@tensorflow/tfjs/dist/tf.es2017.min.js

/**
 * @license
 * Copyright 2022 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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To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await br().read(this.dataId);return"string"===this.dtype?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(br().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return xr.print(this,e)}clone(){return this.throwIfDisposed(),xr.clone(this)}toString(e=!1){return pr(this.dataSync(),this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),xr.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),br().makeVariable(this,e,t,n)}}function kr(){return J("Tensor",(()=>vr))}Object.defineProperty(vr,Symbol.hasInstance,{value:e=>!!e&&null!=e.data&&null!=e.dataSync&&null!=e.throwIfDisposed}),kr();class Nr extends vr{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s),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(!f(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);br().disposeTensor(this),this.dataId=e.dataId,br().incRef(this,null)}dispose(){br().disposeVariable(this),this.isDisposedInternal=!0}}var Ir,Sr,Tr,Cr,$r;Object.defineProperty(Nr,Symbol.hasInstance,{value:e=>e instanceof vr&&null!=e.assign&&e.assign instanceof Function}),(Ir=e.Rank||(e.Rank={})).R0="R0",Ir.R1="R1",Ir.R2="R2",Ir.R3="R3",Ir.R4="R4",Ir.R5="R5",Ir.R6="R6",function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"}(Sr||(Sr={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"}(Tr||(Tr={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"}(Cr||(Cr={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"}($r||($r={}));const Er={float32:Cr,int32:Sr,bool:Tr,complex64:$r};function Ar(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Er[e][t]}function Rr(e){return Ar(e,"int32")}function _r(e,t){if(e.dtype===t.dtype)return[e,t];const n=Ar(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Fr(e,t){u(e.dtype===t.dtype,(()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`))}function Dr(e,t){return t.some((t=>t.id===e.id))}function Or(e){const t=[];return Mr(e,t,new Set),t}function Mr(e,t,n){if(null==e)return;if(e instanceof vr)return void t.push(e);if(s=e,!Array.isArray(s)&&"object"!=typeof s)return;var s;const r=e;for(const e in r){const s=r[e];n.has(s)||(n.add(s),Mr(s,t,n))}}var Lr=Object.freeze({__proto__:null,makeTypesMatch:_r,assertTypesMatch:Fr,isTensorInList:Dr,getTensorsInContainer:Or});function zr(e){return null!=e.kernelName}class Pr{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map((e=>e.name))))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class Br{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Pr}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then((()=>{}));if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(us(`${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 ur(this.backendInstance),!0}setupRegisteredKernels(){fs(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){fs(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 s=t.factory();if(!s||s instanceof n||"function"!=typeof s.then)return this.registry[e]=s,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,n=s.then((n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0))).catch((n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,us(`Initialization of backend ${e} failed`),us(n.stack||n.message)),!1)));return this.pendingBackendInit=n,{success:n,asyncInit:!0}}}catch(t){return us(`Initialization of backend ${e} failed`),us(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n,s=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");s=e}return this.scopedRun((()=>this.startScope(s)),(()=>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 Br.nextTensorId++}nextVariableId(){return Br.nextVariableId++}clone(e){const t=Vr.runKernel(dt,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{const t={x:e},n={dtype:"float32"};return Vr.runKernel(ke,t,n)}})),[],{}),t}runKernel(e,t,n){null==this.backendName&&this.backend;if(!(null!=ps(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 s=this.backend.numDataIds();let r=0;n.forEach((e=>{r+="complex64"===e.dtype?3:1}));const a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;let i,o;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const l=zr(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(zr(e)){const{kernelName:t,inputs:r,attrs:a}=e;null==this.backendName&&this.backend;const l=ps(t,this.backendName);u(null!=l,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),i=()=>{const e=this.backend.numDataIds();o=l.kernelFunc({inputs:r,attrs:a,backend:this.backend});const i=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,i);const u=i.map((e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e)));if(s){const e=this.getTensorsForGradient(t,r,u);n=this.saveTensorsForBackwardMode(e)}return u}}else{const{forwardFunc:t}=e,r=e=>{s&&(n=e.map((e=>this.keep(this.clone(e)))))};i=()=>{const e=this.backend.numDataIds();o=this.tidy((()=>t(this.backend,r)));const n=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,e,n),n}}const{inputs:c,attrs:h}=e,p=zr(e)?null:e.backwardsFunc;let d;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(d=this.profiler.profileKernel(l,c,(()=>i())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs):t=i()})),s&&this.addTapeNode(l,c,t,p,n,h),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map((e=>null!=c[e]?c[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){const t=e.map((e=>this.keep(this.clone(e))));return t}getTensorsForGradient(e,t,n){const s=ds(e);if(null!=s){const e=s.inputsToSave||[],r=s.outputsToSave||[];let a;s.saveAllInputs?(u(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),a=Object.keys(t).map((e=>t[e]))):a=e.map((e=>t[e]));const i=n.filter(((e,t)=>r[t]));return a.concat(i)}return[]}makeTensor(e,t,n,s){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;"string"===n&&A(e[0])&&(r=e.map((e=>ir(e))));const a=s.write(r,t,n),i=new vr(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),"string"===n){const e=this.state.tensorInfo.get(a),t=E(r);this.state.numBytes+=t-e.bytes,e.bytes=t}return i}makeTensorFromDataId(e,t,n,s){const r={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(r,s)}makeTensorFromTensorInfo(e,t){const{dataId:n,shape:s,dtype:r}=e,a=new vr(s,r,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),null!=s&&s!==e.dtype&&(e=e.cast(s));const r=new Nr(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[r.name])throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*$(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 Nr||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*$(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,s,r,a){const i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=ds(e);null!=o&&(s=o.gradFunc),null!=s&&(i.gradient=e=>(e=e.map(((e,t)=>{if(null==e){const e=n[t],s=B(e.size,e.dtype);return this.makeTensor(s,e.shape,e.dtype)}return e})),s(e.length>1?e:e[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){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=Or(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 s=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!==s.id||this.track(e)}))}gradients(e,t,n,s=!1){if(u(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 r=this.scopedRun((()=>this.startTape()),(()=>this.endTape()),(()=>this.tidy("forward",e)));u(r instanceof vr,(()=>"The result y returned by f() must be a tensor."));const a=function(e,t,n){const s={},r={};for(let e=0;e<t.length;e++)s[t[e].id]=!0;for(let n=0;n<e.length;n++){const a=e[n],i=a.inputs;for(const e in i){const n=i[e];let o=!1;for(let e=0;e<t.length;e++)if(s[n.id]){a.outputs.forEach((e=>s[e.id]=!0)),o=!0,r[a.id]=!0;break}if(o)break}}const a={};a[n.id]=!0;const i={};for(let t=e.length-1;t>=0;t--){const n=e[t],s=n.inputs;for(let e=0;e<n.outputs.length;e++)if(a[n.outputs[e].id]){for(const e in s)a[s[e].id]=!0,i[n.id]=!0;break}}const o=[];for(let t=0;t<e.length;t++){const n=e[t];if(r[n.id]&&i[n.id]){const e={};for(const t in n.inputs){const r=n.inputs[t];s[r.id]&&(e[t]=r)}const t=Object.assign({},n);t.inputs=e,t.outputs=n.outputs,o.push(t)}}return o}(this.state.activeTape,t,r);if(!s&&0===a.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",(()=>{const e={};e[r.id]=null==n?function(e){const t=P(d(e),"float32");return Vr.makeTensor(t,e,"float32")}(r.shape):n,function(e,t,n,s){for(let r=t.length-1;r>=0;r--){const a=t[r],i=[];if(a.outputs.forEach((t=>{const n=e[t.id];null!=n?i.push(n):i.push(null)})),null==a.gradient)throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);const o=a.gradient(i);for(const t in a.inputs){if(!(t in o))throw new Error(`Cannot backprop through input ${t}. Available gradients found: ${Object.keys(o)}.`);const r=n((()=>o[t]()));if("float32"!==r.dtype)throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${t} must have 'float32' dtype, but has '${r.dtype}'`);const i=a.inputs[t];if(!f(r.shape,i.shape))throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${t}' has shape '${r.shape}', which does not match the shape of the input '${i.shape}'`);if(null==e[i.id])e[i.id]=r;else{const t=e[i.id];e[i.id]=s(t,r),t.dispose()}}}}(e,a,(e=>this.tidy(e)),Ur);const s=t.map((t=>e[t.id]));return 0===this.state.gradientDepth&&(this.state.activeTape.forEach((e=>{for(const t of e.saved)t.dispose()})),this.state.activeTape=null),{value:r,grads:s}}))}customGrad(e){return u(D(e),(()=>"The f passed in customGrad(f) must be a function.")),(...t)=>{let n;u(t.every((e=>e instanceof vr)),(()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors"));const s={};t.forEach(((e,t)=>{s[t]=e}));return this.runKernelFunc({forwardFunc:(s,r)=>(n=e(...t,r),u(n.value instanceof vr,(()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor")),u(D(n.gradFunc),(()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.")),n.value),backwardsFunc:(e,s)=>{const r=n.gradFunc(e,s),a=Array.isArray(r)?r:[r];u(a.length===t.length,(()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).")),u(a.every((e=>e instanceof vr)),(()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."));const i={};return a.forEach(((e,t)=>{i[t]=()=>e})),i},inputs:s})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){const t=rr(),n=await this.backend.time(e);return n.wallMs=rr()-t,n}track(e){return null!=this.state.activeScope&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return 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Got strides ${n} and dilations '1'`));let i=a,o=!1;3===a.rank&&(o=!0,i=tl(a,[1,a.shape[0],a.shape[1],a.shape[2]])),u(4===i.rank,(()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`)),el("avgPool",s,r);const l={x:i},c={filterSize:t,strides:n,pad:s,dimRoundingMode:r};let h=Vr.runKernel(de,l,c);return h=Ja(h,a.dtype),o?tl(h,[h.shape[1],h.shape[2],h.shape[3]]):h}});const sl=ta({avgPool3d_:function(e,t,n,s,r,a="NDHWC"){const i=Jr(e,"x","avgPool3d","float32");let o=i,l=!1;4===i.rank&&(l=!0,o=tl(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),u(5===o.rank,(()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`)),u("NDHWC"===a,(()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`)),el("avgPool3d",s,r);const c={x:o},h={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a};let p=Vr.runKernel(me,c,h);return p=Ja(p,o.dtype),l?tl(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}});const rl=ta({concat_:function(e,t=0){u(e.length>=1,(()=>"Pass at least one tensor to concat"));const n=Qr(e,"tensors","concat","string_or_numeric");if("complex64"===n[0].dtype&&n.forEach((e=>{if("complex64"!==e.dtype)throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${e.dtype}. `)})),1===n.length)return Qa(n[0]);const s=n,r={axis:t};return Vr.runKernel(Ce,s,r)}});const al=ta({sigmoid_:function(e){const t={x:Jr(e,"x","sigmoid","float32")};return Vr.runKernel($n,t)}});const il=ta({slice_:function(e,t,n){const s=Jr(e,"x","slice","string_or_numeric");if(0===s.rank)throw new Error("Slicing scalar is not possible");const r={x:s},a={begin:t,size:n};return Vr.runKernel(In,r,a)}});const ol=ta({tanh_:function(e){const t={x:Jr(e,"x","tanh","float32")};return Vr.runKernel(Kn,t)}});const ll=ta({basicLSTMCell_:function(e,t,n,s,r,a){const i=Jr(e,"forgetBias","basicLSTMCell"),o=Jr(t,"lstmKernel","basicLSTMCell"),l=Jr(n,"lstmBias","basicLSTMCell"),u=Jr(s,"data","basicLSTMCell"),c=Jr(r,"c","basicLSTMCell"),h=Jr(a,"h","basicLSTMCell"),p=rl([u,h],1),d=bi(p,o),f=Io(d,l),m=f.shape[0],g=f.shape[1]/4,y=[m,g],b=il(f,[0,0],y),x=il(f,[0,g],y),w=il(f,[0,2*g],y),v=il(f,[0,3*g],y),k=Io(Co(al(b),ol(x)),Co(c,al(Io(i,w))));return[k,Co(ol(k),al(v))]}});const ul=ta({batchToSpaceND_:function(e,t,n){const s=Jr(e,"x","batchToSpaceND"),r=t.reduce(((e,t)=>e*t));u(s.rank>=1+t.length,(()=>`input rank is ${s.rank} but should be > than blockShape.length ${t.length}`)),u(n.length===t.length,(()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`)),u(s.shape[0]%r==0,(()=>`input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`));const a={x:s},i={blockShape:t,crops:n};return Vr.runKernel(be,a,i)}});const cl=ta({batchNorm_:function(e,t,n,s,r,a){null==a&&(a=.001);const i=Jr(e,"x","batchNorm"),o=Jr(t,"mean","batchNorm"),l=Jr(n,"variance","batchNorm");let c,h;null!=r&&(c=Jr(r,"scale","batchNorm")),null!=s&&(h=Jr(s,"offset","batchNorm")),u(o.rank===l.rank,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),u(null==h||o.rank===h.rank,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),u(null==c||o.rank===c.rank,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));const p=function(e){let t;return t=0===e.rank||1===e.rank?tl(e,[1,1,1,e.size]):2===e.rank?tl(e,[1,1,e.shape[0],e.shape[1]]):3===e.rank?tl(e,[1,e.shape[0],e.shape[1],e.shape[2]]):e,t}(i),d={x:p,scale:c,offset:h,mean:o,variance:l},f={varianceEpsilon:a},m=Vr.runKernel(lt,d,f);return tl(m,i.shape)}});const hl=ta({batchNorm2d_:function(e,t,n,s,r,a){const i=Jr(e,"x","batchNorm"),o=Jr(t,"mean","batchNorm"),l=Jr(n,"variance","batchNorm");let c,h;return null!=r&&(c=Jr(r,"scale","batchNorm")),null!=s&&(h=Jr(s,"offset","batchNorm")),u(2===i.rank,(()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`)),u(2===o.rank||1===o.rank,(()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`)),u(2===l.rank||1===l.rank,(()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`)),null!=c&&u(2===c.rank||1===c.rank,(()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`)),null!=h&&u(2===h.rank||1===h.rank,(()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${h.rank}.`)),cl(i,o,l,h,c,a)}});const pl=ta({batchNorm3d_:function(e,t,n,s,r,a){const i=Jr(e,"x","batchNorm"),o=Jr(t,"mean","batchNorm"),l=Jr(n,"variance","batchNorm");let c,h;return null!=r&&(c=Jr(r,"scale","batchNorm")),null!=s&&(h=Jr(s,"offset","batchNorm")),u(3===i.rank,(()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`)),u(3===o.rank||1===o.rank,(()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`)),u(3===l.rank||1===l.rank,(()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`)),null!=c&&u(3===c.rank||1===c.rank,(()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`)),null!=h&&u(3===h.rank||1===h.rank,(()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${h.rank}.`)),cl(i,o,l,h,c,a)}});const dl=ta({batchNorm4d_:function(e,t,n,s,r,a){const i=Jr(e,"x","batchNorm"),o=Jr(t,"mean","batchNorm"),l=Jr(n,"variance","batchNorm");let c,h;return null!=r&&(c=Jr(r,"scale","batchNorm")),null!=s&&(h=Jr(s,"offset","batchNorm")),u(4===i.rank,(()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`)),u(4===o.rank||1===o.rank,(()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`)),u(4===l.rank||1===l.rank,(()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`)),null!=c&&u(4===c.rank||1===c.rank,(()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`)),null!=h&&u(4===h.rank||1===h.rank,(()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${h.rank}.`)),cl(i,o,l,h,c,a)}});const fl=ta({bincount_:function(e,t,n){const s=Jr(e,"x","bincount"),r=Jr(t,"weights","bincount");u("int32"===s.dtype,(()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`)),u(n>=0,(()=>`size must be non-negative, but got ${n}.`)),u(r.size===s.size||0===r.size,(()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`));const a={x:s,weights:r},i={size:n};return Vr.runKernel(xe,a,i)}});const ml=ta({broadcastArgs_:function(e,t){const n=Jr(e,"s0","broadcastArgs","int32"),s=Jr(t,"s1","broadcastArgs","int32");if(1!==n.rank)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(1!==s.rank)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);const r={s0:n,s1:s};return Vr.runKernel(ve,r)}});const gl=ta({broadcastTo_:function(e,t){let n=Jr(e,"broadcastTo","x");const s=n.shape;if(V(t),t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){const e=n.shape.slice();for(;e.length<t.length;)e.unshift(1);n=tl(n,e)}const r=n.shape,a=Array.from(t);for(let e=t.length-1;e>=0;e--)if(r[e]===t[e])a[e]=1;else if(1!==n.shape[e])throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);if(0===a.map(((e,t)=>e>1?t:-1)).filter((e=>e>=0)).length)return Qa(n);const i={x:n},o={reps:a};return Vr.runKernel(Xn,i,o)}});const yl=ta({ceil_:function(e){const t={x:Jr(e,"x","ceil","float32")};return Vr.runKernel(Ne,t)}});function bl(e,t,n){V(e);const s={shape:e,value:t,dtype:n};return Vr.runKernel(rt,{},s)}const xl=ta({clipByValue_:function(e,t,n){const s=Jr(e,"x","clipByValue");if(u(t<=n,(()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`)),t===n)return bl(s.shape,t,s.dtype);const r={x:s},a={clipValueMin:t,clipValueMax:n};return Vr.runKernel(Ie,r,a)}});const wl=ta({concat1d_:function(e){return rl(e,0)}});const vl=ta({concat2d_:function(e,t){return rl(e,t)}});const kl=ta({concat3d_:function(e,t){return rl(e,t)}});const Nl=ta({concat4d_:function(e,t){return rl(e,t)}});const Il=ta({conv2d_:function(e,t,n,s,r="NHWC",a=[1,1],i){const o=Jr(e,"x","conv2d","float32"),l=Jr(t,"filter","conv2d","float32");let c=o,h=!1;3===o.rank&&(h=!0,c=tl(o,[1,o.shape[0],o.shape[1],o.shape[2]])),u(4===c.rank,(()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`)),u(4===l.rank,(()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`)),el("conv2d",s,i);const p="NHWC"===r?c.shape[3]:c.shape[1];u(p===l.shape[2],(()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`)),u(Jo(n,a),(()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`));const d={x:c,filter:l},f={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:i},m=Vr.runKernel($e,d,f);return h?tl(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const Sl=ta({conv1d_:function(e,t,n,s,r="NWC",a=1,i){const o=Jr(e,"x","conv1d"),l=Jr(t,"filter","conv1d");let c=o,h=!1;2===o.rank&&(h=!0,c=tl(o,[1,o.shape[0],o.shape[1]])),u(3===c.rank,(()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`)),u(3===l.rank,(()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`)),el("conv1d",s,i),u(c.shape[2]===l.shape[1],(()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`)),u(Jo(n,a),(()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`)),u("NWC"===r,(()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`));const p=tl(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=tl(c,[c.shape[0],1,c.shape[1],c.shape[2]]),f=Il(d,p,[1,n],s,"NHWC",[1,a],i);return tl(f,h?[f.shape[2],f.shape[3]]:[f.shape[0],f.shape[2],f.shape[3]])}});const Tl=ta({conv2DBackpropInput_:function(e,t,n,s,r,a="NHWC",i){u(e.length===t.rank,(()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`));let o=e,l=t,c=!1;3===t.rank&&(c=!0,l=tl(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),u(4===o.length,(()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`)),u(4===l.rank,(()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`)),u(4===n.rank,(()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`));const h="NHWC"===a?o[3]:o[1],p="NHWC"===a?l.shape[3]:l.shape[1];u(h===n.shape[2],(()=>`Error in conv2dDerInput: depth of input (${h}) must match input depth for filter ${n.shape[2]}.