${a};\n          wR += ${r}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${s}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${s} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},zG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,p=l-1-e.padInfo.top,h=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`\n      const ivec3 pads = ivec3(${d}, ${p}, ${h});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(x

2D, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${o};\n           wD += ${a}) {\n          float dyD = float(dyDCorner + wD) / ${t}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${l};\n              wR += ${s}) {\n            float dyR = float(dyRCorner + wR) / ${n}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${u};\n                wC += ${i}) {\n              float dyC = float(dyCCorner + wC) / ${r}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${c} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${l} * ${u} +\n                  wR * ${u} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};var PG={kernelName:rr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=r,p=Im.computePool3DInfo(i.shape,o,l,[1,1,1],u,d),h=new DW(p,"max",!0),c=n.runWebGLProgram(h,[i],i.dtype),f=new zG(p),m=n.runWebGLProgram(f,[a,c],i.dtype);return n.disposeIntermediateTensorInfo(c),m}};var BG={kernelName:tr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;VL([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:p}=r,h=Im.computePool2DInfo(o.shape,l,u,1,d,p),c=new FW(h,"max",!0),f=n.runWebGLProgram(c,[o],o.dtype),m=new LG(h),g=n.runWebGLProgram(m,[a,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}};var WG={kernelName:ar,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;ja.assert(4===r.shape.length,(()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`));let u=[1,1];ja.assert(Im.eitherStridesOrDilationsAreOne(s,u),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`));let d=Im.computePool2DInfo(r.shape,a,s,u,i),[p,h]=function(e,t,n,r){let a=new FW(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");return a=new FW(n,"max",!0,!0,t),[s,r.runWebGLProgram(a,[e],"float32")]}(r,o,d,l);return[p,h]}};var VG={kernelName:sr,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=ja.parseAxisParam(s,r.shape),u=l,d=Im.getAxesPermutation(u,o),p=null!=d,h=i.shouldExecuteOnCPU([r]),c=[],f=r;if(p){if(h){let e=i.texData.get(f.dataId).values,t=new Array(o);for(let e=0;e<t.length;e++)t[e]=r.shape[d[e]];let n=zP(e,r.shape,r.dtype,d,t);f=i.makeTensorInfo(t,r.dtype),i.texData.get(f.dataId).values=n}else f=KB(r,d,i);c.push(f),u=Im.getInnerMostAxes(u.length,o)}Im.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=Im.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=Im.expandShapeToKeepDim(m,l));let b=function(e,t,n,r){let a=ja.sizeFromShape(t),s=WB({inputs:{x:e},attrs:{shape:[ja.sizeFromShape(e.shape)/a,a]},backend:r}),i=HB(s,"float32","mean",r),o=WB({inputs:{x:i},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),o}(f,g,y,i);for(let e of c)i.disposeIntermediateTensorInfo(e);return b}};var UG={kernelName:ir,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=ja.parseAxisParam(s,a.shape),u=l,d=Im.getAxesPermutation(u,o),p=a;null!=d&&(p=ZB({inputs:{x:a},backend:n,attrs:{perm:d}}

2),u=Im.getInnerMostAxes(u.length,a.shape.length)),Im.assertAxesAreInnerMostDims("min",u,o);let h,[c,f]=Im.computeOutAndReduceShapes(p.shape,u),m=WB({inputs:{x:p},backend:n,attrs:{shape:[-1,ja.sizeFromShape(f)]}}),g=HB(m,m.dtype,"min",n);if(i){h=WB({inputs:{x:g},backend:n,attrs:{shape:Im.expandShapeToKeepDim(c,l)}})}else h=WB({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=d&&n.disposeIntermediateTensorInfo(p),h}},GG=FB({opSnippet:yB+"\n  return min(a, b);\n",packedOpSnippet:"\n  vec4 result = vec4(min(a, b));\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  "+xB+"\n  return result;\n",cpuKernelImpl:hP}),HG={kernelName:or,backendName:"webgl",kernelFunc:GG},jG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));let r=e.length,a=oz(r),s=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l="reflect"===n?0:1;this.userCode=1!==r?`\n      ${a} start = ${a}(${s});\n      ${a} end = ${a}(${i});\n\n      void main() {\n        ${a} outC = getOutputCoords();\n        for (int i = 0; i < ${r}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${l};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};\n          }\n        }\n        ${a} coords = outC - start;\n        setOutput(getX(${o}));\n      }\n    `:`\n        int start = ${s};\n        int end = ${i};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${l};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${l};\n          }\n          setOutput(getX(outC - start));\n        }\n      `}},qG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));let r=e.length,a=oz(r),s=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=WP("rc",r),l=WP("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,d=1===r?"source":`vec2(${l.slice(-2).join()})`,p="reflect"===n?0:1,h="";if(1===r){let e=`\n        ${a} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${p};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${p};\n        }\n        source -= start;\n      `;h=`\n        ${a} rc = outputLoc;\n        ${e}\n        result[0] = getChannel(getX(${l.join()}), ${d});\n        ${o[r-1]} += 1;\n        if(${u}) {\n          ${e}\n          result[1] = getChannel(getX(${l.join()}), ${d});\n        }\n      `}else{let e=`\n        ${a} source = rc;\n        ${a} lt = ${a}(lessThan(source, start));\n        ${a} gte = ${a}(greaterThanEqual(source, end));\n        ${a} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${p}) +\n                gte * ((end - 1) * 2 - source + ${p});\n        source -= start;\n      `;h=`\n        ${a} rc = outputLoc;\n        ${e}\n        result[0] = getChannel(getX(${l.join()}), ${d});\n        ${o[r-1]} += 1;\n        if(${u}) {\n          ${e}\n          result[1] = getChannel(getX(${l.join()}), ${d});\n        }\n        rc = outputLoc;\n        ${o[r-2]} += 1;\n        if(${o[r-2]} < ${this.outputShape[r-2]}) {\n          ${e}\n          result[2] = getChannel(getX(${l.join()}), ${d});\n          ${o[r-1]} += 1;\n          if(${u}) {\n            ${e}\n            result[3] = getChannel(getX(${l.join()}), ${d});\n          }\n        }\n      `}this.userCode=`\n      const ${a} start = ${a}(${s});\n      const ${a} end = ${a}(${i});\n\n      void main() {\n        ${a} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${h}\n        setOutput(result);\n      }\n    `}}

2,KG={kernelName:lr,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=ut().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qG(r.shape,a,s):new jG(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)}},XG=FB({opSnippet:"if (b == 0.0) return NAN;\n  return mod(a, b);",packedOpSnippet:"\n  vec4 result = mod(a, b);\n  bvec4 isNaN = equal(b, vec4(0.0));\n  "+xB+"\n  return result;\n"}),YG={kernelName:ur,backendName:"webgl",kernelFunc:XG},ZG=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${t-1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${t-1}));\n      }\n    `}},JG=FB({opSnippet:"\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;",packedOpSnippet:"\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n",checkOutOfBounds:!0}),QG={kernelName:hn,backendName:"webgl",kernelFunc:JG},eH="return a - b;",tH=FB({opSnippet:eH,packedOpSnippet:eH,supportsComplex:!0,cpuKernelImpl:MP}),nH={kernelName:ma,backendName:"webgl",kernelFunc:tH};function rH(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=ja.parseAxisParam([s],a.shape),o=AG({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=Im.expandShapeToKeepDim(o.shape,i),u=WB({inputs:{x:o},backend:n,attrs:{shape:l}}),d=tH({inputs:{a,b:u},backend:n}

2),p=kU({inputs:{x:d},backend:n}),h=XB({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),c=WB({inputs:{x:h},backend:n,attrs:{shape:l}}),f=JG({inputs:{a:p,b:c},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),f}var aH={kernelName:na,backendName:"webgl",kernelFunc:rH};var sH={kernelName:dr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:rH({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],p=new ZG(u,d,s),h=[[i]],c=n.runWebGLProgram(p,[l],"int32",h);return o||n.disposeIntermediateTensorInfo(l),c}},iH=XP+"\n  return -x;\n";var oH={kernelName:hr,backendName:"webgl",kernelFunc:function(e){let t,{inputs:n,backend:r}=e,{x:a}=n;if(r.shouldExecuteOnCPU([a])){let e=r.texData.get(a.dataId),[t,n]=fP(e.values,a.shape,a.dtype);return r.makeTensorInfo(n,a.dtype,t)}return t=ut().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new lB(a.shape,"\n  vec4 result = -x;\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n"):new KP(a.shape,iH),r.runWebGLProgram(t,[a],a.dtype)}},lH=Eg.nonMaxSuppressionV3Impl;var uH={kernelName:fr,backendName:"webgl",kernelFunc:function(e){Im.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),d=n.readSync(s.dataId),{selectedIndices:p}=lH(u,d,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}},dH=Eg.nonMaxSuppressionV4Impl;var pH={kernelName:mr,backendName:"webgl",kernelFunc:function(e){Im.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,d=n.readSync(a.dataId),p=n.readSync(s.dataId),{selectedIndices:h,validOutputs:c}=dH(d,p,i,o,l,u);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([c]))]}},hH=Eg.nonMaxSuppressionV5Impl;var cH={kernelName:gr,backendName:"webgl",kernelFunc:function(e){Im.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,d=n.readSync(a.dataId),p=n.readSync(s.dataId),h=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=hH(d,p,h,c,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}},fH=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${r}), float(${n}),\n                      float(index == coords.y)));\n      }\n    `}},mH={kernelName:br,backendName:"webgl",kernelFunc:e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{dtype:s,depth:i,onValue:o,offValue:l}=r,u=ja.sizeFromShape(a.shape),d=new fH(u,i,o,l),p=WB({inputs:{x:a},backend:n,attrs:{shape:[u]}}),h=n.runWebGLProgram(d,[p],s);n.disposeIntermediateTensorInfo(p);let c=WB({inputs:{x:h},backend:n,attrs:{shape:[...a.shape,i]}});return n.disposeIntermediateTensorInfo(h),c}};function gH(e){let{inputs:t,backend:n}=e,{x:r}=t;if("complex64"===r.dtype){let e=nV({inputs:{input:r},backend:n}),t=gH({inputs:{x:e},backend:n}),a=bV({inputs:{input:r},backend:n}),s=gH({inputs:{x:a},backend:n}),i=IB({inputs:{real:t,imag:s},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),i}return FU({attrs:{shape:r.shape,dtype:r.dtype,value:"string"===r.dtype?"":0},backend:n})}var yH={kernelName:Ta,backendName:"webgl",kernelFunc:gH};var bH={kernelName:yr,backendName:"webgl",kernelFunc:function e(t){let{inputs:n,backend:r}=t,{x:a}=n;if("string"===a.dtype)throw new Error("onesLike is not supported under string dtype");

2if("complex64"===a.dtype){let t=nV({inputs:{input:a},backend:r}),n=e({inputs:{x:t},backend:r}),s=bV({inputs:{input:a},backend:r}),i=gH({inputs:{x:s},backend:r}),o=IB({inputs:{real:n,imag:i},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),o}return FU({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:r})}};var xH={kernelName:xr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(1===t.length)return SU({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach((e=>{ja.assertShapesMatch(s,e.shape,"All tensors passed to stack must have matching shapes"),ja.assert(i===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));let o=[],l=vV({inputs:t.map((e=>{let t=SU({inputs:{input:e},backend:n,attrs:{dim:a}});return o.push(t),t})),backend:n,attrs:{axis:a}});return o.forEach((e=>n.disposeIntermediateTensorInfo(e))),l}},wH=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));let r=e.length,a=oz(r),s=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?`\n      ${a} start = ${a}(${s});\n      ${a} end = ${a}(${i});\n\n      void main() {\n        ${a} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          ${a} coords = outC - start;\n          setOutput(getX(${o}));\n        }\n      }\n    `:`\n        int start = ${s};\n        int end = ${i};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `}},vH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));let r=e.length,a=oz(r),s=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=WP("rc",r),l=WP("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,d=1===r?"source":`vec2(${l.slice(-2).join()})`,p=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;\n       if(${u}) {\n      `,1===r?"":`}\n       rc = outputLoc;\n       ${o[r-2]} += 1;\n       if(${o[r-2]} < ${this.outputShape[r-2]}) {`,1===r?"":`  ${o[r-1]} += 1;\n         if(${u}) {`],h=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",c="";for(let e=0,t=1===r?2:4;e<t;e++)c+=`\n        ${p[e]}\n        if (${h}) {\n          result[${e}] = float(value);\n        } else {\n          ${a} source = rc - start;\n          result[${e}] = getChannel(getX(${l.join()}), ${d});\n        }\n      `;c+=1===r?"} ":"}}",this.userCode=`\n      const ${a} start = ${a}(${s});\n      const ${a} end = ${a}(${i});\n\n      void main() {\n        ${a} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${c}\n        setOutput(result);\n      }\n    `}},kH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r;if(0===ja.sizeFromShape(a.shape)){return FU({backend:n,attrs:{shape:s.map(((e,t)=>e[0]+a.shape[t]+e[1])),value:i,dtype:a.dtype}})}let o=ut().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vH(a.shape,s,i):new wH(a.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[a],a.dtype,l)},IH={kernelName:wr,backendName:"webgl",kernelFunc:kH},SH=FB({opSnippet:"\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",packedOpSnippet:"\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  bvec4 isNaN1 = lessThan(a, vec4(0.0));\n  bvec4 isNaN2 = lessThan(floor(b), b);\n  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.

2w);\n  "+xB+"\n  return result;\n"}),NH={kernelName:kr,backendName:"webgl",kernelFunc:SH};var TH={kernelName:Sr,backendName:"webgl",kernelFunc:function(e){let t,{inputs:n,backend:r,attrs:a}=e,{x:s}=n,{axis:i,keepDims:o}=a,l=s.shape.length,u=[],d=ja.parseAxisParam(i,s.shape),p=d,h=Im.getAxesPermutation(p,l),c=s;if(null!=h&&(c=ZB({inputs:{x:s},backend:r,attrs:{perm:h}}),p=Im.getInnerMostAxes(p.length,l),u.push(c)),Im.assertAxesAreInnerMostDims("prod",p,l),r.shouldExecuteOnCPU([c])){let e=r.texData.get(c.dataId).values,{outVals:n,outShape:a,outDtype:s}=gP(c.shape,c.dtype,e,p);t=r.makeTensorInfo(a,s,n)}else{let[e,n]=Im.computeOutAndReduceShapes(c.shape,p),a=ja.sizeFromShape(n),i=WB({inputs:{x:c},backend:r,attrs:{shape:[-1,a]}}),o=HB(i,Vs(s.dtype),"prod",r);t=WB({inputs:{x:o},backend:r,attrs:{shape:e}}),u.push(i),u.push(o)}if(o){u.push(t);let e=Im.expandShapeToKeepDim(t.shape,d);t=WB({inputs:{x:t},backend:r,attrs:{shape:e}})}return u.forEach((e=>r.disposeIntermediateTensorInfo(e))),t}};var _H={kernelName:Nr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{paramsNestedSplits:a,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=r,l=a.map((e=>n.readSync(e.dataId))),u=a.map((e=>e.shape)),d=n.readSync(s.dataId),p=n.readSync(i.dataId),[h,c,f]=yP(l,u,d,s.shape,s.dtype,p,i.shape,o),m=h.map((e=>n.makeTensorInfo([e.length],"int32",e))),g=n.makeTensorInfo(f,s.dtype,c);return m.concat([g])}};var CH={kernelName:Tr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{starts:r,limits:a,deltas:s}=t,i=n.readSync(r.dataId),o=n.readSync(a.dataId),l=n.readSync(s.dataId),[u,d]=bP(i,r.shape,r.dtype,o,a.shape,l,s.shape);return[n.makeTensorInfo([u.length],"int32",u),n.makeTensorInfo([d.length],r.dtype,d)]}};var EH={kernelName:_r,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{shape:a,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=r,u=n.readSync(a.dataId),d=n.readSync(s.dataId),p=n.readSync(i.dataId),h=o.map((e=>n.readSync(e.dataId))),c=o.map((e=>e.shape)),[f,m]=xP(u,a.shape,d,s.shape,s.dtype,p,i.shape,h,c,l);return n.makeTensorInfo(f,s.dtype,m)}},$H=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=wP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},AH={kernelName:Cr,backendName:"webgl",kernelFunc:$H},RH=RB({opSnippet:"return 1.0 / x;"}),FH={kernelName:$r,backendName:"webgl",kernelFunc:RH},DH=RB({opSnippet:XP+"\n  return (x < 0.0) ? 0.0 : x;\n",packedOpSnippet:"\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n"}),MH={kernelName:Ar,backendName:"webgl",kernelFunc:DH},OH=RB({opSnippet:XP+"\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n",packedOpSnippet:"\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n"}),LH={kernelName:Lr,backendName:"webgl",kernelFunc:OH},zH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u,d=[r&&t>1?i-1:i,r&&n>1?o-1:o],p=[r&&t>1?t-1:t,r&&n>1?n-1:n];u=a?"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${d[0]/p[0]},\n          ${d[1]/p[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${u};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `}},PH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u,d=[r&&t>1?i-1:i,r&&n>1?o-1:o],p=[r&&t>1?t-1:t,r&&n>1?n-1:n];u=a?"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${d[0]/p[0]},\n          ${d[1]/p[1]},\n          ${d[1]/p[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${u};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${l

