/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
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Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||y(t,this.size),this.strides=M(e)}set(e,...t){0===t.length&&(t=[0]),i(t.length===this.rank,(()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`));const n=this.locToIndex(t);this.values[n]=e}get(...e){0===e.length&&(e=[0]);let t=0;for(const n of e){if(n<0||n>=this.shape[t]){const 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];const 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 Es().makeTensor(this.values,this.shape,this.dtype)}}let Es=null,Ss=null;class $s{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=c(e),this.strides=M(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return Ss.buffer(this.shape,this.dtype,e)}bufferSync(){return Ss.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return T(this.shape,e,"complex64"===this.dtype)}arraySync(){return T(this.shape,this.dataSync(),"complex64"===this.dtype)}async data(){this.throwIfDisposed();const e=Es().read(this.dataId);if("string"===this.dtype){const t=await e;try{return t.map((e=>cs(e)))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataToGPU(e){return this.throwIfDisposed(),Es().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();const e=Es().readSync(this.dataId);if("string"===this.dtype)try{return e.map((e=>cs(e)))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Es().read(this.dataId);return"string"===this.dtype?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(this.kerasMask&&this.kerasMask.dispose(),Es().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Ss.print(this,e)}clone(){return this.throwIfDisposed(),Ss.clone(this)}toString(e=!1){return bs(this.dataSync(),this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Ss.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Es().makeVariable(this,e,t,n)}}function As(){return W("Tensor",(()=>$s))}Object.defineProperty($s,Symbol.hasInstance,{value:e=>!!e&&null!=e.data&&null!=e.dataSync&&null!=e.throwIfDisposed}),As();class _s 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 (${this.dtype}) must match`);if(!d(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Es().disposeTensor(this),this.dataId=e.dataId,Es().incRef(this,null)}dispose(){Es().disposeVariable(this),this.isDisposedInternal=!0}}var Is,Ms,Ns,Ts,Ds;Object.defineProperty(_s,Symbol.hasInstance,{value:e=>e instanceof $s&&null!=e.assign&&e.assign instanceof Function}),function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"}(Is||(Is={})),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"}(Ns||(Ns={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"}(Ts||(Ts={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"}(Ds||(Ds={}));const Rs={float32:Ts,int32:Ms,bool:Ns,complex64:Ds};function Fs(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Rs[e][t]}function Bs(e){return Fs(e,"int32")}function Cs(e){return null!=e&&"object"==typeof e&&"texture"in e&&e.texture instanceof WebGLTexture}function Ps(e){return"undefined"!=typeof GPUBuffer&&null!=e&&"object"==typeof e&&"buffer"in e&&e.buffer instanceof GPUBuffer}function Os(e,t){if(e.dtype===t.dtype)return[e,t];const n=Fs(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Ls(e,t){i(e.dtype===t.dtype,(()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`))}function zs(e){const t=[];return Us(e,t,new Set),t}function Us(e,t,n){if(null==e)return;if(e instanceof $s)return void t.push(e);if(r=e,!Array.isArray(r)&&"object"!=typeof r)return;var r;const s=e;for(const e in s){const r=s[e];n.has(r)||(n.add(r),Us(r,t,n))}}var Ws={__proto__:null,assertTypesMatch:Ls,getTensorsInContainer:zs,isTensorInList:function(e,t){return t.some((t=>t.id===e.id))},makeTypesMatch:Os};function Gs(e){return null!=e.kernelName}class Ks{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map((e=>e.name))))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class qs{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Ks}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then((()=>{}));if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${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){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(lr(`${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`);if(this.backendName=e,null==this.registry[e]){this.backendInstance=null;const{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 ps(this.backendInstance),!0}setupRegisteredKernels(){fr(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){fr(e).forEach((t=>{null!=t.disposeFunc&&t.disposeFunc(this.registry[e])}))}initializeBackend(e){const t=this.registryFactory[e];if(null==t)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const r=t.factory();if(!r||r instanceof n||"function"!=typeof r.then)return this.registry[e]=r,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,n=r.then((n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0))).catch((n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,lr(`Initialization of backend ${e} failed`),lr(n.stack||n.message)),!1)));return this.pendingBackendInit=n,{success:n,asyncInit:!0}}}catch(t){return lr(`Initialization of backend ${e} failed`),lr(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:r,asyncInit:s}=this.initializeBackend(n);if(s||r)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),r=n.backend,s=this.readSync(t),o=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,o),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{const e=n();return t(),e}catch(e){throw t(),e}}nextTensorId(){return qs.nextTensorId++}nextVariableId(){return qs.nextVariableId++}clone(e){const t=Hs.runKernel("Identity",{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{const t={x:e},n={dtype:"float32"};return Hs.runKernel("Cast",t,n)}})),[],{}),t}runKernel(e,t,n){null==this.backendName&&this.backend;if(!(null!=hr(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){const r=this.backend.numDataIds();let s=0;n.forEach((e=>{s+="complex64"===e.dtype?3:1}));const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=r-t-s-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),s=this.state.numBytes,o=this.state.numTensors;let a,l;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const u=Gs(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(Gs(e)){const{kernelName:t,inputs:s,attrs:o}=e;null==this.backendName&&this.backend;const u=hr(t,this.backendName);i(null!=u,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),a=()=>{const e=this.backend.numDataIds();l=u.kernelFunc({inputs:s,attrs:o,backend:this.backend});const a=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,a);const i=a.map((e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e)));if(r){const e=this.getTensorsForGradient(t,s,i);n=this.saveTensorsForBackwardMode(e)}return i}}else{const{forwardFunc:t}=e,s=e=>{r&&(n=e.map((e=>this.keep(this.clone(e)))))};a=()=>{const e=this.backend.numDataIds();l=this.tidy((()=>t(this.backend,s)));const n=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,e,n),n}}const{inputs:c,attrs:h}=e,d=Gs(e)?null:e.backwardsFunc;let f;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(f=this.profiler.profileKernel(u,c,(()=>a())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs):t=a()})),r&&this.addTapeNode(u,c,t,d,n,h),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-o,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map((e=>null!=c[e]?c[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){const t=e.map((e=>this.keep(this.clone(e))));return t}getTensorsForGradient(e,t,n){const r=dr(e);if(null!=r){const e=r.inputsToSave||[],s=r.outputsToSave||[];let o;r.saveAllInputs?(i(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),o=Object.keys(t).map((e=>t[e]))):o=e.map((e=>t[e]));const a=n.filter(((e,t)=>s[t]));return o.concat(a)}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 s=e;"string"===n&&E(e[0])&&(s=e.map((e=>us(e))));const o=r.write(s,t,n),a=new $s(t,n,o,this.nextTensorId());if(this.trackTensor(a,r),"string"===n){const e=this.state.tensorInfo.get(o),t=x(s);this.state.numBytes+=t-e.bytes,e.bytes=t}return a}makeTensorFromDataId(e,t,n,r){const s={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(s,r)}makeTensorFromTensorInfo(e,t){const{dataId:n,shape:r,dtype:s}=e,o=new $s(r,s,n,this.nextTensorId());return this.trackTensor(o,t),o}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));const s=new _s(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[s.name])throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*v(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 _s||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;const 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){const t=e.size*v(e.dtype);this.state.numBytes-=t}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(const e in this.state.registeredVariables){const 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(){const 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;const 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(const 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>0&&0===this.state.kernelDepth}addTapeNode(e,t,n,r,s,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},i=dr(e);null!=i&&(r=i.gradFunc),null!=r&&(a.gradient=e=>(e=e.map(((e,t)=>{if(null==e){const e=n[t],r=R(e.size,e.dtype);return this.makeTensor(r,e.shape,e.dtype)}return e})),r(e.length>1?e:e[0],s,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=zs(e),n=new Set(t.map((e=>e.id)));for(let e=0;e<this.state.activeScope.track.length;e++){const t=this.state.activeScope.track[e];t.kept||n.has(t.id)||t.dispose()}const 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(i(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}'`);const s=this.scopedRun((()=>this.startTape()),(()=>this.endTape()),(()=>this.tidy("forward",e)));i(s instanceof $s,(()=>"The result y returned by f() must be a tensor."));const o=function(e,t,n){const r={},s={};for(let e=0;e<t.length;e++)r[t[e].id]=!0;for(let n=0;n<e.length;n++){const o=e[n],a=o.inputs;for(const e in a){const n=a[e];let i=!1;for(let e=0;e<t.length;e++)if(r[n.id]){o.outputs.forEach((e=>r[e.id]=!0)),i=!0,s[o.id]=!0;break}if(i)break}}const o={};o[n.id]=!0;const a={};for(let t=e.length-1;t>=0;t--){const n=e[t],r=n.inputs;for(let e=0;e<n.outputs.length;e++)if(o[n.outputs[e].id]){for(const e in r)o[r[e].id]=!0,a[n.id]=!0;break}}const i=[];for(let t=0;t<e.length;t++){const n=e[t];if(s[n.id]&&a[n.id]){const e={};for(const t in n.inputs){const s=n.inputs[t];r[s.id]&&(e[t]=s)}const t=Object.assign({},n);t.inputs=e,t.outputs=n.outputs,i.push(t)}}return i}(this.state.activeTape,t,s);if(!r&&0===o.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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T;return"channelsFirst"===a?T=[l,N,_,I,M]:"channelsLast"===a&&(T=[l,_,I,M,N]),{batchSize:l,dataFormat:a,inDepth:u,inHeight:c,inWidth:h,inChannels:d,outDepth:_,outHeight:I,outWidth:M,outChannels:N,padInfo:A,strideDepth:b,strideHeight:y,strideWidth:w,filterDepth:f,filterHeight:p,filterWidth:g,effectiveFilterDepth:E,effectiveFilterHeight:S,effectiveFilterWidth:$,dilationDepth:k,dilationHeight:v,dilationWidth:x,inShape:e,outShape:T,filterShape:t}}function Wa(e,t,n,r=1){const s=qa(t,r);return Math.floor((e[0]*(n-1)-n+s)/2)}function Ga(e){return"number"==typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function Ka(e){return"number"==typeof e?[e,e,e]:e}function qa(e,t){return t<=1?e:e+(e-1)*(t-1)}function Va(e,t){if(!t)return Math.trunc(e);switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error(`Unknown roundingMode ${t}`)}}function Ha(e){const[t,n,r]=Ga(e);return 1===t&&1===n&&1===r}function ja(e,t){return 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t=0===e.rank||1===e.rank?Xa(e,[1,1,1,e.size]):2===e.rank?Xa(e,[1,1,e.shape[0],e.shape[1]]):3===e.rank?Xa(e,[1,e.shape[0],e.shape[1],e.shape[2]]):e,t}(a),f={x:d,scale:c,offset:h,mean:l,variance:u},p={varianceEpsilon:o},g=Hs.runKernel("FusedBatchNorm",f,p);return Xa(g,a.shape)}});const ui=oo({batchNorm2d_:function(e,t,n,r,s,o){const a=no(e,"x","batchNorm"),l=no(t,"mean","batchNorm"),u=no(n,"variance","batchNorm");let c,h;return null!=s&&(c=no(s,"scale","batchNorm")),null!=r&&(h=no(r,"offset","batchNorm")),i(2===a.rank,(()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`)),i(2===l.rank||1===l.rank,(()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${l.rank}.`)),i(2===u.rank||1===u.rank,(()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`)),null!=c&&i(2===c.rank||1===c.rank,(()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`)),null!=h&&i(2===h.rank||1===h.rank,(()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${h.rank}.`)),li(a,l,u,h,c,o)}});const ci=oo({batchNorm3d_:function(e,t,n,r,s,o){const a=no(e,"x","batchNorm"),l=no(t,"mean","batchNorm"),u=no(n,"variance","batchNorm");let c,h;return null!=s&&(c=no(s,"scale","batchNorm")),null!=r&&(h=no(r,"offset","batchNorm")),i(3===a.rank,(()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`)),i(3===l.rank||1===l.rank,(()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${l.rank}.`)),i(3===u.rank||1===u.rank,(()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`)),null!=c&&i(3===c.rank||1===c.rank,(()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`)),null!=h&&i(3===h.rank||1===h.rank,(()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${h.rank}.`)),li(a,l,u,h,c,o)}});const hi=oo({batchNorm4d_:function(e,t,n,r,s,o){const a=no(e,"x","batchNorm"),l=no(t,"mean","batchNorm"),u=no(n,"variance","batchNorm");let c,h;return null!=s&&(c=no(s,"scale","batchNorm")),null!=r&&(h=no(r,"offset","batchNorm")),i(4===a.rank,(()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`)),i(4===l.rank||1===l.rank,(()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${l.rank}.`)),i(4===u.rank||1===u.rank,(()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`)),null!=c&&i(4===c.rank||1===c.rank,(()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`)),null!