`)),u(p===n.shape[3],(()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`)),el("conv2dDerInput",r,i);const d={dy:l,filter:n},f={strides:s,pad:r,dataFormat:a,dimRoundingMode:i,inputShape:o},m=Vr.runKernel(Ae,d,f);return c?tl(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const Cl=ta({conv2dTranspose_:function(e,t,n,s,r,a){const i=Jr(e,"x","conv2dTranspose"),o=Jr(t,"filter","conv2dTranspose");return Tl(n,i,o,s,r,"NHWC",a)}});const $l=ta({conv3d_:function(e,t,n,s,r="NDHWC",a=[1,1,1]){const i=Jr(e,"x","conv3d"),o=Jr(t,"filter","conv3d");let l=i,c=!1;4===i.rank&&(c=!0,l=tl(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),u(5===l.rank,(()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`)),u(5===o.rank,(()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`)),u(l.shape[4]===o.shape[3],(()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`)),u(Jo(n,a),(()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`)),u("NDHWC"===r,(()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`));const h={x:l,filter:o},p={strides:n,pad:s,dataFormat:r,dilations:a},d=Vr.runKernel(Re,h,p);return c?tl(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}});const El=ta({conv3DBackpropInput_:function(e,t,n,s,r){u(e.length===t.rank,(()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`));let a=e,i=t,o=!1;4===t.rank&&(o=!0,i=tl(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);const l=a[4],c=i.shape[4];u(5===a.length,(()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`)),u(5===i.rank,(()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`)),u(5===n.rank,(()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`)),u(l===n.shape[3],(()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`)),u(c===n.shape[4],(()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`));const h={dy:i,filter:n},p={pad:r,strides:s,inputShape:a},d=Vr.runKernel(Fe,h,p);return o?tl(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}});const Al=ta({conv3dTranspose_:function(e,t,n,s,r){const a=Jr(e,"x","conv3dTranspose"),i=Jr(t,"filter","conv3dTranspose");return El(n,a,i,s,r)}});const Rl=ta({cos_:function(e){const t={x:Jr(e,"x","cos","float32")};return Vr.runKernel(De,t)}});const _l=ta({cosh_:function(e){const t={x:Jr(e,"x","cosh","float32")};return Vr.runKernel(Oe,t)}});const Fl=ta({cumprod_:function(e,t=0,n=!1,s=!1){const r={x:Jr(e,"x","cumprod")},a={axis:t,exclusive:n,reverse:s};return Vr.runKernel(Me,r,a)}});const Dl=ta({cumsum_:function(e,t=0,n=!1,s=!1){const r={x:Jr(e,"x","cumsum")},a={axis:t,exclusive:n,reverse:s};return Vr.runKernel(Le,r,a)}});const Ol=ta({denseBincount_:function(e,t,n,s=!1){const r=Jr(e,"x","denseBincount"),a=Jr(t,"weights","denseBincount");u("int32"===r.dtype,(()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`)),u(r.rank<=2,(()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`)),u(n>=0,(()=>`size must be non-negative, but got ${n}.`)),u(a.size===r.size||0===a.size,(()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`));const i={x:r,weights:a},o={size:n,binaryOutput:s};return Vr.runKernel(Pe,i,o)}});const Ml=ta({depthToSpace_:function(e,t,n="NHWC"){const s=Jr(e,"x","depthToSpace","float32"),r="NHWC"===n?s.shape[1]:s.shape[2],a="NHWC"===n?s.shape[2]:s.shape[3],i="NHWC"===n?s.shape[3]:s.shape[1];u(t>1,(()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`)),u(r*t>=0,(()=>`Negative dimension size caused by overflow when multiplying\n    ${r} and ${t}  for depthToSpace with input shape\n    ${s.shape}`)),u(a*t>=0,(()=>`Negative dimension size caused by overflow when multiplying\n    ${a} and ${t} for depthToSpace with input shape\n        ${s.shape}`)),u(i%(t*t)==0,(()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${s.shape}`));const o={x:s},l={blockSize:t,dataFormat:n};return Vr.runKernel(Be,o,l)}});const Ll=ta({depthwiseConv2d_:function(e,t,n,s,r="NHWC",a=[1,1],i){const o=Jr(e,"x","depthwiseConv2d","float32"),l=Jr(t,"filter","depthwiseConv2d","float32");let c=o,h=!1;3===o.rank&&(h=!0,c=tl(o,[1,o.shape[0],o.shape[1],o.shape[2]])),u(4===c.rank,(()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`)),u(4===l.rank,(()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`));const p="NHWC"===r?c.shape[3]:c.shape[1];u(p===l.shape[2],(()=>`Error in depthwiseConv2d: number of input channels (${p}) must match the inChannels dimension in filter ${l.shape[2]}.`)),el("depthwiseConv2d",s,i);const d={x:c,filter:l},f={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:i},m=Vr.runKernel(We,d,f);return h?tl(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const zl=ta({diag_:function(e){const t={x:Jr(e,"x","diag")};return Vr.runKernel(Ge,t)}});const Pl=ta({dilation2d_:function(e,t,n,s,r=[1,1],a="NHWC"){const i=Jr(e,"x","dilation2d"),o=Jr(t,"filter","dilation2d");u(3===i.rank||4===i.rank,(()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`)),u(3===o.rank,(()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`)),u("NHWC"===a,(()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`));let l=i,c=!1;3===i.rank&&(l=tl(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);const h={x:l,filter:o},p={strides:n,pad:s,dilations:r},d=Vr.runKernel(He,h,p);return c?tl(d,[d.shape[1],d.shape[2],d.shape[3]]):d}});const Bl=ta({equal_:function(e,t){let n=Jr(e,"a","equal","string_or_numeric"),s=Jr(t,"b","equal","string_or_numeric");[n,s]=_r(n,s),Li(n.shape,s.shape);const r={a:n,b:s};return Vr.runKernel(Qe,r)}});const Wl=ta({where_:function(e,t,n){const s=Jr(t,"a","where"),r=Jr(n,"b","where"),a=Jr(e,"condition","where","bool"),i=Li(Li(a.shape,s.shape),r.shape),o={condition:gl(a,i),t:gl(s,i),e:gl(r,i)};return Vr.runKernel(kn,o)}});const Vl=ta({zerosLike_:function(e){const t={x:Jr(e,"x","zerosLike")};return Vr.runKernel(ns,t)}});const Ul=ta({divNoNan_:function(e,t){let n=Jr(e,"a","div"),s=Jr(t,"b","div");[n,s]=_r(n,s);const r=To(n,s),a=Vl(r),i=Bl(s,a);return Wl(i,a,r)}});const Gl=ta({dot_:function(e,t){const n=Jr(e,"t1","dot"),s=Jr(t,"t2","dot");u(!(1!==n.rank&&2!==n.rank||1!==s.rank&&2!==s.rank),(()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`));const r=1===n.rank?n.size:n.shape[1],a=1===s.rank?s.size:s.shape[0];if(u(r===a,(()=>`Error in dot: inner dimensions of inputs must match, but got ${r} 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a=r,i=!1;3===r.rank&&(i=!0,a=tl(r,[1,r.shape[0],r.shape[1],r.shape[2]]));const[]=t,o={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},c=Vr.runKernel(pn,o,l);return i?tl(c,[c.shape[1],c.shape[2],c.shape[3]]):c}});const Ip=ta({threshold_:function(e,t="binary",n=!1,s=.5){const r=Jr(e,"image","threshold"),a=r.shape[0]*r.shape[1];let i,o,l,c,h=Co(bh([s]),255);if(u(3===r.rank,(()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`)),u(3===r.shape[2]||1===r.shape[2],(()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`)),u("int32"===r.dtype||"float32"===r.dtype,(()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`)),u("otsu"===t||"binary"===t,(()=>`Method must be binary or otsu, but was ${t}`)),3===r.shape[2]){[i,o,l]=ch(r,[1,1,1],-1);const e=Co(i,.2989),t=Co(o,.587),n=Co(l,.114);c=Io(Io(e,t),n)}else c=e;if("otsu"===t){h=function(e,t){let n,s,r,a,i,o,l=bh([-1]),u=bh([0]),c=bh([0]);for(let 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Vr.runKernel(Zn,l,c)}});const Tp=ta({bandPart_:function(e,t,n){u(t%1==0,(()=>`bandPart(): numLower must be an integer, got ${t}.`)),u(n%1==0,(()=>`bandPart(): numUpper must be an integer, got ${n}.`));const s=Jr(e,"a","bandPart");u(s.rank>=2,(()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`));const r=s.shape,[a,i]=s.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=a),n<0&&(n=i);const o=tl(Pc(0,a,1,"int32"),[-1,1]),l=Pc(0,i,1,"int32"),c=Mu(o,l),h=Pu(Tu(c,au(+t,"int32")),wu(c,au(-n,"int32"))),p=Zu([a,i],s.dtype);return tl(fh(Ch(tl(s,[-1,a,i])).map((e=>Wl(h,e,p)))),r)}});const Cp=ta({gramSchmidt_:function(e){let t;if(Array.isArray(e)){t=!1,u(null!=e&&e.length>0,(()=>"Gram-Schmidt process: input must not be null, undefined, or empty"));const n=e[0].shape[0];for(let 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t=il(a,[e,e],[n-e,1]),l=cu(t),u=il(a,[e,e],[1,1]),c=Wl(xu(u,0),xh([[-1]]),xh([[1]])),h=Mu(u,Co(c,l)),p=To(t,h);o=1===p.shape[0]?Qa(i):rl([i,il(p,[1,0],[p.shape[0]-1,p.shape[1]])],0);const d=Ai(To(bi(c,h),l)),f=il(a,[e,0],[n-e,s]),m=Co(d,o),g=_i(o);if(0===e)a=Mu(f,bi(m,bi(g,f)));else{const t=Mu(f,bi(m,bi(g,f)));a=rl([il(a,[0,0],[e,s]),t],0)}const y=_i(m),b=il(r,[0,e],[n,r.shape[1]-e]);if(0===e)r=Mu(b,bi(bi(b,o),y));else{const t=Mu(b,bi(bi(b,o),y));r=rl([il(r,[0,0],[n,e]),t],1)}return[o,a,r]})),Ii([t,l,u])}return!t&&n>s&&(r=il(r,[0,0],[n,s]),a=il(a,[0,0],[s,s])),[r,a]}))}const Ep=ta({qr_:function(e,t=!1){if(u(e.rank>=2,(()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`)),2===e.rank)return $p(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce(((e,t)=>e*t)),s=Ch(tl(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],a=[];s.forEach((e=>{const[n,s]=$p(e,t);r.push(n),a.push(s)}));return[tl(fh(r,0),e.shape),tl(fh(a,0),e.shape)]}}});var Ap;(Ap=e.Reduction||(e.Reduction={}))[Ap.NONE=0]="NONE",Ap[Ap.MEAN=1]="MEAN",Ap[Ap.SUM=2]="SUM",Ap[Ap.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS";const Rp=ta({computeWeightedLoss_:function(t,n,s=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const r=Jr(t,"losses","computeWeightedLoss");let a=null;null!=n&&(a=Jr(n,"weights","computeWeightedLoss"));const i=null==a?r:Co(r,a);if(s===e.Reduction.NONE)return i;if(s===e.Reduction.SUM)return lu(i);if(s===e.Reduction.MEAN){if(null==a)return Yu(i);{const e=r.size/a.size,t=To(lu(i),lu(a));return e>1?To(t,au(e)):t}}if(s===e.Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==a)return To(lu(i),au(r.size));{const e=Co(a,Ju(r.shape)),t=Ja(lu(ic(e,au(0))),"float32");return To(lu(i),t)}}throw Error(`Unknown reduction: ${s}`)}});const _p=ta({absoluteDifference_:function(t,n,s,r=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const a=Jr(t,"labels","absoluteDifference"),i=Jr(n,"predictions","absoluteDifference");let o=null;null!=s&&(o=Jr(s,"weights","absoluteDifference")),c(a.shape,i.shape,"Error in absoluteDifference: ");const l=$o(Mu(a,i));return Rp(l,o,r)}});const Fp=ta({cosineDistance_:function(t,n,s,r,a=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=Jr(t,"labels","cosineDistance"),o=Jr(n,"predictions","cosineDistance");let l=null;null!=r&&(l=Jr(r,"weights","cosineDistance")),c(i.shape,o.shape,"Error in cosineDistance: ");const u=au(1),h=Mu(u,lu(Co(i,o),s,!0));return Rp(h,l,a)}});const Dp=ta({hingeLoss_:function(t,n,s,r=e.Reduction.SUM_BY_NONZERO_WEIGHTS){let a=Jr(t,"labels","hingeLoss");const i=Jr(n,"predictions","hingeLoss");let o=null;null!=s&&(o=Jr(s,"weights","hingeLoss")),c(a.shape,i.shape,"Error in hingeLoss: ");const l=au(1);a=Mu(Co(au(2),a),l);const u=Wc(Mu(l,Co(a,i)));return Rp(u,o,r)}});const Op=ta({huberLoss_:function(t,n,s,r=1,a=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=Jr(t,"labels","huberLoss"),o=Jr(n,"predictions","huberLoss");let l=null;null!=s&&(l=Jr(s,"weights","huberLoss")),c(i.shape,o.shape,"Error in huberLoss: ");const u=au(r),h=$o(Mu(o,i)),p=ec(h,u),d=Mu(h,p),f=Io(Co(au(.5),ou(p)),Co(u,d));return Rp(f,l,a)}});const Mp=ta({logLoss_:function(t,n,s,r=1e-7,a=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=Jr(t,"labels","logLoss"),o=Jr(n,"predictions","logLoss");let l=null;null!=s&&(l=Jr(s,"weights","logLoss")),c(i.shape,o.shape,"Error in logLoss: ");const u=au(1),h=au(r),p=Ai(Co(i,Eu(Io(o,h)))),d=Co(Mu(u,i),Eu(Io(Mu(u,o),h))),f=Mu(p,d);return Rp(f,l,a)}});const Lp=ta({meanSquaredError_:function(t,n,s,r=e.Reduction.SUM_BY_NONZERO_WEIGHTS){const a=Jr(t,"labels","meanSquaredError"),i=Jr(n,"predictions","meanSquaredError");let o=null;null!=s&&(o=Jr(s,"weights","meanSquaredError")),c(a.shape,i.shape,"Error in meanSquaredError: ");const l=ph(a,i);return Rp(l,o,r)}});const zp=ta({sigmoidCrossEntropy_:function(t,n,s,r=0,a=e.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=Jr(t,"multiClassLabels","sigmoidCrossEntropy");const o=Jr(n,"logits","sigmoidCrossEntropy");let l=null;if(null!=s&&(l=Jr(s,"weights","sigmoidCrossEntropy")),c(i.shape,o.shape,"Error in sigmoidCrossEntropy: "),r>0){const e=au(r),t=au(1),n=au(.5);i=Io(Co(i,Mu(t,e)),Co(n,e))}const u=function(e,t){const n=Jr(e,"labels","sigmoidCrossEntropyWithLogits"),s=Jr(t,"logits","sigmoidCrossEntropyWithLogits");c(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const r=Wc(s),a=Co(s,n),i=Au(pu(Ai($o(s))));return Io(Mu(r,a),i)}(i,o);return Rp(u,l,a)}});const Pp=ta({softmaxCrossEntropy_:function(t,n,s,r=0,a=e.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=Jr(t,"onehotLabels","softmaxCrossEntropy");const o=Jr(n,"logits","softmaxCrossEntropy");let l=null;if(null!=s&&(l=Jr(s,"weights","softmaxCrossEntropy")),c(i.shape,o.shape,"Error in softmaxCrossEntropy: "),r>0){const e=au(r),t=au(1),n=au(i.shape[1]);i=Io(Co(i,Mu(t,e)),To(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 s=_u(((e,t,s)=>{const r=zu(t,[n],!0),a=Mu(Ja(t,"float32"),r);s([e,a]);const i=Ai(Co(a,e));return{value:lu(i,[n]),gradFunc:(e,t)=>{const[s,r]=t,a=Zl(e.shape,[n]);return[Co(tl(e,a),Mu(Ja(s,"float32"),pu(r))),Co(tl(e,a),Mu(pu(r),Ja(s,"float32")))]}}}));return s(e,t)}(i,o);return Rp(u,l,a)}});const Bp=ta({sparseFillEmptyRows_:function(e,t,n,s){const r=Jr(e,"indices","sparseFillEmptyRows","int32"),a=Jr(t,"values","sparseFillEmptyRows"),i=Jr(n,"denseShape","sparseFillEmptyRows","int32"),o=Jr(s,"defaultValue","sparseFillEmptyRows",a.dtype);if(2!==r.rank)throw new Error(`Indices should be Tensor2D but received shape\n        ${r.shape}`);if(1!==a.rank)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(1!==i.rank)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(0!==o.rank)throw new Error(`Default value should be a scalar but received shape ${o.shape}`);const l={indices:r,values:a,denseShape:i,defaultValue:o},u=Vr.runKernel(On,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}});const Wp=ta({sparseReshape_:function(e,t,n){const s=Jr(e,"inputIndices","sparseReshape","int32"),r=Jr(t,"inputShape","sparseReshape","int32"),a=Jr(n,"newShape","sparseReshape","int32");if(2!==s.rank)throw new Error(`Input indices should be Tensor2D but received shape\n        ${s.shape}`);if(1!==r.rank)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(1!==a.rank)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);const i={inputIndices:s,inputShape:r,newShape:a},o=Vr.runKernel(Mn,i);return{outputIndices:o[0],outputShape:o[1]}}});const Vp=ta({sparseSegmentMean_:function(e,t,n){const s=Jr(e,"data","sparseSegmentMean"),r=Jr(t,"indices","sparseSegmentMean","int32"),a=Jr(n,"segmentIds","sparseSegmentMean","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==r.rank)throw new Error(`Indices should be Tensor1D but received shape\n          ${r.shape}`);if(1!==a.rank)throw new Error(`Segment ids should be Tensor1D but received shape\n          ${a.shape}`);const i={data:s,indices:r,segmentIds:a};return Vr.runKernel(Ln,i)}});const Up=ta({sparseSegmentSum_:function(e,t,n){const s=Jr(e,"data","sparseSegmentSum"),r=Jr(t,"indices","sparseSegmentSum","int32"),a=Jr(n,"segmentIds","sparseSegmentSum","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==r.rank)throw new Error(`Indices should be Tensor1D but received shape\n         ${r.shape}`);if(1!==a.rank)throw new Error(`Segment ids should be Tensor1D but received shape\n         ${a.shape}`);const i={data:s,indices:r,segmentIds:a};return Vr.runKernel(zn,i)}});const Gp=ta({stringNGrams_:function(e,t,n,s,r,a,i,o){const l=Jr(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=Jr(t,"dataSplits","stringNGrams");if("int32"!==u.dtype)throw new Error("Data splits must be of datatype int32");const c={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:i,preserveShortSequences:o},h={data:l,dataSplits:u},p=Vr.runKernel(Un,h,c);return{nGrams:p[0],nGramsSplits:p[1]}}});const Hp=ta({stringSplit_:function(e,t,n=!0){const s=Jr(e,"input","stringSplit","string"),r=Jr(t,"delimiter","stringSplit","string");if(1!==s.rank)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(0!==r.rank)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);const a={skipEmpty:n},i={input:s,delimiter:r},o=Vr.runKernel(Gn,i,a);return{indices:o[0],values:o[1],shape:o[2]}}});const jp=ta({stringToHashBucketFast_:function(e,t){const n=Jr(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");const r={input:n};return Vr.runKernel(Hn,r,s)}}),qp={fft:oh,ifft:lh,rfft:hh,irfft:uh},Kp={hammingWindow:Jh,hannWindow:Qh,frame:ep,stft:tp},Xp={flipLeftRight:sp,grayscaleToRGB:rp,resizeNearestNeighbor:Np,resizeBilinear:kp,rotateWithOffset:ap,cropAndResize:np,nonMaxSuppression:op,nonMaxSuppressionAsync:yp,nonMaxSuppressionWithScore:bp,nonMaxSuppressionWithScoreAsync:xp,nonMaxSuppressionPadded:wp,nonMaxSuppressionPaddedAsync:vp,threshold:Ip,transform:Sp},Yp={bandPart:Tp,gramSchmidt:Cp,qr:Ep},Zp={absoluteDifference:_p,computeWeightedLoss:Rp,cosineDistance:Fp,hingeLoss:Dp,huberLoss:Op,logLoss:Mp,meanSquaredError:Lp,sigmoidCrossEntropy:zp,softmaxCrossEntropy:Pp},Jp={sparseFillEmptyRows:Bp,sparseReshape:Wp,sparseSegmentMean:Vp,sparseSegmentSum:Up},Qp={stringNGrams:Gp,stringSplit:Hp,stringToHashBucketFast:jp};class ed extends fo{minimize(e,t=!1,n){const{value:s,grads:r}=this.computeGradients(e,n);if(null!=n){const e=n.map((e=>({name:e.name,tensor:r[e.name]})));this.applyGradients(e)}else this.applyGradients(r);return Ii(r),t?s:(s.dispose(),null)}get iterations(){return null==this.iterations_&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Ru(e,t)}dispose(){null!=this.iterations_&&Ii(this.iterations_)}async saveIterations(){return null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:au(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(ed,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});class td extends ed{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=Vr.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const s=Vr.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:Ni((()=>Vl(s).variable(false)))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:Ni((()=>Vl(s).variable(false)))});const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const a=this.accumulatedGrads[n].variable,i=this.accumulatedUpdates[n].variable;Ni((()=>{const e=Io(Co(a,this.rho),Co(ou(r),1-this.rho)),t=Co(To(iu(Io(i,this.epsilon)),iu(Io(a,this.epsilon))),r),n=Io(Co(i,this.rho),Co(ou(t),1-this.rho));a.assign(e),i.assign(n);const o=Io(Co(t,-this.learningRate),s);s.assign(o)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&(Ii(this.accumulatedGrads.map((e=>e.variable))),Ii(this.accumulatedUpdates.map((e=>e.variable))))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){const t=(e=await this.extractIterations(e)).length/2;this.accumulatedGrads=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)}))),this.accumulatedUpdates=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}td.className="Adadelta",go(td);class nd extends ed{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const s=Vr.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:Ni((()=>bl(s.shape,this.initialAccumulatorValue).variable(e)))}}const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const a=this.accumulatedGrads[n].variable;Ni((()=>{const e=Io(a,ou(r));a.assign(e);const t=Io(Co(To(r,iu(Io(e,Vr.backend.epsilon()))),-this.learningRate),s);s.assign(t)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&Ii(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)}}nd.className="Adagrad",go(nd);class sd extends ed{constructor(e,t,n,s=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ni((()=>{this.accBeta1=au(t).variable(),this.accBeta2=au(n).variable()})),null==s&&(this.epsilon=Vr.