2-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}}

2;var BH={kernelName:Mr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,d=ut().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new PH(a.shape,l,u,s,i):new zH(a.shape,l,u,s,i);return n.runWebGLProgram(d,[a],"float32")}},WH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,h=1/d,c=2*Math.ceil(p)+2,f=2*Math.ceil(h)+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${u});\n        const float widthScale = float(${d});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${h});\n\n        const int winHeight = int(${c});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${s}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${a-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};var VH={kernelName:Or,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new WH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}},UH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u,d=[r&&t>1?i-1:i,r&&n>1?o-1:o],p=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0";u=a?"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${d[0]/p[0]},\n          ${d[1]/p[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${u};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}},GH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u,d=[r&&t>1?i-1:i,r&&n>1?o-1:o],p=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0";u=a?"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${d[0]/p[0]},\n          ${d[1]/p[1]},\n          ${d[1]/p[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${u};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${l-1};\n        bool hasNextRow = coords.z < ${n-1};

2\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};var HH={kernelName:Fr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,d=ut().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new GH(a.shape,l,u,s,i):new UH(a.shape,l,u,s,i);return n.runWebGLProgram(d,[a],a.dtype)}},jH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,h=1/d,c=2*Math.ceil(p)+2,f=2*Math.ceil(h)+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${u});\n        const float widthScale = float(${d});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${h});\n\n        const int winHeight = int(${c});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${s}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${l[0]}));\n\n            float sourceFracCol =\n                float(${o[1]}) *\n                  (float(dyC) / float(${l[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${a}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};var qH={kernelName:Dr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new jH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}},KH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,1===n)return void(this.userCode=`\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${e[0]} - coord - 1));\n        }\n      `);let r=e.map(((n,r)=>(n=>-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - coords[${n}] - 1`:`coords[${n}]`)(r))).join(","),a=oz(n);this.userCode=`\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${r}));\n      }\n    `}},XH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=WP("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=oz(n);var o;function l(n){let r=e.map(((r,a)=>function(n,r){return-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - ${r[n]} - 1`:`${r[n]}`}(a,n)));return`getChannel(getX(${r.join(",")}), vec2(${r.slice(-2).join(",")}))`}this.userCode=1===n?`\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${e[0]} - rc - 1),\n            ${e[0]} - rc - 1);\n          if(${a}){\n              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),\n                ${e[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `:`\n        void main() {\n          ${i} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${o=r.slice(),l(o)};\n          if(${a}){\n            result.g = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",l(e)}(r.slice())};\n          }\n          if(${s}) {\n            result.b = ${function(e){return e[n-2]="("+e[n-2]+" + 1)",l(e)}(r.slice())};\n            if(${a}) {\n              result.a = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",l(e)}(r.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `}};var YH={kernelName:zr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=ja.parseAxisParam(s,a.shape);

2if(0===i)return vB({inputs:{x:a},backend:n});let l=ut().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XH(a.shape,o):new KH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}},ZH=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],r=e[2];this.outputShape=e;let a="";a="number"==typeof t?`float outputValue = ${t.toFixed(2)};`:`\n        vec3 fill = vec3(${t.join(",")});\n        float outputValue = fill[coords[3]];`,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${a}\n          if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},JH={kernelName:Ea,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}

2=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new ZH(r.shape,s),[u,d]=Im.getImageCenter(i,r.shape[1],r.shape[2]),p=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[r],r.dtype,p)}},QH=RB({opSnippet:"\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n"}),ej={kernelName:Pr,backendName:"webgl",kernelFunc:QH},tj=RB({opSnippet:"return inversesqrt(x);",cpuKernelImpl:vP}),nj={kernelName:Br,backendName:"webgl",kernelFunc:tj},rj=class{constructor(e,t,n,r,a,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=oz(a.length),u=oz(s.length),d="";1===n?d="i":2===n&&(d="i, j");let p=`getIndices(${d})`,h="";1===r?h="i":2===r&&(h="i, coords[1]");let c=`getUpdates(${h})`,f="";o&&(f="coords[0], coords[1]");let m=`getDefaultValue(${f})`,g=t>1?"strides[j]":"strides";this.userCode=`\n        ${l} strides = ${l}(${a});\n\n        void main() {\n          ${u} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${e}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${t}; j++) {\n              int index = round(${p});\n              flattenedIndex += index * ${g};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${c};\n              found = true;\n            }\n          }\n          setOutput(mix(${m}, sum, float(found)));\n        }\n      `}},aj=class{constructor(e,t,n,r,a,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=oz(a.length),u=oz(s.length),d="";1===n?d="i":2===n&&(d="i, j");let p=`getIndices(${d})`,h="";1===r?h="i":2===r&&(h="i, coords[1]");let c=`getUpdates(${h})`,f="";o&&(f="coords[0], coords[1]");let m=`getDefaultValue(${f})`,g=t>1?"strides[j]":"strides",y=t>1?"strides[j + 1]":"strides";this.userCode=`\n        ${l} strides = ${l}(${a});\n\n        void main() {\n          ${u} coords = getOutputCoords();\n          vec4 sum = vec4(0.);\n          vec4 found = vec4(0.);\n          for (int i = 0; i < ${e}; i+=2) {\n            ivec2 flattenedIndex = ivec2(0);\n            for (int j = 0; j < ${t}; j+=2) {\n              ivec4 index = round(${p});\n              flattenedIndex += index.xz * ${g};\n              if (j + 1 < ${t}) {\n                flattenedIndex += index.yw * ${y};\n              }\n            }\n            if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n                flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n              vec4 updVals = ${c};\n              if (flattenedIndex[0] == coords[0]) {\n                sum.xy += updVals.xy;\n                found.xy = vec2(1.);\n              } else if (flattenedIndex[0] == coords[0] + 1) {\n                sum.zw += updVals.xy;\n                found.zw = vec2(1.);\n              }\n              if (flattenedIndex[1] == coords[0]) {\n                sum.xy += updVals.zw;\n                found.xy = vec2(1.);\n              } else if (flattenedIndex[1] == coords[0] + 1) {\n                sum.zw += updVals.zw;\n                found.zw = vec2(1.);\n              }\n            }\n          }\n          setOutput(mix(${m}, sum, found));\n        }\n      `}};var sj={kernelName:Wr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:p}=Im.calculateShapes(s,a,i),h=[p/u,u];if(0===p)return n.makeTensorInfo(i,a.dtype);let c,f=WB({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),m=WB({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),g=n.makeTensorInfo([],"float32",new Float32Array([0]));c=ut().getBool("WEBGL_PACK")?new aj(l,o,f.shape.length,m.shape.length,d,h):new rj(l,o,f.shape.length,m.shape.length,d,h);

2let y=n.runWebGLProgram(c,[m,f,g],m.dtype),b=WB({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),b}},ij=class{constructor(e,t,n,r){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,s=2===ut().getNumber("WEBGL_VERSION")?"while (left < right) {":a,i="left"===r?"<":"<=";this.userCode=`\n       int findBound(int batch, float value) {\n         int left = 0;\n         int right = numInputs;\n         int mid;\n         ${s}\n           mid = (left + right) / 2;\n           if (getSortedSequence(batch, mid) ${i} value) {\n             left = mid + 1;\n           } else {\n             right = mid;\n           }\n         }\n         return right;\n       }\n\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int valueIndex = coords[1];\n\n         float value = getValues(batch, valueIndex);\n\n         setOutput(float(findBound(batch, value)));\n       }\n     `}};var oj={kernelName:Ur,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{sortedSequence:a,values:s}=t,{side:i}=r,o=new ij(a.shape[0],a.shape[1],s.shape[1],i),l=[[a.shape[1]]];return n.runWebGLProgram(o,[a,s],"int32",l)}},lj=class{constructor(e,t,n){let r,a;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error(`Where for rank ${n} is not yet supported`);if(1===n)a="resRC",r="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],i=[];for(let r=0;r<t.length;r++)i.push(`${n[r]}`),r<e&&s.push(`${n[r]}`);r=s.join(),a=i.join()}let s=oz(n);this.userCode=`\n      void main() {\n        ${s} resRC = getOutputCoords();\n        float cVal = getC(${r});\n        if (cVal >= 1.0) {\n          setOutput(getA(${a}));\n        } else {\n          setOutput(getB(${a}));\n        }\n      }\n    `}};var uj={kernelName:Gr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new lj(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],Ws(a.dtype,s.dtype))}}

2,dj=RB({opSnippet:`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${Im.SELU_SCALEALPHA};\n  float scale = ${Im.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`}),pj={kernelName:Hr,backendName:"webgl",kernelFunc:dj},hj=RB({opSnippet:AB+"\n  return 1.0 / (1.0 + exp(-1.0 * x));\n",packedOpSnippet:"\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n",cpuKernelImpl:IP}),cj={kernelName:Yr,backendName:"webgl",kernelFunc:hj},fj=RB({opSnippet:"\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n"}),mj={kernelName:Xr,backendName:"webgl",kernelFunc:fj},gj=RB({opSnippet:AB+"\n  return sin(x);\n",packedOpSnippet:`\n  vec4 result = sin(x);\n  bvec4 isNaN = isnan(x);\n  ${xB}\n  return result;\n`}),yj={kernelName:qr,backendName:"webgl",kernelFunc:gj},bj=RB({opSnippet:"\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n"}),xj={kernelName:Kr,backendName:"webgl",kernelFunc:bj},wj=RB({opSnippet:"\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n"}),vj={kernelName:Zr,backendName:"webgl",kernelFunc:wj},kj={kernelName:ea,backendName:"webgl",kernelFunc:e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;ja.assert(a.shape.length<=4,(()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"));let o=s.reduce(((e,t)=>e*t)),l=[[0,0]];l.push(...i);for(let e=1+s.length;e<a.shape.length;++e)l.push([0,0]);let u=[],d=kH({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),p=Im.getReshaped(d.shape,s,o,!1),h=Im.getPermuted(p.length,s.length,!1),c=Im.getReshapedPermuted(d.shape,s,o,!1),f=WB({inputs:{x:d},backend:n,attrs:{shape:p}}),m=ZB({inputs:{x:f},backend:n,attrs:{perm:h}}),g=WB({inputs:{x:m},backend:n,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach((e=>n.disposeIntermediateTensorInfo(e))),g}};var Ij={kernelName:ra,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{indices:r,values:a,denseShape:s,defaultValue:i}=t;if(1!==s.shape.length)throw new Error(`Dense shape must be a vector, saw:\n         ${s.shape}`);if(2!==r.shape.length)throw new Error(`Indices must be a matrix, saw:\n         ${r.shape}`);if(1!==a.shape.length)throw new Error(`Values must be a vector, saw:\n         ${a.shape}`);if(0!==i.shape.length)throw new Error(`Default value must be a scalar, saw:\n        ${i.shape}`);let o=n.readSync(r.dataId),l=n.readSync(a.dataId),u=n.readSync(s.dataId),d=n.readSync(i.dataId)[0],[p,h,c,f,m]=TP(o,r.shape,r.dtype,l,a.dtype,u,d);return[n.makeTensorInfo(h,r.dtype,p),n.makeTensorInfo([h[0]],a.dtype,c),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map((e=>Number(e))))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}};var Sj={kernelName:aa,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:a,newShape:s}=t;if(2!==r.shape.length)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(1!==a.shape.length)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(1!==s.shape.length)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(a.dataId)),o=n.readSync(r.dataId),l=Array.from(n.readSync(s.dataId)),[u,d,p]=_P(o,r.shape,r.dtype,i,l);return[n.makeTensorInfo(d,r.dtype,u),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}};var Nj={kernelName:sa,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{data:r,indices:a,segmentIds:s}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");

2if(1!==a.shape.length)throw new Error(`Indices should be a vector but received shape\n              ${a.shape}`);if(1!==s.shape.length)throw new Error(`Segment ids should be a vector but received shape\n              ${s.shape}`);let i=n.readSync(r.dataId),o=n.readSync(a.dataId),l=n.readSync(s.dataId),[u,d]=CP(i,r.shape,r.dtype,o,l,!0);return n.makeTensorInfo(d,r.dtype,u)}};var Tj={kernelName:ia,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n}=e,{data:r,indices:a,segmentIds:s}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.shape.length)throw new Error(`Indices should be a vector but received shape\n             ${a.shape}`);if(1!==s.shape.length)throw new Error(`Segment ids should be a vector but received shape\n             ${s.shape}`);let i=n.readSync(r.dataId),o=n.readSync(a.dataId),l=n.readSync(s.dataId),[u,d]=CP(i,r.shape,r.dtype,o,l);return n.makeTensorInfo(d,r.dtype,u)}};var _j={kernelName:oa,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,sliceSize:d,strides:p,outputSize:h}=Im.calculateShapes(s,a,o);if("string"===s.dtype){let e=n.bufferSync(a),t=n.bufferSync(s),r=ja.decodeString(n.readSync(i.dataId)[0]),c=kP(e,t,o,h,d,u,l,p,r,false);return n.makeTensorInfo(o,c.dtype,c.values)}let c=new rj(u,l,a.shape.length,s.shape.length,p,[h,1],false),f=n.runWebGLProgram(c,[s,a,i],s.dtype),m=WB({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}};var Cj={kernelName:ta,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=ja.parseAxisParam(i,a.shape)[0],l=Im.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),p=a.shape.slice();return l.map((e=>{let t=[...p];t[o]=e;let r=KW({inputs:{x:a},backend:n,attrs:{begin:d,size:t}});return d[o]+=e,r}))}},Ej="return sqrt(x);",$j=RB({opSnippet:Ej,packedOpSnippet:Ej,cpuKernelImpl:EP}),Aj={kernelName:Jr,backendName:"webgl",kernelFunc:$j}

2,Rj=RB({opSnippet:"return x * x;"}),Fj={kernelName:ua,backendName:"webgl",kernelFunc:Rj},Dj="return (a - b) * (a - b);",Mj=FB({opSnippet:Dj,packedOpSnippet:Dj}),Oj={kernelName:la,backendName:"webgl",kernelFunc:Mj};var Lj={kernelName:da,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;if("string"!==a.dtype)throw new Error("Input must be of datatype string");let s=n.readSync(a.dataId),i=Im.fromUint8ToStringArray(s),o=$P(i,"string",r);return n.makeTensorInfo(a.shape,"string",o)}};var zj={kernelName:_a,backendName:"webgl",kernelFunc:function({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=XP+`\n    return x > 0.0 ? 1.0 : float(${t.alpha});\n  `,s=new KP(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}},Pj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=oz(n.length),s=oz(n.length),i="";if(1===r)i="coords * strides + begin";else{let e=0;i=n.map(((t,r)=>(e++,1===n.length?`coords * strides[${r}] + begin[${r}]`:`coords[${e-1}] * strides[${r}] + begin[${r}]`))).join(",")}this.userCode=`\n      ${a} begin = ${a}(${e});\n      ${a} strides = ${a}(${t});\n\n      void main() {\n        ${s} coords = getOutputCoords();\n        setOutput(getX(${i}));\n      }\n    `}};var Bj={kernelName:pa,backendName:"webgl",kernelFunc:function(e){let t,{inputs:n,backend:r,attrs:a}=e,{x:s}=n,{begin:i,end:o,strides:l,beginMask:u,endMask:d,ellipsisMask:p,newAxisMask:h,shrinkAxisMask:c}=a,{finalShapeSparse:f,finalShape:m,isIdentity:g,sliceDim0:y,isSimpleSlice:b,begin:x,end:w,strides:v}=Zf.sliceInfo(s.shape,i,o,l,u,d,p,h,c);if(g)t=WB({inputs:{x:s},backend:r,attrs:{shape:m}});else if(y||b){ja.assert(s.shape.length>=1,(()=>`Input must have rank at least 1, got: ${s.shape.length}`));let e=Zf.computeOutShape(x,w,v),n=KW({inputs:{x:s},backend:r,attrs:{begin:x,size:e}});t=WB({inputs:{x:n},backend:r,attrs:{shape:m}}),r.disposeIntermediateTensorInfo(n)}else if(r.shouldExecuteOnCPU([s])){let e=r.readSync(s.dataId),n=Bo(s.shape,s.dtype,e),a=AP(f,n,v,x);t=r.makeTensorInfo(m,s.dtype,a.values)}else{let e=new Pj(x,v,f);t=r.runWebGLProgram(e,[s],s.dtype)}let k=WB({inputs:{x:t},backend:r,attrs:{shape:m}});return r.disposeIntermediateTensorInfo(t),k}};var Wj={kernelName:ha,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=r,{data:d,dataSplits:p}=t,h=n.readSync(d.dataId),c=n.readSync(p.dataId),[f,m]=RP(h,c,a,s,i,o,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}};var Vj={kernelName:ca,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{skipEmpty:a}=r,{input:s,delimiter:i}=t;if("string"!==s.dtype)throw new Error("Input must be of datatype string");if(1!==s.shape.length)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(0!==i.shape.length)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]=FP(o,l,a),h=d.length;return[n.makeTensorInfo([h,2],"int32",u),n.makeTensorInfo([h],"string",d),n.makeTensorInfo([2],"int32",new Int32Array(p))]}};var Uj={kernelName:fa,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{numBuckets:a}=r,{input:s}=t;if("string"!==s.dtype)throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=DP(i,a);return n.makeTensorInfo(s.shape,"int32",o)}},Gj=RB({opSnippet:"return tan(x);"}),Hj={kernelName:ga,backendName:"webgl",kernelFunc:Gj},jj=RB({opSnippet:"\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n"}),qj={kernelName:ya,backendName:"webgl",kernelFunc:jj};var Kj={kernelName:Vr,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{tensor:a,indices:s,updates:i}=t,{}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:p}=Im.calculateShapes(i,s,a.shape),h=[p/u,u];if(0===p)return n.makeTensorInfo(a.shape,s.dtype);let c=WB({inputs:{x:s},backend:n,attrs:{shape:[l,o]}}