=h&&i(4===h.rank||1===h.rank,(()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${h.rank}.`)),li(a,l,u,h,c,o)}});const di=oo({bincount_:function(e,t,n){const r=no(e,"x","bincount"),s=no(t,"weights","bincount");i("int32"===r.dtype,(()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`)),i(n>=0,(()=>`size must be non-negative, but got ${n}.`)),i(s.size===r.size||0===s.size,(()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${s.shape}.`));const o={x:r,weights:s},a={size:n};return Hs.runKernel("Bincount",o,a)}});const fi=oo({bitwiseAnd_:function(e,t){const n=no(e,"x","bitwiseAnd"),r=no(t,"y","bitwiseAnd");if(!d(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}`);const s={a:n,b:r};return Hs.runKernel("BitwiseAnd",s)}});const pi=oo({broadcastArgs_:function(e,t){const n=no(e,"s0","broadcastArgs","int32"),r=no(t,"s1","broadcastArgs","int32");if(1!==n.rank)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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Has rank ${r.rank}`);const s={s0:n,s1:r};return Hs.runKernel("BroadcastArgs",s)}});const gi=oo({broadcastTo_:function(e,t){let n=no(e,"broadcastTo","x");const r=n.shape;if(F(t),t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){const e=n.shape.slice();for(;e.length<t.length;)e.unshift(1);n=Xa(n,e)}const s=n.shape,o=Array.from(t);for(let e=t.length-1;e>=0;e--)if(s[e]===t[e])o[e]=1;else if(1!==n.shape[e])throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(0===o.map(((e,t)=>e>1?t:-1)).filter((e=>e>=0)).length)return ka(n);const a={x:n},i={reps:o};return Hs.runKernel("Tile",a,i)}});const mi=oo({ceil_:function(e){const t={x:no(e,"x","ceil","float32")};return Hs.runKernel("Ceil",t)}});function bi(e,t,n){F(e);const r={shape:e,value:t,dtype:n=n||A(t)};return Hs.runKernel("Fill",{},r)}const yi=oo({clipByValue_:function(e,t,n){const r=no(e,"x","clipByValue");if(i(t<=n,(()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`)),t===n)return bi(r.shape,t,r.dtype);const s={x:r},o={clipValueMin:t,clipValueMax:n};return Hs.runKernel("ClipByValue",s,o)}});const wi=oo({concat1d_:function(e){return ti(e,0)}});const ki=oo({concat2d_:function(e,t){return ti(e,t)}});const vi=oo({concat3d_:function(e,t){return ti(e,t)}});const xi=oo({concat4d_:function(e,t){return ti(e,t)}});const Ei=oo({conv2d_:function(e,t,n,r,s="NHWC",o=[1,1],a){const l=no(e,"x","conv2d","float32"),u=no(t,"filter","conv2d","float32");let c=l,h=!1;3===l.rank&&(h=!0,c=Xa(l,[1,l.shape[0],l.shape[1],l.shape[2]])),i(4===c.rank,(()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`)),i(4===u.rank,(()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`)),Ya("conv2d",r,a);const d="NHWC"===s?c.shape[3]:c.shape[1];i(d===u.shape[2],(()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${u.shape[2]}.`)),i(ja(n,o),(()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`)),i(Ja(o),(()=>"Error in conv2D: Dilated rates should be larger than 0.")),i(Ja(n),(()=>"Error in conv2D: Strides should be larger than 0."));const f={x:c,filter:u},p={strides:n,pad:r,dataFormat:s,dilations:o,dimRoundingMode:a},g=Hs.runKernel("Conv2D",f,p);return h?Xa(g,[g.shape[1],g.shape[2],g.shape[3]]):g}});const Si=oo({conv1d_:function(e,t,n,r,s="NWC",o=1,a){const l=no(e,"x","conv1d"),u=no(t,"filter","conv1d");let c=l,h=!1;2===l.rank&&(h=!0,c=Xa(l,[1,l.shape[0],l.shape[1]])),i(3===c.rank,(()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`)),i(3===u.rank,(()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`)),Ya("conv1d",r,a),i(c.shape[2]===u.shape[1],(()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${u.shape[1]}.`)),i(ja(n,o),(()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${o}'`)),i(Ja(o),(()=>"Error in conv1D: Dilated rates should be larger than 0.")),i(Ja(n),(()=>"Error in conv1D: Stride should be larger than 0.")),i("NWC"===s,(()=>`Error in conv1d: got dataFormat of ${s} but only NWC is currently supported.`));const d=Xa(u,[1,u.shape[0],u.shape[1],u.shape[2]]),f=Xa(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=Ei(f,d,[1,n],r,"NHWC",[1,o],a);return Xa(p,h?[p.shape[2],p.shape[3]]:[p.shape[0],p.shape[2],p.shape[3]])}});const $i=oo({conv2DBackpropInput_:function(e,t,n,r,s,o="NHWC",a){i(e.length===t.rank,(()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`));let l=e,u=t,c=!1;3===t.rank&&(c=!0,u=Xa(t,[1,t.shape[0],t.shape[1],t.shape[2]]),l=[1,e[0],e[1],e[2]]),i(4===l.length,(()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${l.length}.`)),i(4===u.rank,(()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`)),i(4===n.rank,(()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`));const h="NHWC"===o?l[3]:l[1],d="NHWC"===o?u.shape[3]:u.shape[1];i(h===n.shape[2],(()=>`Error in conv2dDerInput: depth of input (${h}) must match input depth for filter ${n.shape[2]}.`)),i(d===n.shape[3],(()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`)),Ya("conv2dDerInput",s,a);const f={dy:u,filter:n},p={strides:r,pad:s,dataFormat:o,dimRoundingMode:a,inputShape:l},g=Hs.runKernel("Conv2DBackpropInput",f,p);return c?Xa(g,[g.shape[1],g.shape[2],g.shape[3]]):g}});const Ai=oo({conv2dTranspose_:function(e,t,n,r,s,o){const a=no(e,"x","conv2dTranspose"),i=no(t,"filter","conv2dTranspose");return $i(n,a,i,r,s,"NHWC",o)}});const _i=oo({conv3d_:function(e,t,n,r,s="NDHWC",o=[1,1,1]){const a=no(e,"x","conv3d"),l=no(t,"filter","conv3d");let u=a,c=!1;4===a.rank&&(c=!0,u=Xa(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),i(5===u.rank,(()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`)),i(5===l.rank,(()=>`Error in conv3d: filter must be rank 5, but got rank ${l.rank}.`)),i(u.shape[4]===l.shape[3],(()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${l.shape[3]}.`)),i(ja(n,o),(()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`)),i("NDHWC"===s,(()=>`Error in conv3d: got dataFormat of ${s} but only NDHWC is currently supported.`)),i(Ja(o),(()=>"Error in conv3D: Dilated rates should be larger than 0.")),i(Ja(n),(()=>"Error in conv3D: Strides should be larger than 0."));const h={x:u,filter:l},d={strides:n,pad:r,dataFormat:s,dilations:o},f=Hs.runKernel("Conv3D",h,d);return c?Xa(f,[f.shape[1],f.shape[2],f.shape[3],f.shape[4]]):f}});const Ii=oo({conv3DBackpropInput_:function(e,t,n,r,s){i(e.length===t.rank,(()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`));let o=e,a=t,l=!1;4===t.rank&&(l=!0,a=Xa(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),o=[1,e[0],e[1],e[2],e[3]]);const u=o[4],c=a.shape[4];i(5===o.length,(()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${o.length}.`)),i(5===a.rank,(()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`)),i(5===n.rank,(()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`)),i(u===n.shape[3],(()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`)),i(c===n.shape[4],(()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`));const h={dy:a,filter:n},d={pad:s,strides:r,inputShape:o},f=Hs.runKernel("Conv3DBackpropInputV2",h,d);return l?Xa(f,[f.shape[1],f.shape[2],f.shape[3],f.shape[4]]):f}});const Mi=oo({conv3dTranspose_:function(e,t,n,r,s){const o=no(e,"x","conv3dTranspose"),a=no(t,"filter","conv3dTranspose");return Ii(n,o,a,r,s)}});const Ni=oo({cos_:function(e){const t={x:no(e,"x","cos","float32")};return Hs.runKernel("Cos",t)}});const Ti=oo({cosh_:function(e){const t={x:no(e,"x","cosh","float32")};return Hs.runKernel("Cosh",t)}});const Di=oo({cumprod_:function(e,t=0,n=!1,r=!1){const s={x:no(e,"x","cumprod")},o={axis:t,exclusive:n,reverse:r};return Hs.runKernel("Cumprod",s,o)}});const Ri=oo({cumsum_:function(e,t=0,n=!1,r=!1){const s={x:no(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:r};return Hs.runKernel("Cumsum",s,o)}});const Fi=oo({denseBincount_:function(e,t,n,r=!1){const s=no(e,"x","denseBincount"),o=no(t,"weights","denseBincount");i("int32"===s.dtype,(()=>`Error in denseBincount: input dtype must be int32, but got ${s.dtype}`)),i(s.rank<=2,(()=>`Error in denseBincount: input must be at most rank 2, but got rank ${s.rank}.`)),i(n>=0,(()=>`size must be non-negative, but got ${n}.`)),i(o.size===s.size||0===o.size,(()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${s.shape}, weights shape: ${o.shape}.`));const a={x:s,weights:o},l={size:n,binaryOutput:r};return Hs.runKernel("DenseBincount",a,l)}});const Bi=oo({depthToSpace_:function(e,t,n="NHWC"){const r=no(e,"x","depthToSpace","float32"),s="NHWC"===n?r.shape[1]:r.shape[2],o="NHWC"===n?r.shape[2]:r.shape[3],a="NHWC"===n?r.shape[3]:r.shape[1];i(t>1,(()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`)),i(s*t>=0,(()=>`Negative dimension size caused by overflow when multiplying\n    ${s} and ${t}  for depthToSpace with input shape\n    ${r.shape}`)),i(o*t>=0,(()=>`Negative dimension size caused by overflow when multiplying\n    ${o} and ${t} for depthToSpace with input shape\n        ${r.shape}`)),i(a%(t*t)==0,(()=>`Dimension size must be evenly divisible by ${t*t} but is ${a} for depthToSpace with input shape ${r.shape}`));const l={x:r},u={blockSize:t,dataFormat:n};return Hs.runKernel("DepthToSpace",l,u)}});const Ci=oo({depthwiseConv2d_:function(e,t,n,r,s="NHWC",o=[1,1],a){const l=no(e,"x","depthwiseConv2d","float32"),u=no(t,"filter","depthwiseConv2d","float32");let c=l,h=!1;3===l.rank&&(h=!0,c=Xa(l,[1,l.shape[0],l.shape[1],l.shape[2]])),i(4===c.rank,(()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`)),i(4===u.rank,(()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`));const d="NHWC"===s?c.shape[3]:c.shape[1];i(d===u.shape[2],(()=>`Error in depthwiseConv2d: number of input channels (${d}) must match the inChannels dimension in filter ${u.shape[2]}.`)),Ya("depthwiseConv2d",r,a);const f={x:c,filter:u},p={strides:n,pad:r,dataFormat:s,dilations:o,dimRoundingMode:a},g=Hs.runKernel("DepthwiseConv2dNative",f,p);return h?Xa(g,[g.shape[1],g.shape[2],g.shape[3]]):g}});const Pi=oo({diag_:function(e){const t={x:no(e,"x","diag")};return Hs.runKernel("Diag",t)}});const Oi=oo({dilation2d_:function(e,t,n,r,s=[1,1],o="NHWC"){const a=no(e,"x","dilation2d"),l=no(t,"filter","dilation2d");i(3===a.rank||4===a.rank,(()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`)),i(3===l.rank,(()=>`Error in dilation2d: filter must be rank 3, but got rank ${l.rank}.`)),i("NHWC"===o,(()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${o}`));let u=a,c=!1;3===a.rank&&(u=Xa(a,[1,a.shape[0],a.shape[1],a.shape[2]]),c=!0),i(u.shape[3]===l.shape[2],(()=>`Error in dilation2d:  input and filter must have the same depth: ${u.shape[3]} vs ${l.shape[2]}`));const h={x:u,filter:l},d={strides:n,pad:r,dilations:s},f=Hs.runKernel("Dilation2D",h,d);return c?Xa(f,[f.shape[1],f.shape[2],f.shape[3]]):f}});function Li(e,t){const n=e.length,r=[];for(let s=0;s<n;s++){const o=n-1-s,a=e[o]||1;(t[t.length-1-s]||1)>1&&1===a&&r.unshift(o)}return r}function zi(e,t){const n=[];for(let r=0;r<t.length;r++){const s=e[e.length-r-1],o=t.length-r-1,a=t[o];(null==s||1===s&&a>1)&&n.unshift(o)}return n}function Ui(e,t){const n=Math.max(e.length,t.length),r=new Array(n);for(let s=0;s<n;s++){let o=e[e.length-s-1];null==o&&(o=1);let a=t[t.length-s-1];if(null==a&&(a=1),1===o)r[n-s-1]=a;else if(1===a)r[n-s-1]=o;else{if(o!==a){throw Error(`Operands could not be broadcast together with shapes ${e} and ${t}.`)}r[n-s-1]=o}}return r}var Wi={__proto__:null,assertAndGetBroadcastShape:Ui,getBroadcastDims:Li,getReductionAxes:zi};const Gi=oo({equal_:function(e,t){let n=no(e,"a","equal","string_or_numeric"),r=no(t,"b","equal","string_or_numeric");[n,r]=Os(n,r),Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("Equal",s)}});const Ki=oo({where_:function(e,t,n){const r=no(t,"a","where"),s=no(n,"b","where"),o=no(e,"condition","where","bool"),a=Ui(Ui(o.shape,r.shape),s.shape),i={condition:gi(o,a),t:gi(r,a),e:gi(s,a)};return Hs.runKernel("Select",i)}});const qi=oo({zerosLike_:function(e){const t={x:no(e,"x","zerosLike")};return Hs.runKernel("ZerosLike",t)}});const Vi=oo({divNoNan_:function(e,t){let n=no(e,"a","div"),r=no(t,"b","div");[n,r]=Os(n,r);const s=Sa(n,r),o=qi(s),a=Gi(r,o);return Ki(a,o,s)}});const Hi=oo({dot_:function(e,t){const n=no(e,"t1","dot"),r=no(t,"t2","dot");i(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),(()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`));const s=1===n.rank?n.size:n.shape[1],o=1===r.rank?r.size:r.shape[0];if(i(s===o,(()=>`Error in dot: inner dimensions of inputs must match, but got ${s} and ${o}.`)),1===n.rank&&1===r.rank){const e=Xa(n,[1,-1]),t=Xa(r,[-1,1]),s=ni(e,t);return Xa(s,[])}if(1===n.rank&&2===r.rank){const e=Xa(n,[1,-1]),t=Xa(r,[r.shape[0],r.shape[1]]),s=ni(e,t);return Xa(s,[s.size])}if(2===n.rank&&1===r.rank){const e=Xa(r,[-1,1]),t=ni(n,e);return Xa(t,[t.size])}{const e=Xa(r,[r.shape[0],r.shape[1]]);return ni(n,e)}}});const ji=oo({einsum_:function(e,...t){const n=t.map(((e,t)=>no(e,`tensors${t}`,"einsum"))),r={equation:e};return Hs.runKernel("Einsum",n,r)}});const Ji=oo({elu_:function(e){const t={x:no(e,"x","elu","float32")};return Hs.runKernel("Elu",t)}});const Zi=oo({ensureShape_:function(e,t){const n=no(e,"x","ensureShape","string_or_numeric");if(!h(n.shape,t))throw new Error(`EnsureShape: Shape of tensor ${n.shape} is not compatible with expected shape ${t}`);return e}});const Yi=oo({erf_:function(e){let t=no(e,"x","erf");i("int32"===t.dtype||"float32"===t.dtype,(()=>"Input dtype must be `int32` or `float32`.")),"int32"===t.dtype&&(t=wa(t,"float32"));const n={x:t};return Hs.runKernel("Erf",n)}});function Xi(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function Qi(e,t,n){const r=e.length+t.length,s=[];let o=0,a=0;for(let i=0;i<r;i++)-1===n.indexOf(i)?s.push(e[o++]):s.push(t[a++]);return s}function el(e,t){return Qi(e,t.map((e=>1)),t)}const tl=oo({max_:function(e,t=null,n=!1){const r={x:no(e,"x","max")},s={reductionIndices:t,keepDims:n};return Hs.runKernel("Max",r,s)}});const nl=oo({min_:function(e,t=null,n=!1){const r={x:no(e,"x","min")},s={axis:t,keepDims:n};return Hs.runKernel("Min",r,s)}});const rl=oo({pow_:function(e,t){let n=no(e,"base","pow"),r=no(t,"exp","pow");[n,r]=Os(n,r);const s={a:n,b:r};return Hs.runKernel("Pow",s)}});function sl(e,t){if((hs(e)&&"string"!