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);Ni((()=>{const n=Mu(1,this.accBeta1),s=Mu(1,this.accBeta2);t.forEach(((t,r)=>{const a=Vr.registeredVariables[t];null==this.accumulatedFirstMoment[r]&&(this.accumulatedFirstMoment[r]={originalName:`${t}/m`,variable:Ni((()=>Vl(a).variable(false)))}),null==this.accumulatedSecondMoment[r]&&(this.accumulatedSecondMoment[r]={originalName:`${t}/v`,variable:Ni((()=>Vl(a).variable(false)))});const i=Array.isArray(e)?e[r].tensor:e[t];if(null==i)return;const o=this.accumulatedFirstMoment[r].variable,l=this.accumulatedSecondMoment[r].variable,u=Io(Co(o,this.beta1),Co(i,1-this.beta1)),c=Io(Co(l,this.beta2),Co(ou(i),1-this.beta2)),h=To(u,n),p=To(c,s);o.assign(u),l.assign(c);const d=Io(Co(To(h,Io(iu(p),this.epsilon)),-this.learningRate),a);a.assign(d)})),this.accBeta1.assign(Co(this.accBeta1,this.beta1)),this.accBeta2.assign(Co(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&Ii(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&Ii(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),Ni((()=>{this.accBeta1.assign(ru(this.beta1,this.iterations_+1)),this.accBeta2.assign(ru(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)}}sd.className="Adam",go(sd);class rd extends ed{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ni((()=>{this.iteration=au(0).variable(),this.accBeta1=au(t).variable()})),null==s&&(this.epsilon=Vr.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);Ni((()=>{const n=Mu(1,this.accBeta1),s=To(-this.learningRate,Io(Co(this.iteration,this.decay),1));t.forEach(((t,r)=>{const a=Vr.registeredVariables[t];null==this.accumulatedFirstMoment[r]&&(this.accumulatedFirstMoment[r]={originalName:`${t}/m`,variable:Vl(a).variable(false)}),null==this.accumulatedWeightedInfNorm[r]&&(this.accumulatedWeightedInfNorm[r]={originalName:`${t}/v`,variable:Vl(a).variable(false)});const i=Array.isArray(e)?e[r].tensor:e[t];if(null==i)return;const o=this.accumulatedFirstMoment[r].variable,l=this.accumulatedWeightedInfNorm[r].variable,u=Io(Co(o,this.beta1),Co(i,1-this.beta1)),c=Co(l,this.beta2),h=$o(i),p=Xu(c,h);o.assign(u),l.assign(p);const d=Io(Co(To(s,n),To(u,Io(p,this.epsilon))),a);a.assign(d)})),this.iteration.assign(Io(this.iteration,1)),this.accBeta1.assign(Co(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&Ii(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&Ii(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)}}rd.className="Adamax",go(rd);class ad extends ed{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 s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const r=Vr.registeredVariables[t];Ni((()=>{const e=Io(Co(this.c,s),r);r.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=Si(au(-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)}}ad.className="SGD",go(ad);class id extends ad{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=au(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const s=Vr.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:Ni((()=>Vl(s).variable(e)))}}const r=this.accumulations[n].variable,a=Array.isArray(e)?e[n].tensor:e[t];null!=a&&Ni((()=>{let e;const t=Io(Co(this.m,r),a);e=this.useNesterov?Io(Co(this.c,Io(a,Co(t,this.m))),s):Io(Co(this.c,t),s),r.assign(t),s.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&Ii(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)}}id.className="Momentum",go(id);class od extends ed{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,null==s&&(this.epsilon=Vr.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 s=Vr.registeredVariables[t],r=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:Ni((()=>Vl(s).variable(r)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:Ni((()=>Vl(s).variable(r)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:Ni((()=>Vl(s).variable(r)))});const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;Ni((()=>{const e=Io(Co(i,this.decay),Co(ou(a),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,r=Io(Co(t,this.decay),Co(a,1-this.decay)),l=To(Co(a,this.learningRate),iu(Mu(e,Io(ou(r),this.epsilon)))),u=Io(Co(o,this.momentum),l);i.assign(e),t.assign(r),o.assign(u);const c=Mu(s,u);s.assign(c)}else{const e=Io(Co(i,this.decay),Co(ou(a),1-this.decay)),t=Io(Co(o,this.momentum),To(Co(a,this.learningRate),iu(Io(e,this.epsilon))));i.assign(e),o.assign(t);const n=Mu(s,t);s.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&Ii(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&Ii(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&Ii(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)}}od.className="RMSProp",go(od);class ld{static sgd(e){return new ad(e)}static momentum(e,t,n=!1){return new id(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new od(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new sd(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new td(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new rd(e,t,n,s,r)}static adagrad(e,t=.1){return new nd(e,t)}}const ud={sgd:ld.sgd,momentum:ld.momentum,adadelta:ld.adadelta,adagrad:ld.adagrad,rmsprop:ld.rmsprop,adamax:ld.adamax,adam:ld.adam},cd="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:"undefined"!=typeof setImmediate?setImmediate:e=>e();function hd(){return new Promise((e=>cd((()=>e()))))}function pd(e,t){const n=e[0].length;e.forEach(((e,t)=>{u(e.length===n,(()=>`Error in concat${n}D: rank of tensors[${t}] must be the same as the rank of the rest (${n})`))})),u(t>=0&&t<n,(()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`));const s=e[0];e.forEach(((e,r)=>{for(let a=0;a<n;a++)u(a===t||e[a]===s[a],(()=>`Error in concat${n}D: Shape of tensors[${r}] (${e}) does not match the shape of the rest (${s}) along the non-concatenated axis ${r}.`))}))}function dd(e,t){const n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var fd;function md(e,t,n){let s=new Array;if(null==n&&null==t)return s;if(null==t)for(;s.length<e+n.length;)s.push(-1);else s=t.slice();if(null==n)return s;if(e+n.length!==s.length)throw new Error(`rt input.shape and shape=${t} are incompatible: rt input.rank = ${e+n.length}, but shape.rank = ${s.length}`);for(let r=1;r<n.length;++r){const a=n[r],i=s[s.length-n.length+r],o=s[i];if(a>=0)if(o>=0){if(o!==a)throw new Error(`rt input.shape and shape=${t} are incompatible: rt input.shape[${r+e}] = ${a} but shape[${r+e}] = ${o}`)}else s[i]=a}return s}function gd(e){const t={FIRST_DIM_SIZE:fd.FIRST_DIM_SIZE,VALUE_ROWIDS:fd.VALUE_ROWIDS,ROW_LENGTHS:fd.ROW_LENGTHS,ROW_SPLITS:fd.ROW_SPLITS,ROW_LIMITS:fd.ROW_LIMITS,ROW_STARTS:fd.ROW_STARTS},n=[];for(const s of e){if(!(s in t))break;n.push(t[s])}return n}function yd(e){return 0===e.length?0:e[0]===fd.FIRST_DIM_SIZE?e.length-1:e.length}function 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yg(e){eg(pg,"PoolMode",e)}const bg=[];function xg(e,t){bg.push(e);try{const e=t();return bg.pop(),e}catch(e){throw bg.pop(),e}}function wg(e){if(!Ng(e))throw new Error("Not a valid tensor name: '"+e+"'");return(0===bg.length?"":bg.join("/")+"/")+e}function vg(e){if(!Ng(e))throw new Error("Not a valid tensor name: '"+e+"'");fg.has(e)||fg.set(e,0);const t=fg.get(e);if(fg.set(e,fg.get(e)+1),t>0){const n=`${e}_${t}`;return fg.set(n,1),n}return e}const kg=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function Ng(e){return!!e.match(kg)}function Ig(e,t,n){null==t&&(t=0),null==n&&(n=e.length);let s=1;for(let r=t;r<n;++r)s*=e[r];return s}function Sg(e){if(0===e.length)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let n=0;n<e.length;n++){const s=e[n];s<t&&(t=s)}return t}function Tg(e){if(0===e.length)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let n=0;n<e.length;n++){const s=e[n];s>t&&(t=s)}return t}function Cg(e,t){if(t<e)throw new Mm(`end (${t}) < begin (${e}) is 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ah(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new Mm(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}}))}function Dg(e,t,n,s){return Ni((()=>{switch(e.rank){case 1:return nh(e,t,n);case 2:switch(s){case 1:return _g(e,t,n);case 2:return Fg(e,t,n);default:throw new Mm(`The axis is not within the rank of the tensor ${s}`)}case 3:switch(s){case 1:return _g(e,t,n);case 2:return rh(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return Fg(e,t,n);default:throw new Mm(`The axis is not within the rank of the tensor ${s}`)}case 4:switch(s){case 1:return _g(e,t,n);case 2:return ah(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return ah(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return Fg(e,t,n);default:throw new Mm(`The axis is not within the rank of the tensor ${s}`)}default:throw new Mm(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}}))}function Og(e,t=-1){let n;return t<0&&(n=e[0].rank,t=0!==n?n:0),t===e[0].rank&&(t=-1),rl(e,t)}function Mg(e,t){switch(e.rank){case 1:return wl([e,t]);case 2:return vl([e,t],0);case 3:return kl([e,t],0);case 4:return Nl([e,t],0);default:throw new Mm(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function Lg(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new Mm(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return mu(e,t)}function zg(e,t=0,n=1,s,r){return Mc(e,t,n,s,r)}function Pg(e,t,n,s){if(e.rank<2||t.rank<2)throw new Lm(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){if(e.shape.slice(-1)[0]!==t.shape.slice(-2)[0])throw new Lm(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and  y shape = ${t.shape}`)}if(2===e.rank&&2===t.rank){return Yh({a:e,b:t,transposeA:!1,transposeB:!1,bias:s?Vg(e.rank,s,"channelsLast"):null,activation:n})}{const r=e.shape.slice(),a=r.pop();e=tl(e,[-1,a]);const i=t.shape.slice(),o=i.pop(),l=i.pop(),u=[...i,o],c=Array.from({length:t.rank},((e,n)=>0===n?t.rank-2:n<=t.rank-2?n-1:n));t=tl(_i(t,c),[l,-1]);const h=[...r,...u];return tl(Yh({a:e,b:t,transposeA:!1,transposeB:!1,bias:s?Vg(e.rank,s,"channelsLast"):null,activation:n}),h)}}function Bg(e,t,n){return Ni((()=>(t=Array.isArray(t)?bh(t,"int32"):Ja(t,"int32"),bu(e,t,n))))}function Wg(e){return Co(e,e)}function Vg(e,t,n){const s=t.shape;if(1!==t.rank&&t.rank!==e)throw new Mm(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(5===e){if("channelsFirst"===n)return 1===s.length?tl(t,[1,s[0],1,1,1]):tl(t,[1,s[3],s[0],s[1],s[2]]);if("channelsLast"===n)return 1===s.length?tl(t,[1,1,1,1,s[0]]):tl(t,[1].concat(s))}else if(4===e){if("channelsFirst"===n)return 1===s.length?tl(t,[1,s[0],1,1]):tl(t,[1,s[2],s[0],s[1]]);if("channelsLast"===n)return 1===s.length?tl(t,[1,1,1,s[0]]):tl(t,[1].concat(s))}else if(3===e){if("channelsFirst"===n)return 1===s.length?tl(t,[1,s[0],1]):tl(t,[1,s[1],s[0]]);if("channelsLast"===n)return 1===s.length?tl(t,[1,1,s[0]]):tl(t,[1].concat(s))}else if(e<3)return t;throw new Mm(`Unsupported input rank by biasAdd: ${t.rank}`)}function Ug(e,t,n){return Ni((()=>(null==n&&(n="channelsLast"),mg(n),Io(e,Vg(e.rank,t,n)))))}function Gg(e,t,n,s){return Ni((()=>Lh(e,t,n,s)))}function Hg(e,t,n=!1){return n?e():t()}const jg=["fanIn","fanOut","fanAvg"],qg=["normal","uniform","truncatedNormal"];class Kg extends fo{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}}class Xg extends Kg{apply(e,t){return Zu(e,t)}}Xg.className="Zeros",go(Xg);class Yg extends Kg{apply(e,t){return Ju(e,t)}}Yg.className="Ones",go(Yg);class Zg extends Kg{constructor(e){if(super(),"object"!=typeof e)throw new Mm(`Expected argument of type ConstantConfig but got ${e}`);if(void 0===e.value)throw new Mm(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return Ni((()=>Co(au(this.value),Ju(e,t))))}getConfig(){return{value:this.value}}}Zg.className="Constant",go(Zg);class Jg extends Kg{constructor(e){super(),this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=e.minval||this.DEFAULT_MINVAL,this.maxval=e.maxval||this.DEFAULT_MAXVAL,this.seed=e.seed}apply(e,t){return zc(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}}Jg.className="RandomUniform",go(Jg);class Qg extends Kg{constructor(e){super(),this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if("float32"!==(t=t||"float32")&&"int32"!==t)throw new Lm(`randomNormal does not support dType ${t}.`);return zg(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}Qg.className="RandomNormal",go(Qg);class ey extends Kg{constructor(e){super(),this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if("float32"!==(t=t||"float32")&&"int32"!==t)throw new Lm(`truncatedNormal does not support dType ${t}.`);return Ih(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}ey.className="TruncatedNormal",go(ey);class ty extends Kg{constructor(e){super(),this.gain=null!=e.gain?e.gain:1}apply(e,t){return Ni((()=>{if(2!==e.length||e[0]!==e[1])throw new Mm("Identity matrix initializer can only be used for 2D square matrices.");return Co(this.gain,gu(e[0]))}))}getConfig(){return{gain:this.gain}}}ty.className="Identity",go(ty);class ny extends Kg{constructor(e){if(super(),e.scale<0)throw new Mm(`scale must be a positive float. Got: ${e.scale}`);var t;this.scale=null==e.scale?1:e.scale,this.mode=null==e.mode?"fanIn":e.mode,t=this.mode,eg(jg,"FanMode",t),this.distribution=null==e.distribution?"normal":e.distribution,function(e){eg(qg,"Distribution",e)}(this.distribution),this.seed=e.seed}apply(e,t){const n=function(e,t="channelsLast"){let n,s;if(mg(t),2===e.length)n=e[0],s=e[1];else if(-1!==[3,4,5].indexOf(e.length)){if("channelsFirst"===t){const t=Ig(e,2);n=e[1]*t,s=e[0]*t}else if("channelsLast"===t){const t=Ig(e,0,e.length-2);n=e[e.length-2]*t,s=e[e.length-1]*t}}else{const t=Ig(e);n=Math.sqrt(t),s=Math.sqrt(t)}return[n,s]}(e),s=n[0],r=n[1];let a=this.scale;if("fanIn"===this.mode?a/=Math.max(1,s):"fanOut"===this.mode?a/=Math.max(1,r):a/=Math.max(1,(s+r)/2),"normal"===this.distribution){const n=Math.sqrt(a);if("float32"!==(t=t||"float32")&&"int32"!==t)throw new Lm(`${this.getClassName()} does not support dType ${t}.`);return Ih(e,0,n,t,this.seed)}{const n=Math.sqrt(3*a);return zc(e,-n,n,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}}ny.className="VarianceScaling",go(ny);class sy extends ny{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return ny.className}}sy.className="GlorotUniform",go(sy);class ry extends ny{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return ny.className}}ry.className="GlorotNormal",go(ry);class ay extends ny{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return ny.className}}ay.className="HeNormal",go(ay);class iy extends ny{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return ny.className}}iy.className="HeUniform",go(iy);class oy extends ny{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return ny.className}}oy.className="LeCunNormal",go(oy);class ly extends ny{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return ny.className}}ly.className="LeCunNormal",go(ly);class uy extends Kg{constructor(e){if(super(),this.DEFAULT_GAIN=1,this.gain=null==e.gain?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,null!=this.seed)throw new Lm("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return Ni((()=>{if(e.length<2)throw new Lm("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const t=zg(e[0]>e[1]?[e[1],e[0]]:e,0,1,"float32");let n=Yp.gramSchmidt(t);return e[0]>e[1]&&(n=_i(n)),Co(this.gain,n)}))}getConfig(){return{gain:this.gain,seed:this.seed}}}uy.className="Orthogonal",go(uy);const cy={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function hy(e,t={}){return Ym(e,mo.getMap().classNameMap,t,"initializer")}function py(e){return Km(e)}function dy(e){if("string"==typeof e){const t=e in cy?cy[e]:e;if("GlorotNormal"===t)return new ry;if("GlorotUniform"===t)return new sy;if("HeNormal"===t)return new ay;if("HeUniform"===t)return new iy;if("LeCunNormal"===t)return new oy;if("LeCunUniform"===t)return new ly;{const e={};return e.className=t,e.config={},hy(e)}}return e instanceof Kg?e:hy(e)}function fy(e){return Array.isArray(e)&&Array.isArray(e[0])}function my(e){return 0===e.length?[]:Array.isArray(e[0])?e:[e]}function gy(e){let t;if(Array.isArray(e)){if(1!==e.length)throw new Mm(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function yy(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(1===e.length)return e[0];throw new Mm(`Expected exactly 1 Shape; got ${e.length}`)}return e}function by(e){let t=0;for(const n of e)0===n.shape.length?t+=1:t+=n.shape.reduce(((e,t)=>e*t));return t}const xy="Variable";class wy{constructor(e,t="float32",n="Variable",s=!0,r=null){this.dtype=null==t?"float32":t,this.shape=e.shape,this.id=ig(),n=null==n?xy:n,this.originalName=wg(n),this.name=vg(this.originalName),this.trainable_=s,this.constraint=r,this.val=Eh(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),function(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}(this.val,e),this.val.id!==e.id&&(this.val.assign(e),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function vy(e){return e.map((e=>e.read()))}function ky(e){e.forEach((e=>{e[0].write(e[1])}))}class Ny{constructor(e){this.dtype=e.dtype,this.shape=e.shape,null!=e.shape?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class Iy{constructor(e,t,n,s,r,a,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=i,this.id=ig(),null!=a&&(this.originalName=wg(a),this.name=vg(this.originalName)),this.rank=t.length}}let Sy=0;class Ty{constructor(e,t){this.callArgs=t,this.id=Sy++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const t of e.inboundLayers)null!=t&&t.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)null!=t?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let Cy=0;class $y extends fo{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Cy++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const e=this.getClassName();t=Hm(e)+"_"+lg(e)}if(this.name=t,this.trainable_=null==e.trainable||e.trainable,null!=e.inputShape||null!=e.batchInputShape){let t;if(null!=e.batchInputShape)t=e.batchInputShape;else if(null!=e.inputShape){let n=null;null!=e.batchSize&&(n=e.batchSize),t=[n].concat(e.inputShape)}this.batchInputShape=t;let n=e.dtype;null==n&&(n=e.inputDType),null==n&&(n="float32"),this.dtype=n}null!=e.weights?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(0===this.inboundNodes.length)throw new Om(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new Mm(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Um(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Um(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Dm(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(0===this.inboundNodes.length)throw new Dm(`Layer ${this.name} is not connected, no input to return.`);return Um(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(0===this.inboundNodes.length)throw new Dm(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Dm(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Um(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map((e=>e()))}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach((t=>t.trainable=e)),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter((e=>e.trainable)):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter((e=>!e.trainable)).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Gm(e),null==this.inputSpec||0===this.inputSpec.length)return;const t=Gm(this.inputSpec);if(e.length!==t.length)throw new Mm(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const s=e[n],r=t[n];if(null==r)continue;const a=s.rank;if(null!=r.ndim&&a!==r.ndim)throw new Mm(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(null!=r.maxNDim&&a>r.maxNDim)throw new Mm(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(null!