2),f=WB({inputs:{x:i},backend:n,attrs:{shape:[l,u]}}),m=WB({inputs:{x:a},backend:n,attrs:{shape:h}}),g=new rj(l,o,c.shape.length,f.shape.length,d,h,!1,!0),y=n.runWebGLProgram(g,[f,c,m],m.dtype),b=WB({inputs:{x:y},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),b}},Xj=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.rank=n.length;let r=oz(this.rank),a=function(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(1===t)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let t=0;t<e.length;t++)r.push(`imod(${n[t]}, ${e[t]})`);return r.join()}(e);this.userCode=`\n      void main() {\n        ${r} resRC = getOutputCoords();\n        setOutput(getA(${a}));\n      }\n    `}};function Yj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if("string"===a.dtype||a.shape.length>5){let e=n.readSync(a.dataId),t="string"===a.dtype?e.map((e=>ja.decodeString(e))):e,r=Bo(a.shape,a.dtype,t),i=OP(r,s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}let i=new Xj(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var Zj={kernelName:ba,backendName:"webgl",kernelFunc:Yj},Jj=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="\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     "}},Qj=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="\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     "}};function eq(e,t){null!==t&&e.disposeIntermediateTensorInfo(t)}function tq(e){let t=1;for(;t<e;)t*=2;return t}var nq={kernelName:xa,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=ut().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=ut().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(n.shouldExecuteOnCPU([a])||d<o||s>l){let e=n.readSync(a.dataId),[t,r]=LP(e,u,a.dtype,s,i);return[n.makeTensorInfo(t.shape,t.dtype,t.values),n.makeTensorInfo(r.shape,r.dtype,r.values)]}if(0===s)return u[u.length-1]=0,[n.makeTensorInfo(u,a.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(1===d)return[a,FU({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let p=n.texData.get(a.dataId),h=null!==p&&p.isPacked,c=h?n.unpackTensor(a):a,f=ja.sizeFromShape(u)/d,m=WB({input

2s:{x:c},attrs:{shape:[f,d]},backend:n});h&&eq(n,c);let g=tq(s),y=tq(d),b=null,x=()=>null===b?[m,m]:[m,b],w=(e,t,r)=>{let a=x(),s=new Jj(r),i=[[d],[null===b?1:0],[Number.NEGATIVE_INFINITY],[e],[t]],o=b;b=n.runWebGLProgram(s,a,"int32",i),eq(n,o)};for(let e=1;e<g;e*=2){let t=2*e;for(let n=e;n>=1;n/=2)w(t,n,[f,y])}for(let e=y;e>g;e/=2){let t=x(),r=new Qj([f,e/2]),a=[[d],[null===b?1:0],[g]],s=b;b=n.runWebGLProgram(r,t,"int32",a),eq(n,s);let i=g/2,o=2*i;for(let e=i;e>=1;e/=2)w(o,e,b.shape)}let v=b;b=KW({inputs:{x:b},backend:n,attrs:{begin:0,size:[f,s]}}),eq(n,v);let k=JU({inputs:{x:m,indices:b},backend:n,attrs:{axis:1,batchDims:1}});eq(n,m);let I=u.slice(0,-1);I.push(s),v=b,b=WB({inputs:{x:b},attrs:{shape:I},backend:n}),eq(n,v);let S=k;return k=WB({inputs:{x:k},attrs:{shape:I},backend:n}),eq(n,S),[k,b]}},rq=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i,o="nearest"===n?1:2;switch(r){case"constant":default:i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${i} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${i} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${i} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}

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

2\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}};var aq={kernelName:wa,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=r,[d,p,h,c]=a.shape,[f,m]=null!=u?u:[p,h],g=new rq(p,h,i,o,l,[d,f,m,c]);return n.runWebGLProgram(g,[a,s],"float32")}};var sq={kernelName:ka,backendName:"webgl",kernelFunc:function(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;VL(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=PP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}};var iq={kernelName:Ia,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let e=0;e<o;e++)e!==s&&(u[d++]=i.shape[e]);let p=[],h=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let e=0;e<f.length;e++){h[s]=e;let t=KW({inputs:{x:i},backend:n,attrs:{begin:h,size:c}}),r=WB({inputs:{x:t},backend:n,attrs:{shape:u}});f[e]=r,p.push(t)}return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),f}},oq=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o=4*Math.floor(n/4),l=n%4,u="\n        sumValue += dot(values, segFilter);\n    ",d="";a%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return initializationValue;\n        }\n      `);let p="";a%n>0&&(p=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${p}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${s})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${s})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${o}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${o};\n        if (${1===l}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${u}\n        } else if (${2===l}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${u}\n        } else if (${3===l}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}};var lq,uq,dq,pq={kernelName:Sa,backendName:"webgl",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],u=0,d=Im.getAxesPermutation([u],o),p=a;null!=d&&(p=ZB({inputs:{x:a},backend:n,attrs:{perm:d}}),l.push(p),u=Im.getInnerMostAxes(1,o)[0]);let h=Im.segment_util.computeOutShape(p.shape,u,i),c=ja.sizeFromShape([p.shape[u]]),f=WB({input

2s:{x:p},backend:n,attrs:{shape:[-1,c]}});l.push(f);let m=Vs(a.dtype),g=(e,t,r,a,s)=>{let i=e.shape[0],o=e.shape[1],u=Im.segment_util.segOpComputeOptimalWindowSize(o,s),d=new oq({windowSize:u,inSize:o,batchSize:i,numSegments:s},t),p=n.compileAndRun(d,[e,r],a);if(l.push(p),p.shape[1]===s)return p;let h=$H({backend:n,attrs:{start:0,stop:s,step:1,dtype:"float32"}}),c=Yj({inputs:{x:h},backend:n,attrs:{reps:[o/u]}});return l.push(h),l.push(c),g(p,t,c,a,s)},y=WB({inputs:{x:g(f,"unsortedSegmentSum",s,m,i)},backend:n,attrs:{shape:h}}),b=y;if(null!=d){l.push(y);let e=Im.getUndoAxesPermutation(d);b=ZB({inputs:{x:b},backend:n,attrs:{perm:e}})}return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),b}},hq=[tW,rW,sW,oW,dW,cW,fW,mW,vW,kW,SW,TW,CW,$W,RW,MW,OW,PW,BW,WW,GW,YW,ZW,JW,QW,sV,lV,pV,SB,fV,kV,$V,MV,LV,zV,PV,WV,UV,HV,qV,JV,QV,eU,nU,sU,lU,uU,pU,cU,fU,gU,yU,xU,vU,IU,NU,CU,AU,MU,LU,BU,VU,HU,qU,KU,YU,QU,tG,rG,kB,aG,xV,iG,lG,dG,_B,hG,fG,mG,yG,xG,vG,IG,NG,CG,$G,RG,DG,MG,OG,PG,BG,WG,VG,UG,HG,KG,YG,sH,BB,oH,uH,pH,cH,tV,mH,bH,xH,IH,NH,$B,TH,_H,CH,EH,AH,rV,QG,FH,MH,LH,VB,BH,VH,HH,qH,YH,JH,ej,nj,sj,oj,uj,pj,cj,mj,yj,xj,XW,aH,vj,kj,Ij,Sj,Nj,Tj,_j,Cj,Aj,Fj,Oj,Lj,zj,Bj,Wj,Vj,Uj,nH,YB,Hj,qj,Kj,Zj,nq,aq,JB,sq,iq,pq,yH];for(let e of hq)Ba(e);!function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"}(lq||(lq={})),function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"}(uq||(uq={}));var cq={kernelName:$a,backendName:"wasm",setupFunc:function(e){dq=e.wasm.cwrap($a,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if("float32"!==a.dtype||"float32"!==s.dtype)throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:p}=r,h=n.dataIdMap.get(a.dataId).id,c=n.dataIdMap.get(s.dataId).id,f=0;if(null!=i){let e=n.dataIdMap.get(i.dataId);if(1!==e.shape.length)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${e.shape.length}.`);f=e.id}let m=null==o?0:n.dataIdMap.get(o.dataId).id,g=uq[d];if(null==g)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],b=u?s.shape[1]:s.shape[2],x=du.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),w=n.makeOutput([...x,y,b],a.dtype),v=n.dataIdMap.get(w.dataId).id,k=new Uint8Array(new Int32Array(a.shape).buffer),I=new Uint8Array(new Int32Array(s.shape).buffer);return dq(h,k,a.shape.length,c,I,s.shape.length,l,u,g,f,m,p||0,v),w}};function fq(e,t){let n;return{kernelName:e,backendName:"wasm",setupFunc:function(t){n=t.wasm.cwrap(e,null,["number","number","number"])},kernelFunc:function(e){let{backend:r,inputs:{x:a}}=e,s=r.dataIdMap.get(a.dataId).id,i=r.makeOutput(a.shape,t||a.dtype),o=r.dataIdMap.get(i.dataId).id;return 0===ja.sizeFromShape(i.shape)||n(s,lq[a.dtype],o),i}}}var mq=fq(ft),gq=fq(mt),yq=fq(gt);function bq(e,t,n){let r;return{kernelName:e,backendName:"wasm",setupFunc:function(t){r=t.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])},kernelFunc:function(e){let{backend:t,inputs:a}=e,{a:s,b:i}=a,o=t.dataIdMap.get(s.dataId).id,l=t.dataIdMap.get(i.dataId).id,u=null!=n?n:s.dtype,d=Im.assertAndGetBroadcastShape(s.shape,i.shape),p=t.makeOutput(d,u);if(0===ja.sizeFromShape(d))return p;let h=new Uint8Array(new Int32Array(s.shape).buffer),c=new Uint8Array(new Int32Array(i.shape).buffer),f=t.dataIdMap.get(p.dataId).id;return r(o,h,s.shape.length,l,c,i.shape.length,lq[s.dtype],f),p}}}var xq,wq=bq(yt);var vq={kernelName:bt,backendName:"wasm",setupFunc:function(e){xq=e.wasm.cwrap(bt,null,["array","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(0===ja.sizeFromShape(r.shape))return r;let a=t.map((e=>n.dataIdMap.get(e.dataId).id)),s=new Uint8Array(new Int32Array(a).buffer),i=n.dataIdMap.get(r.dataId).id;return xq(s,a.length,lq[r.dtype],i),r}};function kq(e){let{inputs:{x:t},backend:n}=e;if("string"===t.dtype)return yi(n.readSync(t.dataId),t.shape,t.dtype);let r=n.makeOutput(t.shape,t.dtype),a=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(a),r}var Iq,Sq={kernelName:An,backendName:"wasm",kernelFunc:kq};function Nq(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=function(e,t){let n=[],r=[];for(let a=0;a<e.length;++a)1!==e[a]&&n.push(e[a]),1!==e[t[a]]&&r.push(t[a]);for(let e=0;e<r.length;++e){let t=-1;for(let n=0;n<r.length;++n)r[n]>=e&&(-1===t||r[t]>r[n])&&(t=n);r[t]=e}return[n,r]}(t.x.shape,r.perm),i=!0;for(let e=0;e<s.length;e++)s[e]!==e&&(i=!1);let o=function(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let e=kq({inputs:t,backend:n});return e.shape=o,e}let u=n.makeOutput(o,l.dtype),d=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(u.dataId).id,h=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return Iq(d,c,l.shape.length,lq[l.dtype],p,h,s.length),u}var Tq,_q={kernelName:va,backendName:"wasm",kernelFunc:Nq,setupFunc:function(e){Iq=e.wasm.cwrap(va,null,["number","array","number","number","number","array","number"])}};function Cq(e,t,n){let r=e.shape,a=e.shape.length,s=ja.parseAxisParam(t,r),i=s,o=Im.getAxesPermutation(i,a),l=null,u=!1;if(null!=o){let t=new Array(a);for(let e=0;e<t.length;e++)t[e]=r[o[e]];i=Im.getInnerMostAxes(i.length,a),l=Nq({inputs:{x:e},attrs:{perm:o},backend:n});let s=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==s&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var Eq,$q={kernelName:xt,backendName:"wasm",setupFunc:function(e){Tq=e.wasm.cwrap(xt,null,["number, number, number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Cq(i,a,t);if(h){l=u,o=t.dataIdMap.get(u.dataId).id}let c=l.shape.length;Im.assertAxesAreInnerMostDims("all",d,c);let[f,m]=Im.computeOutAndReduceShapes(l.shape,d),g=ja.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(0!==ja.sizeFromShape(l.shape)){let e=t.dataIdMap.get(y.dataId).id;Tq(o,g,e)}if(h&&t.disposeData(u.dataId),s){let e=Im.expandShapeToKeepDim(y.shape,p);y.shape=e}return y}};var Aq={kernelName:wt,backendName:"wasm",setupFunc:function(e){Eq=e.wasm.cwrap(wt,null,["number, number, number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Cq(i,a,t);if(h){l=u,o=t.dataIdMap.get(u.dataId).id}let c=l.shape.length;Im.assertAxesAreInnerMostDims("any",d,c);let[f,m]=Im.computeOutAndReduceShapes(l.shape,d),g=ja.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(0!==ja.sizeFromShape(l.shape)){let e=t.dataIdMap.get(y.dataId).id;Eq(o,g,e)}if(h&&t.disposeData(u.dataId),s){let e=Im.expandShapeToKeepDim(y.shape,p);y.shape=e}return y}};function Rq(e){let t;return{kernelName:e,backendName:"wasm",setupFunc:function(n){t=n.wasm.cwrap(e,null,["number","number","number","number","number"])},kernelFunc:function(e){let{backend:n,inputs:r,attrs:a}=e,{axis:s}=a,{x:i}=r,o=n.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:p,inputWasTransposed:h}=Cq(i,s,n);if(h){let e=n.dataIdMap.get(d.dataId).id;e!==o&&(u=d,l=e)}let c=u.shape.slice(0,-1),f=n.makeOutput(c,"int32"),m=n.dataIdMap.get(f.dataId).id,g=ja.sizeFromShape(f.shape),y=u.shape[p[0]];return t(l,lq[u.dtype],g,y,m),h&&n.disposeData(d.dataId),f}}}var Fq,Dq=Rq(vt),Mq=Rq(kt),Oq=fq(It),Lq=fq(St),zq=fq(Nt),Pq=bq(_t),Bq=fq(Tt);var Wq,Vq={kernelName:Ct,backendName:"wasm",setupFunc:function(e){Fq=e.wasm.cwrap(Ct,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,d=Im.computePool2DInfo(a.shape,i,o,1,l,u),p=d.filterHeight,h=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,b=d.str

2ideWidth,x=d.inChannels;if("channelsLast"!==d.dataFormat)throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(1!==d.dilationWidth||1!==d.dilationHeight)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let w=r.makeOutput(d.outShape,"float32"),v=r.dataIdMap.get(w.dataId).id;return Fq(s,a.shape[0],a.shape[1],a.shape[2],p,h,c,f,m,g,y,b,x,v),w}};var Uq,Gq={kernelName:$t,backendName:"wasm",setupFunc:function(e){Wq=e.wasm.cwrap("AvgPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,d=Im.computePool3DInfo(a.shape,s,i,1,o,l,u),p=n.makeOutput(d.outShape,a.dtype);return Wq(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(p.dataId).id,d.batchSize,d.inChannels,d.inDepth,d.inHeight,d.inWidth,d.outDepth,d.outHeight,d.outWidth,d.strideDepth,d.strideHeight,d.strideWidth,d.dilationDepth,d.dilationHeight,d.dilationWidth,d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth,d.padInfo.front,d.padInfo.top,d.padInfo.left),p}};var Hq,jq={kernelName:At,backendName:"wasm",setupFunc:function(e){Uq=e.wasm.cwrap("AvgPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=Im.computePool3DInfo(s.shape,i,o,1,l,u),p=n.makeOutput(s.shape,s.dtype);return Uq(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(p.dataId).id,d.batchSize,d.inChannels,d.inDepth,d.inHeight,d.inWidth,d.outDepth,d.outHeight,d.outWidth,d.strideDepth,d.strideHeight,d.strideWidth,d.dilationDepth,d.dilationHeight,d.dilationWidth,d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth,d.padInfo.front,d.padInfo.top,d.padInfo.left,d.filterDepth,d.filterHeight,d.filterWidth),p}};var qq={kernelName:Et,backendName:"wasm",setupFunc:function(e){Hq=e.wasm.cwrap("AvgPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l}=r,u=Im.computePool2DInfo(s.shape,i,o,1,l),d=n.makeOutput(s.shape,s.dtype);return Hq(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(d.dataId).id,u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.effectiveFilterHeight,u.effectiveFilterWidth,u.padInfo.top,u.padInfo.left,u.filterHeight,u.filterWidth),d}};function Kq(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=ja.sizeFromShape(r.shape),i=ja.inferFromImplicitShape(a,s);return ja.assert(s===ja.sizeFromShape(i),(()=>`new shape: ${i}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`)),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:i,dtype:r.dtype}}var Xq,Yq={kernelName:Rr,backendName:"wasm",kernelFunc:Kq};var Zq={kernelName:Rt,backendName:"wasm",setupFunc:function(e){Xq=e.wasm.cwrap(Rt,null,["number","array","number","number","array","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;if("float32"!==a.dtype||"float32"!==s.dtype)throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],p=o?s.shape[u-1]:s.shape[u-2],h=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=ja.sizeFromShape(f),y=ja.sizeFromShape(m),b=du.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).c