==t||Array.isArray(e))&&"complex64"!==t)throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if("string"===t&&hs(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return io(e,[],[],t)}const ol=oo({sqrt_:function(e){const t={x:no(e,"x","sqrt","float32")};return Hs.runKernel("Sqrt",t)}});const al=oo({square_:function(e){const t=no(e,"x","square");return Hs.runKernel("Square",{x:t},{})}});const il=oo({sum_:function(e,t=null,n=!1){let r=no(e,"x","sum");"bool"===r.dtype&&(r=wa(r,"int32"));const s={x:r},o={axis:t,keepDims:n};return Hs.runKernel("Sum",s,o)}});function ll(e,t,n=null){if(0===e.rank)return Aa(e);if(1!==e.rank&&null===n)return ll(Xa(e,[-1]),t,n);if(1===e.rank||"number"==typeof n||Array.isArray(n)&&1===n.length){if(1===t)return il(Aa(e),n);if(t===1/0)return tl(Aa(e),n);if(t===-1/0)return nl(Aa(e),n);if("euclidean"===t||2===t)return ol(il(rl(Aa(e),sl(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&2===n.length){if(1===t)return tl(il(Aa(e),n[0]),n[1]-1);if(t===1/0)return tl(il(Aa(e),n[1]),n[0]);if(t===-1/0)return nl(il(Aa(e),n[1]),n[0]);if("fro"===t||"euclidean"===t)return ol(il(al(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}const ul=oo({norm_:function(e,t="euclidean",n=null,r=!1){const s=ll(e=no(e,"x","norm"),t,n);let o=s.shape;if(r){const t=g(n,e.shape);o=el(s.shape,t)}return Xa(s,o)}});const cl=oo({euclideanNorm_:function(e,t=null,n=!1){return ul(e,"euclidean",t,n)}});const hl=oo({exp_:function(e){const t={x:no(e,"x","exp")};return Hs.runKernel("Exp",t)}});const dl=oo({expandDims_:function(e,t=0){const n=no(e,"x","expandDims","string_or_numeric");i(t<=n.rank,(()=>"Axis must be <= rank of the tensor"));const r={input:n},s={dim:t};return Hs.runKernel("ExpandDims",r,s)}});const fl=oo({expm1_:function(e){const t={x:no(e,"x","expm1")};return Hs.runKernel("Expm1",t)}});const pl=oo({tile_:function(e,t){const n=no(e,"x","tile","string_or_numeric");i(n.rank===t.length,(()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`));const r={x:n},s={reps:t};return Hs.runKernel("Tile",r,s)}});const gl=oo({eye_:function(e,t,n,r="float32"){null==t&&(t=e);const s=ya([e,t],r),o=e<=t?e:t;for(let e=0;e<o;++e)s.set(1,e,e);const a=Xa(s.toTensor(),[e,t]);if(null==n)return a;if(1===n.length)return pl(dl(a,0),[n[0],1,1]);if(2===n.length)return pl(dl(dl(a,0),0),[n[0],n[1],1,1]);if(3===n.length)return pl(dl(dl(dl(a,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}});const ml=oo({floor_:function(e){const t={x:no(e,"x","floor","float32")};return Hs.runKernel("Floor",t)}});const bl=oo({gather_:function(e,t,n=0,r=0){const s={x:no(e,"x","gather"),indices:no(t,"indices","gather","int32")},o={axis:n,batchDims:r};return Hs.runKernel("GatherV2",s,o)}});const yl=oo({greater_:function(e,t){let n=no(e,"a","greater","string_or_numeric"),r=no(t,"b","greater","string_or_numeric");[n,r]=Os(n,r),Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("Greater",s)}});const wl=oo({greaterEqual_:function(e,t){let n=no(e,"a","greaterEqual","string_or_numeric"),r=no(t,"b","greaterEqual","string_or_numeric");[n,r]=Os(n,r),Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("GreaterEqual",s)}});const kl=oo({imag_:function(e){const t={input:no(e,"input","imag")};return Hs.runKernel("Imag",t)}});const vl=oo({isFinite_:function(e){const t={x:no(e,"x","isFinite")};return Hs.runKernel("IsFinite",t)}});const xl=oo({isInf_:function(e){const t={x:no(e,"x","isInf")};return Hs.runKernel("IsInf",t)}});const El=oo({isNaN_:function(e){const t={x:no(e,"x","isNaN")};return Hs.runKernel("IsNan",t)}});const Sl=oo({leakyRelu_:function(e,t=.2){const n={x:no(e,"x","leakyRelu")},r={alpha:t};return Hs.runKernel("LeakyRelu",n,r)}});const $l=oo({less_:function(e,t){let n=no(e,"a","less","string_or_numeric"),r=no(t,"b","less","string_or_numeric");[n,r]=Os(n,r),Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("Less",s)}});const Al=oo({lessEqual_:function(e,t){let n=no(e,"a","lessEqual","string_or_numeric"),r=no(t,"b","lessEqual","string_or_numeric");[n,r]=Os(n,r),Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("LessEqual",s)}});function _l(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");const r={start:e,stop:t,num:n};return Hs.runKernel("LinSpace",{},r)}const Il=oo({localResponseNormalization_:function(e,t=5,n=1,r=1,s=.5){const o=no(e,"x","localResponseNormalization");i(4===o.rank||3===o.rank,(()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${o.rank}.`)),i(f(t),(()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`));let a=o,l=!1;3===o.rank&&(l=!0,a=Xa(o,[1,o.shape[0],o.shape[1],o.shape[2]]));const u={x:a},c={depthRadius:t,bias:n,alpha:r,beta:s},h=Hs.runKernel("LRN",u,c);return l?Xa(h,[h.shape[1],h.shape[2],h.shape[3]]):h}});const Ml=oo({log_:function(e){const t={x:no(e,"x","log","float32")};return Hs.runKernel("Log",t)}});const Nl=oo({log1p_:function(e){const t={x:no(e,"x","log1p")};return Hs.runKernel("Log1p",t)}});function Tl(e){return i(_(e),(()=>"The f passed in grad(f) must be a function")),(t,n)=>{const r=no(t,"x","tf.grad","string_or_numeric"),s=null!=n?no(n,"dy","tf.grad"):null;return Hs.tidy((()=>{const{value:t,grads:n}=Hs.gradients((()=>e(r)),[r],s);return null!=s&&l(t.shape,s.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Pl(n),n[0]}))}}function Dl(e){return i(_(e),(()=>"The f passed in grads(f) must be a function")),(t,n)=>{i(Array.isArray(t),(()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s"));const r=ro(t,"args","tf.grads","string_or_numeric"),s=null!=n?no(n,"dy","tf.grads"):null;return Hs.tidy((()=>{const{value:t,grads:n}=Hs.gradients((()=>e(...r)),r,s);return null!=s&&l(t.shape,s.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Pl(n),n}))}}function Rl(e){return i(_(e),(()=>"The f passed in valueAndGrad(f) must be a function")),(t,n)=>{i(t instanceof $s,(()=>"The x passed in valueAndGrad(f)(x) must be a tensor")),i(null==n||n instanceof $s,(()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor"));const{grads:r,value:s}=Hs.gradients((()=>e(t)),[t],n);return Pl(r),{grad:r[0],value:s}}}function Fl(e){return i(_(e),(()=>"The f passed in valueAndGrads(f) must be a function")),(t,n)=>{i(Array.isArray(t)&&t.every((e=>e instanceof $s)),(()=>"The args passed in valueAndGrads(f)(args) must be array of tensors")),i(null==n||n instanceof $s,(()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor"));const r=Hs.gradients((()=>e(...t)),t,n);return null!=n&&l(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Pl(r.grads),r}}function Bl(e,t){i(_(e),(()=>"The f passed in variableGrads(f) must be a function")),i(null==t||Array.isArray(t)&&t.every((e=>e instanceof _s)),(()=>"The varList passed in variableGrads(f, varList) must be an array of variables"));const n=null!=t;if(!n){t=[];for(const e in Hs.registeredVariables)t.push(Hs.registeredVariables[e])}const r=n?t.filter((e=>!e.trainable)):null,s=t.length;t=t.filter((e=>e.trainable)),i(t.length>0,(()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${s} variables is trainable.`));const{value:o,grads:a}=Hs.gradients(e,t,null,!0);i(a.some((e=>null!=e)),(()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().")),i(0===o.rank,(()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`));const l={};return t.forEach(((e,t)=>{null!=a[t]&&(l[e.name]=a[t])})),null!=r&&r.forEach((e=>l[e.name]=null)),{value:o,grads:l}}function Cl(e){return Hs.customGrad(e)}function Pl(e){if(e.filter((e=>null==e)).length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Logits was rank ${n.rank} and axis was ${t}`);const r=Cl(((e,n)=>{const r=tl(e,t,!0),s=Ul(e,r),o=Ul(wa(s,"float32"),Ml(il(hl(s),t,!0)));n([o]);return{value:o,gradFunc:(e,n)=>{const[r]=n,s=hl(r);return Ul(e,$a(il(e,t,!0),s))}}}));return r(n)}});const Gl=oo({logSumExp_:function(e,t=null,n=!1){const r=no(e,"x","logSumExp"),s=g(t,r.shape),o=tl(r,s,!0),a=Ul(r,o),i=hl(a),l=il(i,s),u=Ml(l),c=xa(Xa(o,u.shape),u);if(n){const e=el(c.shape,s);return Xa(c,e)}return c}});const Kl=oo({logicalAnd_:function(e,t){const n=no(e,"a","logicalAnd","bool"),r=no(t,"b","logicalAnd","bool");Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("LogicalAnd",s)}});const ql=oo({logicalNot_:function(e){const t={x:no(e,"x","logicalNot","bool")};return Hs.runKernel("LogicalNot",t)}});const Vl=oo({logicalOr_:function(e,t){const n=no(e,"a","logicalOr","bool"),r=no(t,"b","logicalOr","bool");Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("LogicalOr",s)}});const Hl=oo({logicalXor_:function(e,t){const n=no(e,"a","logicalXor","bool"),r=no(t,"b","logicalXor","bool");return Ui(n.shape,r.shape),Kl(Vl(e,t),ql(Kl(e,t)))}});const jl=oo({searchSorted_:function(e,t,n="left"){const r=no(e,"sortedSequence","searchSorted"),s=no(t,"values","searchSorted"),o=r.shape[r.shape.length-1],a=s.shape[s.shape.length-1],i=Xa(r,[-1,o]),l=Xa(s,[-1,a]);if(i.rank<2)throw new Error("Sorted input argument must be at least 2-dimensional");if(i.shape[0]!==l.shape[0])throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");if(c(l.shape)>=2147483648)throw new Error("values tensor size must less than 2147483648");if(i.shape[1]>=2147483648)throw new Error(`trailing dim_size must less than 2147483648 for int32 output type, was ${i.shape[1]}`);const u={sortedSequence:i,values:l},h={side:n};return Hs.runKernel("SearchSorted",u,h)}});function Jl(e,t){return jl(e,t,"left")}const Zl=oo({maxPool_:function(e,t,n,r,s){const o=no(e,"x","maxPool");let a=o,l=!1;3===o.rank&&(l=!0,a=Xa(o,[1,o.shape[0],o.shape[1],o.shape[2]])),i(4===a.rank,(()=>`Error in maxPool: input must be rank 4 but got rank ${a.rank}.`)),i(ja(n,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '1'`)),Ya("maxPool",r,s);const u={x:a},c={filterSize:t,strides:n,pad:r,dimRoundingMode:s},h=Hs.runKernel("MaxPool",u,c);return l?Xa(h,[h.shape[1],h.shape[2],h.shape[3]]):h}});const Yl=oo({maxPool3d_:function(e,t=[1,1,1],n,r,s,o="NDHWC"){const a=no(e,"x","maxPool3d");let l=a,u=!1;4===a.rank&&(u=!0,l=Xa(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),i(5===l.rank,(()=>`Error in maxPool3d: x must be rank 5 but got rank ${l.rank}.`)),i("NDHWC"===o,(()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${o}`)),Ya("maxPool3d",r,s);const c={x:l},h={filterSize:t,strides:n,pad:r,dimRoundingMode:s,dataFormat:o},d=Hs.runKernel("MaxPool3D",c,h);return u?Xa(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}});const Xl=oo({maxPoolWithArgmax_:function(e,t,n,r,s=!1){const o={x:no(e,"x","maxPoolWithArgmax")},a={filterSize:t,strides:n,pad:r,includeBatchInIndex:s},i=Hs.runKernel("MaxPoolWithArgmax",o,a);return{result:i[0],indexes:i[1]}}});const Ql=oo({maximum_:function(e,t){let n=no(e,"a","maximum"),r=no(t,"b","maximum");[n,r]=Os(n,r),"bool"===n.dtype&&(n=wa(n,"int32"),r=wa(r,"int32")),Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("Maximum",s)}});const eu=oo({mean_:function(e,t=null,n=!1){const r={x:no(e,"x","mean")},s={axis:t,keepDims:n};return Hs.runKernel("Mean",r,s)}});function tu(e,t="float32"){if(F(e),"complex64"===t){const t=tu(e,"float32"),n=tu(e,"float32");return ao(t,n)}const n=R(c(e),t);return Hs.makeTensor(n,e,t)}function nu(e,t="float32"){if(F(e),"complex64"===t){const t=nu(e,"float32"),n=tu(e,"float32");return ao(t,n)}const n=D(c(e),t);return Hs.makeTensor(n,e,t)}function ru(e,t,{indexing:n="xy"}={}){if("xy"!==n&&"ij"!==n)throw new TypeError(`${n} is not a valid third argument to meshgrid`);if(void 0===e)return[];let r=no(e,"x","meshgrid",e instanceof $s?e.dtype:"float32");if(void 0===t)return[r];let s=no(t,"y","meshgrid",t instanceof $s?t.dtype:"float32");const o=c(r.shape),a=c(s.shape);return"xy"===n?(r=Xa(r,[1,-1]),s=Xa(s,[-1,1]),[ni(nu([a,1],r.dtype),r),ni(s,nu([1,o],s.dtype))]):(r=Xa(r,[-1,1]),s=Xa(s,[1,-1]),[ni(r,nu([1,a],r.dtype)),ni(nu([o,1],s.dtype),s)])}const su=oo({minimum_:function(e,t){let n=no(e,"a","minimum"),r=no(t,"b","minimum");[n,r]=Os(n,r),"bool"===n.dtype&&(n=wa(n,"int32"),r=wa(r,"int32")),Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("Minimum",s)}});const ou=oo({mirrorPad_:function(e,t,n){i("reflect"===n||"symmetric"===n,(()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`));const r=no(e,"x","mirrorPad");if(0===r.rank)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");i(t.length===r.rank,(()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`));const s="reflect"===n?1:0;for(let e=0;e<r.rank;e++)i(2===t[e].length,(()=>"Invalid number of paddings. Must be length of 2 each.")),i(t[e][0]>=0&&t[e][0]<=r.shape[e]-s&&t[e][1]>=0&&t[e][1]<=r.shape[e]-s,(()=>`Padding in dimension ${e} cannot be greater than or equal to ${r.shape[e]-s} or less than 0 for input of shape ${r.shape}`));const o={paddings:t,mode:n},a={x:r};return Hs.runKernel("MirrorPad",a,o)}});const au=oo({mod_:function(e,t){let n=no(e,"a","mod"),r=no(t,"b","mod");[n,r]=Os(n,r);const s={a:n,b:r};return Hs.runKernel("Mod",s)}});const iu=oo({moments_:function(e,t=null,n=!1){const r=g(t,(e=no(e,"x","moments")).shape),s=eu(e,r,n);let o=s.shape;n||(o=el(s.shape,r));const a=al(Ul(wa(e,"float32"),Xa(s,o)));return{mean:s,variance:eu(a,r,n)}}});const lu=oo({multiRNNCell_:function(e,t,n,r){const s=no(t,"data","multiRNNCell"),o=ro(n,"c","multiRNNCell"),a=ro(r,"h","multiRNNCell");let i=s;const l=[];for(let t=0;t<e.length;t++){const n=e[t](i,o[t],a[t]);l.push(n[0]),l.push(n[1]),i=n[1]}const u=[],c=[];for(let e=0;e<l.length;e+=2)u.push(l[e]),c.push(l[e+1]);return[u,c]}});const uu=oo({multinomial_:function(e,t,n,r=!1){const s=no(e,"logits","multinomial"),o=s.size,a=s.rank;if(o<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${o}.