=r.minNDim&&a<r.minNDim)throw new Mm(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(null!=r.dtype&&s.dtype!==r.dtype)throw new Mm(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){const e=s.shape;for(const t in r.axes){const s=Number(t),a=r.axes[t],i=s>=0?e[s]:e[e.length+s];if(null!=a&&-1===[a,null].indexOf(i))throw new Mm(`Input ${n} is incompatible with layer ${this.name}: expected axis ${s} of input shape to have value ${a} but got shape ${e}.`)}}if(null!=r.shape)for(let e=0;e<r.shape.length;++e){const t=r.shape[e],a=s.shape[e];if(null!=t&&null!=a&&t!==a)throw new Mm(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){null!=this._callHook&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=Gm(e);let s=!0;for(const e of n)if(!(e instanceof Iy)){s=!1;break}let r=!0;for(const e of n)if(e instanceof Iy){r=!1;break}if(s===r)throw new Mm("Arguments to apply() must be all SymbolicTensors or all Tensors");return xg(this.name,(()=>{if(!this.built){this.assertInputCompatibility(e);const t=[];for(const n of Gm(e))t.push(n.shape);this.build(Um(t)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let s=this.call(e,t);const r=Gm(s),a=[];for(let e of r)-1!==n.indexOf(e)&&(e=e.clone()),a.push(e);if(s=Um(a),null!=this.activityRegularizer)throw new Lm("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}{const n=function(e){e=Gm(e);const t=[];for(const n of e)t.push(n.shape);return Um(t)}(e),s=this.computeOutputShape(n);let r;const a="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(e)?n[0]:n),r=null!=s&&s.length>0&&Array.isArray(s[0])?s.map(((n,s)=>new Iy(a,n,this,Gm(e),t,this.name,s))):new Iy(a,s,this,Gm(e),t,this.name),this.addInboundNode(e,r,null,null,n,s,t),this._refCount++,null!=this.activityRegularizer)throw new Lm("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}}))}warnOnIncompatibleInputShape(e){if(null!=this.batchInputShape)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach(((n,s)=>{null!=n&&null!=e[s]&&e[s]!==n&&(t=!0)})),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new Dm(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);-1===e.indexOf(n)&&e.push(n)}if(1===e.length){const e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&1===e.length?e[0]:e}throw new Dm(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Om(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return by(this.weights)}build(e){this.built=!0}getWeights(e=!1){return vy(e?this.trainableWeights:this.weights)}setWeights(e){Ni((()=>{const t=this.weights;if(t.length!==e.length)throw new Mm(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(0===t.length)return;const n=[],s=vy(t);for(let r=0;r<s.length;++r){const a=s[r],i=t[r],o=e[r];if(!f(a.shape,o.shape))throw new Mm(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}ky(n)}))}addWeight(e,t,n,s,r,a,i,o){if(-1!==this._addedWeightNames.indexOf(e))throw new Mm(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),null==n&&(n="float32"),this.fastWeightInitDuringBuild&&(s=null!=o?o():dy("zeros"));const l=s.apply(t,n),u=new wy(l,n,e,a,i);return l.dispose(),null!=r&&this.addLoss((()=>r.apply(u.read()))),null==a&&(a=!0),a?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){null==e||Array.isArray(e)&&0===e.length||(e=Gm(e),void 0!==this._losses&&null!==this._losses&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(null!=t){if(!Array.isArray(t))throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);t.forEach((e=>{if(null!=e)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)}))}return null}return t}addInboundNode(e,t,n,s,r,a,i=null){const o=Gm(e);t=Gm(t),n=Gm(n),s=Gm(s),r=my(r),a=my(a);const l=[],u=[],c=[];for(const e of o)l.push(e.sourceLayer),u.push(e.nodeIndex),c.push(e.tensorIndex);new Ty({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},i);for(let e=0;e<t.length;e++)t[e].sourceLayer=this,t[e].nodeIndex=this.inboundNodes.length-1,t[e].tensorIndex=e}getConfig(){const e={name:this.name,trainable:this.trainable};return null!=this.batchInputShape&&(e.batchInputShape=this.batchInputShape),null!=this.dtype&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach((e=>e.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(null===this._refCount)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return 0==--this._refCount&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function Ey(e,t,n){if((null==t||null!=n&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),0===t.inboundNodes.length)return[e];{const e=t.inboundNodes[n];if(0===e.inboundLayers.length)return e.inputTensors;{const t=[];for(let n=0;n<e.inboundLayers.length;n++){const s=Ey(e.inputTensors[n],e.inboundLayers[n],e.nodeIndices[n]);for(const e of s)-1===t.indexOf(e)&&t.push(e)}return t}}}class Ay extends $y{constructor(e){if(super({dtype:e.dtype,name:null!=e.name?e.name:lg("input").toString()}),null==e.batchSize&&(e.batchSize=null),null==e.sparse&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,null!=e.inputShape&&null!=e.batchInputShape)throw new Mm("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(null==t){if(null==e.inputShape)throw new Mm("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(null!=e.batchSize)throw new Mm("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const s=new Iy(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Ty({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new Mm(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}function Ry(e){if(null==e.batchShape&&null==e.shape)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(null!=e.batchShape&&null!=e.shape)throw new Mm("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;null!=e.shape&&null==t&&(t=[null].concat(e.shape));let n=e.dtype;null==n&&(n="float32");return new Ay({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}Ay.className="InputLayer",go(Ay);class _y{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof _y)for(const t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(null==e)return;for(const t of e)this.add(t.key,t.value)}}add(e,t,n){if(null!=this.id2Value[e.id])throw new Mm(`Duplicate key: name=${e.name}, id=${e.id}`);return this.id2Value[e.id]=function(e,t){if(null==e.dtype||e.dtype===t.dtype)return t;try{return Ja(t,e.dtype)}catch(n){throw new Mm(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}(e,t),this.name2Id[e.name]=e.id,null!=n&&(this.id2Mask[e.id]=n),this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return null!=this.id2Value[e.id]}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof Iy){if(null==this.id2Value[e.id])throw new Mm(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}{const t=this.name2Id[e];if(null==t)throw new Mm(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Iy){if(null==this.id2Value[e.id])throw new Mm(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}{const t=this.name2Id[e];if(null==t)throw new Mm(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){null!=this.id2Mask&&Ii(this.id2Mask)}}const Fy=new Pm,Dy=new Pm;function Oy(e,t,n,s){const r=null!=n&&n.training,a=Array.isArray(e),i=a?e:[e],o=i.map((e=>e.name)),l=[],c=t.names();for(const e of o)-1!==c.indexOf(e)?l.push(t.getValue(e)):l.push(null);null!=s&&(s.maxNumTensors=-1/0,s.minNumTensors=1/0);const h=o.join(",")+"|"+t.names().sort().join(",");let p,d=Fy.get(h);if(null==d){const e=function(e,t){u(null!=e&&e.length>0,(()=>"Expected at least one fetch, got none"));let n=[],s={};if(1===e.length){const r=Ly(e[0],t);n=r.sorted,s=r.recipientMap}else{const r=new Set;for(const a of e){const{sorted:e,recipientMap:i}=Ly(a,t);for(const t of e)r.has(t.name)||(n.push(t),r.add(t.name));for(const e in i)null==s[e]&&(s[e]=new Set),i[e].forEach((t=>s[e].add(t)))}}return{sorted:n,recipientCounts:My(s)}}(i,t);d=e.sorted,p=e.recipientCounts,Fy.put(h,d),Dy.put(h,p)}p={},r||Object.assign(p,Dy.get(h));const f=new _y(t);for(let e=0;e<d.length;++e){if(null!=s){const e=ki().numTensors;e>s.maxNumTensors&&(s.maxNumTensors=e),e<s.minNumTensors&&(s.minNumTensors=e)}const a=d[e],i=a.sourceLayer;if(i instanceof Ay)continue;const u=[],c=[],h=[];let m=!1;for(const e of a.inputs){const n=f.getValue(e),s=f.getMask(e);u.push(n),c.push(s),null!=s&&(m=!0),r||(p[e.name]--,0!==p[e.name]||t.hasKey(e)||-1!==o.indexOf(e.name)||n.isDisposed||!0===e.sourceLayer.stateful||h.push(n))}m&&((n=n||{}).mask=c[0]);const g=Gm(i.apply(u,n));let y=null;i.supportsMasking&&(y=i.computeMask(u,c));const b=zy(a),x=Array.isArray(b)?b:[b];for(let e=0;e<x.length;++e){f.hasKey(x[e])||f.add(x[e],g[e],Array.isArray(y)?y[0]:y);const t=o.indexOf(x[e].name);-1!==t&&(l[t]=g[e])}r||Ii(h)}return f.disposeMasks(),a?l:l[0]}function My(e){const t={};for(const n in e)t[n]=e[n].size;return t}function Ly(e,t){const n=new Set,s=[],r={};for(const e of t.names())n.add(e);const a=[],i=[];for(a.push(e);a.length>0;){const e=a[a.length-1];if(n.has(e.name)){a.pop();continue}const t=i[i.length-1]===a.length-1;if(0===e.inputs.length||t)a.pop(),s.push(e),n.add(e.name),t&&i.pop();else{i.push(a.length-1);for(const t of e.inputs)null==r[t.name]&&(r[t.name]=new Set),r[t.name].add(e.name),n.has(t.name)||a.push(t)}}return{sorted:s,recipientMap:r}}function zy(e){let t;if(1===e.sourceLayer.inboundNodes.length)t=e.sourceLayer.output;else{let n=null;for(let t=0;t<e.sourceLayer.inboundNodes.length;++t)for(const s of e.sourceLayer.inboundNodes[t].outputTensors)if(s.id===e.id){n=t;break}t=e.sourceLayer.getOutputAt(n)}return t}function Py(e,t){return Ni((()=>iu(lu(Co(e,e),t,!0))))}X().registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES",(()=>100),(function(e){null!=Fy&&Fy.setMaxEntries(e),null!=Dy&&Dy.setMaxEntries(e)}));class By extends fo{getConfig(){return{}}}class Wy extends By{constructor(e){super(),this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=null!=e.maxValue?e.maxValue:this.defaultMaxValue,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return Ni((()=>{const t=Py(e,this.axis),n=xl(t,0,this.maxValue);return Co(e,To(n,Io(Eg(),t)))}))}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}}Wy.className="MaxNorm",go(Wy);class Vy extends By{constructor(e){super(),this.defaultAxis=0,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return Ni((()=>To(e,Io(Eg(),Py(e,this.axis)))))}getConfig(){return{axis:this.axis}}}Vy.className="UnitNorm",go(Vy);class Uy extends By{apply(e){return Wc(e)}}Uy.className="NonNeg",go(Uy);class Gy extends By{constructor(e){super(),this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=null!=e.minValue?e.minValue:this.defaultMinValue,this.maxValue=null!=e.maxValue?e.maxValue:this.defaultMaxValue,this.rate=null!=e.rate?e.rate:this.defaultRate,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return Ni((()=>{const t=Py(e,this.axis),n=Io(Co(this.rate,xl(t,this.minValue,this.maxValue)),Co(1-this.rate,t));return Co(e,To(n,Io(Eg(),t)))}))}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}}Gy.className="MinMaxNorm",go(Gy);const Hy={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function jy(e){return Km(e)}function qy(e,t={}){return Ym(e,mo.getMap().classNameMap,t,"constraint")}function Ky(e){if(null==e)return null;if("string"==typeof e){return qy({className:e in Hy?Hy[e]:e,config:{}})}return e instanceof By?e:qy(e)}var Xy=Object.freeze({__proto__:null,maxNorm:function(e){return new Wy(e)},unitNorm:function(e){return new Vy(e)},nonNeg:function(){return new Uy},minMaxNorm:function(e){return new Gy(e)}});var Yy,Zy=Object.freeze({__proto__:null,zeros:function(){return new Xg},ones:function(){return new Yg},constant:function(e){return new Zg(e)},randomUniform:function(e){return new Jg(e)},randomNormal:function(e){return new Qg(e)},truncatedNormal:function(e){return new ey(e)},identity:function(e){return new ty(e)},varianceScaling:function(e){return new ny(e)},glorotUniform:function(e){return new sy(e)},glorotNormal:function(e){return new ry(e)},heNormal:function(e){return new ay(e)},heUniform:function(e){return new iy(e)},leCunNormal:function(e){return new oy(e)},leCunUniform:function(e){return new ly(e)},orthogonal:function(e){return new uy(e)}});async function Jy(e){if(null==e)return;const t=[],n=[],s=[];for(const r in e){const a=e[r];if("number"!=typeof a){const e=a;t.push(e.data()),n.push(r),s.push(e)}}if(t.length>0){const r=await Promise.all(t);for(let t=0;t<r.length;++t)e[n[t]]=r[t][0];Ii(s)}}function Qy(e){if(null!=e)for(const t in e){const n=e[t];"number"!=typeof n&&n.dispose()}}!function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"}(Yy||(Yy={}));class eb{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}}class tb{constructor(e,t=10){null==e&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(const t of this.callbacks)t.setParams(e)}setModel(e){for(const t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){null==t&&(t={});for(const n of this.callbacks)await n.onEpochBegin(e,t)}async onEpochEnd(e,t){null==t&&(t={});for(const n of this.callbacks)await n.onEpochEnd(e,t)}async onBatchBegin(e,t){null==t&&(t={});for(const n of this.callbacks)await n.onBatchBegin(e,t)}async onBatchEnd(e,t){null==t&&(t={});for(const n of this.callbacks)await n.onBatchEnd(e,t)}async onTrainBegin(e){null==e&&(e={});for(const t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){null==e&&(e={});for(const t of this.callbacks)await t.onTrainEnd(e)}}class nb extends eb{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){null==t&&(t={});const n=null==t.size?0:t.size;this.seen+=n;for(const e in t){const s=t[e];if("number"==typeof s)this.totals.hasOwnProperty(e)||(this.totals[e]=0),this.totals[e]=this.totals[e]+s*n;else{let t;e in this.totals?t=this.totals[e]:this.totals[e]=0;const r=Ni((()=>Io(this.totals[e],Co(s,n))));this.totals[e]=r,null!=t&&t.dispose()}}}async onEpochEnd(e,t){if(null!=t)for(const e of this.params.metrics)null!=this.totals[e]&&("number"==typeof this.totals[e]?t[e]=this.totals[e]/this.seen:Ni((()=>{const n=Co(To(1,this.seen),this.totals[e]);t[e]=n,this.totals[e].dispose(),Si(t[e])})))}}class sb extends eb{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){null==t&&(t={}),this.epoch.push(e);for(const e in t)null==this.history[e]&&(this.history[e]=[]),this.history[e].push(t[e])}async syncData(){const e=[],t=[],n=[];for(const s in this.history){const r=this.history[s];for(let a=0;a<r.length;++a)if("number"!=typeof r[a]){const i=r[a];e.push(i.data()),t.push(s),n.push(a)}}const s=await Promise.all(e);for(let e=0;e<s.length;++e){this.history[t[e]][n[e]].dispose(),this.history[t[e]][n[e]]=s[e][0]}}}class rb extends eb{constructor(e,t){if(super(),this.currentEpoch=0,this.nowFunc=e.nowFunc,this.nextFrameFunc=e.nextFrameFunc||hd,this.yieldEvery=t||"auto","auto"===this.yieldEvery&&(this.yieldEvery=125),"never"===this.yieldEvery&&null!=e.onYield)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");_(this.yieldEvery)&&(this.maybeWait=function(e,t,n){let s,r=null!=n?n():rr();return(...a)=>{const i=null!=n?n():rr();return i-r<t||(r=i,s=e(...a)),s}}(this.maybeWait.bind(this),this.yieldEvery,this.nowFunc)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,n){const s=[];null!=this.yield&&(await Jy(n),s.push(this.yield(e,t,n))),s.push(this.nextFrameFunc()),await Promise.all(s)}async onEpochBegin(e,t){this.currentEpoch=e,null!=this.epochBegin&&(await Jy(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){const n=[];null!=this.epochEnd&&(await Jy(t),n.push(this.epochEnd(e,t))),"epoch"===this.yieldEvery&&n.push(this.nextFrameFunc()),await Promise.all(n)}async onBatchBegin(e,t){null!=this.batchBegin&&(await Jy(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){const n=[];null!=this.batchEnd&&(await Jy(t),n.push(this.batchEnd(e,t))),"batch"===this.yieldEvery?n.push(this.nextFrameFunc()):_(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){null!=this.trainBegin&&(await Jy(e),await this.trainBegin(e))}async onTrainEnd(e){null!=this.trainEnd&&(await Jy(e),await this.trainEnd(e))}}function ab(e,t){if(null==e&&(e={}),e instanceof eb)return[e];if(Array.isArray(e)&&e[0]instanceof eb)return e;return Gm(e).map((e=>new rb(e,t)))}class ib{constructor(){}static registerCallbackConstructor(e,t){u(e>=0&&Number.isInteger(e),(()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`)),ib.checkForDuplicate(t),null==ib.constructors[e]&&(ib.constructors[e]=[]),ib.constructors[e].push(t)}static checkForDuplicate(e){for(const t in ib.constructors){ib.constructors[+t].forEach((t=>{if(t===e)throw new Mm("Duplicate callback constructor.")}))}}static clear(){ib.constructors={}}static createCallbacks(e){const t=[];for(const n in ib.constructors){const s=+n;e>=s&&t.push(...ib.constructors[s])}return t.map((e=>new e))}}function ob(e,t,n,s,r,a,i,o,l){const u=new sb,c=[new nb,...ib.createCallbacks(t)];null!=e&&c.push(...e),c.push(u);const h=new tb(c);return h.setParams({epochs:n,initialEpoch:s,samples:r,steps:a,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:h,history:u}}function lb(e,t={},n=!1){return Ym(e,mo.getMap().classNameMap,t,"layer",n)}function ub(e,t){return Ni((()=>{"float32"!==e.dtype&&(e=Ja(e,"float32"));const n=lu(Wg(e),t,!0),s=bl(n.shape,Eg()),r=iu(Xu(n,s));return To(e,r)}))}function cb(e,t){return Ni((()=>Yu(Wg(Mu(t,e)),-1)))}function hb(e,t){return Ni((()=>Yu($o(Mu(t,e)),-1)))}function pb(e,t){return Ni((()=>{const n=Mu(e,t),s=xl($o(e),Eg(),Number.MAX_VALUE),r=$o(To(n,s));return Co(100,Yu(r,-1))}))}function db(e,t){return Ni((()=>{const n=xl(t,Eg(),Number.MAX_VALUE),s=Eu(Io(1,n)),r=xl(e,Eg(),Number.MAX_VALUE),a=Eu(Io(1,r));return Yu(Wg(Mu(s,a)),-1)}))}function fb(e,t,n=!1){return Ni((()=>{if(n)t=ih(t);else{const e=lu(t,t.shape.length-1,!0);t=To(t,e)}return t=xl(t,Eg(),1-Eg()),Ai(lu(Co(Ja(e,"float32"),Eu(t)),t.shape.length-1))}))}function mb(e,t,n=!1){return Ni((()=>{const s=Ja(yu(function(e){const t=[Ig(e.shape)];return tl(e,t)}(e)),"int32"),r=(t=xl(t,Eg(),1-Eg())).shape;return fb(tl(xi(s,r[r.length-1]),r),t,n)}))}function gb(e,t){return Ni((()=>{let n;return n=xl(t,Eg(),1-Eg()),n=Eu(To(n,Mu(1,n))),Yu(function(e,t){if(!f(e.shape,t.shape))throw new Mm(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return Ni((()=>{const n=Wc(t),s=Ai($o(t));return Io(Mu(n,Co(t,e)),Au(pu(s)))}))}(e,n),-1)}))}function yb(e,t){return Ni((()=>{const n=xl(e,Eg(),1),s=xl(t,Eg(),1);return lu(Co(e,Eu(To(n,s))),-1)}))}function bb(e,t){return Ni((()=>{const n=ub(e,-1),s=ub(t,-1),r=Co(n,s);return Ai(lu(r,-1))}))}ib.constructors={};const xb={meanSquaredError:cb,meanAbsoluteError:hb,meanAbsolutePercentageError:pb,meanSquaredLogarithmicError:db,squaredHinge:function(e,t){return Ni((()=>{const n=Xu(0,Mu(1,Co(e,t)));return Yu(Wg(n),-1)}))},hinge:function(e,t){return Ni((()=>{const n=Xu(0,Mu(1,Co(e,t)));return Yu(n,-1)}))},categoricalHinge:function(e,t){return Ni((()=>{const n=lu(Co(e,t),-1),s=nu(Co(Mu(1,e),t),-1);return Xu(0,Io(1,Mu(s,n)))}))},logcosh:function(e,t){return Ni((()=>{const n=Math.log(2),s=Mu(t,e),r=Mu(Io(s,Du(Co(-2,s))),n);return Yu(r,-1)}))},categoricalCrossentropy:fb,sparseCategoricalCrossentropy:mb,binaryCrossentropy:gb,kullbackLeiblerDivergence:yb,poisson:function(e,t){return Ni((()=>{const n=Eu(Io(Eg(),t));return Yu(Mu(t,Co(e,n)),-1)}))},cosineProximity:bb};function wb(e){if("string"==typeof e){if(e in xb)return xb[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new Mm(t)}return e}function vb(e,t){return Ni((()=>{const n=Co(.5,oc(t)),s=Ag(xu(t,n),e.dtype);return Yu(Bl(e,s),-1)}))}function kb(e,t){return Ni((()=>Ag(Bl(Do(e,-1),Do(t,-1)),"float32")))}function Nb(e,t){return Ni((()=>Ja(lu(Pu(Bl(e,1),Bl(t,1))),"float32")))}function Ib(e,t){return Ni((()=>{const n=Nb(e,t),s=function(e,t){return Ni((()=>Ja(lu(Pu(Bl(e,0),Bl(t,1))),"float32")))}(e,t),r=Io(n,s);return Ja(Wl(xu(r,0),To(n,r),0),"float32")}))}function Sb(e,t){return Ni((()=>{const n=Nb(e,t),s=function(e,t){return Ni((()=>Ja(lu(Pu(Bl(e,1),Bl(t,0))),"float32")))}(e,t),r=Io(n,s);return Ja(Wl(xu(r,0),To(n,r),0),"float32")}))}function Tb(e,t){return gb(e,t)}function Cb(e,t){return e.rank===t.rank&&(e=dh(e,[e.rank-1])),(t=Do(t,-1)).dtype!