2oncat([h,c]);ja.assert(d===p,(()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`));let x=o?[y,c,p]:[y,p,c],w=Kq({inputs:{x:a},backend:n,attrs:{shape:i?[g,d,h]:[g,h,d]}}),v=Kq({inputs:{x:s},backend:n,attrs:{shape:x}}),k=n.dataIdMap.get(w.dataId).id,I=n.dataIdMap.get(v.dataId).id,S=i?w.shape[2]:w.shape[1],N=o?v.shape[1]:v.shape[2],T=Math.max(g,y),_=n.makeOutput([T,S,N],w.dtype),C=n.dataIdMap.get(_.dataId).id,E=new Uint8Array(new Int32Array(w.shape).buffer),$=new Uint8Array(new Int32Array(v.shape).buffer);return Xq(k,E,w.shape.length,I,$,v.shape.length,i,o,C),n.disposeData(w.dataId),n.disposeData(v.dataId),_.shape=b,_}};function Jq(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:a}=e,[s,i]=Zf.parseSliceParams(t,n,r),o=Zf.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=ja.computeStrides(t.shape),p=a.dataIdMap.get(u.dataId);if(o){let e=Zf.computeFlatOffset(s,d);return"string"===t.dtype?p.stringBytes=l.slice(e,e+ja.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(e,e+ja.sizeFromShape(i))),u}if("string"===t.dtype){let e=YA(l,s,i,t.shape,t.dtype);return p.stringBytes=e,u}let h=a.typedArrayFromHeap(u),c=t.shape.length;if(2===c)!function(e,t,n,r,a){let s=0,i=r[0],o=r[1],l=i+a[0];for(let r=i;r<l;r++){let i=r*t+o;n.set(e.subarray(i,i+a[1]),s),s+=a[1]}}(l,d[0],h,s,i);else if(3===c)!function(e,t,n,r,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],p=l+s[1];for(let a=o;a<d;a++)for(let o=l;o<p;o++){let l=a*t+o*n+u;r.set(e.subarray(l,l+s[2]),i),i+=s[2]}}(l,d[0],d[1],h,s,i);else if(4===c)!function(e,t,n,r,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],p=l+i[0],h=u+i[1],c=d+i[2],f=s[3];for(let s=l;s<p;s++)for(let l=u;l<h;l++)for(let u=d;u<c;u++){let d=s*t+l*n+u*r+f;a.set(e.subarray(d,d+i[3]),o),o+=i[3]}}(l,d[0],d[1],d[2],h,s,i);else{let e=YA(l,s,i,t.shape,t.dtype);h.set(e)}return u}var Qq={kernelName:jr,backendName:"wasm",kernelFunc:Jq};var eK,tK={kernelName:Ft,backendName:"wasm",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r,o=s.reduce(((e,t)=>e*t)),l=Im.getReshaped(a.shape,s,o),u=Im.getPermuted(l.length,s.length),d=Im.getReshapedPermuted(a.shape,s,o),p=Im.getSliceBeginCoords(i,s.length),h=Im.getSliceSize(d,i,s.length),c=Kq({inputs:{x:a},backend:n,attrs:{shape:l}}),f=Nq({inputs:{x:c},backend:n,attrs:{perm:u}}),m=Kq({inputs:{x:f},backend:n,attrs:{shape:d}}),g=Jq({inputs:{x:m},backend:n,attrs:{begin:p,size:h}});return n.disposeData(c.dataId),n.disposeData(f.dataId),n.disposeData(m.dataId),g}};var nK={kernelName:Dt,backendName:"wasm",setupFunc:function(e){eK=e.wasm.cwrap(Dt,null,["number","number","boolean","number","number","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,weights:s}=n,{size:i}=r,o=0!==s.shape.reduce(((e,t)=>e*t),1),l=1===a.shape.length?[i]:[a.shape[0],i],u=t.makeOutput(l,s.dtype);function d(e){return t.dataIdMap.get(e.dataId).id}return eK(d(a),i,o,d(s),lq[s.dtype],d(u)),u}},rK=bq(Mt);var aK={kernelName:Lt,backendName:"wasm",kernelFunc:function(e){let{inputs:t,backend:n}=e,{s0:r,s1:a}=t,s=n.typedArrayFromHeap(r),i=n.typedArrayFromHeap(a),o=Im.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeOutput([o.length],"int32",void 0,new Int32Array(o))}};function sK(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,a=r.makeOutput(t.shape,n),s=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(a).set(s),a}var iK,oK={kernelName:zt,backendName:"wasm",kernelFunc:sK},lK=fq(Pt);var uK={kernelName:Bt,backendName:"wasm",setupFunc:function(e){iK=e.wasm.cwrap(Bt,null,["number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(l.dataId).id;return iK(o,s,i,u),l}};function dK(e){let{inputs:t,backend:n}=e,r=ja.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=t.map((e=>e.shape));Im.assertParamsConsistent(a,r);let s=Im.computeOutShape(t.map((e=>e.shape)),r),i=t.filter((e=>ja.sizeFromShape(e.shape)>0));

2if(1===i.length)return kq({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(0===ja.sizeFromShape(s))return o;if("string"===i[0].dtype){let e=i.map((e=>{let t=[-1,ja.sizeFromShape(e.shape.slice(r))];return Kq({inputs:{x:e},backend:n,attrs:{shape:t}})})),a=e.map((e=>({vals:n.readSync(e.dataId),shape:e.shape})));s=Im.computeOutShape(e.map((e=>e.shape)),1);let l=1===e[0].shape[0],u=$$(a,s,t[0].dtype,l),d=Im.computeOutShape(i.map((e=>e.shape)),r);return o.shape=d,n.dataIdMap.get(o.dataId).stringBytes=Im.fromStringArrayToUint8(u),e.forEach((e=>n.disposeData(e.dataId))),o}let l=ja.sizeFromShape(i[0].shape.slice(0,r)),u=0,d=i.map((e=>{let t=ja.sizeFromShape(e.shape.slice(r));return u+=t,t})),p=i.map((e=>n.typedArrayFromHeap(e))),h=n.typedArrayFromHeap(o);for(let e=0;e<l;e++){let t=e*u;for(let n=0;n<p.length;n++){let r=d[n],a=e*r,s=p[n].subarray(a,a+r);h.set(s,t),t+=r}}return o}var pK,hK={kernelName:Ut,backendName:"wasm",kernelFunc:dK};var cK,fK={kernelName:Gt,backendName:"wasm",setupFunc:function(e){pK=e.wasm.cwrap(Gt,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:p,dataFormat:h}=n,c=Im.convertConv2DDataFormat(h),f=Im.computeConv2DInfo(a.shape,s.shape,l,u,d,p,!1,c),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,b=f.padInfo.right,x=f.padInfo.bottom,w=f.padInfo.left,v=f.dilationHeight,k=f.dilationWidth,I=f.strideHeight,S=f.strideWidth,N=f.inChannels,T=f.outChannels,_="SAME"===f.padInfo.type?1:0;if("channelsLast"!==f.dataFormat)throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let C=r.makeOutput(f.outShape,"float32"),E=r.dataIdMap.get(C.dataId).id;return pK(i,a.shape[0],a.shape[1],a.shape[2],o,m,g,y,b,x,w,_,v,k,I,S,N,T,E),C}};var mK,gK={kernelName:jt,backendName:"wasm",setupFunc:function(e){cK=e.wasm.cwrap(jt,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{dy:a,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:d}=r,p=Im.convertConv2DDataFormat(l),h=Im.computeConv2DInfo(d,s.shape,i,1,o,u,!1,p),{batchSize:c,filterHeight:f,filterWidth:m,inChannels:g,inHeight:y,inWidth:b,outChannels:x,outHeight:w,outWidth:v,strideHeight:k,strideWidth:I}=h,S=f-1-h.padInfo.top,N=m-1-h.padInfo.left,T="channelsLast"===h.dataFormat,_=ja.computeStrides(h.inShape),C=ja.computeStrides(a.shape),[E,$,A]=ja.computeStrides(s.shape),R=_[0],F=T?_[1]:_[2],D=T?_[2]:1,M=T?1:_[1],O=C[0],L=T?C[1]:C[2],z=T?C[2]:1,P=T?1:C[1],B=t.makeOutput(h.inShape,"float32"),W=t.dataIdMap.get(B.dataId).id,V=t.dataIdMap.get(a.dataId).id,U=t.dataIdMap.get(s.dataId).id;return cK(V,U,c,f,m,y,b,g,w,v,x,k,I,S,N,E,$,A,R,F,D,M,O,L,z,P,W),B}};var yK,bK={kernelName:qt,backendName:"wasm",setupFunc:function(e){mK=e.wasm.cwrap(qt,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;if("float32"!==a.dtype)throw new Error(`Tensor x must have dtype float32, got ${a.dtype}`);if("float32"!==s.dtype)throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let u=Im.computeConv3DInfo(a.shape,s.shape,i,l,o),d=n.makeOutput(u.outShape,a.dtype);return mK(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(d.dataId).id,u.batchSize,u.inDepth,u.inHeight,u.inWidth,u.inChannels,u.outDepth,u.outHeight,u.outWidth,u.outChannels,u.strideDepth,u.strideHeight,u.strideWidth,u.dilationDepth,u.dilationHeight,u.dilationWidth,u.filterDepth,u.filterHeight,u.filterWidth,u.padInfo.front,u.padInfo.top,u.padInfo.left),d}};var xK,wK={kernelName:Kt,backendName:"wasm",setupFunc:function(e){yK=e.wasm.cwrap(Kt,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;if("float32"!==a.dtype)throw new Error(`Tensor dy must have dtype float32, got ${a.dtype}`);if("float32"!==s.dtype)throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let u=Im.computeConv3DInfo(a.shape,l,i,1,o),d=n.makeOutput(u.filterShape,s.dtype);return yK(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(d.dataId).id,u.batchSize,u.inDepth,u.inHeight,u.inWidth,u.inChannels,u.outDepth,u.outHeight,u.outWidth,u.outChannels,u.strideDepth,u.strideHeight,u.strideWidth,u.dilationDepth,u.dilationHeight,u.dilationWidth,u.filterDepth,u.filterHeight,u.filterWidth,u.padInfo.front,u.padInfo.top,u.padInfo.left),d}};var vK,kK,IK={kernelName:Xt,backendName:"wasm",setupFunc:function(e){xK=e.wasm.cwrap(Xt,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;if("float32"!==a.dtype)throw new Error(`Tensor dy must have dtype float32, got ${a.dtype}`);if("float32"!==s.dtype)throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let u=Im.computeConv3DInfo(l,s.shape,o,1,i),d=n.makeOutput(u.inShape,a.dtype);return xK(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(d.dataId).id,u.batchSize,u.inDepth,u.inHeight,u.inWidth,u.inChannels,u.outDepth,u.outHeight,u.outWidth,u.outChannels,u.strideDepth,u.strideHeight,u.strideWidth,u.dilationDepth,u.dilationHeight,u.dilationWidth,u.filterDepth,u.filterHeight,u.filterWidth,u.padInfo.front,u.padInfo.top,u.padInfo.left),d}},SK=fq(Yt),NK=fq(Zt);!function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"}(vK||(vK={}));var TK,_K={kernelName:en,backendName:"wasm",setupFunc:function(e){kK=e.wasm.cwrap(en,null,["number","number","number","number","array","number","number","number","number","number"])},kernelFunc:function(e){let t,{backend:n,inputs:r,attrs:a}=e,{method:s,extrapolationValue:i,cropSize:o}=a,{image:l,boxes:u,boxInd:d}=r,p=u.shape[0],[h,c]=o,f=[p,h,c,l.shape[3]],m=n.dataIdMap.get(l.dataId);"float32"!==l.dtype&&(t=sK({backend:n,inputs:{x:l},attrs:{dtype:"float32"}}),m=n.dataIdMap.get(t.dataId));let g=m.id,y=n.dataIdMap.get(u.dataId).id,b=n.dataIdMap.get(d.dataId).id,x=n.makeOutput(f,"float32"),w=n.dataIdMap.get(x.dataId).id,v=new Uint8Array(new Int32Array(l.shape).buffer);return kK(g,y,b,p,v,h,c,vK[s],i,w),null!=t&&n.disposeData(t.dataId),x}};

2var CK,EK={kernelName:Jt,backendName:"wasm",setupFunc:function(e){TK=e.wasm.cwrap(Jt,null,["number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length;ja.assert("float32"===a.dtype||"int32"===a.dtype,(()=>`cumprod does not support ${a.dtype} tensors in the WASM backend`));let u=Im.getAxesPermutation([s],l),d=a;null!==u&&(d=Nq({inputs:{x:a},attrs:{perm:u},backend:n}));let p=Im.getInnerMostAxes(1,l)[0];Im.assertAxesAreInnerMostDims("cumprod",[p],l);let h=n.makeOutput(d.shape,d.dtype),c=d.shape[p],f=n.dataIdMap.get(d.dataId).id,m=n.dataIdMap.get(h.dataId).id;TK(f,i?1:0,o?1:0,c,m,lq[a.dtype]);let g=h;if(null!==u){g=Nq({inputs:{x:h},attrs:{perm:Im.getUndoAxesPermutation(u)},backend:n}),n.disposeData(d.dataId),n.disposeData(h.dataId)}return g}};var $K,AK={kernelName:Qt,backendName:"wasm",setupFunc:function(e){CK=e.wasm.cwrap(Qt,null,["number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length;ja.assert("float32"===a.dtype||"int32"===a.dtype,(()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`));let u=Im.getAxesPermutation([s],l),d=a;null!==u&&(d=Nq({inputs:{x:a},attrs:{perm:u},backend:n}));let p=Im.getInnerMostAxes(1,l)[0];Im.assertAxesAreInnerMostDims("cumsum",[p],l);let h=n.makeOutput(d.shape,d.dtype),c=d.shape[p],f=n.dataIdMap.get(d.dataId).id,m=n.dataIdMap.get(h.dataId).id;CK(f,i?1:0,o?1:0,c,m,lq[a.dtype]);let g=h;if(null!==u){g=Nq({inputs:{x:h},attrs:{perm:Im.getUndoAxesPermutation(u)},backend:n}),n.disposeData(d.dataId),n.disposeData(h.dataId)}return g}};var RK,FK={kernelName:tn,backendName:"wasm",setupFunc:function(e){$K=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,weights:s}=n,{size:i,binaryOutput:o}=r,l=0!==s.shape.reduce(((e,t)=>e*t),1),u=1===a.shape.length?[i]:[a.shape[0],i],d=t.makeOutput(u,s.dtype);function p(e){return t.dataIdMap.get(e.dataId).id}return $K(p(a),new Uint8Array(new Int32Array(a.shape).buffer),a.shape.length,i,l,p(s),lq[s.dtype],o,p(d)),d}};var DK,MK={kernelName:nn,backendName:"wasm",setupFunc:function(e){RK=e.wasm.cwrap(nn,null,["number","number","number","array","number","array","array","number","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{blockSize:s,dataFormat:i}=r,o=a.shape[0],l=("NHWC"===i?a.shape[1]:a.shape[2])*s,u=("NHWC"===i?a.shape[2]:a.shape[3])*s,d=("NHWC"===i?a.shape[3]:a.shape[1])/(s*s),p="NHWC"===i?[o,l,u,d]:[o,d,l,u],h=t.makeOutput(p,"float32"),c=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(ja.computeStrides(a.shape)).buffer),m=new Uint8Array(new Int32Array(p).buffer),g=new Uint8Array(new Int32Array(ja.computeStrides(p)).buffer),y=t.dataIdMap.get(h.dataId).id;return RK(c,s,"NHWC"===i?1:0,f,a.shape.length-1,m,g,p.length,y),h}};var OK,LK={kernelName:rn,backendName:"wasm",setupFunc:function(e){DK=e.wasm.cwrap(rn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:p}=n,h=null==u?[1,1]:u,c=Im.computeConv2DInfo(a.shape,s.shape,l,h,d,p,!0),f=c.filterHeight,m=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,b=c.padInfo.bottom,x=c.padInfo.left,w=c.dilationHeight,v=c.dilationWidth,k=c.strideHeight,I=c.strideWidth,S=c.inChannels,N=c.outChannels,T="SAME"===c.padInfo.type?1:0;if("channelsLast"!==c.dataFormat)throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let _=r.makeOutput(c.outShape,"float32"),C=r.dataIdMap.get(_.dataId).id;return DK(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,g,y,b,x,T,w,v,k,I,S,N,C),_}};var zK,PK={kernelName:on,backendName:"wasm",setupFunc:function(e){OK=e.w