`);if(a>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${a}`);n=n||Math.random();const i={logits:1===a?Xa(s,[1,-1]):s},l={numSamples:t,seed:n,normalized:r},u=Hs.runKernel("Multinomial",i,l);return 1===a?Xa(u,[u.size]):u}});const cu=oo({notEqual_:function(e,t){let n=no(e,"a","notEqual","string_or_numeric"),r=no(t,"b","notEqual","string_or_numeric");[n,r]=Os(n,r),Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("NotEqual",s)}});const hu=oo({oneHot_:function(e,t,n=1,r=0,s="int32"){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);const o={indices:no(e,"indices","oneHot","int32")},a={dtype:s,depth:t,onValue:n,offValue:r};return Hs.runKernel("OneHot",o,a)}});const du=oo({onesLike_:function(e){const t={x:no(e,"x","onesLike")};return Hs.runKernel("OnesLike",t)}});const fu=oo({outerProduct_:function(e,t){const n=no(e,"v1","outerProduct"),r=no(t,"v2","outerProduct");i(1===n.rank&&1===r.rank,(()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`));const s=Xa(n,[-1,1]),o=Xa(r,[1,-1]);return ni(s,o)}});const pu=oo({pad_:function(e,t,n=0){const r=no(e,"x","pad");if(0===r.rank)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");const s={paddings:t,constantValue:n},o={x:r};return Hs.runKernel("PadV2",o,s)}});const gu=oo({pad1d_:function(e,t,n=0){return i(2===t.length,(()=>"Invalid number of paddings. Must be length of 2.")),pu(e,[t],n)}});const mu=oo({pad2d_:function(e,t,n=0){return i(2===t.length&&2===t[0].length&&2===t[1].length,(()=>"Invalid number of paddings. Must be length of 2 each.")),pu(e,t,n)}});const bu=oo({pad3d_:function(e,t,n=0){return i(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.")),pu(e,t,n)}});const yu=oo({pad4d_:function(e,t,n=0){return i(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.")),pu(e,t,n)}});const wu=oo({spaceToBatchND_:function(e,t,n){const r=no(e,"x","spaceToBatchND");i(r.rank>=1+t.length,(()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`)),i(n.length===t.length,(()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`)),i(r.shape.reduce(((e,r,s)=>s>0&&s<=t.length?e&&(r+n[s-1][0]+n[s-1][1])%t[s-1]==0:e),!0),(()=>`input spatial dimensions ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`));const s={x:r},o={blockShape:t,paddings:n};return Hs.runKernel("SpaceToBatchND",s,o)}});const ku=oo({pool_:function(e,t,n,r,s,o,a){null==s&&(s=[1,1]),null==o&&(o=1),0===r&&(r="valid");const l=no(e,"x","maxPool");let u=l,c=!1;3===l.rank&&(c=!0,u=Xa(l,[1,l.shape[0],l.shape[1],l.shape[2]])),i(ja(o,s),(()=>`Error in pool: Either strides or dilations must be 1. 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s={x:r},o={axis:t,keepDims:n};return Hs.runKernel("Prod",s,o)}});const Eu=oo({raggedGather_:function(e,t,n,r){const s={paramsNestedSplits:e.map(((e,t)=>no(e,`tensors${t}`,"raggedGather","int32"))),paramsDenseValues:no(t,"paramsDenseValues","raggedGather"),indices:no(n,"indices","raggedGather","int32")},o={outputRaggedRank:r},a=Hs.runKernel("RaggedGather",s,o);return{outputNestedSplits:a.slice(0,a.length-1),outputDenseValues:a[a.length-1]}}});const Su=oo({raggedRange_:function(e,t,n){const r=no(e,"starts","raggedRange"),s={starts:r,limits:no(t,"limits","raggedRange",r.dtype),deltas:no(n,"deltas","raggedRange",r.dtype)},o=Hs.runKernel("RaggedRange",s);return{rtNestedSplits:o[0],rtDenseValues:o[1]}}});const $u=oo({raggedTensorToTensor_:function(e,t,n,r,s){const 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${t.rank}.`)),dc(t,0)}});const pc=oo({reverse2d_:function(e,t){const n=no(e,"x","reverse");return i(2===n.rank,(()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`)),dc(n,t)}});const gc=oo({reverse3d_:function(e,t){const n=no(e,"x","reverse");return i(3===n.rank,(()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`)),dc(n,t)}});const mc=oo({reverse4d_:function(e,t){const n=no(e,"x","reverse");return i(4===n.rank,(()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`)),dc(n,t)}});const bc=oo({round_:function(e){const t={x:no(e,"x","round")};return Hs.runKernel("Round",t)}});const yc=oo({rsqrt_:function(e){const t={x:no(e,"x","rsqrt","float32")};return Hs.runKernel("Rsqrt",t)}});const wc=oo({selu_:function(e){const t={x:no(e,"x","selu")};return Hs.runKernel("Selu",t)}});const kc=oo({separableConv2d_:function(e,t,n,r,s,o=[1,1],a="NHWC"){const l=no(e,"x","separableConv2d"),u=no(t,"depthwiseFilter","separableConv2d"),c=no(n,"pointwiseFilter","separableConv2d");let h=l,d=!1;if(3===l.rank&&(d=!0,h=Xa(l,[1,l.shape[0],l.shape[1],l.shape[2]])),"NCHW"===a)throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");i(4===h.rank,(()=>`Error in separableConv2d: input must be rank 4, but got rank ${h.rank}.`)),i(4===u.rank,(()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`)),i(4===c.rank,(()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`)),i(1===c.shape[0],(()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${c.shape[0]}.`)),i(1===c.shape[1],(()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${c.shape[1]}.`));const f=u.shape[2],p=u.shape[3];i(c.shape[2]===f*p,(()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${f*p}, but got ${c.shape[2]}.`));const g=Ci(h,u,r,s,a,o),m=Ei(g,c,1,"valid",a);return d?Xa(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const vc=async function(e,t){const n=no(e,"x","setdiff1d"),r=no(t,"y","setdiff1d");i(n.dtype===r.dtype,(()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`)),i(1===n.rank,(()=>`x should be 1D tensor, but got x (${n.shape}).`)),i(1===r.rank,(()=>`y should be 1D tensor, but got y (${r.shape}).`));const s=await n.data(),o=await r.data(),a=new Set(o);let l=0;for(let e=0;e<s.length;e++)a.has(s[e])||l++;const u=new xs([l],n.dtype),c=new xs([l],"int32");for(let e=0,t=0;e<s.length;e++)a.has(s[e])||(u.values[t]=s[e],c.values[t]=e,t++);return[u.toTensor(),c.toTensor()]};const xc=oo({sign_:function(e){const t={x:no(e,"x","sign")};return Hs.runKernel("Sign",t)}});const Ec=oo({sin_:function(e){const t={x:no(e,"x","sin","float32")};return Hs.runKernel("Sin",t)}});const Sc=oo({sinh_:function(e){const t={x:no(e,"x","sinh")};return Hs.runKernel("Sinh",t)}});const $c=oo({slice1d_:function(e,t,n){const r=no(e,"x","slice1d");return i(1===r.rank,(()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`)),si(r,[t],[n])}});const Ac=oo({slice2d_:function(e,t,n){const r=no(e,"x","slice2d");return i(2===r.rank,(()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`)),si(r,t,n)}});const _c=oo({slice3d_:function(e,t,n){const r=no(e,"x","slice3d");return i(3===r.rank,(()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`)),si(r,t,n)}});const Ic=oo({slice4d_:function(e,t,n){const r=no(e,"x","slice4d");return i(4===r.rank,(()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`)),si(r,t,n)}});const Mc=oo({softmax_:function(e,t=-1){const n=no(e,"logits","softmax","float32");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);const r={logits:n},s={dim:t};return Hs.runKernel("Softmax",r,s)}});const Nc=oo({fft_:function(e){i("complex64"===e.dtype,(()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`));const t={input:e};return Hs.runKernel("FFT",t)}});const Tc=oo({ifft_:function(e){i("complex64"===e.dtype,(()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`));const t={input:e};return Hs.runKernel("IFFT",t)}});const Dc=oo({irfft_:function(e){const t=e.shape[e.shape.length-1],n=e.size/t;let r;if(t<=2){const s=Xa(e,[n,t]);r=Tc(s)}else{const s=[n,2*(t-1)],o=Xa(lc(e),[n,t]),a=Xa(kl(e),[n,t]),i=dc(si(o,[0,1],[n,t-2]),1),l=$a(dc(si(a,[0,1],[n,t-2]),1),sl(-1)),u=ti([o,i],1),c=ti([a,l],1),h=Xa(ao(u,c),[s[0],s[1]]);r=Tc(h)}if(r=lc(r),3===e.rank&&0!==e.shape[0]){const t=r,n=e.shape[0];r=Xa(r,[n,r.shape[0]/n,r.shape[1]]),t.dispose()}return r}});const Rc=oo({split_:function(e,t,n=0){const r={x:no(e,"x","split")},s={numOrSizeSplits:t,axis:n};return Hs.runKernel("SplitV",r,s)}});const Fc=oo({rfft_:function(e,t){i("float32"===e.dtype,(()=>`The dtype for rfft() must be real value but got ${e.dtype}`));let n=e.shape[e.shape.length-1];const r=e.size/n;let s;if(null!=t&&t<n){const r=e.shape.map((e=>0)),o=e.shape.map((e=>e));o[e.shape.length-1]=t,s=si(e,r,o),n=t}else if(null!=t&&t>n){const r=e.shape.map((e=>e));r[e.shape.length-1]=t-n,s=ti([e,tu(r)],e.shape.length-1),n=t}else s=e;const o=qi(s),a=Xa(ao(s,o),[r,n]),l=Nc(a),u=Math.floor(n/2)+1,c=lc(l),h=kl(l),d=Rc(c,[u,n-u],c.shape.length-1),f=Rc(h,[u,n-u],h.shape.length-1),p=s.shape.slice();return p[s.shape.length-1]=u,Xa(ao(d[0],f[0]),p)}});const Bc=oo({squaredDifference_:function(e,t){let n=no(e,"a","squaredDifference"),r=no(t,"b","squaredDifference");[n,r]=Os(n,r),Ui(n.shape,r.shape);const s={a:n,b:r};return Hs.runKernel("SquaredDifference",s,{})}});const Cc=oo({squeeze_:function(e,t){const n=no(e,"x","squeeze","string_or_numeric");return Xa(n,m(n.shape,t).newShape)}});const Pc=oo({stack_:function(e,t=0){const n=ro(e,"tensors","stack","string_or_numeric");i(n.length>=1,(()=>"Pass at least one tensor to tf.stack")),n.length>0&&i(t<=n[0].rank,(()=>"Axis must be <= rank of the tensor"));const r=n,s={axis:t};return Hs.runKernel("Pack",r,s)}});const Oc=oo({step_:function(e,t=0){const n={x:no(e,"x","step")},r={alpha:t};return Hs.runKernel("Step",n,r)}});const Lc=oo({stridedSlice_:function(e,t,n,r,s=0,o=0,a=0,i=0,l=0){const u={x:no(e,"x","stridedSlice","string_or_numeric")},c={begin:t,end:n,strides:r,beginMask:s,endMask:o,ellipsisMask:a,newAxisMask:i,shrinkAxisMask:l};return Hs.runKernel("StridedSlice",u,c)}});const zc=oo({tan_:function(e){const t={x:no(e,"x","tan","float32")};return Hs.runKernel("Tan",t)}});function Uc(e,t){u(e);const n=Qs(e,t);if(1!==n.length)throw new Error("tensor1d() requires values to be a flat/TypedArray");return io(e,null,n,t)}function Wc(e,t,n){if(u(e),null!=t&&2!==t.length)throw new Error("tensor2d() requires shape to have two numbers");const r=Qs(e,n);if(2!==r.length&&1!==r.length)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return io(e,t,r,n)}function Gc(e,t,n){if(u(e),null!=t&&3!==t.length)throw new Error("tensor3d() requires shape to have three numbers");const r=Qs(e,n);if(3!==r.length&&1!==r.length)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return io(e,t,r,n)}function Kc(e,t,n){if(u(e),null!=t&&4!==t.length)throw new Error("tensor4d() requires shape to have four numbers");const r=Qs(e,n);if(4!==r.length&&1!==r.length)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return io(e,t,r,n)}function qc(e,t,n){if(u(e),null!=t&&5!==t.length)throw new Error("tensor5d() requires shape to have five numbers");const r=Qs(e,n);if(5!==r.length&&1!==r.length)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return io(e,t,r,n)}function Vc(e,t,n){if(u(e),null!=t&&6!==t.length)throw new Error("tensor6d() requires shape to have six numbers");const r=Qs(e,n);if(6!==r.length&&1!==r.length)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return io(e,t=t||r,r,n)}function Hc(e,t,n){const r=t.rank>1?t.shape[t.rank-1]:1,s=t.rank>1?t.rank-1:1,o=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${r}, and batchDim: ${s}.`;if(n.rank<s)throw new Error(o+` update.rank < ${s}. `);if(e.length<r+(n.rank-s))throw new Error(o+` Output shape length < ${r+(n.rank-s)}`);if(n.rank!==s+e.length-r)throw new Error(o+" update.rank != "+(s+e.length-r));for(let e=0;e<s;++e)if(n.shape[e]!==t.shape[e])throw new Error(o+` updates.shape[${e}] (${n.shape[e]}) != indices.shape[${e}] (${t.shape[e]}).`);for(let t=0;t<n.rank-s;++t)if(n.shape[t+s]!==e[t+r])throw new Error(o+` updates.shape[${t+s}] (${n.shape[t+s]}) != shape[${t+s}] (${e[t+s]})`)}function jc(e,t,n){if(t.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(e.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${e.rank}.`);if("int32"!==t.dtype)throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);if(n.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${n}`);if(0===n.length){if(0===t.size)throw new Error(`Indices specified for empty output. indices shape: ${t.shape}`);if(0===e.size)throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`)}Hc(n,t,e)}function Jc(e,t,n){const r=t.shape.length,s=r>1?t.shape[r-1]:1,o=n.length;let a=1;for(let e=s;e<o;++e)a*=n[e];const i=s<1?1:s;return{sliceRank:s,numUpdates:c(t.shape)/i,sliceSize:a,strides:[...M(n.slice(0,s)),1],outputSize:c(n)}}var Zc={__proto__:null,calculateShapes:Jc,validateInput:jc,validateUpdateShape:Hc};const Yc=oo({tensorScatterUpdate_:function(e,t,n){const r=no(e,"tensor","tensorScatterupdate"),s=no(t,"indices","tensorScatterupdate","int32"),o=no(n,"updates","tensorScatterupdate");if(jc(o,s,r.shape),r.dtype!==o.dtype)throw new Error(`tensor and updates must have the same dtype, instead they are ${r.dtype} and ${o.dtype}.`);const a={tensor:r,indices:s,updates:o};return Hs.runKernel("TensorScatterUpdate",a,{})}});const Xc=oo({topk_:function(e,t=1,n=!0){const r=no(e,"x","topk");if(0===r.rank)throw new Error("topk() expects the input to be of rank 1 or higher");const s=r.shape[r.shape.length-1];if(t<0)throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);if(t>s)throw new Error(`'k' passed to topk() must be <= the last dimension (${s}) but got ${t}`);const o={x:r},a={k:t,sorted:n},[i,l]=Hs.runKernel("TopK",o,a);return{values:i,indices:l}}});const Qc=oo({truncatedNormal_:function(e,t=0,n=1,r,s){if(F(e),null!=r&&"bool"===r)throw new Error("Unsupported data type $ { dtype }");const o=new Qu(t,n,r,!0,s),a=ya(e,r);for(let e=0;e<a.values.length;e++)a.values[e]=o.nextValue();return a.toTensor()}});const eh=oo({unique_:function(e,t=0){const n=no(e,"x","unique","string_or_numeric");i(n.rank>0,(()=>"The input tensor must be at least 1D"));const r={x:n},s={axis:t},[o,a]=Hs.runKernel("Unique",r,s);return{values:o,indices:a}}});const th=oo({unsortedSegmentSum_:function(e,t,n){const r=no(e,"x","unsortedSegmentSum"),s=no(t,"segmentIds","unsortedSegmentSum","int32");i(f(n),(()=>"numSegments must be of dtype int"));const o={x:r,segmentIds:s},a={numSegments:n};return Hs.runKernel("UnsortedSegmentSum",o,a)}});const nh=oo({unstack_:function(e,t=0){const n=no(e,"x","unstack","string_or_numeric");i(t>=-n.shape.length&&t<n.shape.length,(()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`));const r={value:n},s={axis:t};return Hs.runKernel("Unpack",r,s)}});function rh(e,t){return jl(e,t,"right")}function sh(e,t=!0,n,r){return Hs.makeVariable(e,t,n,r)}function oh(e,t){const n=[];for(let e=0;e<t.length;e++)t[e]&&n.push(e);const r=ya(e,"int32"),s=ya([n.length,e.length],"int32");for(let t=0;t<n.length;t++){const o=r.indexToLoc(n[t]),a=t*e.length;s.values.set(o,a)}return s.toTensor()}const ah=async function(e){const t=no(e,"condition","whereAsync","bool"),n=await t.data(),r=oh(t.shape,n);return e!