==e.dtype&&(t=Ja(t,e.dtype)),Ja(Bl(e,t),"float32")}const $b=fb,Eb=mb,Ab={binaryAccuracy:vb,categoricalAccuracy:kb,precision:Ib,categoricalCrossentropy:$b,sparseCategoricalCrossentropy:Eb,mse:cb,MSE:cb,mae:hb,MAE:hb,mape:pb,MAPE:pb,cosine:bb};function Rb(e){if("string"==typeof e&&e in Ab)return Ab[e];if("string"!=typeof e&&null!=e)return e;throw new Mm(`Unknown metric ${e}`)}function _b(e){if(Wm(null!==e,`Unknown LossOrMetricFn ${e}`),"string"==typeof e)return e;{let t;for(const n of Object.keys(xb))if(xb[n]===e){t=n;break}if(void 0!==t)return t;for(const n of Object.keys(Ab))if(Ab[n]===e){t=n;break}return void 0!==t?t:e.name}}const Fb=1048576;function Db(e,t,n=!1){if(null==e||"object"!=typeof e||Object.getPrototypeOf(e)!==Object.prototype||!Ob(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){const n=JSON.stringify(e);n.length>Fb&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${n.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= 1048576.`)}}function Ob(e){if(null===e)return!0;if("object"==typeof e){if(Object.getPrototypeOf(e)===Object.prototype){const t=Object.keys(e);for(const n of t){if("string"!=typeof n)return!1;if(!Ob(e[n]))return!1}return!0}if(Array.isArray(e)){for(const t of e)if(!Ob(t))return!1;return!0}return!1}{const t=typeof e;return"string"===t||"number"===t||"boolean"===t}}function Mb(e,t,n,s=console.log){const r=function(e){let t=!0;const n=[],s=[];for(const t in e.nodesByDepth)n.push(e.nodesByDepth[t]);for(const e of n){if(e.length>1||1===e.length&&e[0].inboundLayers.length>1){t=!1;break}s.push(...e)}if(t)for(const n of e.layers){let e=!1;for(const r of n.inboundNodes)if(-1!==s.indexOf(r)){if(e){t=!1;break}e=!0}if(!t)break}return t}(e),a=["Layer (type)","Input Shape","Output shape","Param #"];let i;if(r?(t=t||90,n=n||[.32,.61,.89,1]):(t=t||115,n=n||[.24,.48,.7,.8,1]),n[n.length-1]<=1&&(n=n.map((e=>Math.floor(t*e)))),!r){a.push("Receives inputs"),i=[];for(const t in e.nodesByDepth)i.push(...e.nodesByDepth[t])}s("_".repeat(t)),Lb(a,n,s),s("=".repeat(t));const o=e.layers;for(let e=0;e<o.length;++e)r?zb(o[e],n,s):Pb(o[e],n,i,s),s((e===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();const l=function(e){let t;t=null!=e.collectedTrainableWeights?by(e.collectedTrainableWeights):by(e.trainableWeights);return t}(e),u=by(e.nonTrainableWeights);s(`Total params: ${l+u}`),s(`Trainable params: ${l}`),s(`Non-trainable params: ${u}`),s("_".repeat(t))}function Lb(e,t,n=console.log){let s="";for(let n=0;n<e.length;++n)n>0&&(s=s.slice(0,s.length-1)+" "),s+=e[n],s=s.slice(0,t[n]),s+=" ".repeat(t[n]-s.length);n(s)}function zb(e,t,n){let s,r;try{r=e.inboundNodes.map((e=>JSON.stringify(e.inputShapes))).join(",")}catch(e){r="multiple"}try{s=JSON.stringify(e.outputShape)}catch(e){s="multiple"}Lb([`${e.name} (${e.getClassName()})`,r,s,e.countParams().toString()],t,n)}function Pb(e,t,n,s){let r,a;try{a=e.inboundNodes.map((e=>JSON.stringify(e.inputShapes))).join(",")}catch(e){a="multiple"}try{r=JSON.stringify(e.outputShape)}catch(e){r="multiple"}const i=[];for(const t of e.inboundNodes)if(!(null!=n&&n.length>0&&-1===n.indexOf(t)))for(let e=0;e<t.inboundLayers.length;++e){const n=t.inboundLayers[e].name,s=t.nodeIndices[e],r=t.tensorIndices[e];i.push(`${n}[${s}][${r}]`)}const o=e.name,l=e.getClassName(),u=0===i.length?"":i[0];Lb([`${o} (${l})`,a,r,e.countParams().toString(),u],t,s);for(let e=1;e<i.length;++e)Lb(["","","","",i[e]],t,s)}function Bb(e,t,n){return("inboundNodes"===e||"outputLayers"===e||"inputLayers"===e)&&0===t&&"string"==typeof n}function Wb(e,t){if(null===e)return null;if("string"==typeof e)return jm(e);if("number"==typeof e||"boolean"==typeof e)return e;if(e instanceof Array){const n=[],s=e.length;for(let r=0;r<s;++r){const s=e[r];Bb(t,r,s)?n.push(s):n.push(Wb(s,t))}return n}{const t={};for(const n of Object.keys(e)){const s=e[n];if("name"===n&&"string"==typeof s)t[n]=s;else{const e=jm(n);t[e]=Wb(s,e)}}return t}}function Vb(e,t){if(null==e)return null;if("string"==typeof e)return Hm(e);if("number"==typeof e||"boolean"==typeof e)return e;if(e instanceof Array){const n=[],s=e.length;for(let r=0;r<s;++r){const s=e[r];Bb(t,r,s)?n.push(s):n.push(Vb(s,t))}return n}{const t={};for(const n of Object.keys(e)){const s=e[n],r=Hm(n);t[r]="name"!==n&&"className"!==n||"string"!=typeof s?Vb(s,n):s}return t}}const Ub="4.1.0";class Gb extends $y{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,null==this.name){const e=this.getClassName().toLowerCase();this.name=lg(e)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Jm(this.inputs).length!==this.inputs.length)throw new Mm(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((e=>e.name))}`);Jm(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((e=>e.name))}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const e of this.outputs){const t=e.sourceLayer,n=e.nodeIndex,s=e.tensorIndex;this.outputLayers.push(t),this.outputLayersNodeIndices.push(n),this.outputLayersTensorIndices.push(s)}for(const e of this.inputs){const t=e.sourceLayer,n=e.nodeIndex,s=e.tensorIndex;Wm(0===n,"input layer has >1 nodes"),Wm(0===s,"input layer has >1 tensors"),this.inputLayers.push(t),this.inputLayersNodeIndices.push(n),this.inputLayersTensorIndices.push(s)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let t=0;t<this.inputLayers.length;t++){const n=this.inputLayers[t];if(!(n instanceof Ay))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${t} (0-based) originates from layer type ${n.getClassName()}.`);this.inputNames.push(n.name),this.feedInputShapes.push(n.batchInputShape),this.feedInputNames.push(n.name)}for(const e of this.outputLayers)this.outputNames.push(e.name);this.internalInputShapes=this.inputs.map((e=>e.shape)),this.internalOutputShapes=this.outputs.map((e=>e.shape));const t={},n={},s={},r={},a={},i=[],o=(e,t,n,s,r,l)=>{null!=s&&null!=r&&null!=l||(s=e.sourceLayer,r=e.nodeIndex,l=e.tensorIndex);const u=s.inboundNodes[r];if(-1!==n.indexOf(u))throw new Om(`The tensor ${e.name} at layer "${s.name}" is part of a cycle.`);if(-1!==t.indexOf(u))return;this.containerNodes.add(Gb.nodeKey(s,r)),s.id in a||(a[s.id]=Object.keys(a).length),-1===n.indexOf(u)&&n.push(u);const c=u.inboundLayers.length;for(let e=0;e<c;e++){const s=u.inputTensors[e],r=u.inboundLayers[e],a=u.nodeIndices[e],i=u.tensorIndices[e];o(s,t,n,r,a,i)}for(t.push(u);n.indexOf(u)>=0;)n.splice(n.indexOf(u),1);i.push(u)},l=[],u=[];for(const e of this.outputs)o(e,l,u);const c=i.slice().reverse();for(const e of c){n[e.id]=e,e.id in t||(t[e.id]=0);let a=t[e.id];const i=null==s[e.outboundLayer.id]?0:s[e.outboundLayer.id];a=Math.max(a,i),s[e.outboundLayer.id]=a,r[e.outboundLayer.id]=e.outboundLayer,t[e.id]=a;for(let s=0;s<e.inboundLayers.length;s++){const r=e.inboundLayers[s],i=e.nodeIndices[s],o=r.inboundNodes[i],l=null==t[o.id]?0:t[o.id];t[o.id]=Math.max(a+1,l),n[o.id]=o}}const h={};for(const e in t){const s=t[e];s in h||(h[s]=[]),h[s].push(n[e])}const p={};for(const e in s){const t=s[e];t in p||(p[t]=[]),p[t].push(r[e])}let d=Object.keys(p).map((e=>parseInt(e,10))).sort(Zm);this.layers=[];for(const e of d){const t=p[e];t.sort(((e,t)=>{const n=a[e.id],s=a[t.id];return n<s?-1:n>s?1:0}));for(const e of t)e instanceof Gb&&this.internalContainerRefs.push(e),this.layers.push(e)}this.layersByDepth=p,d=Object.keys(h).map((e=>parseInt(e,10))).sort(Zm);const f=this.inputs.slice(),m=[];for(const e of d)for(const t of h[e]){const e=t.outboundLayer;if(null!=e){for(const n of t.inputTensors)if(-1===f.indexOf(n))throw new Om(`Graph disconnected: cannot obtain value for tensor ${n} at layer "${e.name}". The following previous layers were accessed without issue: ${m}`);for(const e of t.outputTensors)f.push(e);m.push(e.name)}}this.nodesByDepth=h;const g=this.layers.map((e=>e.name));for(const e of g){const t=g.filter((t=>t===e)).length;if(1!==t)throw new Om(`The name "${e}" is used ${t} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Ty({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map((e=>null)),outputMasks:this.outputs.map((e=>null)),inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs.map((e=>e.shape))}),this.built=!0,this._refCount=1}assertNotDisposed(){if(0===this._refCount)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(0==--this._refCount){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach((t=>{t._trainableWeights.forEach((t=>t.trainable=e))})),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new Mm("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.layers)t.push(...e.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let s=0;for(const e of this.layers)for(const t of e.weights){if(null!=n[t.originalName])throw new Mm(`Duplicate weight name: ${t.originalName}`);n[t.originalName]=t,s++}const r=[];for(const s in e){let a=s;if(null==n[s]){const e=s.split("/");a=e.slice(0,-2).concat([e[e.length-1]]).join("/")}if(null!=n[a])r.push([n[a],e[s]]);else if(t)throw new Mm(`Provided weight data has no target variable: ${s}`);delete n[a]}if(t){const e=[];for(const t in n)e.push(t);if(e.length>0)throw new Mm(`${e.length} of ${s} weights are not set: ${e}`)}ky(r)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion="tfjs-layers 4.1.0",t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=Vb(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return Ni((()=>{e=Gm(e);const n=new _y;for(let t=0;t<this.inputs.length;++t)n.add(this.inputs[t],e[t]);return Oy(this.outputs,n,t)}))}computeMask(e,t){return Ni((()=>{let n;return e=Gm(e),n=null==t?Bm(null,e.length):Gm(t),this.runInternalGraph(e,n)[1]}))}computeOutputShape(e){const t=my(e);if(t.length!==this.inputLayers.length)throw new Mm(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let e=0;e<t.length;e++){const s=this.inputLayers[e],r=t[e];n[s.name+"_0_0"]=r}const s=Object.keys(this.nodesByDepth).map((e=>parseInt(e,10))).sort(Zm);if(s.length>1)for(const e of s){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer;if(-1!==this.inputLayers.map((e=>e.id)).indexOf(t.id))continue;const s=[];for(let t=0;t<e.inboundLayers.length;t++){const r=e.inboundLayers[t],a=e.nodeIndices[t],i=e.tensorIndices[t],o=n[`${r.name}_${a}_${i}`];s.push(o)}const r=my(t.computeOutputShape(Um(s))),a=t.inboundNodes.indexOf(e);for(let e=0;e<r.length;e++){n[`${t.name}_${a}_${e}`]=r[e]}}}const r=[],a=[];for(let e=0;e<this.outputLayers.length;e++){const t=this.outputLayers[e],n=this.outputLayersNodeIndices[e],s=this.outputLayersTensorIndices[e],r=`${t.name}_${n}_${s}`;a.push(r)}for(let e=0;e<a.length;e++){const t=a[e];Wm(t in n),r.push(n[t])}return Um(r)}runInternalGraph(e,t){null==t&&(t=Bm(null,e.length));const n={};for(let s=0;s<this.inputs.length;++s){const r=this.inputs[s],a=e[s],i=t[s];n[r.id]=[a,i]}const s=Object.keys(this.nodesByDepth).map((e=>parseInt(e,10))).sort(Zm);for(const e of s){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer,s=e.inputTensors,r=e.outputTensors,a=new Array;for(const e of s)e.id in n&&a.push(n[e.id]);if(a.length===s.length){let s,i,o,l,u={};if(null!=e.callArgs&&(u=e.callArgs),1===a.length){const[e,n]=a[0];null==u.mask&&(u.mask=n),o=Gm(t.call(e,u)),l=Gm(t.computeMask(e,n)),s=[e],i=[n]}else s=a.map((e=>e[0])),i=a.map((e=>e[1])),null==u.mask&&(u.mask=i),o=Gm(t.call(s,u)),l=Gm(t.computeMask(s,i));if(t.activityRegularizer)throw new Lm("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let e=0;e<r.length;++e){const t=r[e],s=o[e],a=l[e];n[t.id]=[s,a]}}}}const r=[],a=[],i=[];for(const e of this.outputs){Wm(e.id in n,`Could not compute output ${e.name} : ${e.id}`);const[t,s]=n[e.id];i.push(t.shape),r.push(t),a.push(s)}return[r,a,i]}buildNodeConversionMap(e){const t={};let n;for(const e of this.layers){n=e instanceof Gb?1:0;for(let s=0;s<e.inboundNodes.length;s++){const r=Gb.nodeKey(e,s);this.containerNodes.has(r)&&(t[r]=n,n+=1)}}return t}getLayer(e,t){if(null!=t){if(this.layers.length<=t)throw new Mm(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}if(null==e)throw new Mm("Provide either a layer name or layer index");for(const t of this.layers)if(t.name===e)return t;throw new Mm(`No such layer: ${e}`)}calculateLosses(){return Ni((()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=Gb.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e}))}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const e of this.layers){const s=e.getClassName(),r=e.getConfig(),a=[];for(let n=0;n<e.inboundNodes.length;n++){const s=e.inboundNodes[n],r=Gb.nodeKey(e,n);let i={};if(this.containerNodes.has(r)){if(s.callArgs)try{JSON.stringify(s.callArgs),i=s.callArgs}catch(t){console.warn(`Layer ${e.name} was passed non-serializable keyword arguments: ${s.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),i={}}if(s.inboundLayers.length>0){const e=[];for(let n=0;n<s.inboundLayers.length;n++){const r=s.inboundLayers[n],a=s.nodeIndices[n],o=s.tensorIndices[n];let l=t[Gb.nodeKey(r,a)];null==l&&(l=0),e.push([r.name,l,o,i])}a.push(e)}}}const i={};i.name=e.name,i.className=s,i.config=r,i.inboundNodes=a,n.push(i)}e.layers=n;const s=[];for(let e=0;e<this.inputLayers.length;e++){const n=this.inputLayers[e],r=this.inputLayersNodeIndices[e],a=Gb.nodeKey(n,r);if(!this.containerNodes.has(a))continue;let i=t[a];null==i&&(i=0);const o=this.inputLayersTensorIndices[e];s.push([n.name,i,o])}e.inputLayers=s;const r=[];for(let e=0;e<this.outputLayers.length;e++){const n=this.outputLayers[e],s=this.outputLayersNodeIndices[e],a=Gb.nodeKey(n,s);if(!this.containerNodes.has(a))continue;let i=t[a];null==i&&(i=0);const o=this.outputLayersTensorIndices[e];r.push([n.name,i,o])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){const r={},a={};function i(e,t){e.name in a?a[e.name].push(t):a[e.name]=[t]}function o(e,t){const n=[];let s;for(const a of t){const o=a[0],l=a[1],u=a[2];if(s=null==a[3]?{}:a[3],!(o in r))return void i(e,t);const c=r[o];if(c.inboundNodes.length<=l)return void i(e,t);const h=c.inboundNodes[l];n.push(h.outputTensors[u])}n.length>0&&e.apply(Um(n),s)}function l(e){const n=e.name,a=lb(e,null!=t.customObjects?t.customObjects:{});a.setFastWeightInitDuringBuild(s),r[n]=a;e.inboundNodes.forEach((e=>{if(!(e instanceof Array))throw new Mm(`Corrupted configuration, expected array for nodeData: ${e}`);i(a,e)}))}const u=t.name,c=t.layers;for(const e of c)l(e);for(;!Qm(a);)for(const e of c){const t=r[e.name];if(t.name in a){const e=a[t.name];delete a[t.name];for(const n of e)o(t,n)}}const h=[],p=[],d=t.inputLayers;for(const e of d){const t=e[0],n=e[1],s=e[2];Wm(t in r);const a=r[t].inboundNodes[n].outputTensors;h.push(a[s])}const f=t.outputLayers;for(const e of f){const t=e[0],n=e[1],s=e[2];Wm(t in r);const a=r[t].inboundNodes[n].outputTensors;p.push(a[s])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new Mm("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){Ni((()=>{this.layers.forEach((e=>{e.stateful&&e.resetStates()}))}))}}function Hb(e,t,n){const s=t.length;if(null==e||Array.isArray(e)&&0===e.length)return t.map((e=>null));if(1===s)return Array.isArray(e)&&1===e.length?e:"object"==typeof e&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}if("object"==typeof e&&Object.keys(e).length>0&&"object"==typeof e[Object.keys(e)[0]]){const n=[];return t.forEach((t=>{t in e?n.push(e[t]):n.push(null)})),n}throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function jb(e,t){return Hb(e,t,"classWeight")}async function qb(e,t,n,s){if(null!=t||null!=s)throw new Error("Support sampleWeight is not implemented yet");if(null!=n){const t=Ni((()=>{if(1===e.shape.length)return Qa(e);if(2===e.shape.length){if(e.shape[1]>1){return Do(e,1)}if(1===e.shape[1])return tl(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)})),s=Array.from(await t.data());Ii(t);const r=[];return s.forEach((e=>{if(null==n[e])throw new Error(`classWeight must contain all classes in the training data. The class ${e} exists in the data but not in classWeight`);r.push(n[e])})),bh(r,"float32")}return null}function Kb(e,t){return Co(e,t)}function Xb(e,t){let n,s;const r=t;n=r.xs,s=r.ys,u(null!=n&&null!=s,(()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`));const a=Yb("input",e.inputNames,n),i=Yb("output",e.outputNames,s),o=a[0].shape[0];u(a.length===e.inputs.length,(()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`)),u(i.length===e.outputs.length,(()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`));for(let t=0;t<a.length;t++)u(a[t].shape[0]===o,(()=>`Batch size mismatch: input ${e.inputNames[t]} has ${a[t].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`));for(let t=0;t<i.length;t++)u(i[t].shape[0]===o,(()=>`Batch size mismatch: output ${e.outputNames[t]} has ${i[t].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`));return{xs:a,ys:i}}function Yb(e,t,n){if(n instanceof vr)return[n];if(Array.isArray(n))return u(n.length===t.length,(()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`)),n;{const s=[];for(const r of t){if(null==n[r])throw new Mm(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}async function Zb(e,t,n){const s=null!=n.batchesPerEpoch;if(u(null!=e.optimizer,(()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).")),u(null!=n,(()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.")),u(null!=n.epochs&&n.epochs>0&&Number.isInteger(n.epochs),(()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`)),u(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),(()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`)),u(null==n.validationSplit,(()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead.")),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const r=null!=n.validationData;let a,i;if(r)if(Jb(n.validationData))u(null==n.validationBatches||n.validationBatches>0&&Number.isInteger(n.validationBatches),(()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`));else{const e=function(e){if(3===e.length)throw new Lm("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}(n.validationData);a=e.xs,i=e.ys}const o=e.makeTrainFunction(),l=e.getDedupedMetricsNames();let c;c=r?l.slice().concat(l.map((e=>"val_"+e))):l.slice();const h=ab(n.callbacks,n.yieldEvery),p=null==n.verbose?1:n.verbose,{callbackList:d,history:f}=ob(h,p,n.epochs,null,null,function(e,t){let n=null;null!=t.batchesPerEpoch?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size);return n}(t,n),null,r,c);d.setModel(e),e.history=f,await d.onTrainBegin(),e.stopTraining_=!1;let m=null==n.initialEpoch?0:n.initialEpoch,g=await t.iterator();for(;m<n.epochs;){const u={};await d.onEpochBegin(m);let c=0,h=0;for(s||(g=await t.iterator());!s||c<n.batchesPerEpoch;){const t=await g.next();if(s&&t.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${c} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, `+n.batchesPerEpoch*n.epochs+" batches). You may need to use the repeat() function when building your dataset.");break}if(null!=t.value){const{xs:s,ys:r}=Xb(e,t.value),a={};a.batch=h,a.size=s[0].shape[0],await d.onBatchBegin(h,a);const i=[];if(null!=n.classWeight){const t=jb(n.classWeight,e.outputNames);for(let e=0;e<t.length;++e)i.push(await qb(r[e],null,t[e]))}const u=s.concat(r).concat(i),p=o(u);Ii(u);for(let e=0;e<l.length;++e){const t=l[e],n=p[e];a[t]=n,Si(n)}await d.onBatchEnd(h,a),Qy(a),h++,c++}if(s?c>=n.batchesPerEpoch:t.done){if(r){let t;t=Jb(n.validationData)?Gm(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):Gm(e.evaluate(a,i,{batchSize:null==n.validationBatchSize?32:n.validationBatchSize,verbose:0}));for(let n=0;n<e.metricsNames.length;++n)u[`val_${e.metricsNames[n]}`]=t[n]}break}if(e.stopTraining_)break}if(await d.onEpochEnd(m,u),m++,e.stopTraining_)break}return await d.