2asm.cwrap("Diag",null,["number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n}=e,{x:r}=t,a=ja.sizeFromShape(r.shape),s=n.makeOutput([...r.shape,...r.shape],r.dtype);return OK(n.dataIdMap.get(r.dataId).id,lq[r.dtype],a,n.dataIdMap.get(s.dataId).id),s}};var BK,WK={kernelName:ln,backendName:"wasm",setupFunc:function(e){zK=e.wasm.cwrap(ln,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;if(a.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${a.dtype} and ${s.dtype}`);let u=Im.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d=n.makeOutput(u.outShape,a.dtype);return zK(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(d.dataId).id,lq[a.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),d}};var VK,UK={kernelName:dn,backendName:"wasm",setupFunc:function(e){BK=e.wasm.cwrap(dn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=r;if(a.dtype!==s.dtype||a.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${a.dtype}, ${s.dtype}, and ${i.dtype}`);let d=Im.computeDilation2DInfo(a.shape,s.shape,o,l,"NHWC",u),p=n.makeOutput(s.shape,s.dtype);return BK(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(p.dataId).id,lq[a.dtype],d.batchSize,d.inChannels,d.inHeight,d.inWidth,d.outHeight,d.outWidth,d.strideHeight,d.strideWidth,d.dilationHeight,d.dilationWidth,d.filterHeight,d.filterWidth,d.padInfo.top,d.padInfo.left),p}};var GK,HK={kernelName:un,backendName:"wasm",setupFunc:function(e){VK=e.wasm.cwrap(un,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=r;if(a.dtype!==s.dtype||a.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${a.dtype}, ${s.dtype}, and ${i.dtype}`);let d=Im.computeDilation2DInfo(a.shape,s.shape,o,l,"NHWC",u),p=n.makeOutput(a.shape,a.dtype);return VK(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(p.dataId).id,lq[a.dtype],d.batchSize,d.inChannels,d.inHeight,d.inWidth,d.outHeight,d.outWidth,d.strideHeight,d.strideWidth,d.dilationHeight,d.dilationWidth,d.filterHeight,d.filterWidth,d.padInfo.top,d.padInfo.left),p}},jK=fq(fn);var qK={kernelName:mn,backendName:"wasm",setupFunc:function(e){GK=e.wasm.cwrap(mn,null,["number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=n.makeOutput(a.shape,"float32"),i=e=>n.dataIdMap.get(e.dataId).id;return GK(i(a),i(r),i(s)),s}},KK=bq(yn,0,"bool"),XK=fq(gn),YK=fq(bn,"float32");function ZK(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(ja.assert(-(i+1)<=s,(()=>`Axis must be in the interval [${-(i+1)}, ${i}]`)),l=i+s+1),o.splice(l,0,1),Kq({inputs:{x:a},backend:r,attrs:{shape:o}})}var JK={kernelName:xn,backendName:"wasm",kernelFunc:ZK},QK=fq(wn,"float32");function eX(e){let{attrs:{shape:t,value:n},backend:r}=e,{attrs:{dtype:a}}=e;a=a||ja.inferDtype(n);let s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var tX,nX={kernelName:kn,backendName:"wasm",kernelFunc:eX};var rX,aX={kernelName:In,backendName:"wasm",kernelFunc:function(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,u,d]=r.shape;return tX(s,o,l,u,d,i),a},setupFunc:function(e){tX=e.wasm.cwrap(In,null,["number","number","number","number","number","number"])}},sX=fq(Sn),iX=bq(Nn);var oX,lX={kernelName:Tn,backendName:"wasm",setupFunc:function(e){rX=e.wasm.cwrap(Tn,null,["number","number","number","number","number","number","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:u}=n,d=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=t.dataIdMap.get(o.dataId).id,c=null!=l?t.dataIdMap.get(l.dataId).id:0,f=null!=u?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(0===ja.sizeFromShape(s.shape))return m;let g=t.dataIdMap.get(m.dataId).id;return rX(d,p,h,c,f,a,g),m}};var uX,dX={kernelName:Aa,backendName:"wasm",setupFunc:function(e){oX=e.wasm.cwrap(Aa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:p,dimRoundingMode:h,activation:c,leakyreluAlpha:f}=n,m=Im.computeConv2DInfo(a.shape,s.shape,l,d,u,h),g=uq[c];if(null==g)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,b=r.dataIdMap.get(s.dataId).id,x=m.outChannels,w=0;if(null!=i){let e=r.dataIdMap.get(i.dataId);if(1!==e.shape.length)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${e.shape.length}.`);if(e.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${e.shape}) does not match the number of output channels (${x})`);w=e.id}let v=m.filterHeight,k=m.filterWidth,I=m.padInfo.top,S=m.padInfo.right,N=m.padInfo.bottom,T=m.padInfo.left,_=m.dilationHeight,C=m.dilationWidth,E=m.strideHeight,$=m.strideWidth,A=m.inChannels,R="SAME"===m.padInfo.type?1:0,F=m.batchSize,D=m.inHeight,M=m.inWidth;

2if("NHWC"!==p)throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let O=r.makeOutput(m.outShape,"float32"),L=r.dataIdMap.get(O.dataId).id,z=null==o?0:r.dataIdMap.get(o.dataId).id;return oX(y,F,D,M,b,v,k,w,I,S,N,T,R,_,C,E,$,A,x,g,z,f||0,L),O}};var pX,hX={kernelName:Ra,backendName:"wasm",setupFunc:function(e){uX=e.wasm.cwrap(Ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:p,dimRoundingMode:h,activation:c,leakyreluAlpha:f}=n,m=Im.computeConv2DInfo(a.shape,s.shape,l,d,u,h,!0),g=uq[c];if(null==g)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,b=r.dataIdMap.get(s.dataId).id,x=m.outChannels,w=0;if(null!=i){let e=r.dataIdMap.get(i.dataId);if(1!==e.shape.length)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${e.shape.length}.`);if(e.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${e.shape}) does not match the number of output channels (${x})`);w=e.id}let v=m.filterHeight,k=m.filterWidth,I=m.padInfo.top,S=m.padInfo.right,N=m.padInfo.bottom,T=m.padInfo.left,_=m.dilationHeight,C=m.dilationWidth,E=m.strideHeight,$=m.strideWidth,A=m.inChannels,R="SAME"===m.padInfo.type?1:0,F=m.batchSize,D=m.inHeight,M=m.inWidth;if("NHWC"!==p)throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let O=r.makeOutput(m.outShape,"float32"),L=r.dataIdMap.get(O.dataId).id,z=null==o?0:r.dataIdMap.get(o.dataId).id;return uX(y,F,D,M,b,v,k,w,I,S,N,T,R,_,C,E,$,A,x,g,z,f||0,L),O}};var cX,fX={kernelName:Cn,backendName:"wasm",setupFunc:function(e){pX=e.wasm.cwrap(Cn,null,["number","number","number","number","number","number","array","number"])},kernelFunc:function(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=Xf.prepareAndValidate(r,a),u=t.makeOutput(s,r.dtype);if(0===i)return u;let d=a.shape,p=d[d.length-1],h=t.dataIdMap.get(r.dataId).id,c=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(u.dataId).id;return pX(h,lq[r.dtype],c,i,p,o,f,m),u}};var mX,gX={kernelName:_n,backendName:"wasm",setupFunc:function(e){cX=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=ja.parseAxisParam(i,a.shape)[0],u=t.readSync(s.dataId),d=a.shape[l];for(let e=0;e<u.length;++e){let t=u[e];ja.assert(t<=d-1&&t>=0,(()=>`GatherV2: the index value ${t} is not in [0, ${d-1}]`))}let p=Im.segment_util.collectGatherOpShapeInfo(a,s,l,o),h=Kq({inputs:{x:a},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),c=ja.sizeFromShape(s.shape),f=Kq({inputs:{x:s},attrs:{shape:[p.batchSize,c/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,c/p.batchSize,p.sliceSize],g=t.makeOutput(m,a.dtype);if(0===ja.sizeFromShape(a.shape))return g;let y=h.shape.length-1,b=t.dataIdMap.get(h.dataId).id,x=t.dataIdMap.get(f.dataId).id,w=t.dataIdMap.get(g.dataId).id,v=new Uint8Array(new Int32Array(ja.computeStrides(h.shape)).buffer),k=new Uint8Array(new Int32Array(ja.computeStrides(m)).buffer);return cX(b,lq[a.dtype],v,y,x,p.batchSize,k,w),t.disposeData(h.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}},yX=bq(En,0,"bool"),bX=bq($n,0,"bool"),xX=fq(Dn,"bool"),wX=fq(Mn,"bool"),vX=fq(On,"bool");var kX,IX={kernelName:Ln,backendName:"wasm",setupFunc:function(e){mX=e.wasm.cwrap(Ln,null,["number","number","number","number"])},kernelFunc:function(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,"float32");if(0!==ja.sizeFromShape(t.shape)){let e=r.dataIdMap.get(s.dataId).id;mX(a,lq[t.dtype],n,e)}return s}},SX=bq(zn,0,"bool"),NX=bq(Pn,0,"bool");var TX,_X={kernelName:Bn,backendName:"wasm",setupFunc:function(e){kX=e.wasm.cwrap(Bn,null,["number","number","number","number"])},kernelFunc:function(e){let{attrs:t,backend:n}=e,{start:r,stop:a,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return kX(n.dataIdMap.get(o.dataId).id,r,a,i),o}},CX=fq(Wn),EX=fq(Vn),$X=bq(Un,0,"bool"),AX=fq(Gn),RX=bq(Hn,0,"bool"),FX=bq(jn,0,"bool");var DX,MX={kernelName:Xn,backendName:"wasm",setupFunc:function(e){TX=e.w

2asm.cwrap(Xn,null,["number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;if("float32"!==a.dtype)throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(a.shape,a.dtype);return TX(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(u.dataId).id,a.shape[3],s,i,o,l),u}};var OX,LX={kernelName:Yn,backendName:"wasm",setupFunc:function(e){DX=e.wasm.cwrap(Yn,null,["number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=r;if("float32"!==a.dtype||"float32"!==s.dtype||"float32"!==i.dtype)throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let p=n.makeOutput(a.shape,a.dtype);return DX(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(p.dataId).id,i.shape[3],o,l,u,d),p}};var zX,PX={kernelName:Jn,backendName:"wasm",setupFunc:function(e){OX=e.wasm.cwrap(Jn,null,["number","number","number","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Cq(i,a,t);if(h){l=u,o=t.dataIdMap.get(u.dataId).id}let c=l.shape.length;Im.assertAxesAreInnerMostDims("max",d,c);let[f,m]=Im.computeOutAndReduceShapes(l.shape,d),g=ja.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(0!==ja.sizeFromShape(l.shape)){let e=t.dataIdMap.get(y.dataId).id;OX(o,lq[i.dtype],g,e)}if(h&&t.disposeData(u.dataId),s){let e=Im.expandShapeToKeepDim(y.shape,p);y.shape=e}return y}},BX=bq(Qn);var WX,VX={kernelName:er,backendName:"wasm",setupFunc:function(e){zX=e.wasm.cwrap(er,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id;ja.assert("float32"===a.dtype,(()=>`Error in MaxPool: only float32 input is supported. Got ${a.dtype}.`));let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,d=Im.computePool2DInfo(a.shape,i,o,1,l,u),p=d.filterHeight,h=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,b=d.dilationWidth,x=d.strideHeight,w=d.strideWidth,v=d.inChannels,k=d.outChannels;if("channelsLast"!==d.dataFormat)throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let I=r.makeOutput(d.outShape,"float32"),S=r.dataIdMap.get(I.dataId).id;return zX(s,a.shape[0],a.shape[1],a.shape[2],p,h,c,f,m,g,y,b,x,w,v,k,S),I}};var UX,GX={kernelName:nr,backendName:"wasm",setupFunc:function(e){WX=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,d=Im.computePool3DInfo(a.shape,s,i,1,o,l,u),p=n.makeOutput(d.outShape,a.dtype);return WX(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(p.dataId).id,d.batchSize,d.inChannels,d.inDepth,d.inHeight,d.inWidth,d.outDepth,d.outHeight,d.outWidth,d.strideDepth,d.strideHeight,d.strideWidth,d.dilationDepth,d.dilationHeight,d.dilationWidth,d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth,d.padInfo.front,d.padInfo.top,d.padInfo.left),p}};var HX,jX={kernelName:rr,backendName:"wasm",setupFunc:function(e){UX=e.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=Im.computePool3DInfo(s.shape,i,o,1,l,u),p=n.makeOutput(s.shape,s.dtype);return UX(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(p.dataId).id,d.batchSize,d.inChannels,d.inDepth,d.inHeight,d.inWidth,d.outDepth,d.outHeight,d.outWidth,d.strideDepth,d.strideHeight,d.strideWidth,d.dilationDepth,d.dilationHeight,d.dilationWidth,d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth,d.padInfo.front,d.padInfo.top,d.padInfo.left),p}};var qX,KX={kernelName:tr,backendName:"wasm",setupFunc:function(e){HX=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=Im.computePool2DInfo(s.shape,i,o,1,l,u),p=n.makeOutput(s.shape,s.dtype);return HX(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(p.dataId).id,d.batchSize,d.inChannels,d.inHeight,d.inWidth,d.outHeight,d.outWidth,d.strideHeight,d.strideWidth,d.dilationHeight,d.dilationWidth,d.effectiveFilterHeight,d.effectiveFilterWidth,d.padInfo.top,d.padInfo.left),p}};var XX,YX={kernelName:ar,backendName:"wasm",setupFunc:function(e){qX=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,includeBatchInIndex:l}=r;ja.assert(4===a.shape.length,(()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`));let u=[1,1];ja.assert(Im.eitherStridesOrDilationsAreOne(i,u),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`));let d=Im.computePool2DInfo(a.shape,s,i,[1,1],o),p=n.makeOutput(d.outShape,a.dtype),h=n.makeOutput(d.outShape,"int32");return qX(n.dataIdMap.get(a.dataId).id,n.dataIdMap.get(p.dataId).id,n.dataIdMap.get(h.dataId).id,lq[a.dtype],l,d.batchSize,d.inChannels,d.inHeight,d.inWidth,d.outHeight,d.outWidth,d.strideHeight,d.strideWidth,d.dilationHeight,d.dilationWidth,d.effectiveFilterHeight,d.effectiveFilterWidth,d.padInfo.top,d.padInfo.left),[p,h]}};var ZX,JX={kernelName:sr,backendName:"wasm",setupFunc:function(e){XX=e.wasm.cwrap(sr,null,["number, number, number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:p,originalAxes:h,inputWasTransposed:c}=Cq(i,a,t),f=p;if(c){let e=t.dataIdMap.get(d.dataId).id;e!==o&&(u=d,l=e,f=Im.getInnerMostAxes(f.length,u.shape.length))}Im.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,g]=Im.computeOutAndReduceShapes(u.shape,f),y=ja.sizeFromShape(g),b=u;"float32"!==u.dtype&&(b=sK({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(m,"float32");if(0!==ja.sizeFromShape(u.shape)){let e=t.dataIdMap.get(x.dataId).id;XX(l,y,e)}if(c&&t.disposeData(d.dataId),s){let e=Im.expandShapeToKeepDim(x.shape,h);x.shape=e}return"float32"!==u.dtype&&t.disposeData(b.dataId),x}};var QX,eY,tY={kernelName:ir,backendName:"wasm",setupFunc:function(e){ZX=e.wasm.cwrap(ir,null,["number","number","number","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:p,originalAxes:h,inputWasTransposed:c}=Cq(i,a,t);if(c){let e=t.dataIdMap.get(d.dataId).id;e!==o&&(u=d,l=e)}let f=u.shape.length;Im.assertAxesAreInnerMostDims("min",p,f);let[m,g]=Im.computeOutAndReduceShapes(u.shape,p),y=ja.sizeFromShape(g),b=t.makeOutput(m,u.dtype);if(0!==ja.sizeFromShape(u.shape)){let e=t.dataIdMap.get(b.dataId).id;ZX(l,lq[i.dtype],y,e)}if(c&&t.disposeData(d.dataId),s){let e=Im.expandShapeToKeepDim(b.shape,h);b.shape=e}return b}},nY=bq(or);!function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"}(QX||(QX={}));var rY,aY={kernelName:lr,backendName:"wasm",kernelFunc:function(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,mode:a}}=e,s=r.map(((e,n)=>e[0]+t.shape[n]+e[1])),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=r.map((e=>e[0])),p=r.map((e=>e[1])),h=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(p).buffer);return eY(i,u,t.shape.length,lq[t.dtype],h,c,QX[a],l),o}