==t&&t.dispose(),r};const ih=async function(e,t,n){const r=no(e,"tensor","boolMask"),s=no(t,"mask","boolMask","bool"),o=null==n?0:n,a=s.rank,u=r.shape;i(a>0,(()=>"mask cannot be scalar")),l(u.slice(o,o+a),s.shape,"mask's shape must match the first K dimensions of tensor's shape,");let c=1;for(let e=o;e<o+a;e++)c*=u[e];const h=u.slice(0,o).concat([c],u.slice(o+a)),d=Xa(r,h),f=Xa(s,[-1]),p=await ah(f),g=Cc(p,[1]),m=bl(d,g,o);return e!==r&&r.dispose(),t!==s&&s.dispose(),g.dispose(),d.dispose(),f.dispose(),p.dispose(),m};const lh=oo({transpose_:function(e,t,n){const r=no(e,"x","transpose");if(null==t&&(t=r.shape.map(((e,t)=>t)).reverse()),i(r.rank===t.length,(()=>`Error in transpose: rank of input ${r.rank} must match length of perm ${t}.`)),t.forEach((e=>{i(e>=0&&e<r.rank,(()=>"All entries in 'perm' must be between 0 and "+(r.rank-1)+` but got ${t}`))})),r.rank<=1)return r.clone();const s={x:r},o={perm:t};return"complex64"===r.dtype?ko((()=>{let e=lc(r),t=kl(r);return e=Hs.runKernel("Transpose",{x:e},o),t=Hs.runKernel("Transpose",{x:t},o),n&&(t=Ol(t)),ao(e,t)})):Hs.runKernel("Transpose",s,o)}});const uh=oo({movingAverage_:function(e,t,n,r,s=!0){const o=no(e,"v","movingAverage"),a=no(t,"x","movingAverage"),l=no(n,"decay","movingAverage");Ls(o,a),i(d(o.shape,a.shape),(()=>"Shape mismatch in v and x"));const u=sl(1),c=Ul(u,l);let h=$a(Ul(a,o),c);if(s){i(null!=r,(()=>"When using zeroDebias: true, step is required."));const e=no(r,"step","movingAverage");h=Sa(h,Ul(u,rl(l,e)))}return xa(o,h)}});const ch=oo({scatterND_:function(e,t,n){F(n);const r=no(e,"indices","scatterND","int32"),s=no(t,"updates","scatterND");jc(s,r,n);const o={indices:r,updates:s},a={shape:n};return Hs.runKernel("ScatterNd",o,a)}});const hh=oo({sparseToDense_:function(e,t,n,r=0){F(n);const s=no(e,"sparseIndices","sparseToDense","int32"),o=no(t,"sparseValues","sparseToDense","string_or_numeric"),a=no(r,"defaultValue","sparseToDense",o.dtype);!function(e,t,n,r){if("int32"!==e.dtype)throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${e.shape}.`);const s=e.rank>0?e.shape[0]:1,o=e.rank>1?e.shape[1]:1;if(n.length!==o)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${o}.`);const a=t.size;if(0!==t.rank&&(1!==t.rank||a!==s))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${s}]`);if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}(s,o,n,a);const i={sparseIndices:s,sparseValues:o,defaultValue:a},l={outputShape:n};return Hs.runKernel("SparseToDense",i,l)}});const dh=oo({gatherND_:function(e,t){const n=no(t,"indices","gatherND","int32"),r={params:no(e,"x","gatherND","string_or_numeric"),indices:n};return Hs.runKernel("GatherNd",r)}});const fh=oo({dropout_:function(e,t,n,r){const s=no(e,"x","dropout");if(i("float32"===s.dtype,(()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${s.dtype} tensor instead.`)),i(t>=0&&t<1,(()=>`rate must be a float in the range [0, 1), but got ${t}.`)),0===t)return e instanceof $s?s.clone():s;const o=function(e,t){if(null==t)return e.shape.slice();if(d(e.shape,t))return t;if(e.shape.length===t.length){const n=[];for(let r=0;r<e.shape.length;r++)null==t[r]&&null!=e.shape[r]?n.push(e.shape[r]):n.push(t[r]);return n}return t}(s,n),a=1-t,l=Sa(ml(xa(oc(o,0,1,"float32",r),a)),a);return $a(s,l)}});function ph(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function gh(e,t,n){const r=1-e%2,s=new Float32Array(e);for(let o=0;o<e;++o){const a=2*Math.PI*o/(e+r-1);s[o]=t-n*Math.cos(a)}return Uc(s,"float32")}const mh=async function(e,t,n=1){const r=no(e,"predictions","inTopK"),s=no(t,"targets","inTopK");i(r.rank>1,(()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`)),i(r.rank-1===s.rank,(()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${s.rank}`)),l(r.shape.slice(0,r.shape.length-1),s.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");const o=r.shape[r.shape.length-1];i(n>0&&n<=o,(()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${o}), but got ${n}`));const a=await r.data(),u=await s.data(),[c,h]=[a.length/o,o],d=b("bool",c);for(let e=0;e<c;e++){const t=e*h,r=a.subarray(t,t+h),s=[];for(let e=0;e<r.length;e++)s.push({value:r[e],index:e});s.sort(((e,t)=>t.value-e.value)),d[e]=0;for(let t=0;t<n;t++)if(s[t].index===u[e]){d[e]=1;break}}return e!==r&&r.dispose(),t!==s&&s.dispose(),lo(d,s.shape,"bool")};const bh=oo({conv2DBackpropFilter_:function(e,t,n,r,s,o="NHWC",a){let l=e;3===e.rank&&(l=Xa(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let u=t;3===u.rank&&(u=Xa(t,[1,t.shape[0],t.shape[1],t.shape[2]])),i(4===l.rank,(()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${l.shape}.`)),i(4===u.rank,(()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`)),i(4===n.length,(()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`));const c="NHWC"===o?l.shape[3]:l.shape[1],h="NHWC"===o?u.shape[3]:u.shape[1];i(c===n[2],(()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`)),i(h===n[3],(()=>`Error in conv2dDerFilter: depth of dy (${h}) must match output depth for filter (${n[3]}).`)),Ya("conv2dDerFilter",s,a);const d={x:l,dy:u},f={strides:r,pad:s,dataFormat:o,dimRoundingMode:a,filterShape:n};return Hs.runKernel("Conv2DBackpropFilter",d,f)}});function yh(e,t,n){if(null==n||"linear"===n)return e;if("relu"===n)return $a(e,Oc(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function wh(e,t){let n=t;const r=zi(e.shape,t.shape);return r.length>0&&(n=il(n,r)),Xa(n,e.shape)}function kh(e,t,n,r){if("linear"===t)return e;if("relu"===t)return cc(e);if("elu"===t)return Ji(e);if("relu6"===t)return hc(e);if("prelu"===t)return vu(e,n);if("leakyrelu"===t)return Sl(e,r);if("sigmoid"===t)return ri(e);throw new Error(`Unknown fused activation ${t}.`)}const vh=(e,t)=>!(e>0)||"linear"===t;const xh=oo({fusedConv2d_:function({x:e,filter:t,strides:n,pad:r,dataFormat:s="NHWC",dilations:o=[1,1],dimRoundingMode:a,bias:l,activation:u="linear",preluActivationWeights:c,leakyreluAlpha:h}){if(u=u||"linear",!1===vh(Hs.state.gradientDepth,u)){i("NHWC"===s,(()=>`Error in fused conv2d: got dataFormat of ${s} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`));let d=Ei(e,t,n,r,s,o,a);return null!=l&&(d=xa(d,l)),kh(d,u,c,h)}const d=no(e,"x","conv2d","float32"),f=no(t,"filter","conv2d","float32");let p=d,g=!1;3===d.rank&&(g=!0,p=Xa(d,[1,d.shape[0],d.shape[1],d.shape[2]])),i(4===p.rank,(()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`)),i(4===f.rank,(()=>`Error in fused conv2d: filter must be rank 4, but got rank ${f.rank}.`)),Ya("fused conv2d",r,a);const m="NHWC"===s?p.shape[3]:p.shape[1];i(f.shape[2]===m,(()=>`Error in conv2d: depth of input (${m}) must match input depth for filter ${f.shape[2]}.`)),i(ja(n,o),(()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`));const b=za(p.shape,f.shape,n,o,r,a);let y,w;if(null!=l&&(y=no(l,"bias","fused conv2d"),[y]=Os(y,d),"NHWC"===s?Ui(b.outShape,y.shape):(i(y.shape.length<=1,(()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${y.shape.length}.`)),i(0===y.shape.length||y.shape[0]===b.outChannels||1===y.shape[0],(()=>`Error in fused conv2d: bias shape (${y.shape}) is not compatible with the number of output channels (${b.outChannels})`)))),null!=c){const e=c.shape;if(i(e.length<=1||3===e.length,(()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${e.length}.`)),1===e.length)i(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{Ui(e,b.outShape)}catch(t){const n=`Error in fused conv2d: PReLU activation weights (${e}) is not compatible with the output shape of the conv2d (${b.outShape}).`;throw Error(n)}w=no(c,"prelu weights","fused conv2d")}const k=(e,t)=>{i("NHWC"===s,(()=>`Error in gradient of fused conv2D: got dataFormat of ${s} but only NHWC is currently supported.`));const[a,l,c,h]=t,d=yh(e,c,u);i(Ha(o),(()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. 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Got strides ${n} and dilations '${o}'`)),Ya("fused depthwiseConv2d",r,a);const m=za(p.shape,f.shape,n,o,r,a,!0);let b,y;null!=l&&(b=no(l,"bias","fused conv2d"),[b]=Os(b,d),Ui(m.outShape,b.shape)),null!=c&&(y=no(c,"prelu weights","fused depthwiseConv2d"));const w=(e,t)=>{i(Ha(o),(()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`));const[s,l,c,h]=t,d=yh(e,c,u),f=Sh(l.shape,d,s,n,r,o,a),p=Eh(l,d,s.shape,n,r,o,a);if(null!=h){return[f,p,wh(b,d)]}return[f,p]},k={x:p,filter:f,bias:b,preluActivationWeights:y},v={strides:n,pad:r,dataFormat:s,dilations:o,dimRoundingMode:a,activation:u,leakyreluAlpha:h};if(null==l){const e=Cl(((e,t,n)=>{let r=Hs.runKernel("FusedDepthwiseConv2D",k,v);return n([t,e,r]),g&&(r=Xa(r,[r.shape[1],r.shape[2],r.shape[3]])),{value:r,gradFunc:w}}));return e(p,f)}{const e=Cl(((e,t,n,r)=>{let s=Hs.runKernel("FusedDepthwiseConv2D",k,v);return r([t,e,s,n]),g&&(s=Xa(s,[s.shape[1],s.shape[2],s.shape[3]])),{value:s,gradFunc:w}}));return e(p,f,b)}}});const Ah=oo({fusedMatMul_:function({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:s,activation:o="linear",preluActivationWeights:a,leakyreluAlpha:l=.2}){if(!1===vh(Hs.state.gradientDepth,o)){let i=ni(e,t,n,r);return null!=s&&(i=xa(i,s)),kh(i,o,a,l)}let u=no(e,"a","fused matMul"),h=no(t,"b","fused matMul");[u,h]=Os(u,h);const d=n?u.shape[u.rank-2]:u.shape[u.rank-1],f=r?h.shape[h.rank-1]:h.shape[h.rank-2],p=n?u.shape[u.rank-1]:u.shape[u.rank-2],g=r?h.shape[h.rank-2]:h.shape[h.rank-1],m=u.shape.slice(0,-2),b=h.shape.slice(0,-2),y=c(m),w=c(b);i(d===f,(()=>`Error in fused matMul: inner shapes (${d}) and (${f}) of Tensors with shapes ${u.shape} and ${h.shape} and transposeA=${n} and transposeB=${r} must match.`));const k=Ui(u.shape.slice(0,-2),h.shape.slice(0,-2)).concat([p,g]),v=Xa(u,n?[y,d,p]:[y,p,d]),x=Xa(h,r?[w,g,f]:[w,f,g]);let E,S;null!=s&&(E=no(s,"bias","fused matMul"),[E]=Os(E,u),Ui(k,E.shape)),null!=a&&(S=no(a,"prelu weights","fused matMul"));const $=(e,t)=>{const[a,i,l,u]=t,c=yh(Xa(e,l.shape),l,o);let h,d;if(n||r?!n&&r?(h=ni(c,i,!1,!1),d=ni(c,a,!0,!1)):n&&!r?(h=ni(i,c,!1,!0),d=ni(a,c,!1,!1)):(h=ni(i,c,!0,!0),d=ni(c,a,!0,!0)):(h=ni(c,i,!1,!0),d=ni(a,c,!0,!1)),null!=s){return[h,d,wh(u,c)]}return[h,d]},A={a:v,b:x,bias:E,preluActivationWeights:S},_={transposeA:n,transposeB:r,activation:o,leakyreluAlpha:l};if(null==s){const e=Cl(((e,t,n)=>{const r=Hs.runKernel("_FusedMatMul",A,_);return n([e,t,r]),{value:Xa(r,k),gradFunc:$}}));return e(v,x)}{const e=Cl(((e,t,n,r)=>{const s=Hs.runKernel("_FusedMatMul",A,_);return r([e,t,s,n]),{value:Xa(s,k),gradFunc:$}}));return e(v,x,E)}}});var _h={__proto__:null,conv2d:xh,depthwiseConv2d:$h,matMul:Ah};const Ih=oo({hammingWindow_:function(e){return gh(e,.54,.46)}});const Mh=oo({hannWindow_:function(e){return gh(e,.5,.5)}});const Nh=oo({frame_:function(e,t,n,r=!1,s=0){let o=0;const a=[];for(;o+t<=e.size;)a.push(si(e,o,t)),o+=n;if(r)for(;o<e.size;){const r=o+t-e.size,i=ti([si(e,o,t-r),bi([r],s)]);a.push(i),o+=n}return 0===a.length?Wc([],[0,t]):Xa(ti(a),[a.length,t])}});const Th=oo({stft_:function(e,t,n,r,s=Mh){null==r&&(r=ph(t));const o=Nh(e,t,n),a=$a(o,s(t));return Fc(a,r)}});const Dh=oo({cropAndResize_:function(e,t,n,r,s="bilinear",o=0){const a=no(e,"image","cropAndResize"),l=no(t,"boxes","cropAndResize","float32"),u=no(n,"boxInd","cropAndResize","int32"),c=l.shape[0];i(4===a.rank,(()=>`Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`)),i(2===l.rank&&4===l.shape[1],(()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${l.shape}.`)),i(1===u.rank&&u.shape[0]===c,(()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${l.shape}.`)),i(2===r.length,(()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`)),i(r[0]>=1&&r[1]>=1,(()=>`cropSize must be atleast [1,1], but was ${r}`)),i("bilinear"===s||"nearest"===s,(()=>`method must be bilinear or nearest, but was ${s}`));const h={image:a,boxes:l,boxInd:u},d={method:s,extrapolationValue:o,cropSize:r};return Hs.runKernel("CropAndResize",h,d)}});const Rh=oo({flipLeftRight_:function(e){const t=no(e,"image","flipLeftRight","float32");i(4===t.rank,(()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`));const n={image:t};return Hs.runKernel("FlipLeftRight",n,{})}});const Fh=oo({grayscaleToRGB_:function(e){const t=no(e,"image","grayscaleToRGB"),n=t.rank-1,r=t.shape[n];i(t.rank>=2,(()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`)),i(1===r,(()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${r}.`));const s=new Array(t.rank);return s.fill(1,0,n),s[n]=3,pl(t,s)}});const Bh=oo({rgbToGrayscale_:function(e){const t=no(e,"image","RGBToGrayscale"),n=t.rank-1,r=t.shape[n];i(t.rank>=2,(()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`)),i(3===r,(()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${r}.`));const s=t.dtype,o=wa(t,"float32"),a=Uc([.2989,.587,.114]);let l;switch(t.rank){case 2:l=ji("ij,j->i",o,a);break;case 3:l=ji("ijk,k->ij",o,a);break;case 4:l=ji("ijkl,l->ijk",o,a);break;case 5:l=ji("ijklm,m->ijkl",o,a);break;case 6:l=ji("ijklmn,n->ijklm",o,a);break;default:throw new Error("Not a valid tensor rank.")}return l=dl(l,-1),wa(l,s)}});const Ch=oo({rotateWithOffset_:function(e,t,n=0,r=.5){const s=no(e,"image","rotateWithOffset","float32");i(4===s.rank,(()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${s.rank}.`));const o={image:s},a={radians:t,fillValue:n,center:r};return Hs.runKernel("RotateWithOffset",o,a)}});function Ph(e,t,n,r,s,o){null==r&&(r=.5),null==s&&(s=Number.NEGATIVE_INFINITY),null==o&&(o=0);const a=e.shape[0];return n=Math.min(n,a),i(0<=r&&r<=1,(()=>`iouThreshold must be in [0, 1], but was '${r}'`)),i(2===e.rank,(()=>`boxes must be a 2D tensor, but was of rank 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Expected ${a}, but was ${t.shape[0]}`)),i(0<=o&&o<=1,(()=>`softNmsSigma must be in [0, 1], but was '${o}'`)),{maxOutputSize:n,iouThreshold:r,scoreThreshold:s,softNmsSigma:o}}const Oh=oo({nonMaxSuppression_:function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY){const o=no(e,"boxes","nonMaxSuppression","float32"),a=no(t,"scores","nonMaxSuppression","float32"),i=Ph(o,a,n,r,s),l={maxOutputSize:n=i.maxOutputSize,iouThreshold:r=i.iouThreshold,scoreThreshold:s=i.scoreThreshold};return Hs.runKernel("NonMaxSuppressionV3",{boxes:o,scores:a},l)}});function Lh(e,t,n){const r=function(e,t,n){return function(e,t,n){let r=0,s=e.length,o=0,a=!1;for(;r<s;){o=r+(s-r>>>1);const i=n(t,e[o]);i>0?r=o+1:(s=o,a=!i)}return a?r:-r-1}(e,t,n||zh)}(e,t,n),s=r<0?