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function Jb(e){return"function"==typeof e.iterator}function Qb(e){u(e>0&&Number.isInteger(e),(()=>`batchSize is required to be a positive integer, but got ${e}`))}function ex(e,t,n){return null==e?[null]:Array.isArray(e)?e.map((e=>_g(e,t,n-t))):_g(e,t,n-t)}function tx(e,t){return Ni((()=>null==e?null:Array.isArray(e)?e.map((e=>tx(e,t))):Bg(e,"int32"===t.dtype?t:Ja(t,"int32"))))}function nx(e,t){const n=[];let s=0,r=null;for(;s<e;)r=s+t,r>=e&&(r=e),n.push([s,r]),s=r;return n}function sx(e){const t=[];e instanceof vr&&(e=[e]);for(let n=0;n<e.length;++n){const s=e[n];if(1===s.rank)t.push(Rg(s,1));else{if(0===s.rank)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(s)}}return t}function rx(e,t){if(null==e)return;const n=[];if(t instanceof vr)n.push(t.id);else if(Array.isArray(t))t.forEach((e=>n.push(e.id)));else if(null!=t)for(const e in t){const s=t[e];n.push(s.id)}const s=[];if(e instanceof vr)-1===n.indexOf(e.id)&&s.push(e);else if(Array.isArray(e))e.forEach((e=>{-1===n.indexOf(e.id)&&s.push(e)}));else if(null!=e)for(const t in e){const r=e[t];-1===n.indexOf(r.id)&&s.push(r)}s.forEach((e=>{e.isDisposed||e.dispose()}))}function ax(e){return Array.isArray(e)}function ix(e){return!function(e){return e instanceof vr}(e)&&!ax(e)}function ox(e,t,n,s=!0,r=""){if(null==t||0===t.length){if(null!=e){let t=!1;if(ax(e)&&e.length>0)t=!0;else if(ix(e)){for(const n in e)if(e.hasOwnProperty(n)){t=!0;break}}else t=!0;if(t)throw new Mm(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(null==e)return t.map((e=>null));let a;if(ix(e)){a=[];for(const n of t){if(null==e[n])throw new Mm(`No data provided for "${n}". Need data for each key in: ${t}`);a.push(e[n])}}else if(ax(e)){if(e.length!==t.length)throw new Mm(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);a=e}else{if(t.length>1)throw new Mm(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);a=[e]}if(a=sx(a),null!=n)for(let e=0;e<t.length;++e){if(null==n[e])continue;const i=a[e];if(i.shape.length!==n[e].length)throw new Mm(`Error when checking ${r}: expected ${t[e]} to have ${n[e].length} dimension(s). but got array with shape ${i.shape}`);for(let t=0;t<n[e].length;++t){if(0===t&&!s)continue;const a=i.shape[t],o=n[e][t];if(null!=o&&o>=0&&a!==o)throw new Mm(`${r} expected a batch of elements where each example has shape [${n[e].slice(1,n[e].length)}] (i.e.,tensor shape [*,${n[e].slice(1,n[e].length)}]) but the ${r} received an input with ${i.shape[0]} examples, each with shape [${i.shape.slice(1,i.shape.length)}] (tensor shape [${i.shape}])`)}}return a}function lx(e,t,n,s=!0,r=""){let a;if(Array.isArray(e)){if(e.length!==t.length)throw new Mm(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);a=e}else{if(t.length>1)throw new Mm(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);a=[e]}if(null!=n)for(let e=0;e<t.length;++e){if(null==n[e])continue;const i=a[e];if(i.shape.length!==n[e].length)throw new Mm(`Error when checking ${r}: expected ${t[e]} to have ${n[e].length} dimension(s), but got array with shape ${JSON.stringify(i.shape)}`);for(let a=0;a<n[e].length;++a){if(0===a&&!s)continue;const o=i.shape[a],l=n[e][a];if(null!=l&&l!==o)throw new Mm(`Error when checking ${r}: expected ${t[e]} to have shape ${JSON.stringify(n[e])} but got array with shape ${JSON.stringify(i.shape)}.`)}}}class ux extends Gb{constructor(e){super(e),this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new Mm("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");Mb(this,e,t,n)}compile(e){if(null==e.loss&&(e.loss=[]),this.loss=e.loss,"string"==typeof e.optimizer)this.optimizer_=function(e){const t={Adagrad:()=>ud.adagrad(.01),Adadelta:()=>ud.adadelta(1,.95,Eg()),Adam:()=>ud.adam(.001,.9,.999,Eg()),Adamax:()=>ud.adamax(.002,.9,.999,Eg(),0),RMSProp:()=>ud.rmsprop(.001,.9,0,Eg()),SGD:()=>ud.sgd(.01)};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,e in t)return t[e]();throw new Mm(`Unknown Optimizer ${e}`)}(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof ed))throw new Mm("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(Array.isArray(e.loss)||"string"==typeof e.loss||"function"==typeof e.loss)if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new Mm(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);const n=e.loss;t=n.map((e=>wb(e)))}else{const n=wb(e.loss);this.outputs.forEach((e=>{t.push(n)}))}else{e.loss=e.loss;for(const t in e.loss)if(-1===this.outputNames.indexOf(t))throw new Mm(`Unknown entry in loss dictionary: "${t}". Only expected the following keys: ${this.outputNames}`);for(const n of this.outputNames)null==e.loss[n]&&console.warn(`Output "${n}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${n} during training`),t.push(wb(e.loss[n]))}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let e=0;e<this.outputs.length;++e){const t=this.internalOutputShapes[e],n=this.outputNames[e];this.feedOutputNames.push(n),this.feedOutputShapes.push(t),this.feedLossFns.push(this.lossFunctions[e])}const n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],xg("loss",(()=>{for(let e=0;e<this.outputs.length;++e){if(-1!==n.indexOf(e))continue;const t=this.lossFunctions[e];this.outputs.length>1&&(this.metricsTensors.push([t,e]),this.metricsNames.push(this.outputNames[e]+"_loss"))}}));const s=function(e,t){if(null==e||Array.isArray(e)&&0===e.length)return t.map((e=>[]));let n;if("string"==typeof e||"function"==typeof e)n=[e];else{if(!Array.isArray(e)&&"object"!=typeof e)throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);n=e}if(Array.isArray(n))return t.map((e=>n));{const e=[];for(const s of t){let t=n.hasOwnProperty(s)?n[s]:[];Array.isArray(t)||(t=[t]),e.push(t)}return e}}(e.metrics,this.outputNames),r=(e,t,n)=>{this.outputNames.length>1&&(t=this.outputNames[e]+"_"+t),this.metricsNames.push(t),this.metricsTensors.push([n,e])};xg("metric",(()=>{for(let e=0;e<this.outputs.length;++e){if(-1!==n.indexOf(e))continue;(t=>{let n,s,a;for(const i of t){if("string"==typeof i&&-1!==["accuracy","acc","crossentropy","ce"].indexOf(i)){const t=this.internalOutputShapes[e];let r;1===t[t.length-1]||this.lossFunctions[e]===gb?-1!==["accuracy","acc"].indexOf(i)?s=vb:-1!==["crossentropy","ce"].indexOf(i)&&(s=Tb):this.lossFunctions[e]===mb?-1!==["accuracy","acc"].indexOf(i)?s=Cb:-1!==["crossentropy","ce"].indexOf(i)&&(s=Eb):-1!==["accuracy","acc"].indexOf(i)?s=kb:-1!==["crossentropy","ce"].indexOf(i)&&(s=$b),-1!==["accuracy","acc"].indexOf(i)?r="acc":-1!==["crossentropy","ce"].indexOf(i)&&(r="ce"),a=s,n=""+r}else{const e=Rb(i);a=e,n=""+_b(i)}let t;xg(n,(()=>{t=a})),r(e,n,t)}})(s[e])}})),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){null!=this.collectedTrainableWeights&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){const s=null==n.batchSize?32:n.batchSize;Qb(s);const r=this.standardizeUserDataXY(e,t,!0,s);try{const a=r[0].concat(r[1]);this.makeTestFunction();const i=this.testFunction;return Um(this.testLoop(i,a,s,n.verbose,n.steps))}finally{rx(r[0],e),rx(r[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),async function(e,t,n){const s=null!=(n=n||{}).batches,r=e.testFunction;let a=[];if(n.verbose>0)throw new Lm("Verbose mode is not implemented yet.");u(!s||n.batches>0&&Number.isInteger(n.batches),(()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`));const i="function"==typeof t.next?t:await t.iterator();let o=0,l=0;for(;!s||l<n.batches;){const t=await i.next();if(a=Ni((()=>{if(t.value){const{xs:n,ys:s}=Xb(e,t.value),i=n.concat(s),u=Ni((()=>r(i)));if(Ii(i),0===l)for(let e=0;e<u.length;++e)a.push(au(0));const c=i[0].shape[0];for(let e=0;e<u.length;++e){const t=u[e],n=a[e];a[e]=Ni((()=>Io(a[e],Co(c,t)))),l>0&&Ii(n)}Ii(u),o+=c,++l}return a})),t.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let e=0;e<a.length;++e){const t=a[e];a[e]=To(a[e],o),Ii(t)}return Um(a)}(this,e,t)}checkNumSamples(e,t,n,s="steps"){let r;if(null!=n){if(r=null,null!=t)throw new Mm(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else{if(null==e)throw new Mm(`Either the input data should have a defined shape, or ${s} shoud be specified.`);r=Array.isArray(e)?e[0].shape[0]:e.shape[0]}return r}execute(e,t){if(Array.isArray(t)&&0===t.length)throw new Mm("`outputs` is an empty Array, which is not allowed.");const n=Array.isArray(t),s=n?t:[t],r=this.retrieveSymbolicTensors(s),a=new _y;if(e instanceof vr&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new Mm(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let t=0;t<this.inputs.length;++t)a.add(this.inputs[t],e[t])}else for(const t of this.inputs){const n=e[t.name];if(null==n)throw new Mm(`No value is provided for the model's input ${t.name}`);a.add(t,n)}const i=Oy(r,a);return n?i:i[0]}retrieveSymbolicTensors(e){const t=Bm(null,e.length);let n=e.length;for(const s of this.layers){const r=Array.isArray(s.output)?s.output:[s.output],a=r.map((e=>e.name));for(let s=0;s<e.length;++s){const i=a.indexOf(e[s]);if(-1!==i&&(t[s]=r[i],n--),0===n)break}if(0===n)break}if(n>0){const n=[];throw t.forEach(((t,s)=>{null==t&&n.push(e[s])})),new Mm(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(n)}`)}return t}predictLoop(e,t=32,n=!1){return Ni((()=>{const s=this.checkNumSamples(e);if(n)throw new Lm("Verbose predictLoop() is not implemented yet.");const r=nx(s,t),a=this.outputs.map((e=>[]));for(let t=0;t<r.length;++t){Ni((()=>{const n=r[t][0],s=r[t][1],a=ex(e,n,s),i=[];if(Array.isArray(a))for(let e=0;e<a.length;++e)i.push({key:this.inputs[e],value:a[e]});else i.push({key:this.inputs[0],value:a});const o=new _y(i);return Oy(this.outputs,o)})).forEach(((e,t)=>a[t].push(e)))}return Um(a.map((e=>rl(e,0))))}))}predict(e,t={}){const n=sx(e);lx(n,this.inputNames,this.feedInputShapes,!1);try{const s=null==t.batchSize?32:t.batchSize;return Qb(s),this.predictLoop(n,s)}finally{rx(n,e)}}predictOnBatch(e){lx(e,this.inputNames,this.feedInputShapes,!0);const t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,s){if(null==this.optimizer_)throw new Om("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");const r=[];for(let e=0;e<this.feedOutputShapes.length;++e){const t=this.feedOutputShapes[e];this.feedLossFns[e]===mb?r.push(t.slice(0,t.length-1).concat([1])):r.push(t)}if(function(e,t,n){const s=Jm(e.map((e=>e.shape[0])));s.sort();const r=Jm(t.map((e=>e.shape[0])));if(r.sort(),s.length>1)throw new Mm(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map((e=>e.shape)))}`);if(r.length>1)throw new Mm(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map((e=>e.shape)))}`);if(s.length>0&&r.length>0&&!f(s,r))throw new Mm(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`)}(e=ox(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=ox(t,this.feedOutputNames,r,!1,"target")),function(e,t,n){const s=[cb,gb,fb];for(let r=0;r<e.length;++r){const a=e[r],i=t[r],o=n[r];if(null!=i){if(i===fb&&1===a.shape[a.shape.length-1])throw new Mm(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(-1!==s.indexOf(i)){const e=a.shape.slice(1),t=o.slice(1);for(let n=0;n<e.length;++n){const s=e[n],r=t[n];if(null!=r&&s!==r)throw new Mm(`A target Tensor with shape ${a.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&null!=s&&s>0&&e[0].shape[0]%s!=0)throw new Mm(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){const[i,o]=this.standardizeUserDataXY(e,t,r,a);if(null!=n)throw new Error("sample weight is not supported yet.");let l=null;if(null!=s){const e=jb(s,this.outputNames);l=[];for(let t=0;t<e.length;++t)l.push(await qb(o[t],null,e[t]))}return[i,o,l]}testLoop(e,t,n,s=0,r){return Ni((()=>{const a=this.checkNumSamples(t,n,r,"steps"),i=[];if(s>0)throw new Lm("Verbose mode is not implemented yet.");if(null!=r)throw new Lm("steps mode in testLoop() is not implemented yet");{const s=nx(a,n),r=bh(Cg(0,a));for(let n=0;n<s.length;++n){const a=s[n][0],o=s[n][1],l=_g(r,a,o-a),u=tx(t,l),c=e(u);if(0===n)for(let e=0;e<c.length;++e)i.push(au(0));for(let e=0;e<c.length;++e){const t=c[e];i[e]=Io(i[e],Co(o-a,t))}}for(let e=0;e<i.length;++e)i[e]=To(i[e],a)}return i}))}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let r=s;if(Vm(e,s)>1){r+=`_${Vm(e.slice(0,n),s)}`}t.push(r)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+2*this.outputs.length),a=[],i=this.collectedTrainableWeights.map((e=>e.read()));return[this.optimizer_.minimize((()=>{const e=[];for(let t=0;t<this.inputs.length;++t)e.push({key:this.inputs[t],value:n[t]});const i=new _y(e),o=Oy(this.outputs,i,{training:!0});let l;for(let e=0;e<this.lossFunctions.length;++e){let n=(0,this.lossFunctions[e])(s[e],o[e]);null!=r[e]&&(n=Kb(n,r[e]));const a=Yu(n);t.push(a),l=0===e?n:Io(l,n)}for(let e=0;e<this.metricsTensors.length;++e){let n;if(this.outputs.length>1&&e<this.outputs.length)n=t[e];else{const t=this.metricsTensors[e][0],r=this.metricsTensors[e][1];n=Yu(t(s[r],o[r]))}Si(n),a.push(n)}return l=Yu(l),this.calculateLosses().forEach((e=>{l=Io(l,e)})),l}),!0,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>Ni((()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let e=0;e<this.inputs.length;++e)a.push({key:this.inputs[e],value:s[e]});const i=new _y(a),o=Oy(this.outputs,i);for(let e=0;e<this.lossFunctions.length;++e){const s=this.lossFunctions[e],a=Yu(s(r[e],o[e]));n=0===e?a:Io(n,a),t.push(n)}for(let e=0;e<this.metricsTensors.length;++e){const n=this.metricsTensors[e][0],s=this.metricsTensors[e][1],a=Yu(n(r[s],o[s]));t.push(a)}return t}))}async fit(e,t,n={}){if(this.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");let s,r,a,i,o,l,u,c,h;this.isTraining=!0;try{const p=null==n.batchSize?32:n.batchSize;Qb(p);const d=!1,f=await this.standardizeUserData(e,t,n.sampleWeight,n.classWeight,d,p);s=f[0],r=f[1],h=f[2];let m,g=!1;if(null!=n.validationData&&n.validationData.length>0){if(g=!0,2!==n.validationData.length)throw 3===n.validationData.length?new Lm("validationData including sample weights is not supported yet."):new Mm(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${n.validationData} is invalid.`);o=n.validationData[0],l=n.validationData[1];const e=!0,t=await this.standardizeUserData(o,l,null,null,e,p);u=t[0],c=t[1],m=u.concat(c)}else if(null!=n.validationSplit&&n.validationSplit>0&&n.validationSplit<1){g=!0;const e=Math.floor(s[0].shape[0]*(1-n.validationSplit)),t=s[0].shape[0];u=ex(s,e,t),a=s,s=ex(s,0,e),c=ex(r,e,t),i=r,r=ex(r,0,e),m=u.concat(c)}else null!=n.validationSteps&&(g=!0);const y=s.concat(r).concat(h);this.checkTrainableWeightsConsistency();const b=this.makeTrainFunction(),x=this.getDedupedMetricsNames();let w,v;g?(this.makeTestFunction(),w=this.testFunction,v=x.slice().concat(x.map((e=>"val_"+e)))):(w=null,m=[],v=x.slice());const k=ab(n.callbacks,n.yieldEvery);return await this.fitLoop(b,y,x,p,n.epochs,n.verbose,k,w,m,n.shuffle,v,n.initialEpoch,null,null)}finally{this.isTraining=!1,rx(s,e),rx(r,t),rx(a,e),rx(i,t),rx(u,o),rx(c,l),null!=h&&Ii(h)}}async fitLoop(e,t,n,s,a,i,o,l,u,c,h,p,d,f){null==s&&(s=32),null==a&&(a=1),null==c&&(c=!0),null==p&&(p=0);let m=!1;if(null!=l&&null!=u&&(m=!0),null!=f&&(m=!0,null==d))throw new Mm("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");const g=this.checkNumSamples(t,s,d,"steps_per_epoch");let y;null!=g&&(y=Cg(0,g)),null==i&&(i=1);const{callbackList:b,history:x}=ob(o,i,a,p,g,d,s,m,h);b.setModel(this),this.history=x,await b.onTrainBegin(),this.stopTraining_=!1;for(let i=p;i<a;++i){await b.onEpochBegin(i);const a={};if(null!=d)throw new Lm("stepsPerEpoch mode is not implemented yet.");{if("batch"===c)throw new Lm("batch shuffling is not implemneted yet");c&&r(y);const i=bh(y),o=nx(g,s);for(let r=0;r<o.length;++r){const c={};if(await b.onBatchBegin(r,c),Ni((()=>{const h=o[r][0],p=o[r][1],d=_g(i,h,p-h);c.batch=r,c.size=p-h;const f=tx(t,d),g=e(f);for(let e=0;e<n.length;++e){const t=n[e],s=g[e];c[t]=s,Si(s)}if(r===o.length-1&&m){const e=this.testLoop(l,u,s);for(let t=0;t<n.length;++t){const s=n[t],r=e[t];Si(r),a["val_"+s]=r}}})),await b.onBatchEnd(r,c),Qy(c),this.stopTraining_)break}i.dispose()}if(await b.onEpochEnd(i,a),this.stopTraining_)break}return await b.onTrainEnd(),await this.history.syncData(),this.history}async fitDataset(e,t){return Zb(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],r=n[1],a=this.makeTrainFunction()(s.concat(r)),i=[];for(const e of a){const t=await e.data();i.push(t[0])}return Ii(a),rx(n[0],e),rx(n[1],t),Um(i)}getNamedWeights(e){const t=[],n=null!=e&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let e=0;e<s.length;++e)n&&!s[e].trainable||t.push({name:s[e].originalName,tensor:r[e]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(0===e.refCountAfterDispose&&null!=this.optimizer&&this.isOptimizerOwned){const t=ki().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-ki().numTensors}return e}getLossIdentifiers(){let e;if("string"==typeof this.loss)e=Hm(this.loss);else if(Array.isArray(this.loss)){for(const e of this.loss)if("string"!=typeof e)throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map((e=>Hm(e)))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t){if("string"!=typeof n[s])throw new Error("Serialization of non-string loss is not supported.");e[s]=Hm(n[s])}}return e}getMetricIdentifiers(){if("string"==typeof this.metrics||"function"==typeof this.metrics)return[Hm(_b(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map((e=>Hm(_b(e))));{const e={};for(const t in this.metrics)e[t]=Hm(_b(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(null!=e.weighted_metrics)throw new Error("Loading weight_metrics is not supported yet.");if(null!=e.loss_weights)throw new Error("Loading loss_weights is not supported yet.");if(null!=e.sample_weight_mode)throw new Error("Loading sample_weight_mode is not supported yet.");const t=lb(Wb(e.optimizer_config));let n,s;if("string"==typeof e.loss)n=jm(e.loss);else if(Array.isArray(e.loss))n=e.loss.map((e=>jm(e)));else if(null!=e.loss){n={};for(const t in e.loss)n[t]=jm(e.loss[t])}if(Array.isArray(e.metrics))s=e.metrics.map((e=>jm(e)));else if(null!=e.metrics){s={};for(const t in e.metrics)s[t]=jm(e.metrics[t])}this.compile({loss:n,metrics:s,optimizer:t})}async save(e,t){if("string"==typeof e){const t=wa(e);if(0===t.length)throw new Mm(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Mm(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)throw new Mm("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await ia(this.getNamedWeights(t)),s={modelTopology:this.toJSON(null,!1),format:"layers-model",generatedBy:"TensorFlow.js tfjs-layers v4.1.0",convertedBy:null};if(null!=t&&t.includeOptimizer&&null!=this.optimizer){s.trainingConfig=this.getTrainingConfig();const e="optimizer",{data:t,specs:r}=await ia(await this.optimizer.getWeights(),e);n.specs.push(...r),n.data=ha([n.data,t])}if(null!=this.userDefinedMetadata){const e=!0;Db(this.userDefinedMetadata,this.name,e),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){Db(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}ux.className="Model",go(ux);class cx extends ux{}cx.className="Functional",go(cx);class hx extends ux{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=null!=e.name?e.name:lg("sequential_"),null!=e.layers)for(const t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some((e=>e<0)))throw new Mm(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof hx||e instanceof ux;let n;if(t){if(n=e,1!==n.outputs.length)throw new Mm("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(1!==n.inputs.length)throw new Mm("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(0===this.outputs.length){if(0===e.inboundNodes.length){if(null==e.batchInputShape)throw new Mm("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const t=Ry({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(t)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(1!