2,setupFunc:function(e){eY=e.wasm.cwrap(lr,null,["number","array","number","number","array","array","number","number"])}};function sY(e){let{backend:t,inputs:{logits:n},attrs:{dim:r}}=e,a=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[r],l=ja.sizeFromShape(n.shape)/o;return 0===ja.sizeFromShape(s.shape)||rY(a,i,o,l),s}var iY,oY={kernelName:na,backendName:"wasm",setupFunc:function(e){rY=e.wasm.cwrap(na,null,["number","number","number","number"])},kernelFunc:sY};var lY,uY={kernelName:dr,backendName:"wasm",setupFunc:function(e){iY=e.wasm.cwrap(dr,null,["number","number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r;if("float32"!==a.dtype)throw new Error(`Tensor logits must have dtype float32, got ${a.dtype}`);let l=o?a:sY({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),[u,d]=l.shape,p=n.makeOutput([u,s],"int32");return iY(n.dataIdMap.get(l.dataId).id,u,d,s,i,n.dataIdMap.get(p.dataId).id),o||n.disposeData(l.dataId),p}},dY=bq(ur),pY=bq(pr),hY=fq(hr);function cY(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],a=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var fY,mY={kernelName:fr,backendName:"wasm",setupFunc:function(e){lY=e.wasm.cwrap(fr,"number",["number","number","number","number","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=r,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,p=lY(u,d,s,a,i),{pSelectedIndices:h,selectedSize:c,pSelectedScores:f,pValidOutputs:m}=cY(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([c],"int32",h)}};var gY,yY={kernelName:mr,backendName:"wasm",setupFunc:function(e){fY=e.wasm.cwrap(mr,"number",["number","number","number","number","number","bool"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:u}=n,d=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,h=fY(d,p,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=cY(t,h);return t.wasm._free(m),[t.makeOutput([f],"int32",c),t.makeOutput([],"int32",g)]}};var bY,xY={kernelName:gr,backendName:"wasm",setupFunc:function(e){gY=e.wasm.cwrap(gr,"number",["number","number","number","number","number","number"])},kernelFunc:function(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:u}=n,d=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,h=gY(d,p,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=cY(t,h);return t.wasm._free(g),[t.makeOutput([f],"int32",c),t.makeOutput([f],"float32",m)]}},wY=bq(cr,0,"bool");var vY={kernelName:br,backendName:"wasm",setupFunc:function(e){bY=e.wasm.cwrap(br,null,["number","number","number","number","number"])},kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{dtype:s,depth:i,onValue:o,offValue:l}=r,u=n.makeOutput([...a.shape,i],s),d=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(a.dataId).id;return bY(p,i,o,l,d),u}};var kY={kernelName:yr,backendName:"wasm",kernelFunc:function(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}};var IY,SY={kernelName:xr,backendName:"wasm",kernelFunc:function(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(1===t.length)return ZK({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach((e=>{ja.assertShapesMatch(s,e.shape,"All tensors passed to stack must have matching shapes"),ja.assert(i===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));let o=[],l=dK({inputs:t.map((e=>{let t=ZK({inputs:{input:e},backend:n,attrs:{dim:a}});return o.push(t),t})),backend:n,attrs:{axis:a}});return o.forEach((e=>n.disposeData(e.dataId))),l}};var NY,TY={kernelName:wr,backendName:"wasm",kernelFunc:function(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:a}}=e,s=r.map(((e,n)=>e[0]+t.shape[n]+e[1]));

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2function hQ(e,t){(Array.isArray(t)?t:[t]).forEach((t=>{let n=t instanceof BJ?t.score:KJ(t)?t.detection.score:void 0,r=t instanceof BJ?t.box:KJ(t)?t.detection.box:new LJ(t),a=n?`${EJ(n)}`:void 0;new pQ(r,{label:a}).draw(e)}))}function cQ(e){let{Image:t,Video:n}=rQ.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function fQ(e){let{Image:t,Video:n}=rQ.getEnv();return e instanceof t?new vJ(e.naturalWidth,e.naturalHeight):e instanceof n?new vJ(e.videoWidth,e.videoHeight):new vJ(e.width,e.height)}function mQ({width:e,height:t}){let{createCanvasElement:n}=rQ.getEnv(),r=n();return r.width=e,r.height=t,r}function gQ(e,t){let{ImageData:n}=rQ.getEnv();if(!(e instanceof n||cQ(e)))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:r,height:a}=t||fQ(e),s=mQ({width:r,height:a});return e instanceof n?sQ(s).putImageData(e,0,0):sQ(s).drawImage(e,0,0,r,a),s}function yQ(e){let{Image:t,Canvas:n,Video:r}=rQ.getEnv();return e instanceof t||e instanceof n||e instanceof r}var bQ=class{constructor(e,t=!1){if(this._imageTensors=[],this._canvases=[],this._treatAsBatchInput=!1,this._inputDimensions=[],this._inputSize=0,!Array.isArray(e))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${e}`);this._treatAsBatchInput=t,this._batchSize=e.length,e.forEach(((e,t)=>{if(NJ(e))return this._imageTensors[t]=e,void(this._inputDimensions[t]=e.shape);if(TJ(e)){let n=e.shape[0];if(1!==n)throw new Error(`NetInput - tf.Tensor4D with batchSize ${n} passed, but not supported in input array`);return this._imageTensors[t]=e,void(this._inputDimensions[t]=e.shape.slice(1))}let n=e instanceof rQ.getEnv().Canvas?e:gQ(e);this._canvases[t]=n,this._inputDimensions[t]=[n.height,n.width,3]}))}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return FJ(this.batchSize,0,1).map(((e,t)=>this.getReshapedInputDimensions(t)))}getInput(e){return this.canvases[e]||this.imageTensors[e]}getInputDimensions(e){return this._inputDimensions[e]}getInputHeight(e){return this._inputDimensions[e][0]}getInputWidth(e){return this._inputDimensions[e][1]}getReshapedInputDimensions(e){if("number"!=typeof this.inputSize)throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");return AJ({width:this.getInputWidth(e),height:this.getInputHeight(e)},this.inputSize)}toBatchTensor(e,t=!0){return this._inputSize=e,Ci((()=>{let n=FJ(this.batchSize,0,1).map((n=>{let r=this.getInput(n);if(r instanceof $s){let n=TJ(r)?r:Vu(r);return n=function(e,t=!1){return Ci((()=>{let[n,r]=e.shape.slice(1);if(n===r)return e;let a=Math.abs(n-r),s=Math.round(a*(t?.5:1)),i=n>r?2:1,o=t=>{let n=e.shape.slice();return n[i]=t,Vl(n,0,"float32")},l=o(s),u=a-l.shape[i],d=[t&&u?o(u):null,e,l].filter((e=>!!e)).map((e=>Wo(e,"float32")));return Nl(d,i)}))}(n,t),(n.shape[1]!==e||n.shape[2]!==e)&&(n=Kc.resizeBilinear(n,[e,e],!1,!1)),n.as3D(e,e,3)}if(r instanceof rQ.getEnv().Canvas)return Pf.fromPixels(function(e,t,n=!1){let{Image:r,Canvas:a}=rQ.getEnv();if(!(e instanceof r||e instanceof a))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return mQ({width:1,height:1});let s=fQ(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,u=mQ({width:t,height:t}),d=e instanceof a?e:gQ(e),p=Math.abs(o-l)/2,h=n&&o<l?p:0,c=n&&l<o?p:0;return d.width>0&&d.height>0&&sQ(u).drawImage(d,h,c,o,l),u}(r,e,t));throw new Error(`toBatchTensor - at batchIdx ${n}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${r}`)}));return ah(n.map((e=>Wo(e,"float32")))).as4D(this.batchSize,e,e,3)}))}};async function xQ(e){if(e instanceof bQ)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=t=>Array.isArray(e)?` at input index ${t}:`:"",r=t.map(aQ);return r.forEach(((e,r)=>{if(!yQ(e)&&!NJ(e)&&!TJ(e))throw"string"==typeof t[r]?new Error(`toNetInput -${n(r)} string passed, but could not resolve HTMLElement for element id ${t[r]}`):new Error(`toNetInput -${n(r)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);

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expected image tensor to be 3D or 4D");if(TJ(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return Ci((()=>{let[n,r,a]=e.shape.slice(TJ(e)?1:0);return t.map((e=>e instanceof BJ?e.forSize(r,n).box:e)).map((e=>e.clipAtImageBorders(r,n))).filter((e=>e.width>0&&e.height>0)).map((({x:t,y:s,width:i,height:o})=>Kp(e.as3D(n,r,a),[s,t,0],[o,i,a])))}))}async function kQ(e,t){let{fetch:n}=rQ.getEnv(),r=await n(e,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}function IQ(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if("/"===e)return{modelBaseUri:"/",manifestUri:`/${n}`};let r=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"",a=(e=e.replace(r,"")).split("/").filter((e=>e)),s=e.endsWith(".json")?a[a.length-1]:n,i=r+(e.endsWith(".json")?a.slice(0,a.length-1):a).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:"/"===i?`/${s}`:`${i}/${s}`}}async function SQ(e,t){let{manifestUri:n,modelBaseUri:r}=IQ(e,t),a=await async function(e){return(await kQ(e)).json()}(n);return gf.loadWeights(a,r)}function NQ(e,t,n=!1){let{width:r,height:a}=n?fQ(t):t;return e.width=r,e.height=a,{width:r,height:a}}var TQ=class{constructor(e){this._params=void 0,this._paramMappings=[],this._name=e}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(e){let{obj:t,objProp:n}=this.traversePropertyPath(e);return t[n]}reassignParamFromPath(e,t){let{obj:n,objProp:r}=this.traversePropertyPath(e);n[r].dispose(),n[r]=t}getParamList(){return this._paramMappings.map((({paramPath:e})=>({path:e,tensor:this.getParamFromPath(e)})))}getTrainableParams(){return this.getParamList().filter((e=>e.tensor instanceof Rs))}getFrozenParams(){return this.getParamList().filter((e=>!(e.tensor instanceof Rs)))}variable(){this.getFrozenParams().forEach((({path:e,tensor:t})=>{this.reassignParamFromPath(e,t.variable())}))}freeze(){this.getTrainableParams().forEach((({path:e,tensor:t})=>{let n=yi(t.dataSync());t.dispose(),this.reassignParamFromPath(e,n)}))}dispose(e=!0){this.getParamList().forEach((t=>{if(e&&t.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${t.path}`);t.tensor.dispose()})),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map((({tensor:e})=>Array.from(e.dataSync()))).reduce(((e,t)=>e.concat(t))))}async load(e){e instanceof Float32Array?this.extractWeights(e):await this.loadFromUri(e)}async loadFromUri(e){if(e&&"string"!=typeof e)throw new Error(`${this._name}.loadFromUri - expected model uri`);let t=await SQ(e,this.getDefaultModelName());this.loadFromWeightMap(t)}async loadFromDisk(e){if(e&&"string"!=typeof e)throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:t}=rQ.getEnv(),{manifestUri:n,modelBaseUri:r}=IQ(e,this.getDefaultModelName()),a=gf.weightsLoaderFactory((e=>Promise.all(e.map((e=>t(e).then((e=>"string"==typeof e?E.from(e):e.buffer))))))),s=JSON.parse((await t(n)).toString()),i=await a(s,r);this.loadFromWeightMap(i)}loadFromWeightMap(e){let{paramMappings:t,params:n}=this.extractParamsFromWeightMap(e);this._paramMappings=t,this._params=n}extractWeights(e){let{paramMappings:t,params:n}=this.extractParams(e);this._paramMappings=t,this._params=n}traversePropertyPath(e){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let t=e.split("/").reduce(((t,n)=>{if(!t.nextObj.hasOwnProperty(n))throw new Error(`traversePropertyPath - object does not have property ${n}, for path ${e}`);

2return{obj:t.nextObj,objProp:n,nextObj:t.nextObj[n]}}),{nextObj:this.params}),{obj:n,objProp:r}=t;if(!(n&&r&&n[r]instanceof $s))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${e}`);return{obj:n,objProp:r}}};function _Q(e,t,n){return Ci((()=>{let r=Wp(e,t.depthwise_filter,t.pointwise_filter,n,"same");return r=Go(r,t.bias),r}))}function CQ(e,t,n=!1){return Ci((()=>{let r=Ap(n?Go(Kl(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):_Q(e,t.conv0,[2,2])),a=_Q(r,t.conv1,[1,1]),s=_Q(Ap(Go(r,a)),t.conv2,[1,1]);return Ap(Go(r,Go(a,s)))}))}function EQ(e,t,n=!1,r=!0){return Ci((()=>{let a=Ap(n?Go(Kl(e,t.conv0.filters,r?[2,2]:[1,1],"same"),t.conv0.bias):_Q(e,t.conv0,r?[2,2]:[1,1])),s=_Q(a,t.conv1,[1,1]),i=_Q(Ap(Go(a,s)),t.conv2,[1,1]),o=_Q(Ap(Go(a,Go(s,i))),t.conv3,[1,1]);return Ap(Go(a,Go(s,Go(i,o))))}))}function $Q(e,t,n="same",r=!1){return Ci((()=>{let a=Go(Kl(e,t.filters,[1,1],n),t.bias);return r?Ap(a):a}))}function AQ(e,t){Object.keys(e).forEach((n=>{t.some((e=>e.originalPath===n))||e[n].dispose()}))}function RQ(e,t){return(n,r,a,s)=>{let i=ph(e(n*r*a*a),[a,a,n,r]),o=lh(e(r));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function FQ(e,t){return(n,r,a)=>{let s=uh(e(n*r),[n,r]),i=lh(e(r));return t.push({paramPath:`${a}/weights`},{paramPath:`${a}/bias`}),{weights:s,bias:i}}}var DQ=class{constructor(e,t,n){this.depthwise_filter=e,this.pointwise_filter=t,this.bias=n}};function MQ(e,t){return(n,r,a)=>{let s=ph(e(9*n),[3,3,n,1]),i=ph(e(n*r),[1,1,n,r]),o=lh(e(r));return t.push({paramPath:`${a}/depthwise_filter`},{paramPath:`${a}/pointwise_filter`},{paramPath:`${a}/bias`}),new DQ(s,i,o)}}function OQ(e){return t=>{let n=e(`${t}/depthwise_filter`,4),r=e(`${t}/pointwise_filter`,4),a=e(`${t}/bias`,1);return new DQ(n,r,a)}}function LQ(e,t){return(n,r,a)=>{let s=e[n];if(!kJ(s,r))throw new Error(`expected weightMap[${n}] to be a Tensor${r}D, instead have ${s}`);return t.push({originalPath:n,paramPath:a||n}),s}}function zQ(e){let t=e;return{extractWeights:function(e){let n=t.slice(0,e);return t=t.slice(e),n},getRemainingWeights:function(){return t}}}function PQ(e,t){let n=RQ(e,t),r=MQ(e,t);function a(e,t,a,s=!1){return{conv0:s?n(e,t,3,`${a}/conv0`):r(e,t,`${a}/conv0`),conv1:r(t,t,`${a}/conv1`),conv2:r(t,t,`${a}/conv2`)}}return{extractDenseBlock3Params:a,extractDenseBlock4Params:function(e,t,n,s=!1){let{conv0:i,conv1:o,conv2:l}=a(e,t,n,s);return{conv0:i,conv1:o,conv2:l,conv3:r(t,t,`${n}/conv3`)}}}}function BQ(e){return t=>({filters:e(`${t}/filters`,4),bias:e(`${t}/bias`,1)})}function WQ(e,t){let n=LQ(e,t),r=BQ(n),a=OQ(n);return{extractDenseBlock3Params:function(e,t=!1){return{conv0:t?r(`${e}/conv0`):a(`${e}/conv0`),conv1:a(`${e}/conv1`),conv2:a(`${e}/conv2`)}},extractDenseBlock4Params:function(e,t=!1){return{conv0:t?r(`${e}/conv0`):a(`${e}/conv0`),conv1:a(`${e}/conv1`),conv2:a(`${e}/conv2`),conv3:a(`${e}/conv3`)}}}}var VQ=class extends TQ{constructor(){super("FaceFeatureExtractor")}forwardInput(e){let{params:t}=this;if(!t)throw new Error("FaceFeatureExtractor - load model before inference");return Ci((()=>{let n=EQ(VJ(Wo(e.toBatchTensor(112,!0),"float32"),[122.782,117.001,104.298]).div(255),t.dense0,!0);return n=EQ(n,t.dense1),n=EQ(n,t.dense2),n=EQ(n,t.dense3),n=Il(n,[7,7],[2,2],"valid"),n}))}async forward(e){return this.forwardInput(await xQ(e))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(e){return function(e){let t=[],{extractDenseBlock4Params:n}=WQ(e,t),r={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return AQ(e,t),{params:r,paramMappings:t}}(e)}extractParams(e){return function(e){let t=[],{extractWeights:n,getRemainingWeights:r}=zQ(e),{extractDenseBlock4Params:a}=PQ(n,t),s=a(3,32,"dense0",!0),i=a(32,64,"dense1"),o=a(64,128,"dense2"),l=a(128,256,"dense3");if(0!==r().length)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}(e)}};function UQ(e,t){return Ci((()=>Go(Tl(e,t.weights),t.bias)))}function GQ(e){let t={},n={};return Object.keys(e).forEach((r=>{(r.startsWith("fc")?n:t)[r]=e[r]})),{featureExtractorMap:t,classifierMap:n}}var HQ=class extends TQ{constructor(e,t){super(e),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return Ci((()=>{let n=e instanceof bQ?this.faceFeatureExtractor.forwardInput(e):e;return UQ(n.as2D(n.shape[0],-1),t.fc)}))}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){let{params:t,paramMappings:n}=this.extractClassifierParams(e);this._params=t,this._paramMappings=n}extractClassifierParams(e){return function(e,t,n){let r=[],{extractWeights:a,getRemainingWeights:s}=zQ(e),i=FQ(a,r)(t,n,"fc");if(0!==s().length)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:r,params:{fc:i}}}(e,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(e){let{featureExtractorMap:t,classifierMap:n}=GQ(e);return this.faceFeatureExtractor.loadFromWeightMap(t),function(e){let t=[],n=LQ(e,t),r={fc:(a="fc",{weights:n(`${a}/weights`,2),bias:n(`${a}/bias`,1)})};var a;return AQ(e,t),{params:r,paramMappings:t}}(n)}extractParams(e){let t=this.getClassifierChannelsIn(),n=this.getClassifierChannelsOut(),r=n*t+n,a=e.slice(0,e.length-r),s=e.slice(e.length-r);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(s)}},jQ=["neutral","happy","sad","angry","fearful","disgusted","surprised"],qQ=class{constructor(e){if(this.neutral=0,this.happy=0,this.sad=0,this.angry=0,this.fearful=0,this.disgusted=0,this.surprised=0,7!==e.length)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${e.length}`);