-(r+1):r;e.splice(s,0,t)}function zh(e,t){return e>t?1:e<t?-1:0}function Uh(e,t,n,r,s){return Kh(e,t,n,r,s,0)}function Wh(e,t,n,r,s,o){return Kh(e,t,n,r,s,0,!1,o,!0)}function Gh(e,t,n,r,s,o){return Kh(e,t,n,r,s,o,!0)}function Kh(e,t,n,r,s,o,a=!1,i=!1,l=!1){const u=[];for(let e=0;e<t.length;e++)t[e]>s&&u.push({score:t[e],boxIndex:e,suppressBeginIndex:0});u.sort(Hh);const c=o>0?-.5/o:0,h=[],d=[];for(;h.length<n&&u.length>0;){const t=u.pop(),{score:n,boxIndex:o,suppressBeginIndex:a}=t;if(n<s)break;let i=!1;for(let n=h.length-1;n>=a;--n){const a=qh(e,o,h[n]);if(a>=r){i=!0;break}if(t.score=t.score*Vh(r,c,a),t.score<=s)break}t.suppressBeginIndex=h.length,i||(t.score===n?(h.push(o),d.push(t.score)):t.score>s&&Lh(u,t,Hh))}const f=h.length,p=n-f;i&&p>0&&(h.push(...new Array(p).fill(0)),d.push(...new Array(p).fill(0)));const g={selectedIndices:h};return a&&(g.selectedScores=d),l&&(g.validOutputs=f),g}function qh(e,t,n){const r=e.subarray(4*t,4*t+4),s=e.subarray(4*n,4*n+4),o=Math.min(r[0],r[2]),a=Math.min(r[1],r[3]),i=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),u=Math.min(s[0],s[2]),c=Math.min(s[1],s[3]),h=Math.max(s[0],s[2]),d=Math.max(s[1],s[3]),f=(i-o)*(l-a),p=(h-u)*(d-c);if(f<=0||p<=0)return 0;const g=Math.max(o,u),m=Math.max(a,c),b=Math.min(i,h),y=Math.min(l,d),w=Math.max(b-g,0)*Math.max(y-m,0);return w/(f+p-w)}function Vh(e,t,n){const r=Math.exp(t*n*n);return n<=e?r:0}function Hh(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}const jh=async function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY){const o=no(e,"boxes","nonMaxSuppressionAsync"),a=no(t,"scores","nonMaxSuppressionAsync"),i=Ph(o,a,n,r,s);n=i.maxOutputSize,r=i.iouThreshold,s=i.scoreThreshold;const l=await Promise.all([o.data(),a.data()]),u=l[0],c=l[1],{selectedIndices:h}=Uh(u,c,n,r,s);return o!==e&&o.dispose(),a!==t&&a.dispose(),Uc(h,"int32")};const Jh=oo({nonMaxSuppressionWithScore_:function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,o=0){const a=no(e,"boxes","nonMaxSuppression"),i=no(t,"scores","nonMaxSuppression"),l=Ph(a,i,n,r,s,o),u={boxes:a,scores:i},c={maxOutputSize:n=l.maxOutputSize,iouThreshold:r=l.iouThreshold,scoreThreshold:s=l.scoreThreshold,softNmsSigma:o=l.softNmsSigma},h=Hs.runKernel("NonMaxSuppressionV5",u,c);return{selectedIndices:h[0],selectedScores:h[1]}}});const Zh=async function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,o=0){const a=no(e,"boxes","nonMaxSuppressionAsync"),i=no(t,"scores","nonMaxSuppressionAsync"),l=Ph(a,i,n,r,s,o);n=l.maxOutputSize,r=l.iouThreshold,s=l.scoreThreshold,o=l.softNmsSigma;const u=await Promise.all([a.data(),i.data()]),c=u[0],h=u[1],{selectedIndices:d,selectedScores:f}=Gh(c,h,n,r,s,o);return a!==e&&a.dispose(),i!==t&&i.dispose(),{selectedIndices:Uc(d,"int32"),selectedScores:Uc(f)}};const Yh=oo({nonMaxSuppressionPadded_:function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,o=!1){const a=no(e,"boxes","nonMaxSuppression"),i=no(t,"scores","nonMaxSuppression"),l=Ph(a,i,n,r,s,null),u={boxes:a,scores:i},c={maxOutputSize:l.maxOutputSize,iouThreshold:l.iouThreshold,scoreThreshold:l.scoreThreshold,padToMaxOutputSize:o},h=Hs.runKernel("NonMaxSuppressionV4",u,c);return{selectedIndices:h[0],validOutputs:h[1]}}});const Xh=async function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,o=!1){const a=no(e,"boxes","nonMaxSuppressionAsync"),i=no(t,"scores","nonMaxSuppressionAsync"),l=Ph(a,i,n,r,s,null),u=l.maxOutputSize,c=l.iouThreshold,h=l.scoreThreshold,[d,f]=await Promise.all([a.data(),i.data()]),{selectedIndices:p,validOutputs:g}=Wh(d,f,u,c,h,o);return a!==e&&a.dispose(),i!==t&&i.dispose(),{selectedIndices:Uc(p,"int32"),validOutputs:sl(g,"int32")}};const Qh=oo({resizeBilinear_:function(e,t,n=!1,r=!1){const s=no(e,"images","resizeBilinear");i(3===s.rank||4===s.rank,(()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${s.rank}.`)),i(2===t.length,(()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`)),i(!1===r||!1===n,(()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false."));let o=s,a=!1;3===s.rank&&(a=!0,o=Xa(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const l={images:o},u={alignCorners:n,halfPixelCenters:r,size:t},c=Hs.runKernel("ResizeBilinear",l,u);return a?Xa(c,[c.shape[1],c.shape[2],c.shape[3]]):c}});const ed=oo({resizeNearestNeighbor_:function(e,t,n=!1,r=!1){const s=no(e,"images","resizeNearestNeighbor");i(3===s.rank||4===s.rank,(()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${s.rank}.`)),i(2===t.length,(()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`)),i("float32"===s.dtype||"int32"===s.dtype,(()=>"`images` must have `int32` or `float32` as dtype")),i(!1===r||!1===n,(()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false."));let o=s,a=!1;3===s.rank&&(a=!0,o=Xa(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const l={images:o},u={alignCorners:n,halfPixelCenters:r,size:t},c=Hs.runKernel("ResizeNearestNeighbor",l,u);return a?Xa(c,[c.shape[1],c.shape[2],c.shape[3]]):c}});const td=oo({threshold_:function(e,t="binary",n=!1,r=.5){const s=no(e,"image","threshold"),o=s.shape[0]*s.shape[1];let a,l,u,c,h=$a(Uc([r]),255);if(i(3===s.rank,(()=>`Error in threshold: image must be rank 3,but got rank ${s.rank}.`)),i(3===s.shape[2]||1===s.shape[2],(()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${s.shape[2]}.`)),i("int32"===s.dtype||"float32"===s.dtype,(()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${s.dtype}.`)),i("otsu"===t||"binary"===t,(()=>`Method must be binary or otsu, but was ${t}`)),3===s.shape[2]){[a,l,u]=Rc(s,[1,1,1],-1);const e=$a(a,.2989),t=$a(l,.587),n=$a(u,.114);c=xa(xa(e,t),n)}else c=e;if("otsu"===t){h=function(e,t){let n,r,s,o,a,i,l=Uc([-1]),u=Uc([0]),c=Uc([0]);for(let h=0;h<e.size-1;h++){n=si(e,0,h+1),r=si(e,h+1),a=Sa(il(n),t),i=Sa(il(r),t);const d=il($a(n,ic(0,n.size)));s=Sa(d,il(n));const f=bi(r.shape,n.size),p=xa(ic(0,r.size),f),g=$a(r,p);o=Sa(il(g),il(r));const m=Ul(s,o),b=Ul(s,o),y=$a(a,i);c=$a($a(y,m),b);const w=yl(c,u);u=Ki(w,c,u),l=Ki(w,Uc([h]),l)}return l}(di(wa(bc(c),"int32"),lo([]),256),o)}const d=n?Al(c,h):yl(c,h);return wa($a(d,255),"int32")}});const nd=oo({transform_:function(e,t,n="nearest",r="constant",s=0,o){const a=no(e,"image","transform","float32"),l=no(t,"transforms","transform","float32");i(4===a.rank,(()=>`Error in transform: image must be rank 4,but got rank ${a.rank}.`)),i(2===l.rank&&(l.shape[0]===a.shape[0]||1===l.shape[0])&&8===l.shape[1],(()=>"Error in transform: Input transform should be batch x 8 or 1 x 8")),i(null==o||2===o.length,(()=>`Error in transform: outputShape must be [height, width] or null, but got ${o}.`));const u={image:a,transforms:l},c={interpolation:n,fillMode:r,fillValue:s,outputShape:o};return Hs.runKernel("Transform",u,c)}});const rd=oo({bandPart_:function(e,t,n){const r=no(e,"a","bandPart");i(r.rank>=2,(()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`));const s=r.shape,[o,a]=r.shape.slice(-2);let l,u;"number"==typeof t?(i(t%1==0,(()=>`bandPart(): numLower must be an integer, got ${t}.`)),i(t<=o,(()=>`bandPart(): numLower (${t}) must not be greater than the number of rows (${o}).`)),l=no(t<0?o:t,"numLower","bandPart")):(i("int32"===t.dtype,(()=>"bandPart(): numLower's dtype must be an int32.")),l=Ki($l(t,0),o,su(t,o))),"number"==typeof n?(i(n%1==0,(()=>`bandPart(): numUpper must be an integer, got ${n}.`)),i(n<=a,(()=>`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`)),u=no(n<0?a:n,"numUpper","bandPart")):(i("int32"===n.dtype,(()=>"bandPart(): numUpper's dtype must be an int32.")),u=Ki($l(n,0),a,su(n,a)));const c=Xa(ic(0,o,1,"int32"),[-1,1]),h=ic(0,a,1,"int32"),d=Ul(c,h),f=Kl(Al(d,l),wl(d,Ol(u))),p=tu([o,a],r.dtype);return Xa(Pc(nh(Xa(r,[-1,o,a])).map((e=>Ki(f,e,p)))),s)}});const sd=oo({gramSchmidt_:function(e){let t;if(Array.isArray(e)){t=!1,i(null!=e&&e.length>0,(()=>"Gram-Schmidt process: input must not be null, undefined, or empty"));const n=e[0].shape[0];for(let t=1;t<e.length;++t)i(e[t].shape[0]===n,(()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[t].shape[0]} vs. ${n})`))}else t=!0,e=Rc(e,e.shape[0],0).map((e=>Cc(e,[0])));i(e.length<=e[0].shape[0],(()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`));const n=[],r=e;for(let t=0;t<e.length;++t)n.push(Hs.tidy((()=>{let e=r[t];if(t>0)for(let r=0;r<t;++r){const t=$a(il($a(n[r],e)),n[r]);e=Ul(e,t)}return Sa(e,ul(e,"euclidean"))})));return t?Pc(n,0):n}});function od(e,t=!1){return Hs.tidy((()=>{i(2===e.shape.length,(()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`));const n=e.shape[0],r=e.shape[1];let s=gl(n),o=ka(e);const a=Wc([[1]],[1,1]);let l=ka(a);const u=n>=r?r:n;for(let e=0;e<u;++e){const t=o,i=l,u=s;[l,o,s]=Hs.tidy((()=>{const t=si(o,[e,e],[n-e,1]),i=ul(t),u=si(o,[e,e],[1,1]),c=Ki(yl(u,0),Wc([[-1]]),Wc([[1]])),h=Ul(u,$a(c,i)),d=Sa(t,h);l=1===d.shape[0]?ka(a):ti([a,si(d,[1,0],[d.shape[0]-1,d.shape[1]])],0);const f=Ol(Sa(ni(c,h),i)),p=si(o,[e,0],[n-e,r]),g=$a(f,l),m=lh(l);if(0===e)o=Ul(p,ni(g,ni(m,p)));else{const t=Ul(p,ni(g,ni(m,p)));o=ti([si(o,[0,0],[e,r]),t],0)}const b=lh(g),y=si(s,[0,e],[n,s.shape[1]-e]);if(0===e)s=Ul(y,ni(ni(y,l),b));else{const t=Ul(y,ni(ni(y,l),b));s=ti([si(s,[0,0],[n,e]),t],1)}return[l,o,s]})),vo([t,i,u])}return!t&&n>r&&(s=si(s,[0,0],[n,r]),o=si(o,[0,0],[r,r])),[s,o]}))}const ad=oo({qr_:function(e,t=!1){if(i(e.rank>=2,(()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`)),2===e.rank)return od(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce(((e,t)=>e*t)),r=nh(Xa(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),s=[],o=[];r.forEach((e=>{const[n,r]=od(e,t);s.push(n),o.push(r)}));return[Xa(Pc(s,0),e.shape),Xa(Pc(o,0),e.shape)]}}});var id;!function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"}(id||(id={}));const ld=oo({computeWeightedLoss_:function(e,t,n=id.SUM_BY_NONZERO_WEIGHTS){const r=no(e,"losses","computeWeightedLoss");let s=null;null!=t&&(s=no(t,"weights","computeWeightedLoss"));const o=null==s?r:$a(r,s);if(n===id.NONE)return o;if(n===id.SUM)return il(o);if(n===id.MEAN){if(null==s)return eu(o);{const e=r.size/s.size,t=Sa(il(o),il(s));return e>1?Sa(t,sl(e)):t}}if(n===id.SUM_BY_NONZERO_WEIGHTS){if(null==s)return Sa(il(o),sl(r.size));{const e=$a(s,nu(r.shape)),t=wa(il(cu(e,sl(0))),"float32");return Sa(il(o),t)}}throw Error(`Unknown reduction: ${n}`)}});const ud=oo({absoluteDifference_:function(e,t,n,r=id.SUM_BY_NONZERO_WEIGHTS){const s=no(e,"labels","absoluteDifference"),o=no(t,"predictions","absoluteDifference");let a=null;null!=n&&(a=no(n,"weights","absoluteDifference")),l(s.shape,o.shape,"Error in absoluteDifference: ");const i=Aa(Ul(s,o));return ld(i,a,r)}});const cd=oo({cosineDistance_:function(e,t,n,r,s=id.SUM_BY_NONZERO_WEIGHTS){const o=no(e,"labels","cosineDistance"),a=no(t,"predictions","cosineDistance");let i=null;null!=r&&(i=no(r,"weights","cosineDistance")),l(o.shape,a.shape,"Error in cosineDistance: ");const u=sl(1),c=Ul(u,il($a(o,a),n,!0));return ld(c,i,s)}});const hd=oo({hingeLoss_:function(e,t,n,r=id.SUM_BY_NONZERO_WEIGHTS){let s=no(e,"labels","hingeLoss");const o=no(t,"predictions","hingeLoss");let a=null;null!=n&&(a=no(n,"weights","hingeLoss")),l(s.shape,o.shape,"Error in hingeLoss: ");const i=sl(1);s=Ul($a(sl(2),s),i);const u=cc(Ul(i,$a(s,o)));return ld(u,a,r)}});const dd=oo({huberLoss_:function(e,t,n,r=1,s=id.SUM_BY_NONZERO_WEIGHTS){const o=no(e,"labels","huberLoss"),a=no(t,"predictions","huberLoss");let i=null;null!=n&&(i=no(n,"weights","huberLoss")),l(o.shape,a.shape,"Error in huberLoss: ");const u=sl(r),c=Aa(Ul(a,o)),h=su(c,u),d=Ul(c,h),f=xa($a(sl(.5),al(h)),$a(u,d));return ld(f,i,s)}});const fd=oo({logLoss_:function(e,t,n,r=1e-7,s=id.SUM_BY_NONZERO_WEIGHTS){const o=no(e,"labels","logLoss"),a=no(t,"predictions","logLoss");let i=null;null!=n&&(i=no(n,"weights","logLoss")),l(o.shape,a.shape,"Error in logLoss: ");const u=sl(1),c=sl(r),h=Ol($a(o,Ml(xa(a,c)))),d=$a(Ul(u,o),Ml(xa(Ul(u,a),c))),f=Ul(h,d);return ld(f,i,s)}});const pd=oo({meanSquaredError_:function(e,t,n,r=id.SUM_BY_NONZERO_WEIGHTS){const s=no(e,"labels","meanSquaredError"),o=no(t,"predictions","meanSquaredError");let a=null;null!=n&&(a=no(n,"weights","meanSquaredError")),l(s.shape,o.shape,"Error in meanSquaredError: ");const i=Bc(s,o);return ld(i,a,r)}});const gd=oo({sigmoidCrossEntropy_:function(e,t,n,r=0,s=id.SUM_BY_NONZERO_WEIGHTS){let o=no(e,"multiClassLabels","sigmoidCrossEntropy");const a=no(t,"logits","sigmoidCrossEntropy");let i=null;if(null!=n&&(i=no(n,"weights","sigmoidCrossEntropy")),l(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),r>0){const e=sl(r),t=sl(1),n=sl(.5);o=xa($a(o,Ul(t,e)),$a(n,e))}const u=function(e,t){const n=no(e,"labels","sigmoidCrossEntropyWithLogits"),r=no(t,"logits","sigmoidCrossEntropyWithLogits");l(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");const s=cc(r),o=$a(r,n),a=Nl(hl(Ol(Aa(r))));return xa(Ul(s,o),a)}(o,a);return ld(u,i,s)}});const md=oo({softmaxCrossEntropy_:function(e,t,n,r=0,s=id.SUM_BY_NONZERO_WEIGHTS){let o=no(e,"onehotLabels","softmaxCrossEntropy");const a=no(t,"logits","softmaxCrossEntropy");let i=null;if(null!=n&&(i=no(n,"weights","softmaxCrossEntropy")),l(o.shape,a.shape,"Error in softmaxCrossEntropy: "),r>0){const e=sl(r),t=sl(1),n=sl(o.shape[1]);o=xa($a(o,Ul(t,e)),Sa(e,n))}const u=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. Labels / logits was rank ${t.rank} and dim was ${n}`);const r=Cl(((e,t,r)=>{const s=Gl(t,[n],!0),o=Ul(wa(t,"float32"),s);r([e,o]);const a=Ol($a(o,e));return{value:il(a,[n]),gradFunc:(e,t)=>{const[r,s]=t,o=el(e.shape,[n]);return[$a(Xa(e,o),Ul(wa(r,"float32"),hl(s))),$a(Xa(e,o),Ul(hl(s),wa(r,"float32")))]}}}));return r(e,t)}(o,a);return ld(u,i,s)}});const bd=oo({sparseFillEmptyRows_:function(e,t,n,r){const s=no(e,"indices","sparseFillEmptyRows","int32"),o=no(t,"values","sparseFillEmptyRows"),a=no(n,"denseShape","sparseFillEmptyRows","int32"),i=no(r,"defaultValue","sparseFillEmptyRows",o.dtype);if(2!