==e.inboundNodes.length)throw new Mm(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(1!==e.inboundNodes[0].outputTensors.length)throw new Mm("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=Ey(this.outputs[0])}this.inboundNodes=[],new Ty({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Bm(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs[0].shape})}else{const t=e.apply(this.outputs[0]);if(Array.isArray(t))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[t],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(0===this.layers.length)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),0===this.layers.length)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return null==this.model&&this.build(),this.model.call(e,t)}build(e){if(yy(e),0===this.inputs.length||0===this.outputs.length)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ux({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){null==this.model&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Om("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Om("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return null==this.model&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return null==this.model&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return null==this.model?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Om("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Om("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(null==t[0].className||"Merge"===t[0].className)throw new Mm("Legacy serialization format not supported yet.");r=t}else u(null!=t.layers,(()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.")),r=t.layers,delete t.layers,a=t;const i=new e(a);if(!(i instanceof hx))throw new Lm(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(const e of r){const t=lb(e,void 0,s);s&&t.setFastWeightInitDuringBuild(!0),i.add(t)}return i}set stopTraining(e){if(null==this.model)throw new Mm("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(null==this.model)throw new Mm("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}function px(e){return Ry(e)}hx.className="Sequential",go(hx);class dx extends fo{getConfig(){return{}}}class fx extends dx{apply(e,t=1){return function(e,t=1){if(1!==t)throw new Lm(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return jl(e)}(e,t)}}fx.className="elu",go(fx);class mx extends dx{apply(e){return Yc(e)}}mx.className="selu",go(mx);class gx extends dx{apply(e){return Wc(e)}}gx.className="relu",go(gx);class yx extends dx{apply(e){return Ni((()=>ec(6,Wc(e))))}}yx.className="relu6",go(yx);class bx extends dx{apply(e){return e}}bx.className="linear",go(bx);class xx extends dx{apply(e){return al(e)}}xx.className="sigmoid",go(xx);class wx extends dx{apply(e){return function(e){return Ni((()=>{const t=Io(.5,Co(.2,e));return xl(t,0,1)}))}(e)}}wx.className="hardSigmoid",go(wx);class vx extends dx{apply(e){return Du(e)}}vx.className="softplus",go(vx);class kx extends dx{apply(e){return function(e){return Ni((()=>To(e,Io($o(e),1))))}(e)}}kx.className="softsign",go(kx);class Nx extends dx{apply(e){return ol(e)}}Nx.className="tanh",go(Nx);class Ix extends dx{apply(e,t=-1){return ih(e,t)}}Ix.className="softmax",go(Ix);class Sx extends dx{apply(e,t=-1){return Lu(e,t)}}Sx.className="logSoftmax",go(Sx);class Tx extends dx{apply(e,t=1){return Ni((()=>Co(al(Co(e,t)),e)))}}Tx.className="swish",go(Tx);class Cx extends dx{apply(e){return Ni((()=>Co(e,ol(Du(e)))))}}function $x(e){return e.getClassName()}function Ex(e,t={}){return Ym(e,mo.getMap().classNameMap,t,"activation")}function Ax(e){if(null==e){const e={className:"linear",config:{}};return Ex(e)}if("string"==typeof e){const t={};return t.className=e,t.config={},Ex(t)}return e instanceof dx?e:Ex(e)}function Rx(e){if(null!=e&&"object"!=typeof e)throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}Cx.className="mish",go(Cx);class _x extends fo{}class Fx extends _x{constructor(e){super(),Rx(e),this.l1=null==e||null==e.l1?.01:e.l1,this.l2=null==e||null==e.l2?.01:e.l2,this.hasL1=0!==this.l1,this.hasL2=0!==this.l2}apply(e){return Ni((()=>{let t=Zu([1]);return this.hasL1&&(t=Io(t,lu(Co(this.l1,$o(e))))),this.hasL2&&(t=Io(t,lu(Co(this.l2,Wg(e))))),tl(t,[])}))}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Fx.className="L1L2",go(Fx);const Dx={l1l2:"L1L2"};function Ox(e){return Km(e)}function Mx(e,t={}){return Ym(e,mo.getMap().classNameMap,t,"regularizer")}function Lx(e){if(null==e)return null;if("string"==typeof e){return Mx({className:e in Dx?Dx[e]:e,config:{}})}return e instanceof _x?e:Mx(e)}class zx extends $y{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,null!=e&&(this.maxValue=e.maxValue)}call(e,t){e=gy(e);let n=Wc(e);return null!=this.maxValue&&(n=xl(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}zx.className="ReLU",go(zx);class Px extends $y{constructor(e){super(null==e?{}:e),this.DEFAULT_ALPHA=.3,null==e&&(e={}),this.alpha=null==e.alpha?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=gy(e);return Iu(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Px.className="LeakyReLU",go(Px);class Bx extends $y{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",null==e&&(e={}),this.supportsMasking=!0,this.alphaInitializer=dy(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Lx(e.alphaRegularizer),this.alphaConstraint=Ky(e.alphaConstraint),null==e.sharedAxes)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else{if("number"!=typeof e.sharedAxes)throw new Mm(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`);this.sharedAxes=[e.sharedAxes]}}build(e){const t=(e=yy(e)).slice(1);if(null!=this.sharedAxes)for(const e of this.sharedAxes)t[e-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(null!=this.sharedAxes)for(let t=1;t<e.length;++t)n[t]=e[t];this.inputSpec=[new Ny({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=gy(e),gc(e,this.alpha.read())}getConfig(){const e={alphaInitializer:py(this.alphaInitializer),alphaRegularizer:Ox(this.alphaRegularizer),alphaConstraint:jy(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}Bx.className="PReLU",go(Bx);class Wx extends $y{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA=1,null==e&&(e={}),null!=e.alpha&&e.alpha!==this.DEFAULT_ALPHA)throw new Lm(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=null==e.alpha?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=gy(e);return jl(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Wx.className="ELU",go(Wx);class Vx extends $y{constructor(e){super(null==e?{}:e),this.DEFAULT_THETA=1,null==e&&(e={}),this.theta=null==e.theta?this.DEFAULT_THETA:e.theta}call(e,t){const n=gy(e);return Co(n,Ja(xu(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Vx.className="ThresholdedReLU",go(Vx);class Ux extends $y{constructor(e){super(null==e?{}:e),this.DEFAULT_AXIS=1,null==e&&(e={}),this.softmax=(new Ix).apply,this.axis=null==e.axis?this.DEFAULT_AXIS:e.axis}call(e,t){const n=gy(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}function Gx(e,t,n){if("number"==typeof e)return Bm(e,t);if(e.length!==t)throw new Mm(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){const a=e[r];if((s=a)!==parseInt(s.toString(),10))throw new Mm(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e;var s}function Hx(e,t,n,s,r=1){if(null==e)return e;let a;return a="same"===n?e:e-(t+(t-1)*(r-1))+1,Math.floor((a+s-1)/s)}function jx(e,t,n,s){if(null==e)return null;if("valid"===s)e=e*t+Tg([n-t,0]);else{if("same"!==s)throw new Mm(`Unsupport padding mode: ${s}.`);e*=t}return e}function qx(e,t){return Ni((()=>(mg(t),"channelsFirst"===t?_i(e,[0,2,3,1]):e)))}function Kx(e,t){return Ni((()=>(mg(t),"channelsFirst"===t?_i(e,[0,2,3,4,1]):e)))}function Xx(e,t,n,s=1,r="valid",a,i=1){return Ni((()=>{if(null==a&&(a="channelsLast"),mg(a),3!==e.shape.length)throw new Mm(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(3!==t.shape.length)throw new Mm(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(null!=n&&1!==n.shape.length)throw new Mm(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if("channelsFirst"===a&&(e=_i(e,[0,2,1])),"causal"===r)throw new Lm("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Sl(e,t,s,"same"===r?"same":"valid","NWC",i);return null!=n&&(o=Ug(o,n)),o}))}function Yx(e,t,n,s=[1,1],r="valid",a,i,o=null){return Ni((()=>{if(null==a&&(a="channelsLast"),mg(a),3!==e.rank&&4!==e.rank)throw new Mm(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(3!==t.rank&&4!==t.rank)throw new Mm(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=qx(e,a);if("causal"===r)throw new Lm("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=jh({x:l,filter:t,strides:s,pad:"same"===r?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),"channelsFirst"===a&&(l=_i(l,[0,3,1,2])),l}))}function Zx(e,t,n,s=[1,1,1],r="valid",a,i){return Ni((()=>{if(null==a&&(a="channelsLast"),mg(a),4!==e.rank&&5!==e.rank)throw new Mm(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(4!==t.rank&&5!==t.rank)throw new Mm(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=Kx(e,a);if("causal"===r)throw new Lm("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=$l(o,t,s,"same"===r?"same":"valid","NDHWC",i),null!=n&&(o=Ug(o,n)),"channelsFirst"===a&&(o=_i(o,[0,4,1,2,3])),o}))}Ux.className="Softmax",go(Ux);class Jx extends $y{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Jx.verifyArgs(t),this.rank=e,ng(this.rank,"rank"),1!==this.rank&&2!==this.rank&&3!==this.rank)throw new Lm(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gx(t.kernelSize,e,"kernelSize"),this.strides=Gx(null==t.strides?1:t.strides,e,"strides"),this.padding=null==t.padding?"valid":t.padding,gg(this.padding),this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,mg(this.dataFormat),this.activation=Ax(t.activation),this.useBias=null==t.useBias||t.useBias,this.biasInitializer=dy(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ky(t.biasConstraint),this.biasRegularizer=Lx(t.biasRegularizer),this.activityRegularizer=Lx(t.activityRegularizer),this.dilationRate=Gx(null==t.dilationRate?1:t.dilationRate,e,"dilationRate"),1===this.rank&&Array.isArray(this.dilationRate)&&1!==this.dilationRate.length)throw new Mm(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(2===this.rank){if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate];else if(2!==this.dilationRate.length)throw new Mm(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(3===this.rank)if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(3!==this.dilationRate.length)throw new Mm(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}static verifyArgs(e){if(Wm("kernelSize"in e,"required key 'kernelSize' not in config"),"number"!=typeof e.kernelSize&&!tg(e.kernelSize,"number",1,3))throw new Mm(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:$x(this.activation),useBias:this.useBias,biasInitializer:py(this.biasInitializer),biasRegularizer:Ox(this.biasRegularizer),activityRegularizer:Ox(this.activityRegularizer),biasConstraint:jy(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Qx extends Jx{constructor(e,t){super(e,t),this.kernel=null,Qx.verifyArgs(t),this.filters=t.filters,ng(this.filters,"filters"),this.kernelInitializer=dy(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ky(t.kernelConstraint),this.kernelRegularizer=Lx(t.kernelRegularizer)}build(e){e=yy(e);const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Mm(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return Ni((()=>{let t;e=gy(e);const n=null==this.bias?null:this.bias.read(),s=rg(this.activation.getClassName());if(null!=s&&2===this.rank)t=Yx(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(1===this.rank)t=Xx(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(2===this.rank)t=Yx(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else{if(3!==this.rank)throw new Lm("convolutions greater than 3D are not implemented yet.");t=Zx(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate)}null!=this.activation&&(t=this.activation.apply(t))}return t}))}computeOutputShape(e){e=yy(e);const t=[],n="channelsLast"===this.dataFormat?e.slice(1,e.length-1):e.slice(2);for(let e=0;e<n.length;++e){const s=Hx(n[e],this.kernelSize[e],this.padding,this.strides[e],"number"==typeof this.dilationRate?this.dilationRate:this.dilationRate[e]);t.push(s)}let s=[e[0]];return"channelsLast"===this.dataFormat?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){const e={filters:this.filters,kernelInitializer:py(this.kernelInitializer),kernelRegularizer:Ox(this.kernelRegularizer),kernelConstraint:jy(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||"number"!=typeof e.filters||e.filters<1)throw new Mm(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class ew extends Qx{constructor(e){super(2,e),ew.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!tg(e.kernelSize,"number",1,2))throw new Mm(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}ew.className="Conv2D",go(ew);class tw extends Qx{constructor(e){super(3,e),tw.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&(!Array.isArray(e.kernelSize)||1!==e.kernelSize.length&&3!==e.kernelSize.length))throw new Mm(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}tw.className="Conv3D",go(tw);class nw extends ew{constructor(e){if(super(e),this.inputSpec=[new Ny({ndim:4})],"same"!==this.padding&&"valid"!==this.padding)throw new Mm(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(4!==(e=yy(e)).length)throw new Mm("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Mm("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ny({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return Ni((()=>{let t=gy(e);if(4!==t.shape.length)throw new Mm(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);const n=t.shape,s=n[0];let r,a;"channelsFirst"===this.dataFormat?(r=2,a=3):(r=1,a=2);const i=n[r],o=n[a],l=this.kernelSize[0],u=this.kernelSize[1],c=this.strides[0],h=this.strides[1],p=[s,jx(i,c,l,this.padding),jx(o,h,u,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=_i(t,[0,2,3,1]));let d=Cl(t,this.kernel.read(),p,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(d=_i(d,[0,3,1,2])),null!=this.bias&&(d=Ug(d,this.bias.read(),this.dataFormat)),null!=this.activation&&(d=this.activation.apply(d)),d}))}computeOutputShape(e){const t=(e=yy(e)).slice();let n,s,r;"channelsFirst"===this.dataFormat?(n=1,s=2,r=3):(n=3,s=1,r=2);const a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=jx(t[s],o,a,this.padding),t[r]=jx(t[r],l,i,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}nw.className="Conv2DTranspose",go(nw);class sw extends tw{constructor(e){if(super(e),this.inputSpec=[new Ny({ndim:5})],"same"!==this.padding&&"valid"!==this.padding)throw new Mm(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(5!==(e=yy(e)).length)throw new Mm("Input should have rank 5; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Mm("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ny({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return Ni((()=>{let t=gy(e);if(5!==t.shape.length)throw new Mm(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);const n=t.shape,s=n[0];let r,a,i;"channelsFirst"===this.dataFormat?(i=2,r=3,a=4):(i=1,r=2,a=3);const o=n[i],l=n[r],u=n[a],c=this.kernelSize[0],h=this.kernelSize[1],p=this.kernelSize[2],d=this.strides[0],f=this.strides[1],m=this.strides[2],g=[s,jx(o,d,c,this.padding),jx(l,f,h,this.padding),jx(u,m,p,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=_i(t,[0,2,3,4,1]));let y=Al(t,this.kernel.read(),g,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(y=_i(y,[0,4,1,2,3])),null!==this.bias&&(y=Ug(y,this.bias.read(),this.dataFormat)),null!==this.activation&&(y=this.activation.apply(y)),y}))}computeOutputShape(e){const t=(e=yy(e)).slice();let n,s,r,a;"channelsFirst"===this.dataFormat?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);const i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],h=this.strides[2];return t[n]=this.filters,t[s]=jx(t[s],u,i,this.padding),t[r]=jx(t[r],c,o,this.padding),t[a]=jx(t[a],h,l,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}sw.className="Conv3DTranspose",go(sw);class rw extends Qx{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,null==t.filters)throw new Mm("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=t.kernelInitializer||null!=t.kernelRegularizer||null!=t.kernelConstraint)throw new Mm("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=t.padding&&"same"!==t.padding&&"valid"!==t.padding)throw new Mm(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,this.depthwiseInitializer=dy(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Lx(t.depthwiseRegularizer),this.depthwiseConstraint=Ky(t.depthwiseConstraint),this.pointwiseInitializer=dy(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Lx(t.pointwiseRegularizer),this.pointwiseConstraint=Ky(t.pointwiseConstraint)}build(e){if((e=yy(e)).length<this.rank+2)throw new Mm(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t]||e[t]<0)throw new Mm(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let e=0;e<this.rank;++e)r.push(1);r.push(n*this.depthMultiplier,this.filters);const a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Ny({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return Ni((()=>{let t;if(e=gy(e),1===this.rank)throw new Lm("1D separable convolution is not implemented yet.");return 2===this.rank&&("channelsFirst"===this.dataFormat&&(e=_i(e,[0,2,3,1])),t=Zc(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(t=Ug(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),"channelsFirst"===this.dataFormat&&(t=_i(t,[0,3,1,2])),t}))}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=py(this.depthwiseInitializer),e.pointwiseInitializer=py(this.pointwiseInitializer),e.depthwiseRegularizer=Ox(this.depthwiseRegularizer),e.pointwiseRegularizer=Ox(this.pointwiseRegularizer),e.depthwiseConstraint=jy(this.depthwiseConstraint),e.pointwiseConstraint=jy(this.pointwiseConstraint),e}}rw.className="SeparableConv";class aw extends rw{constructor(e){super(2,e)}}aw.className="SeparableConv2D",go(aw);class iw extends Qx{constructor(e){super(1,e),iw.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!tg(e.kernelSize,"number",1,1))throw new Mm(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}iw.className="Conv1D",go(iw);class ow extends $y{constructor(e){super(e),"number"==typeof e.cropping?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:"number"==typeof e.cropping[0]?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=void 0===e.dataFormat?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return"channelsFirst"===this.dataFormat?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return Ni((()=>{if(e=gy(e),"channelsLast"===this.dataFormat){const t=Dg(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Dg(t,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}{const t=Dg(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Dg(t,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}}))}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}ow.className="Cropping2D",go(ow);class lw extends $y{constructor(e){var t;super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=null==e.size?this.DEFAULT_SIZE:e.size,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,mg(this.dataFormat),this.interpolation=null==e.interpolation?"nearest":e.interpolation,t=this.interpolation,eg(cg,"InterpolationFormat",t)}computeOutputShape(e){if("channelsFirst"===this.dataFormat){const t=null==e[2]?null:this.size[0]*e[2],n=null==e[3]?null:this.size[1]*e[3];return[e[0],e[1],t,n]}{const t=null==e[1]?null:this.size[0]*e[1],n=null==e[2]?