2jQ.forEach(((t,n)=>{this[t]=e[n]}))}asSortedArray(){return jQ.map((e=>({expression:e,probability:this[e]}))).sort(((e,t)=>t.probability-e.probability))}};function KQ(e,t){return{...e,expressions:t}}function XQ(e,t,n=.1,r){(Array.isArray(t)?t:[t]).forEach((t=>{let a=t instanceof qQ?t:function(e){return e.expressions instanceof qQ}(t)?t.expressions:void 0;if(!a)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let s=a.asSortedArray().filter((e=>e.probability>n)),i=KJ(t)?t.detection.box.bottomLeft:r||new OJ(0,0);new uQ(s.map((e=>`${e.expression} (${EJ(e.probability)})`)),i).draw(e)}))}function YQ(e){return KJ(e)&&e.landmarks instanceof jJ&&e.unshiftedLandmarks instanceof jJ&&e.alignedRect instanceof BJ}function ZQ(e,t){let{box:n}=e.detection,r=t.shiftBy(n.x,n.y),a=r.align(),{imageDims:s}=e.detection,i=new BJ(e.detection.score,a.rescale(s.reverse()),s),o=function(e){let t=e=>180*e/Math.PI,n=(e,t)=>Math.sqrt((e.x-t.x)**2+(e.y-t.y)**2),r={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e.positions||68!==e.positions.length)return r;let a=e.positions;return r.roll=((e,n)=>{let r=Math.hypot(n.x-e.x,n.y-e.y),a=n.y-e.y,s=Math.asin(a/r),i=t(s);return Math.floor(90-i)*(n.x-e.x<0?-1:1)})(a[27],a[66]),r.pitch=((e,r,a)=>{let s=n(e,a),i=new OJ((e.x+a.x)/2,(e.y+a.y)/2),o=n(r,i),l=Math.atan(o/s);return Math.floor(t(l))*(i.y-r.y<0?-1:1)})(a[14],a[30],a[2]),r.yaw=(s=a[14],i=a[33],o=a[2],Math.floor(s.x-i.x)-Math.floor(i.x-o.x)),r;var s,i,o}(t);return{...e,landmarks:r,unshiftedLandmarks:t,alignedRect:i,angle:o}}var JQ=class{constructor(e={}){let{drawLines:t=!0,drawPoints:n=!0,lineWidth:r,lineColor:a,pointSize:s,pointColor:i}=e;this.drawLines=t,this.drawPoints=n,this.lineWidth=r||1,this.pointSize=s||2,this.lineColor=a||"rgba(0, 255, 255, 1)",this.pointColor=i||"rgba(255, 0, 255, 1)"}},QQ=class{constructor(e,t={}){this.faceLandmarks=e,this.options=new JQ(t)}draw(e){let t=sQ(e),{drawLines:n,drawPoints:r,lineWidth:a,lineColor:s,pointSize:i,pointColor:o}=this.options;if(n&&this.faceLandmarks instanceof qJ&&(t.strokeStyle=s,t.lineWidth=a,wJ(t,this.faceLandmarks.getJawOutline()),wJ(t,this.faceLandmarks.getLeftEyeBrow()),wJ(t,this.faceLandmarks.getRightEyeBrow()),wJ(t,this.faceLandmarks.getNose()),wJ(t,this.faceLandmarks.getLeftEye(),!0),wJ(t,this.faceLandmarks.getRightEye(),!0),wJ(t,this.faceLandmarks.getMouth(),!0)),r){t.strokeStyle=o,t.fillStyle=o;let e=e=>{t.beginPath(),t.arc(e.x,e.y,i,0,2*Math.PI),t.fill()};this.faceLandmarks.positions.forEach(e)}}};function e0(e,t){(Array.isArray(t)?t:[t]).forEach((t=>{let n=t instanceof jJ?t:YQ(t)?t.landmarks:void 0;if(!n)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new QQ(n).draw(e)}))}function t0(e,t){let n=[],{extractWeights:r,getRemainingWeights:a}=zQ(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=function(e,t){let n=RQ(e,t),r=MQ(e,t);return{extractConvParams:n,extractSeparableConvParams:r,extractReductionBlockParams:function(e,t,a){return{separable_conv0:r(e,t,`${a}/separable_conv0`),separable_conv1:r(t,t,`${a}/separable_conv1`),expansion_conv:n(e,t,1,`${a}/expansion_conv`)}},extractMainBlockParams:function(e,t){return{separable_conv0:r(e,e,`${t}/separable_conv0`),separable_conv1:r(e,e,`${t}/separable_conv1`),separable_conv2:r(e,e,`${t}/separable_conv2`)}}}}(r,n),u={conv_in:s(3,32,3,"entry_flow/conv_in"),reduction_block_0:o(32,64,"entry_flow/reduction_block_0"),reduction_block_1:o(64,128,"entry_flow/reduction_block_1")},d={};FJ(t,0,1).forEach((e=>{d[`main_block_${e}`]=l(128,`middle_flow/main_block_${e}`)}));let p={reduction_block:o(128,256,"exit_flow/reduction_block"),separable_conv:i(256,512,"exit_flow/separable_conv")};if(0!==a().length)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:n,params:{entry_flow:u,middle_flow:d,exit_flow:p}}}function n0(e,t){let n=[],{extractConvParams:r,extractSeparableConvParams:a,extractReductionBlockParams:s,extractMainBlockParams:i}=function(e,t){let n=LQ(e,t),r=BQ(n),a=OQ(n);return{extractConvParams:r,extractSeparableConvParams:a,extractReductionBlockParams:function(e){return{separable_conv0:a(`${e}/separable_conv0`),separable_conv1:a(`${e}/separable_conv1`),expansion_conv:r(`${e}

2/expansion_conv`)}},extractMainBlockParams:function(e){return{separable_conv0:a(`${e}/separable_conv0`),separable_conv1:a(`${e}/separable_conv1`),separable_conv2:a(`${e}/separable_conv2`)}}}}(e,n),o={conv_in:r("entry_flow/conv_in"),reduction_block_0:s("entry_flow/reduction_block_0"),reduction_block_1:s("entry_flow/reduction_block_1")},l={};FJ(t,0,1).forEach((e=>{l[`main_block_${e}`]=i(`middle_flow/main_block_${e}`)}));let u={reduction_block:s("exit_flow/reduction_block"),separable_conv:a("exit_flow/separable_conv")};return AQ(e,n),{params:{entry_flow:o,middle_flow:l,exit_flow:u},paramMappings:n}}function r0(e,t,n){return Go(Kl(e,t.filters,n,"same"),t.bias)}function a0(e,t,n=!0){let r=n?Ap(e):e;return r=_Q(r,t.separable_conv0,[1,1]),r=_Q(Ap(r),t.separable_conv1,[1,1]),r=_d(r,[3,3],[2,2],"same"),r=Go(r,r0(e,t.expansion_conv,[2,2])),r}var s0=class extends TQ{constructor(e){super("TinyXception"),this._numMainBlocks=e}forwardInput(e){let{params:t}=this;if(!t)throw new Error("TinyXception - load model before inference");return Ci((()=>{let n=VJ(Wo(e.toBatchTensor(112,!0),"float32"),[122.782,117.001,104.298]).div(255),r=Ap(r0(n,t.entry_flow.conv_in,[2,2]));return r=a0(r,t.entry_flow.reduction_block_0,!1),r=a0(r,t.entry_flow.reduction_block_1),FJ(this._numMainBlocks,0,1).forEach((e=>{r=function(e,t){let n=_Q(Ap(e),t.separable_conv0,[1,1]);return n=_Q(Ap(n),t.separable_conv1,[1,1]),n=_Q(Ap(n),t.separable_conv2,[1,1]),n=Go(n,e),n}(r,t.middle_flow[`main_block_${e}`])})),r=a0(r,t.exit_flow.reduction_block),r=Ap(_Q(r,t.exit_flow.separable_conv,[1,1])),r}))}async forward(e){return this.forwardInput(await xQ(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(e){return n0(e,this._numMainBlocks)}extractParams(e){return t0(e,this._numMainBlocks)}};var i0,o0=((i0=o0||{}).FEMALE="female",i0.MALE="male",i0),l0=class extends HQ{postProcess(e,t,n){let r=n.map((({width:e,height:n})=>{let r=t/Math.max(n,e);return{width:e*r,height:n*r}})),a=r.length;return Ci((()=>{let n=(e,t)=>ah([Vl([68],e,"float32"),Vl([68],t,"float32")],1).as2D(1,136).as1D(),s=(e,t)=>{let{width:n,height:a}=r[e];return t(n,a)?Math.abs(n-a)/2:0};return e.mul(Vl([a,136],t,"float32")).sub(ah(Array.from(Array(a),((e,t)=>n((e=>s(e,((e,t)=>e<t)))(t),(e=>s(e,((e,t)=>t<e)))(t)))))).div(ah(Array.from(Array(a),((e,t)=>n(r[t].width,r[t].height)))))}))}forwardInput(e){return Ci((()=>{let t=this.runNet(e);return this.postProcess(t,e.inputSize,e.inputDimensions.map((([e,t])=>({height:e,width:t}))))}))}async forward(e){return this.forwardInput(await xQ(e))}async detectLandmarks(e){let t=await xQ(e),n=Ci((()=>Ih(this.forwardInput(t)))),r=await Promise.all(n.map((async(e,n)=>{let r=Array.from(e.dataSync()),a=r.filter(((e,t)=>CJ(t))),s=r.filter(((e,t)=>!CJ(t)));return new qJ(Array(68).fill(0).map(((e,t)=>new OJ(a[t],s[t]))),{height:t.getInputHeight(n),width:t.getInputWidth(n)})})));return n.forEach((e=>e.dispose())),t.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}},u0=class extends l0{constructor(e=new VQ){super("FaceLandmark68Net",e)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var d0=class extends TQ{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(e){let{params:t}=this;if(!t)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Ci((()=>{let n=CQ(VJ(Wo(e.toBatchTensor(112,!0),"float32"),[122.782,117.001,104.298]).div(255),t.dense0,!0);return n=CQ(n,t.dense1),n=CQ(n,t.dense2),n=Il(n,[14,14],[2,2],"valid"),n}))}async forward(e){return this.forwardInput(await xQ(e))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(e){return function(e){let t=[],{extractDenseBlock3Params:n}=WQ(e,t),r={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return AQ(e,t),{params:r,paramMappings:t}}(e)}extractParams(e){return function(e){let t=[],{extractWeights:n,getRemainingWeights:r}=zQ(e),{extractDenseBlock3Params:a}=PQ(n,t),s=a(3,32,"dense0",!0),i=a(32,64,"dense1"),o=a(64,128,"dense2");if(0!==r().length)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}(e)}};function p0(e,t,n,r,a="same"){let{filters:s,bias:i}=t.conv,o=Kl(e,s,n,a);return o=Go(o,i),o=function(e,t){return Go(qo(e,t.weights),t.biases)}(o,t.scale),r?Ap(o):o}function h0(e,t){return p0(e,t,[1,1],!1)}function c0(e,t){return p0(e,t,[2,2],!0,"valid")}function f0(e,t){function n(n,r,a,s){let i=function(t,n,r){let a=e(t),s=a.length/(n*r*r);if(_J(s))throw new Error(`depth has to be an integer: ${s}, weights.length: ${a.length}, numFilters: ${n}, filterSize: ${r}`);return Ci((()=>Eh(ph(a,[n,s,r,r]),[2,3,1,0])))}(n,r,a),o=lh(e(r));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}function r(r,a,s,i){let o=n(r,a,s,`${i}/conv`),l=function(n,r){let a=lh(e(n)),s=lh(e(n));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/biases`}),{weights:a,biases:s}}(a,`${i}/scale`);return{conv:o,scale:l}}return{extractConvLayerParams:r,extractResidualLayerParams:function(e,t,n,a,s=!1){return{conv1:r((s?.5:1)*e,t,n,`${a}/conv1`),conv2:r(e,t,n,`${a}/conv2`)}}}}function m0(e,t){let n=LQ(e,t);function r(e){let t=n(`${e}/conv/filters`,4),r=n(`${e}/conv/bias`,1),a=function(e){return{weights:n(`${e}/scale/weights`,1),biases:n(`${e}/scale/biases`,1)}}(e);return{conv:{filters:t,bias:r},scale:a}}return{extractConvLayerParams:r,extractResidualLayerParams:function(e){return{conv1:r(`${e}/conv1`),conv2:r(`${e}/conv2`)}}}}function g0(e,t){let n=function(e,t){return p0(e,t,[1,1],!0)}(e,t.conv1);return n=h0(n,t.conv2),n=Go(n,e),n=Ap(n),n}function y0(e,t){let n=c0(e,t.conv1);n=h0(n,t.conv2);let r=Il(e,2,2,"valid"),a=Rd(r.shape),s=r.shape[3]!==n.shape[3];if(r.shape[1]!==n.shape[1]||r.shape[2]!==n.shape[2]){let e=[...n.shape];e[1]=1;let t=Rd(e);n=Nl([n,t],1);let r=[...n.shape];r[2]=1;let a=Rd(r);n=Nl([n,a],2)}return r=s?Nl([r,a],3):r,n=Go(r,n),n=Ap(n),n}var b0=class extends TQ{constructor(){super("FaceRecognitionNet")}forwardInput(e){let{params:t}=this;if(!t)throw new Error("FaceRecognitionNet - load model before inference");return Ci((()=>{let n=c0(VJ(Wo(e.toBatchTensor(150,!0),"float32"),[122.782,117.001,104.298]).div(255),t.conv32_down);n=_d(n,3,2,"valid"),n=g0(n,t.conv32_1),n=g0(n,t.conv32_2),n=g0(n,t.conv32_3),n=y0(n,t.conv64_down),n=g0(n,t.conv64_1),n=g0(n,t.conv64_2),n=g0(n,t.conv64_3),n=y0(n,t.conv128_down),n=g0(n,t.conv128_1),n=g0(n,t.conv128_2),n=y0(n,t.conv256_down),n=g0(n,t.conv256_1),n=g0(n,t.conv256_2),n=y0(n,t.conv256_down_out);let r=n.mean([1,2]);return Tl(r,t.fc)}))}async forward(e){return this.forwardInput(await xQ(e))}async computeFaceDescriptor(e){var t;if(null!=(t=null==e?void 0:e.shape)&&t.some((e=>e<=0)))return new Float32Array(128);let n=await xQ(e),r=Ci((()=>Ih(this.forwardInput(n)))),a=await Promise.all(r.map((e=>e.data())));return r.forEach((e=>e.dispose())),n.isBatchInput?a:a[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(e){return function(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:r}=m0(e,t),a=n("conv32_down"),s=r("conv32_1"),i=r("conv32_2"),o=r("conv32_3"),l=r("conv64_down"),u=r("conv64_1"),d=r("conv64_2"),p=r("conv64_3"),h=r("conv128_down"),c=r("conv128_1"),f=r("conv128_2"),m=r("conv256_down"),g=r("conv256_1"),y=r("conv256_2"),b=r("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!SJ(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);

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I0(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:r}=function(e,t){let n=LQ(e,t);function r(e,t,r){return{filters:n(`${e}/Conv2d_${t}_pointwise/weights`,4,`${r}/filters`),batch_norm_offset:n(`${e}/Conv2d_${t}_pointwise/convolution_bn_offset`,1,`${r}/batch_norm_offset`)}}function a(e){let t=`mobilenetv1/conv_${e}`,a=`MobilenetV1/Conv2d_${e}_depthwise`,s=`${t}/depthwise_conv`,i=`${t}/pointwise_conv`;return{depthwise_conv:{filters:n(`${a}/depthwise_weights`,4,`${s}/filters`),batch_norm_scale:n(`${a}/BatchNorm/gamma`,1,`${s}/batch_norm_scale`),batch_norm_offset:n(`${a}/BatchNorm/beta`,1,`${s}/batch_norm_offset`),batch_norm_mean:n(`${a}/BatchNorm/moving_mean`,1,`${s}/batch_norm_mean`),batch_norm_variance:n(`${a}/BatchNorm/moving_variance`,1,`${s}/batch_norm_variance`)}