==s.rank)throw new Error(`Indices should be Tensor2D but received shape\n        ${s.shape}`);if(1!==o.rank)throw new Error(`Values should be Tensor1D but received shape ${o.shape}`);if(1!==a.rank)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(0!==i.rank)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);const l={indices:s,values:o,denseShape:a,defaultValue:i},u=Hs.runKernel("SparseFillEmptyRows",l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}});const yd=oo({sparseReshape_:function(e,t,n){const r=no(e,"inputIndices","sparseReshape","int32"),s=no(t,"inputShape","sparseReshape","int32"),o=no(n,"newShape","sparseReshape","int32");if(2!==r.rank)throw new Error(`Input indices should be Tensor2D but received shape\n        ${r.shape}`);if(1!==s.rank)throw new Error(`Input shape should be Tensor1D but received shape ${s.shape}`);if(1!==o.rank)throw new Error(`New shape should be Tensor1D but received shape ${o.shape}`);const a={inputIndices:r,inputShape:s,newShape:o},i=Hs.runKernel("SparseReshape",a);return{outputIndices:i[0],outputShape:i[1]}}});const wd=oo({sparseSegmentMean_:function(e,t,n){const r=no(e,"data","sparseSegmentMean"),s=no(t,"indices","sparseSegmentMean","int32"),o=no(n,"segmentIds","sparseSegmentMean","int32");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==s.rank)throw new Error(`Indices should be Tensor1D but received shape\n          ${s.shape}`);if(1!==o.rank)throw new Error(`Segment ids should be Tensor1D but received shape\n          ${o.shape}`);const a={data:r,indices:s,segmentIds:o};return Hs.runKernel("SparseSegmentMean",a)}});const kd=oo({sparseSegmentSum_:function(e,t,n){const r=no(e,"data","sparseSegmentSum"),s=no(t,"indices","sparseSegmentSum","int32"),o=no(n,"segmentIds","sparseSegmentSum","int32");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==s.rank)throw new Error(`Indices should be Tensor1D but received shape\n         ${s.shape}`);if(1!==o.rank)throw new Error(`Segment ids should be Tensor1D but received shape\n         ${o.shape}`);const a={data:r,indices:s,segmentIds:o};return Hs.runKernel("SparseSegmentSum",a)}});const vd=oo({stringNGrams_:function(e,t,n,r,s,o,a,i){const l=no(e,"data","stringNGrams","string");if("string"!==l.dtype)throw new Error("Data must be of datatype string");if(1!==l.shape.length)throw new Error(`Data must be a vector, saw: ${l.shape}`);const u=no(t,"dataSplits","stringNGrams");if("int32"!==u.dtype)throw new Error("Data splits must be of datatype int32");const c={separator:n,nGramWidths:r,leftPad:s,rightPad:o,padWidth:a,preserveShortSequences:i},h={data:l,dataSplits:u},d=Hs.runKernel("StringNGrams",h,c);return{nGrams:d[0],nGramsSplits:d[1]}}});const xd={fft:Nc,ifft:Tc,rfft:Fc,irfft:Dc},Ed={hammingWindow:Ih,hannWindow:Mh,frame:Nh,stft:Th},Sd={flipLeftRight:Rh,grayscaleToRGB:Fh,resizeNearestNeighbor:ed,resizeBilinear:Qh,rgbToGrayscale:Bh,rotateWithOffset:Ch,cropAndResize:Dh,nonMaxSuppression:Oh,nonMaxSuppressionAsync:jh,nonMaxSuppressionWithScore:Jh,nonMaxSuppressionWithScoreAsync:Zh,nonMaxSuppressionPadded:Yh,nonMaxSuppressionPaddedAsync:Xh,threshold:td,transform:nd},$d={bandPart:rd,gramSchmidt:sd,qr:ad},Ad={absoluteDifference:ud,computeWeightedLoss:ld,cosineDistance:cd,hingeLoss:hd,huberLoss:dd,logLoss:fd,meanSquaredError:pd,sigmoidCrossEntropy:gd,softmaxCrossEntropy:md},_d={sparseFillEmptyRows:bd,sparseReshape:yd,sparseSegmentMean:wd,sparseSegmentSum:kd},Id={stringNGrams:vd,stringSplit:oo({stringSplit_:function(e,t,n=!0){const r=no(e,"input","stringSplit","string"),s=no(t,"delimiter","stringSplit","string");if(1!==r.rank)throw new Error(`Input should be Tensor1D but received shape ${r.shape}`);if(0!==s.rank)throw new Error(`Delimiter should be a scalar but received shape ${s.shape}`);const o={skipEmpty:n},a={input:r,delimiter:s},i=Hs.runKernel("StringSplit",a,o);return{indices:i[0],values:i[1],shape:i[2]}}}),stringToHashBucketFast:oo({stringToHashBucketFast_:function(e,t){const n=no(e,"input","stringToHashBucketFast","string"),r={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");const s={input:n};return Hs.runKernel("StringToHashBucketFast",s,r)}}),staticRegexReplace:oo({staticRegexReplace_:function(e,t,n,r=!0){const s=no(e,"input","staticRegexReplace","string"),o={pattern:t,rewrite:n,replaceGlobal:r};return Hs.runKernel("StaticRegexReplace",{x:s},o)}})},Md=new Map,Nd=new Map;class Td{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}}class Dd{constructor(){this.classNameMap={}}static getMap(){return null==Dd.instance&&(Dd.instance=new Dd),Dd.instance}static register(e){Dd.getMap().classNameMap[e.className]=[e,e.fromConfig]}}function Rd(e,t,n){i(null!=e.className,(()=>"Class being registered does not have the static className property defined.")),i("string"==typeof e.className,(()=>"className is required to be a string, but got type "+typeof e.className)),i(e.className.length>0,(()=>"Class being registered has an empty-string as its className, which is disallowed.")),"undefined"==typeof t&&(t="Custom"),"undefined"==typeof n&&(n=e.className);const r=t+">"+n;return Dd.register(e),Md.set(r,e),Nd.set(e,r),e}var Fd={__proto__:null,Serializable:Td,SerializationMap:Dd,getRegisteredName:function(e){return Nd.has(e)?Nd.get(e):e.className},registerClass:Rd};class Bd extends Td{minimize(e,t=!1,n){const{value:r,grads:s}=this.computeGradients(e,n);if(null!=n){const e=n.map((e=>({name:e.name,tensor:s[e.name]})));this.applyGradients(e)}else this.applyGradients(s);return vo(s),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 Bl(e,t)}dispose(){null!=this.iterations_&&vo(this.iterations_)}async saveIterations(){return null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:sl(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Bd,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});class Cd extends Bd{static get className(){return"Adadelta"}constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=Hs.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Hs.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:ko((()=>qi(r).variable(false)))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:ko((()=>qi(r).variable(false)))});const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const o=this.accumulatedGrads[n].variable,a=this.accumulatedUpdates[n].variable;ko((()=>{const e=xa($a(o,this.rho),$a(al(s),1-this.rho)),t=$a(Sa(ol(xa(a,this.epsilon)),ol(xa(o,this.epsilon))),s),n=xa($a(a,this.rho),$a(al(t),1-this.rho));o.assign(e),a.assign(n);const i=xa($a(t,-this.learningRate),r);r.assign(i)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&(vo(this.accumulatedGrads.map((e=>e.variable))),vo(this.accumulatedUpdates.map((e=>e.variable))))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){const t=(e=await this.extractIterations(e)).length/2;this.accumulatedGrads=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)}))),this.accumulatedUpdates=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}class Pd extends Bd{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Hs.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:ko((()=>bi(r.shape,this.initialAccumulatorValue).variable(e)))}}const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const o=this.accumulatedGrads[n].variable;ko((()=>{const e=xa(o,al(s));o.assign(e);const t=xa($a(Sa(s,ol(xa(e,Hs.backend.epsilon()))),-this.learningRate),r);r.assign(t)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&vo(this.accumulatedGrads.map((e=>e.variable)))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);this.accumulatedGrads=e.map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}class Od extends Bd{static get className(){return"Adam"}constructor(e,t,n,r=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ko((()=>{this.accBeta1=sl(t).variable(),this.accBeta2=sl(n).variable()})),null==r&&(this.epsilon=Hs.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);ko((()=>{const n=Ul(1,this.accBeta1),r=Ul(1,this.accBeta2);t.forEach(((t,s)=>{const o=Hs.registeredVariables[t];null==this.accumulatedFirstMoment[s]&&(this.accumulatedFirstMoment[s]={originalName:`${t}/m`,variable:ko((()=>qi(o).variable(false)))}),null==this.accumulatedSecondMoment[s]&&(this.accumulatedSecondMoment[s]={originalName:`${t}/v`,variable:ko((()=>qi(o).variable(false)))});const a=Array.isArray(e)?e[s].tensor:e[t];if(null==a)return;const i=this.accumulatedFirstMoment[s].variable,l=this.accumulatedSecondMoment[s].variable,u=xa($a(i,this.beta1),$a(a,1-this.beta1)),c=xa($a(l,this.beta2),$a(al(a),1-this.beta2)),h=Sa(u,n),d=Sa(c,r);i.assign(u),l.assign(c);const f=xa($a(Sa(h,xa(ol(d),this.epsilon)),-this.learningRate),o);o.assign(f)})),this.accBeta1.assign($a(this.accBeta1,this.beta1)),this.accBeta2.assign($a(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&vo(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&vo(this.accumulatedSecondMoment.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),ko((()=>{this.accBeta1.assign(rl(this.beta1,this.iterations_+1)),this.accBeta2.assign(rl(this.beta2,this.iterations_+1))}));const 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)}}class Ld extends Bd{static get className(){return"Adamax"}constructor(e,t,n,r=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ko((()=>{this.iteration=sl(0).variable(),this.accBeta1=sl(t).variable()})),null==r&&(this.epsilon=Hs.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);ko((()=>{const n=Ul(1,this.accBeta1),r=Sa(-this.learningRate,xa($a(this.iteration,this.decay),1));t.forEach(((t,s)=>{const o=Hs.registeredVariables[t];null==this.accumulatedFirstMoment[s]&&(this.accumulatedFirstMoment[s]={originalName:`${t}/m`,variable:qi(o).variable(false)}),null==this.accumulatedWeightedInfNorm[s]&&(this.accumulatedWeightedInfNorm[s]={originalName:`${t}/v`,variable:qi(o).variable(false)});const a=Array.isArray(e)?e[s].tensor:e[t];if(null==a)return;const i=this.accumulatedFirstMoment[s].variable,l=this.accumulatedWeightedInfNorm[s].variable,u=xa($a(i,this.beta1),$a(a,1-this.beta1)),c=$a(l,this.beta2),h=Aa(a),d=Ql(c,h);i.assign(u),l.assign(d);const f=xa($a(Sa(r,n),Sa(u,xa(d,this.epsilon))),o);o.assign(f)})),this.iteration.assign(xa(this.iteration,1)),this.accBeta1.assign($a(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&vo(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&vo(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)}}class zd extends Bd{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)=>{const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const s=Hs.registeredVariables[t];ko((()=>{const e=xa($a(this.c,r),s);s.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=xo(sl(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).length)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}class Ud extends zd{static get className(){return"Momentum"}constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=sl(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Hs.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:ko((()=>qi(r).variable(e)))}}const s=this.accumulations[n].variable,o=Array.isArray(e)?e[n].tensor:e[t];null!=o&&ko((()=>{let e;const t=xa($a(this.m,s),o);e=this.useNesterov?xa($a(this.c,xa(o,$a(t,this.m))),r):xa($a(this.c,t),r),s.assign(t),r.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&vo(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}))))}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)}}class Wd extends Bd{static get className(){return"RMSProp"}constructor(e,t=.9,n=0,r=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,null==r&&(this.epsilon=Hs.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)=>{const r=Hs.registeredVariables[t],s=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:ko((()=>qi(r).variable(s)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:ko((()=>qi(r).variable(s)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:ko((()=>qi(r).variable(s)))});const o=Array.isArray(e)?e[n].tensor:e[t];if(null==o)return;const a=this.accumulatedMeanSquares[n].variable,i=this.accumulatedMoments[n].variable;ko((()=>{const e=xa($a(a,this.decay),$a(al(o),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,s=xa($a(t,this.decay),$a(o,1-this.decay)),l=Sa($a(o,this.learningRate),ol(Ul(e,xa(al(s),this.epsilon)))),u=xa($a(i,this.momentum),l);a.assign(e),t.assign(s),i.assign(u);const c=Ul(r,u);r.assign(c)}else{const e=xa($a(a,this.decay),$a(al(o),1-this.decay)),t=xa($a(i,this.momentum),Sa($a(o,this.learningRate),ol(xa(e,this.epsilon))));a.assign(e),i.assign(t);const n=Ul(r,t);r.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&vo(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&vo(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&vo(this.accumulatedMoments.map((e=>e.variable)))}async getWeights(){const 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);const 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)}}const Gd=[Cd,Pd,Od,Ld,Ud,Wd,zd];function Kd(e){return new Promise((e=>setTimeout(e))).then(e)}class qd{constructor(e){if(!O().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(qd.URL_SCHEME)&&(e=e.slice(qd.URL_SCHEME.length)),null!=e&&0!==e.length||(e="model"),this.modelJsonFileName=e+".json",this.weightDataFileName=e+".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");const t=co.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.");{const t=Wo(e,[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}]),r=window.URL.createObjectURL(new Blob([JSON.stringify(t)],{type:"application/json"})),s=null==this.modelJsonAnchor?document.createElement("a"):this.modelJsonAnchor;if(s.download=this.modelJsonFileName,s.href=r,await Kd((()=>s.dispatchEvent(new MouseEvent("click")))),null!=e.weightData){const e=null==this.weightDataAnchor?document.createElement("a"):this.weightDataAnchor;e.download=this.weightDataFileName,e.href=n,await Kd((()=>e.dispatchEvent(new MouseEvent("click"))))}return{modelArtifactsInfo:qo(e)}}}}qd.URL_SCHEME="downloads://";class Vd{constructor(e){if(null==e||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.jsonFile=e[0],this.weightsFiles=e.slice(1)}async load(){return new Promise(((e,t)=>{const n=new FileReader;n.onload=n=>{const r=JSON.parse(n.target.result),s=r.modelTopology;if(null==s)return void t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));if(null==r.weightsManifest)return void t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));if(0===this.weightsFiles.length)return void e({modelTopology:s});const o=Ko(r,(e=>this.loadWeights(e)));e(o)},n.onerror=e=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),n.readAsText(this.jsonFile)}))}loadWeights(e){const t=[],n=[];for(const r of e)t.push(...r.weights),n.push(...r.paths);const r=this.checkManifestAndWeightFiles(e),s=n.map((e=>this.loadWeightsFile(e,r[e])));return Promise.all(s).then((e=>[t,e]))}loadWeightsFile(e,t){return new Promise(((n,r)=>{const s=new FileReader;s.onload=e=>{const t=e.target.result;n(t)},s.onerror=t=>r(`Failed to weights data from file of path '${e}'.`),s.readAsArrayBuffer(t)}))}checkManifestAndWeightFiles(e){const t=[],n=this.weightsFiles.map((e=>Uo(e.name))),r={};for(const s of e)s.paths.forEach((e=>{const s=Uo(e);if(-1!==t.indexOf(s))throw new Error(`Duplicate file basename found in weights manifest: '${s}'`);if(t.push(s),-1===n.indexOf(s))throw new Error(`Weight file with basename '${s}' is not provided.`);r[e]=this.weightsFiles[n.indexOf(s)]}));if(t.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return r}}function Hd(e,t,n,r){!function(e){i(null!=e&&Array.isArray(e)&&e.length>0,(()=>"promises must be a none empty array"))}(e),function(e,t){i(e>=0&&e<=1,(()=>`Progress fraction must be in range [0, 1], but got startFraction ${e}`)),i(t>=0&&t<=1,(()=>`Progress fraction must be in range [0, 1], but got endFraction ${t}`)),i(t>=e,(()=>`startFraction must be no more than endFraction, but got startFraction ${e} and endFraction ${t}`))}(n=null==n?0:n,r=null==r?1:r);let s=0;return Promise.all(e.map((o=>(o.then((o=>{const a=n+ ++s/e.length*(r-n);return t(a),o})),o))))}async function jd(e,t){null==t&&(t={});const n=null==t.fetchFunc?O().platform.fetch:t.fetchFunc,r=e.map((e=>n(e,t.requestInit,{isBinary:!0}))),s=(null==t.onProgress?await Promise.all(r):await Hd(r,t.onProgress,0,.5)).map((e=>e.arrayBuffer()));return null==t.onProgress?await Promise.all(s):await Hd(s,t.onProgress,.5,1)}function Jd(e){return async(t,n="",r)=>{const s=t.map((()=>!1)),o={},a=null!=r?r.map((()=>!1)):[],i=[];if(t.forEach(((e,t)=>{let n=0;e.weights.forEach((e=>{const l="quantization"in e?e.quantization.dtype:e.dtype,u=uo[l]*c(e.shape),h=()=>{s[t]=!0,null==o[t]&&(o[t]=[]),o[t].push({manifestEntry:e,groupOffset:n,sizeBytes:u})};null!=r?r.forEach(((t,n)=>{t===e.name&&(h(),a[n]=!0)})):h(),i.push(e.name),n+=u}))})),!a.every((e=>e))){const e=r.filter(((e,t)=>!a[t]));throw new Error(`Could not find weights in manifest with names: ${e.join(", ")}. \nManifest JSON has weights with names: ${i.join(", ")}.`)}const l=s.reduce(((e,t,n)=>(t&&e.push(n),e)),[]),u=[];l.forEach((e=>{t[e].paths.forEach((e=>{const t=n+(n.endsWith("/")?"":"/")+e;u.push(t)}))}));const h=await e(u),d={};let f=0;return l.forEach((e=>{const n=t[e].paths.length,r=new co(h.slice(f,f+n));o[e].forEach((e=>{const t=Ro(r.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(const e in t)d[e]=t[e]})),f+=n})),d}}Ho.registerSaveRouter((e=>O().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(qd.URL_SCHEME)?function(e="model"){return new qd(e)}(e.slice(qd.URL_SCHEME.length)):null));class Zd{constructor(e,t){if(this.DEFAULT_METHOD="POST",null==t&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.weightUrlConverter=t.weightUrlConverter,null!=t.fetchFunc?(i("function"==typeof t.fetchFunc,(()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)")),this.fetch=t.fetchFunc):this.fetch=O().platform.fetch,i(null!=e&&e.length>0,(()=>"URL path for http must not be null, undefined or empty.")),Array.isArray(e)&&i(2===e.length,(()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`)),this.path=e,null!=t.requestInit&&null!=t.requestInit.body)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{},this.loadOptions=t}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");const t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;const n=Wo(e,[{paths:["./model.weights.bin"],weights:e.weightSpecs}]);if(t.body.append("model.json",new Blob([JSON.stringify(n)],{type:"application/json"}),"model.json"),null!=e.weightData){const n=co.join(e.weightData);t.body.append("model.weights.bin",new Blob([n],{type:"application/octet-stream"}),"model.weights.bin")}const r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:qo(e),responses:[r]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`)}async loadModelJSON(){const e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(e){let t=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?t+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":t+=" Please make sure the server is serving valid JSON for this request.",new Error(t)}const n=t.modelTopology,r=t.weightsManifest;if(null==n&&null==r)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return t}async load(){if(this.loadOptions.streamWeights)return this.loadStream();return Ko(await this.loadModelJSON(),(e=>this.loadWeights(e)))}async loadStream(){const e=await this.loadModelJSON(),t=await this.getWeightUrls(e.weightsManifest),n=Vo(e.weightsManifest);return Object.assign(Object.assign({},e),{weightSpecs:n,getWeightStream:()=>function(e,t){var n;const r=null==t.fetchFunc?O().platform.fetch:t.fetchFunc;let s,o=0;return null===(n=t.onProgress)||void 0===n||n.call(t,0),new ReadableStream({pull:async n=>{for(var a;o<e.length;){if(!s){const n=(await r(e[o],t.requestInit,{isBinary:!0})).body;s=n.getReader()}const{done:i,value:l}=await s.read();if(!i)return void n.enqueue(l);o++,s=void 0,null===(a=t.onProgress)||void 0===a||a.call(t,o/e.length)}n.close()}})}(t,this.loadOptions)})}async getWeightUrls(e){const t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=function(e){const t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),s=n>t?e.substring(n):"";return[r+"/",s]}(t),s=this.weightPathPrefix||n,o=[],a=[];for(const t of e)for(const e of t.paths)null!=this.weightUrlConverter?a.push(this.weightUrlConverter(e)):o.push(s+e+r);return this.weightUrlConverter&&o.push(...await Promise.all(a)),o}async loadWeights(e){const t=await this.getWeightUrls(e);return[Vo(e),await jd(t,this.loadOptions)]}}function Yd(e){return null!=e.match(Zd.URL_SCHEME_REGEX)}Zd.URL_SCHEME_REGEX=/^https?:\/\//;const Xd=(e,t)=>{if("undefined"==typeof fetch&&(null==t||null==t.fetchFunc))return null;{let n=!0;if(n=Array.isArray(e)?e.every((e=>Yd(e))):Yd(e),n)return Qd(e,t)}return null};function Qd(e,t){return new Zd(e,t)}Ho.registerSaveRouter(Xd),Ho.registerLoadRouter(Xd);class ef{constructor(e){this.modelArtifacts=e}load(){return this.modelArtifacts}}class tf{constructor(e){this.saveHandler=e}save(e){return this.saveHandler(e)}}class nf{constructor(e){e.load&&(this.load=()=>Promise.resolve(e.load())),e.save&&(this.save=t=>Promise.resolve(e.save(t)))}}function rf(e,t,n,r){if(1===arguments.length){return null!=e.modelTopology||null!=e.weightSpecs?new ef(e):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new ef({modelTopology:e}))}return console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new ef({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r})}var sf={__proto__:null,CompositeArrayBuffer:co,browserFiles:function(e){return new Vd(e)},browserHTTPRequest:function(e,t){return Qd(e,t)},concatenateArrayBuffers:function(e){return co.join(e)},copyModel:async function(e,t){return fa(e,t,!1)},decodeWeights:Ro,decodeWeightsStream:async function(e,t){const n={},r=e.getReader();let s=new ArrayBuffer(0);for(const e of t){const t=await Bo(e,(async(e,t)=>(s=await Po(r,s,t),s.slice(e,t))));s=await Po(r,s,t);const o=s.slice(0,t);s=s.slice(t);const a=Co(e,o);if(n[e.name]=a,"webgpu"===Ao()){const e=To();"uploadToGPU"in e&&c(a.shape)>=O().get("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD")&&e.uploadToGPU(a.dataId)}}return n},encodeWeights:async function(e,t){const n=[],r=[],s=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);for(let o=0;o<s.length;++o){const a=s[o],i=Array.isArray(e)?e[o].tensor:e[a];if("float32"!==i.dtype&&"int32"!==i.dtype&&"bool"!==i.dtype&&"string"!==i.dtype&&"complex64"!==i.dtype)throw new Error(`Unsupported dtype in weight '${a}': ${i.dtype}`);const l={name:a,shape:i.shape,dtype:i.dtype};if("string"===i.dtype){const e=new Promise((async e=>{const t=await i.bytes(),n=t.reduce(((e,t)=>e+t.length),0)+4*t.length,r=new Uint8Array(n);let s=0;for(let e=0;e<t.length;e++){const n=t[e],o=new Uint8Array(new Uint32Array([n.length]).buffer);r.set(o,s),s+=4,r.set(n,s),s+=n.length}e(r)}));r.push(e)}else r.push(i.data());null!=t&&(l.group=t),n.push(l)}return{data:Oo(await Promise.all(r)),specs:n}},fromMemory:function(e,t,n,r){const s=arguments;return new nf(rf(...s))},fromMemorySync:rf,getLoadHandlers:(e,t)=>Ho.getLoadHandlers(e,t),getModelArtifactsForJSON:Ko,getModelArtifactsForJSONSync:Go,getModelArtifactsInfoForJSON:qo,getSaveHandlers:e=>Ho.getSaveHandlers(e),getWeightSpecs:Vo,http:Qd,isHTTPScheme:Yd,listModels:async function(){const e=ha.getSchemes(),t={};for(const n of e){const e=await ha.getManager(n).listModels();for(const r in e){t[n+"://"+r]=e[r]}}return t},loadWeights:async function(e,t="",n,r){return Jd((e=>jd(e,{requestInit:r})))(e,t,n)},moveModel:async function(e,t){return fa(e,t,!0)},registerLoadRouter:e=>Ho.registerLoadRouter(e),registerSaveRouter:e=>Ho.registerSaveRouter(e),removeModel:async function(e){const t=da(e);return ha.getManager(t.scheme).removeModel(t.path)},weightsLoaderFactory:Jd,withSaveHandler:function(e){return new tf(e)},withSaveHandlerSync:function(e){return new tf(e)}};var of={__proto__:null,confusionMatrix:oo({confusionMatrix_:function(e,t,n){const r=no(e,"labels","confusionMatrix"),s=no(t,"predictions","confusionMatrix");i(null==n||n>0&&Number.isInteger(n),(()=>`If provided, numClasses must be a positive integer, but got ${n}`)),i(1===r.rank,(()=>`Expected the rank of labels to be 1, but got ${r.rank}`)),i(1===s.rank,(()=>`Expected the rank of predictions to be 1, but got ${s.rank}`)),i(r.shape[0]===s.shape[0],(()=>`Mismatch in the number of examples: ${r.shape[0]} vs. ${s.shape[0]}. Labels and predictions should have the same number of elements.`)),i(n>0&&Number.isInteger(n),(()=>`numClasses is required to be a positive integer, but got ${n}`));const o=hu(wa(r,"int32"),n),a=hu(wa(s,"int32"),n),l=lh(o),u=ni(l,a);return wa(u,"int32")}})};let af,lf=!1;function uf(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(null==e)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,r=!1,s=!1,o=!1,a=!1,i=!1;if(e.data instanceof Uint8Array)n=!0;else if("undefined"!=typeof ImageData&&e instanceof ImageData)r=!0;else if("undefined"!=typeof HTMLVideoElement&&e instanceof HTMLVideoElement)s=!0;else if("undefined"!=typeof HTMLImageElement&&e instanceof HTMLImageElement)o=!0;else if(null!=e.getContext)a=!0;else{if(!("undefined"!=typeof ImageBitmap&&e instanceof ImageBitmap))throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);i=!0}if(null!=hr("FromPixels",Hs.backendName)){const n={pixels:e},r={numChannels:t};return Hs.runKernel("FromPixels",n,r)}const[l,u]=s?[e.videoWidth,e.videoHeight]:[e.width,e.height];let c,h;if(a)c=e.getContext("2d").getImageData(0,0,l,u).data;else if(r||n)c=e.data;else if(o||s||i){if(null==af)if("undefined"==typeof document){if("undefined"==typeof OffscreenCanvas||"undefined"==typeof OffscreenCanvasRenderingContext2D)throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");af=new OffscreenCanvas(1,1).getContext("2d")}else af=document.createElement("canvas").getContext("2d",{willReadFrequently:!0});af.canvas.width=l,af.canvas.height=u,af.drawImage(e,0,0,l,u),c=af.getImageData(0,0,l,u).data}if(4===t)h=new Int32Array(c);else{const e=l*u;h=new Int32Array(e*t);for(let n=0;n<e;n++)for(let e=0;e<t;++e)h[n*t+e]=c[4*n+e]}return Gc(h,[u,l,t],"int32")}function cf(e){return"undefined"!=typeof window&&"undefined"!=typeof ImageBitmap&&window.hasOwnProperty("createImageBitmap")&&!(e instanceof ImageBitmap)&&function(e){return null!=e&&0!==e.width&&0!==e.height}(e)&&!function(e){return null!=e&&e.data instanceof Uint8Array}(e)}function hf(e){if(2!==e.rank&&3!==e.rank)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${e.rank}.`);const t=2===e.rank?1:e.shape[2];if(t>4||2===t)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${t}`);if("float32"!==e.dtype&&"int32"!==e.dtype)throw new Error(`Unsupported type for toPixels: ${e.dtype}. Please use float32 or int32 tensors.`)}var df={__proto__:null,draw:function(e,t,n){let r=no(e,"img","draw");if(!(e instanceof $s)){const e=r;r=wa(e,"int32"),e.dispose()}hf(r),function(e){const t=(null==e?void 0:e.alpha)||1;if(t>1||t<0)throw new Error(`Alpha value ${t} is suppoed to be in range [0 - 1].`)}(null==n?void 0:n.imageOptions);const s={image:r},o={canvas:t,options:n};Hs.runKernel("Draw",s,o)},fromPixels:oo({fromPixels_:uf}),fromPixelsAsync:async function(e,t=3){let n=null;if(O().getBool("WRAP_TO_IMAGEBITMAP")&&cf(e)){let t;try{t=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(e){t=null}n=null!=t&&t.width===e.width&&t.height===e.height?t:e}else n=e;return uf(n,t)},toPixels:async function(e,t){let n=no(e,"img","toPixels");if(!(e instanceof $s)){const e=n;n=wa(e,"int32"),e.dispose()}hf(n);const[r,s]=n.shape.slice(0,2),o=2===n.rank?1:n.shape[2],a=await n.data(),i="float32"===n.dtype?255:1,l=new Uint8ClampedArray(s*r*4);for(let e=0;e<r*s;++e){const t=[0,0,0,255];for(let r=0;r<o;r++){const s=a[e*o+r];if("float32"===n.dtype){if(s<0||s>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${s}.`)}else if("int32"===n.dtype&&(s<0||s>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${s}.`);1===o?(t[0]=s*i,t[1]=s*i,t[2]=s*i):t[r]=s*i}const r=4*e;l[r+0]=Math.round(t[0]),l[r+1]=Math.round(t[1]),l[r+2]=Math.round(t[2]),l[r+3]=Math.round(t[3])}if(null!=t){if(!lf){null!=hr("Draw",Hs.backendName)&&(console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. 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