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output of conv layer 11 is null");return{out:r,conv11:n}}))}function _0(e,t,n){let r=e.arraySync(),a=Math.min(r[t][0],r[t][2]),s=Math.min(r[t][1],r[t][3]),i=Math.max(r[t][0],r[t][2]),o=Math.max(r[t][1],r[t][3]),l=Math.min(r[n][0],r[n][2]),u=Math.min(r[n][1],r[n][3]),d=Math.max(r[n][0],r[n][2]),p=Math.max(r[n][1],r[n][3]),h=(i-a)*(o-s),c=(d-l)*(p-u);if(h<=0||c<=0)return 0;let f=Math.max(a,l),m=Math.max(s,u),g=Math.min(i,d),y=Math.min(o,p),b=Math.max(g-f,0)*Math.max(y-m,0);return b/(h+c-b)}function C0(e,t){let{sizes:n,centers:r}=function(e){let t=Ih(Eh(e,[1,0])),n=[yd(t[2],t[0]),yd(t[3],t[1])];return{sizes:n,centers:[Go(t[0],jo(n[0],2)),Go(t[1],jo(n[1],2))]}}(e),a=Ih(Eh(t,[1,0])),s=jo(qo(Wu(jo(a[2],5)),n[0]),2),i=Go(qo(jo(a[0],10),n[0]),r[0]),o=jo(qo(Wu(jo(a[3],5)),n[1]),2),l=Go(qo(jo(a[1],10),n[1]),r[1]);return Eh(ah([yd(i,s),yd(l,o),Go(i,s),Go(l,o)]),[1,0])}function E0(e,t){return Ci((()=>{let n=e.shape[0];return{boxPredictionEncoding:kl($Q(e,t.box_encoding_predictor),[n,-1,1,4]),classPrediction:kl($Q(e,t.class_predictor),[n,-1,3])}}))}var $0=class{constructor({minConfidence:e,maxResults:t}={}){if(this._name="SsdMobilenetv1Options",this._minConfidence=e||.5,this._maxResults=t||100,"number"!=typeof this._minConfidence||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if("number"!=typeof this._maxResults)throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},A0=class extends TQ{constructor(){super("SsdMobilenetv1")}forwardInput(e){let{params:t}=this;if(!t)throw new Error("SsdMobilenetv1 - load model before inference");return Ci((()=>{let n=Wo(e.toBatchTensor(512,!1),"float32"),r=T0(yd(jo(n,127.5),1),t.mobilenetv1),{boxPredictions:a,classPredictions:s}=function(e,t,n){return Ci((()=>{let r=S0(e,n.conv_0,[1,1]),a=S0(r,n.conv_1,[2,2]),s=S0(a,n.conv_2,[1,1]),i=S0(s,n.conv_3,[2,2]),o=S0(i,n.conv_4,[1,1]),l=S0(o,n.conv_5,[2,2]),u=S0(l,n.conv_6,[1,1]),d=S0(u,n.conv_7,[2,2]),p=E0(t,n.box_predictor_0),h=E0(e,n.box_predictor_1),c=E0(a,n.box_predictor_2),f=E0(i,n.box_predictor_3),m=E0(l,n.box_predictor_4),g=E0(d,n.box_predictor_5);return{boxPredictions:Nl([p.boxPredictionEncoding,h.boxPredictionEncoding,c.boxPredictionEncoding,f.boxPredictionEncoding,m.boxPredictionEncoding,g.boxPredictionEncoding],1),classPredictions:Nl([p.classPrediction,h.classPrediction,c.classPrediction,f.classPrediction,m.classPrediction,g.classPrediction],1)}}))}(r.out,r.conv11,t.prediction_layer);return function(e,t,n){return Ci((()=>{let r=e.shape[0],a=C0(kl(Gu(n.extra_dim,[r,1,1]),[-1,4]),kl(e,[-1,4]));a=kl(a,[r,a.shape[0]/r,4]);let s=_l(Cl(t,[0,0,1],[-1,-1,-1])),i=Cl(s,[0,0,0],[-1,-1,1]);return i=kl(i,[r,i.shape[1]]),{boxes:Ih(a),scores:Ih(i)}}))}(a,s,t.output_layer)}

2))}async forward(e){return this.forwardInput(await xQ(e))}async locateFaces(e,t={}){let{maxResults:n,minConfidence:r}=new $0(t),a=await xQ(e),{boxes:s,scores:i}=this.forwardInput(a),o=s[0],l=i[0];for(let e=1;e<s.length;e++)s[e].dispose(),i[e].dispose();let u=Array.from(l.dataSync()),d=function(e,t,n,r,a){let s=e.shape[0],i=Math.min(n,s),o=t.map(((e,t)=>({score:e,boxIndex:t}))).filter((e=>e.score>a)).sort(((e,t)=>t.score-e.score)),l=e=>e<=r?1:0,u=[];return o.forEach((t=>{if(u.length>=i)return;let n=t.score;for(let n=u.length-1;n>=0;--n){let r=_0(e,t.boxIndex,u[n]);if(0!==r&&(t.score*=l(r),t.score<=a))break}n===t.score&&u.push(t.boxIndex)})),u}(o,u,n,.5,r),p=a.getReshapedInputDimensions(0),h=a.inputSize,c=h/p.width,f=h/p.height,m=o.arraySync(),g=d.map((e=>{let[t,n]=[Math.max(0,m[e][0]),Math.min(1,m[e][2])].map((e=>e*f)),[r,s]=[Math.max(0,m[e][1]),Math.min(1,m[e][3])].map((e=>e*c));return new BJ(u[e],new HJ(r,t,s-r,n-t),{height:a.getInputHeight(0),width:a.getInputWidth(0)})}));return o.dispose(),l.dispose(),g}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(e){return I0(e)}extractParams(e){return function(e){let t=[],{extractWeights:n,getRemainingWeights:r}=zQ(e),{extractMobilenetV1Params:a,extractPredictionLayerParams:s}=k0(n,t),i=a(),o=s(),l={extra_dim:dh(n(20472),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),0!==r().length)throw new Error(`weights remaing after extract: ${r().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:l},paramMappings:t}}(e)}};var R0=[new OJ(.738768,.874946),new OJ(2.42204,2.65704),new OJ(4.30971,7.04493),new OJ(10.246,4.59428),new OJ(12.6868,11.8741)],F0=[new OJ(1.603231,2.094468),new OJ(6.041143,7.080126),new OJ(2.882459,3.518061),new OJ(4.266906,5.178857),new OJ(9.041765,10.66308)],D0=[117.001,114.697,97.404],M0=e=>"number"==typeof e;function O0(e){return Ci((()=>{let t=qo(e,Du(.10000000149011612));return Go(Ap(yd(e,t)),t)}))}function L0(e,t){return Ci((()=>{let n=Hd(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Kl(n,t.conv.filters,[1,1],"valid"),n=yd(n,t.bn.sub),n=qo(n,t.bn.truediv),n=Go(n,t.conv.bias),O0(n)}))}function z0(e,t){return Ci((()=>{let n=Hd(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Wp(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Go(n,t.bias),O0(n)}))}function P0(e,t){let n=RQ(e,t);let r=MQ(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:function(r,a,s){let i=n(r,a,3,`${s}/conv`),o=function(n,r){let a=lh(e(n)),s=lh(e(n));return t.push({paramPath:`${r}/sub`},{paramPath:`${r}/truediv`}),{sub:a,truediv:s}}(a,`${s}/bn`);return{conv:i,bn:o}},extractSeparableConvParams:r}}function B0(e,t){let n=LQ(e,t);function r(e){return{filters:n(`${e}/filters`,4),bias:n(`${e}/bias`,1)}}return{extractConvParams:r,extractConvWithBatchNormParams:function(e){let t=r(`${e}/conv`),a=function(e){return{sub:n(`${e}/sub`,1),truediv:n(`${e}/truediv`,1)}}(`${e}/bn`);return{conv:t,bn:a}},extractSeparableConvParams:OQ(n)}}var W0=class{constructor({inputSize:e,scoreThreshold:t}={}){if(this._name="TinyYolov2Options",this._inputSize=e||416,this._scoreThreshold=t||.5,"number"!=typeof this._inputSize||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if("number"!=typeof this._scoreThreshold||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}},V0=class e extends TQ{constructor(e){super("TinyYolov2"),function(e){if(!e)throw new Error(`invalid config: ${e}`);if("boolean"!=typeof e.withSeparableConvs)throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!M0(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every((e=>"string"==typeof e)))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map((e=>e||{})).every((e=>M0(e.x)&&M0(e.y))))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||3!==e.meanRgb.length||!e.meanRgb.every(M0))

2)throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,t){let n=L0(e,t.conv0);return n=_d(n,[2,2],[2,2],"same"),n=L0(n,t.conv1),n=_d(n,[2,2],[2,2],"same"),n=L0(n,t.conv2),n=_d(n,[2,2],[2,2],"same"),n=L0(n,t.conv3),n=_d(n,[2,2],[2,2],"same"),n=L0(n,t.conv4),n=_d(n,[2,2],[2,2],"same"),n=L0(n,t.conv5),n=_d(n,[2,2],[1,1],"same"),n=L0(n,t.conv6),n=L0(n,t.conv7),$Q(n,t.conv8,"valid",!1)}runMobilenet(e,t){let n=this.config.isFirstLayerConv2d?O0($Q(e,t.conv0,"valid",!1)):z0(e,t.conv0);return n=_d(n,[2,2],[2,2],"same"),n=z0(n,t.conv1),n=_d(n,[2,2],[2,2],"same"),n=z0(n,t.conv2),n=_d(n,[2,2],[2,2],"same"),n=z0(n,t.conv3),n=_d(n,[2,2],[2,2],"same"),n=z0(n,t.conv4),n=_d(n,[2,2],[2,2],"same"),n=z0(n,t.conv5),n=_d(n,[2,2],[1,1],"same"),n=t.conv6?z0(n,t.conv6):n,n=t.conv7?z0(n,t.conv7):n,$Q(n,t.conv8,"valid",!1)}forwardInput(e,t){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return Ci((()=>{let r=Wo(e.toBatchTensor(t,!1),"float32");return r=this.config.meanRgb?VJ(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,n):this.runTinyYolov2(r,n)}))}async forward(e,t){return this.forwardInput(await xQ(e),t)}async detect(e,t={}){let{inputSize:n,scoreThreshold:r}=new W0(t),a=await xQ(e),s=await this.forwardInput(a,n),i=Ci((()=>Ih(s)[0].expandDims())),o={width:a.getInputWidth(0),height:a.getInputHeight(0)},l=await this.extractBoxes(i,a.getReshapedInputDimensions(0),r);s.dispose(),i.dispose();let u=l.map((e=>e.box)),d=l.map((e=>e.score)),p=l.map((e=>e.classScore)),h=l.map((e=>this.config.classes[e.label]));return function(e,t,n,r=!0){let a=t.map(((e,t)=>({score:e,boxIndex:t}))).sort(((e,t)=>e.score-t.score)).map((e=>e.boxIndex)),s=[];for(;a.length>0;){let t=a.pop();s.push(t);let i=a,o=[];for(let n=0;n<i.length;n++){let a=i[n],s=e[t],l=e[a];o.push(WJ(s,l,r))}a=a.filter(((e,t)=>o[t]<=n))}return s}(u.map((e=>e.rescale(n))),d,this.config.iouThreshold,!0).map((e=>new PJ(d[e],p[e],h[e],u[e],o)))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return function(e,t){let n,r=[],{extractConvParams:a,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}=B0(e,r);if(t.withSeparableConvs){let e=t.filterSizes&&t.filterSizes.length||9;n={conv0:t.isFirstLayerConv2d?a("conv0"):i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:e>7?i("conv6"):void 0,conv7:e>8?i("conv7"):void 0,conv8:a("conv8")}}else n={conv0:s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:s("conv6"),conv7:s("conv7"),conv8:a("conv8")};return AQ(e,r),{params:n,paramMappings:r}}(e,this.config)}extractParams(t){let n=this.config.filterSizes||e.DEFAULT_FILTER_SIZES,r=n?n.length:void 0;if(7!==r&&8!==r&&9!==r)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return function(e,t,n,r){let a,{extractWeights:s,getRemainingWeights:i}=zQ(e),o=[],{extractConvParams:l,extractConvWithBatchNormParams:u,extractSeparableConvParams:d}=P0(s,o);if(t.withSeparableConvs){let[e,s,i,o,u,p,h,c,f]=r;a={conv0:t.isFirstLayerConv2d?l(e,s,3,"conv0"):d(e,s,"conv0"),conv1:d(s,i,"conv1"),conv2:d(i,o,"conv2"),conv3:d(o,u,"conv3"),conv4:d(u,p,"conv4"),conv5:d(p,h,"conv5"),conv6:c?d(h,c,"conv6"):void 0,conv7:f?d(c,f,"conv7"):void 0,conv8:l(f||c||h,5*n,1,"conv8")}}else{let[e,t,s,i,o,d,p,h,c]=r;a={conv0:u(e,t,"conv0"),conv1:u(t,s,"conv1"),conv2:u(s,i,"conv2"),conv3:u(i,o,"conv3"),conv4:u(o,d,"conv4"),conv5:u(d,p,"conv5"),conv6:u(p,h,"conv6"),conv7:u(h,c,"conv7"),conv8:l(c,5*n,1,"conv8")}}if(0!==i().length)throw new Error(`weights remaing after extract: ${i().length}`);return{params:a,paramMappings:o}}(t,this.config,this.boxEncodingSize,n)}async extractBoxes(e,t,n){let{width:r,height:a}=t,s=Math.max(r,a),i=s/r,o=s/a,l=e.shape[1],u=this.config.anchors.length,[d,p,h]=Ci((()=>{let t=e.reshape([l,l,u,this.boxEncodingSize]);return[t.slice([0,0,0,0],[l,l,u,4]),t.slice([0,0,0,4],[l,l,u,1]),this.withClassScores?Yp(t.slice([0,0,0,5],[l,l,u,this.config.classes.length]),3):Du(0)]})),c=[],f=await p.array(),m=await d.array();for(let e=0;e<l;e++)for(let t=0;t<l;t++)for(let r=0;r<u;r++){let a=UJ(f[e][t][r][0]);if(!n||a>n){let n=(t+UJ(m[e][t][r][0]))/l*i,s=(e+UJ(m[e][t][r][1]))/l*o,u=Math.exp(m[e][t][r][2])*this.config.anchors[r].x/l*i,d=Math.exp(m[e][t][r][3])*this.config.anchors[r].y/l*o,p=n-u/2,f=s-d/2,g={row:e,col:t,anchor:r},{classScore:y,label:b}=this.withClassScores?await this.extractPredictedClass(h,g):{classScore:1,label:0};c.push({box:new zJ(p,f,p+u,f+d),score:a,classScore:a*y,label:b,...g})}}return d.dispose(),p.dispose(),h.dispose(),c}async extractPredictedClass(e,t){let{row:n,col:r,anchor:a}=t,s=await e.array();return Array(this.config.classes.length).fill(0).map(((e,t)=>s[n][r][a][t])).map(((e,t)=>({classScore:e,label:t}))).reduce(((e,t)=>e.classScore>t.classScore?e:t))}};V0.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var U0=V0,G0=class extends U0{constructor(e=!0){super({withSeparableConvs:e,iouThreshold:.4,classes:["face"],...e?{anchors:F0,meanRgb:D0}:{anchors:R0,withClassScores:!0}})}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(e,t){return(await this.detect(e,t)).map((e=>new BJ(e.score,e.relativeBox,{width:e.imageWidth,height:e.imageHeight})))}getDefaultModelName(){return this.withSeparableConvs?"tiny_yolov2_separable_conv_model":"tiny_yolov2_model"}extractParamsFromWeightMap(e){return super.extractParamsFromWeightMap(e)}};var H0=class extends W0{constructor(){super(...arguments),this._name="TinyFaceDetectorOptions"}},j0=class{async then(e){return e(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function q0(e,t,n,r,a=(({alignedRect:e})=>e)){let s=e.map((e=>YQ(e)?a(e):e.detection)),i=r||(t instanceof $s?await vQ(t,s):await wQ(t,s)),o=await n(i);

2return i.forEach((e=>e instanceof $s&&e.dispose())),o}async function K0(e,t,n,r,a){return q0([e],t,(async e=>n(e[0])),r,a)}var X0=[new OJ(1.603231,2.094468),new OJ(6.041143,7.080126),new OJ(2.882459,3.518061),new OJ(4.266906,5.178857),new OJ(9.041765,10.66308)],Y0=[117.001,114.697,97.404],Z0=class extends U0{constructor(){super({withSeparableConvs:!0,iouThreshold:.4,classes:["face"],anchors:X0,meanRgb:Y0,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]})}get anchors(){return this.config.anchors}async locateFaces(e,t){return(await this.detect(e,t)).map((e=>new BJ(e.score,e.relativeBox,{width:e.imageWidth,height:e.imageHeight})))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(e){return super.extractParamsFromWeightMap(e)}},J0={ssdMobilenetv1:new A0,tinyFaceDetector:new Z0,tinyYolov2:new G0,faceLandmark68Net:new u0,faceLandmark68TinyNet:new class extends l0{constructor(e=new d0){super("FaceLandmark68TinyNet",e)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},faceRecognitionNet:new b0,faceExpressionNet:new class extends HQ{constructor(e=new VQ){super("FaceExpressionNet",e)}forwardInput(e){return Ci((()=>Yp(this.runNet(e))))}async forward(e){return this.forwardInput(await xQ(e))}async predictExpressions(e){let t=await xQ(e),n=await this.forwardInput(t),r=await Promise.all(Ih(n).map((async e=>{let t=e.dataSync();return e.dispose(),t})));n.dispose();let a=r.map((e=>new qQ(e)));return t.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}},ageGenderNet:new class extends TQ{constructor(e=new s0(2)){super("AgeGenderNet"),this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:t}=this;if(!t)throw new Error(`${this._name} - 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