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return Ni((()=>{let t=gy(e);const n=t.shape;if("channelsFirst"===this.dataFormat){t=_i(t,[0,2,3,1]);const e=this.size[0]*n[2],s=this.size[1]*n[3],r="nearest"===this.interpolation?Xp.resizeNearestNeighbor(t,[e,s]):Xp.resizeBilinear(t,[e,s]);return _i(r,[0,3,1,2])}{const e=this.size[0]*n[1],s=this.size[1]*n[2];return"nearest"===this.interpolation?Xp.resizeNearestNeighbor(t,[e,s]):Xp.resizeBilinear(t,[e,s])}}))}getConfig(){const e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}}lw.className="UpSampling2D",go(lw);class uw extends Jx{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=dy(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ky(e.depthwiseConstraint),this.depthwiseRegularizer=Lx(e.depthwiseRegularizer)}build(e){if((e=yy(e)).length<4)throw new Mm(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t="channelsFirst"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new Mm(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Ni((()=>{let t=function(e,t,n=[1,1],s="valid",r,a){return Ni((()=>{null==r&&(r="channelsLast"),mg(r);let i=qx(e,r);if(4!==e.rank)throw new Mm(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(4!==t.rank)throw new Mm(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ll(i,t,n,"same"===s?"same":"valid","NHWC",a),"channelsFirst"===r&&(i=_i(i,[0,3,1,2])),i}))}(e=gy(e),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(t=Ug(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),t}))}computeOutputShape(e){e=yy(e);const t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],s="channelsFirst"===this.dataFormat?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Hx(t,this.kernelSize[0],this.padding,this.strides[0]),a=Hx(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=py(this.depthwiseInitializer),e.depthwiseRegularizer=Ox(this.depthwiseRegularizer),e.depthwiseConstraint=jy(this.depthwiseRegularizer),e}}function cw(e,t,n,s){if(Array.isArray(e)){if(null!=t||null!=n)throw new Mm("When inputs is an array, neither initialState or constants should be provided");null!=s&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(e){return null==e||Array.isArray(e)?e:[e]}return{inputs:e,initialState:t=r(t),constants:n=r(n)}}function hw(e,t,n,s=!1,r,a,i=!1,o=!1){return Ni((()=>{const l=t.shape.length;if(l<3)throw new Mm(`Input should be at least 3D, but is ${l}D.`);const u=[1,0].concat(Cg(2,l));if(t=_i(t,u),null!=a)throw new Lm("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),null!=r&&((r=Ja(Ja(r,"bool"),"float32")).rank===l-1&&(r=du(r,-1)),r=_i(r,u)),s&&(t=Uc(t,0),null!=r&&(r=Uc(r,0)));const c=[];let h,p=n;const d=t.shape[0],f=Ch(t);let m,g;null!=r&&(m=Ch(r));for(let t=0;t<d;++t){const n=f[t],s=Ni((()=>e(n,p)));if(null==r)h=s[0],p=s[1];else{const e=Ni((()=>{const e=m[t],n=Mu(oc(e),e);return{output:Io(Co(s[0],e),Co(p[0],n)),newStates:p.map(((t,r)=>Io(Co(s[1][r],e),Co(t,n))))}}));h=e.output,p=e.newStates}o&&c.push(h)}if(o){g=fh(c,1)}return[h,g,p]}))}uw.className="DepthwiseConv2D",go(uw);class pw extends $y{constructor(e){let t;if(super(e),null==e.cell)throw new Mm("cell property is missing for the constructor of RNN.");if(t=Array.isArray(e.cell)?new ww({cells:e.cell}):e.cell,null==t.stateSize)throw new Mm("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=null!=e.returnSequences&&e.returnSequences,this.returnState=null!=e.returnState&&e.returnState,this.goBackwards=null!=e.goBackwards&&e.goBackwards,this._stateful=null!=e.stateful&&e.stateful,this.unroll=null!=e.unroll&&e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ny({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(null==this.states_){return Cg(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map((e=>null))}return this.states_}setStates(e){this.states_=e}computeOutputShape(e){fy(e)&&(e=e[0]);let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let s;if(s=this.returnSequences?[e[0],e[1],n]:[e[0],n],this.returnState){const n=[];for(const s of t)n.push([e[0],s]);return[s].concat(n)}return s}computeMask(e,t){return Ni((()=>{Array.isArray(t)&&(t=t[0]);const e=this.returnSequences?t:null;if(this.returnState){const t=this.states.map((e=>null));return[e].concat(t)}return e}))}get states(){if(null==this.states_){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}return this.states_}set states(e){this.states_=e}build(e){if(null!=this.numConstants)throw new Lm("Constants support is not implemented in RNN yet.");fy(e)&&(e=e[0]);const t=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new Ny({shape:[t,null,...n]});const s=[e[0]].concat(e.slice(2));let r;if(this.cell.build(s),r=Array.isArray(this.cell.stateSize)?this.cell.stateSize:[this.cell.stateSize],null!=this.stateSpec){if(!f(this.stateSpec.map((e=>e.shape[e.shape.length-1])),r))throw new Mm(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=r.map((e=>new Ny({shape:[null,e]})));this.stateful&&this.resetStates()}resetStates(e,t=!1){Ni((()=>{if(!this.stateful)throw new Dm("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(null==n)throw new Mm("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==this.states_)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>Zu([n,e]))):this.states_=[Zu([n,this.cell.stateSize])];else if(null==e)Ii(this.states_),null!=this.keptStates&&(Ii(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>Zu([n,e]))):this.states_[0]=Zu([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Mm(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);!0===t?this.keptStates.push(this.states_.slice()):Ii(this.states_);for(let t=0;t<this.states_.length;++t){const s=e[t],r=Array.isArray(this.cell.stateSize)?this.cell.stateSize[t]:this.cell.stateSize,a=[n,r];if(!f(s.shape,a))throw new Mm(`State ${t} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${s.shape}`);this.states_[t]=s}}this.states_=this.states_.map((e=>Si(e.clone())))}))}apply(e,t){let n=null==t?null:t.initialState,s=null==t?null:t.constants;null==t&&(t={});const r=cw(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(null!=n){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(const e of n)this.stateSpec.push(new Ny({shape:e.shape}));i=i.concat(this.stateSpec)}null!=s&&(t.constants=s,a=a.concat(s),this.numConstants=s.length);if(a[0]instanceof Iy){const n=[e].concat(a),s=this.inputSpec.concat(i),r=this.inputSpec;this.inputSpec=s;const o=super.apply(n,t);return this.inputSpec=r,o}return super.apply(e,t)}call(e,t){return Ni((()=>{const n=null==t?null:t.mask,s=null==t?null:t.training;let r=null==t?null:t.initialState;e=gy(e),null==r&&(r=this.stateful?this.states_:this.getInitialState(e));const a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new Mm(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const i={training:s},o=hw(((e,t)=>{const n=this.cell.call([e].concat(t),i);return[n[0],n.slice(1)]}),e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,s);const h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h}))}getInitialState(e){return Ni((()=>{let t=Zu(e.shape);return t=lu(t,[1,2]),t=Rg(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map((e=>e>1?Lg(t,[1,e]):t)):this.cell.stateSize>1?[Lg(t,[1,this.cell.stateSize])]:[t]}))}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===pw.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){const s=lb(t.cell,n);return new e(Object.assign(t,{cell:s}))}}pw.className="RNN",go(pw);class dw extends $y{}class fw extends dw{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,ng(this.units,"units"),this.activation=Ax(null==e.activation?this.DEFAULT_ACTIVATION:e.activation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=dy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dy(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Lx(e.kernelRegularizer),this.recurrentRegularizer=Lx(e.recurrentRegularizer),this.biasRegularizer=Lx(e.biasRegularizer),this.kernelConstraint=Ky(e.kernelConstraint),this.recurrentConstraint=Ky(e.recurrentConstraint),this.biasConstraint=Ky(e.biasConstraint),this.dropout=Sg([1,Tg([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=Sg([1,Tg([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=yy(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Ni((()=>{if(2!==e.length)throw new Mm(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=null!=t.training&&t.training;let r;0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=vw({ones:()=>oc(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=vw({ones:()=>oc(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));const a=this.dropoutMask,i=this.recurrentDropoutMask;r=Pg(null!=a?Co(e,a):e,this.kernel.read()),null!=this.bias&&(r=Ug(r,this.bias.read())),null!=i&&(n=Co(n,i));let o=Io(r,Pg(n,this.recurrentKernel.read()));return null!=this.activation&&(o=this.activation.apply(o)),[o,o]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:$x(this.activation),useBias:this.useBias,kernelInitializer:py(this.kernelInitializer),recurrentInitializer:py(this.recurrentInitializer),biasInitializer:py(this.biasInitializer),kernelRegularizer:Ox(this.kernelRegularizer),recurrentRegularizer:Ox(this.recurrentRegularizer),biasRegularizer:Ox(this.biasRegularizer),activityRegularizer:Ox(this.activityRegularizer),kernelConstraint:jy(this.kernelConstraint),recurrentConstraint:jy(this.recurrentConstraint),biasConstraint:jy(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}}fw.className="SimpleRNNCell",go(fw);class mw extends pw{constructor(e){e.cell=new fw(e),super(e)}call(e,t){return Ni((()=>{null!=this.cell.dropoutMask&&(Ii(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Ii(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,s=null==t?null:t.training,r=null==t?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})}))}static fromConfig(e,t){return new e(t)}}mw.className="SimpleRNN",go(mw);class gw extends dw{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new Mm("GRUCell does not support reset_after parameter set to true.");this.units=e.units,ng(this.units,"units"),this.activation=Ax(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ax(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=dy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dy(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Lx(e.kernelRegularizer),this.recurrentRegularizer=Lx(e.recurrentRegularizer),this.biasRegularizer=Lx(e.biasRegularizer),this.kernelConstraint=Ky(e.kernelConstraint),this.recurrentConstraint=Ky(e.recurrentConstraint),this.biasConstraint=Ky(e.biasConstraint),this.dropout=Sg([1,Tg([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=Sg([1,Tg([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){const t=(e=yy(e))[e.length-1];this.kernel=this.addWeight("kernel",[t,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Ni((()=>{if(2!==e.length)throw new Mm(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=null!=t.training&&t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=vw({ones:()=>oc(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=vw({ones:()=>oc(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));const r=this.dropoutMask,a=this.recurrentDropoutMask;let i,o,l;0<this.dropout&&this.dropout<1&&(e=Co(e,r[0]));let u=Pg(e,this.kernel.read());this.useBias&&(u=Ug(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=Co(s,a[0]));const c=this.recurrentKernel.read(),[h,p]=ch(c,[2*this.units,this.units],c.rank-1),d=Pg(s,h),[f,m,g]=ch(u,3,u.rank-1),[y,b]=ch(d,2,d.rank-1);i=this.recurrentActivation.apply(Io(f,y)),o=this.recurrentActivation.apply(Io(m,b));const x=Pg(Co(o,s),p);l=this.activation.apply(Io(g,x));const w=Io(Co(i,s),Co(Io(1,Ai(i)),l));return[w,w]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:$x(this.activation),recurrentActivation:$x(this.recurrentActivation),useBias:this.useBias,kernelInitializer:py(this.kernelInitializer),recurrentInitializer:py(this.recurrentInitializer),biasInitializer:py(this.biasInitializer),kernelRegularizer:Ox(this.kernelRegularizer),recurrentRegularizer:Ox(this.recurrentRegularizer),biasRegularizer:Ox(this.biasRegularizer),activityRegularizer:Ox(this.activityRegularizer),kernelConstraint:jy(this.kernelConstraint),recurrentConstraint:jy(this.recurrentConstraint),biasConstraint:jy(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},e),t)}}gw.className="GRUCell",go(gw);class yw extends pw{constructor(e){0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new gw(e),super(e)}call(e,t){return Ni((()=>{null!=this.cell.dropoutMask&&(Ii(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Ii(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,s=null==t?null:t.training,r=null==t?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}yw.className="GRU",go(yw);class bw extends dw{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,ng(this.units,"units"),this.activation=Ax(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ax(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=dy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dy(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Lx(e.kernelRegularizer),this.recurrentRegularizer=Lx(e.recurrentRegularizer),this.biasRegularizer=Lx(e.biasRegularizer),this.kernelConstraint=Ky(e.kernelConstraint),this.recurrentConstraint=Ky(e.recurrentConstraint),this.biasConstraint=Ky(e.biasConstraint),this.dropout=Sg([1,Tg([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=Sg([1,Tg([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;const n=(e=yy(e))[e.length-1];let s;if(this.kernel=this.addWeight("kernel",[n,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){const e=this.biasInitializer,n=this.units;s=new((t=class extends Kg{apply(t,s){const r=e.apply([n]),a=(new Yg).apply([n]),i=e.apply([2*n]);return Mg(Mg(r,a),i)}}).className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return Ni((()=>{const n=null!=t.training&&t.training;if(3!==e.length)throw new Mm(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=vw({ones:()=>oc(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=vw({ones:()=>oc(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const a=this.dropoutMask,i=this.recurrentDropoutMask;let o,l,u,c;0<this.dropout&&this.dropout<1&&(e=Co(e,a[0]));let h=Pg(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=Co(s,i[0])),h=Io(h,Pg(s,this.recurrentKernel.read())),this.useBias&&(h=Ug(h,this.bias.read()));const[p,d,f,m]=ch(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(d),u=Io(Co(l,r),Co(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);const g=Co(c,this.activation.apply(u));return[g,g,u]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:$x(this.activation),recurrentActivation:$x(this.recurrentActivation),useBias:this.useBias,kernelInitializer:py(this.kernelInitializer),recurrentInitializer:py(this.recurrentInitializer),biasInitializer:py(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Ox(this.kernelRegularizer),recurrentRegularizer:Ox(this.recurrentRegularizer),biasRegularizer:Ox(this.biasRegularizer),activityRegularizer:Ox(this.activityRegularizer),kernelConstraint:jy(this.kernelConstraint),recurrentConstraint:jy(this.recurrentConstraint),biasConstraint:jy(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},e),t)}}bw.className="LSTMCell",go(bw);class xw extends pw{constructor(e){0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new bw(e),super(e)}call(e,t){return Ni((()=>{null!=this.cell.dropoutMask&&(Ii(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Ii(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,s=null==t?null:t.training,r=null==t?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}xw.className="LSTM",go(xw);class ww extends dw{constructor(e){super(e),this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return Ni((()=>{let n=e.slice(1);const s=[];for(const e of this.cells.slice().reverse())Array.isArray(e.stateSize)?s.push(n.splice(0,e.stateSize.length)):s.push(n.splice(0,1));s.reverse();const r=[];let a;for(let i=0;i<this.cells.length;++i){const o=this.cells[i];n=s[i],a=0===i?[e[0]].concat(n):[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(const e of r.slice().reverse())n.push(...e);return[a[0]].concat(n)}))}build(e){let t;fy(e)&&(e=e[0]),this.cells.forEach(((n,s)=>{xg(`RNNCell_${s}`,(()=>{n.build(e),t=Array.isArray(n.stateSize)?n.stateSize[0]:n.stateSize,e=[e[0],t]}))})),this.built=!0}getConfig(){const e=super.getConfig(),t={cells:this.cells.map((e=>({className:e.getClassName(),config:e.getConfig()})))};return Object.assign(Object.assign({},e),t)}static fromConfig(e,t,n={}){const s=[];for(const e of t.cells)s.push(lb(e,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.cells)t.push(...e.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return vy(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,r=e.splice(s);for(let e=0;e<n.weights.length;++e)t.push([n.weights[e],r[e]])}ky(t)}}function vw(e){const{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>null!=a?a(t(),n):Gg(t(),n),o=()=>Hg(i,t,s);if(!r||r<=1)return Si(o().clone());return Array(r).fill(void 0).map(o).map((e=>Si(e.clone())))}ww.className="StackedRNNCells",go(ww);var kw=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols){var r=0;for(s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]])}return n};class Nw extends pw{constructor(e){if(e.unroll)throw new Lm("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Lm("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Ny({ndim:5})]}call(e,t){return Ni((()=>{if(null!=this.cell.dropoutMask&&(Ii(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Ii(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new Mm("ConvRNN2D cell does not support constants");const n=null==t?null:t.mask,s=null==t?null:t.training,r=null==t?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})}))}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return Ni((()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=Zu([s[0],...s.slice(2)]);return Array.isArray(t)?Array(t.length).fill(r):[r]}))}resetStates(e,t=!1){Ni((()=>{if(!this.stateful)throw new Dm("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(null==n[0])throw new Mm("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==this.getStates())Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>Zu(r))):this.states_=[Zu(r)];else if(null==e)Ii(this.states_),null!=this.keptStates&&(Ii(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>Zu(r))):this.states_[0]=Zu(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Mm(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ii(this.states_);for(let t=0;t<this.states_.length;++t){const n=e[t],s=r;if(!f(n.shape,s))throw new Mm(`State ${t} is incompatible with layer ${this.name}: expected shape=${s}, received shape=${n.shape}`);this.states_[t]=n}}this.states_=this.states_.map((e=>Si(e.clone())))}))}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o="channelsFirst"===t,l=e[o?3:2],u=e[o?4:3],c=Hx(l,s[0],r,a[0],i[0]),h=Hx(u,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}}Nw.className="ConvRNN2D";class Iw extends bw{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,ng(this.filters,"filters"),this.kernelSize=Gx(n,2,"kernelSize"),this.kernelSize.forEach((e=>ng(e,"kernelSize"))),this.strides=Gx(s||1,2,"strides"),this.strides.forEach((e=>ng(e,"strides"))),this.padding=r||"valid",gg(this.padding),this.dataFormat=a||"channelsLast",mg(this.dataFormat),this.dilationRate=Gx(i||1,2,"dilationRate"),this.dilationRate.forEach((e=>ng(e,"dilationRate")))}build(e){var t;e=yy(e);const n="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[n])throw new Mm(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],r=this.kernelSize.concat([s,4*this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let e;if(this.unitForgetBias){const n=this.biasInitializer,s=this.filters;e=new((t=class extends Kg{apply(e,t){return Og([n.apply([s]),Ju([s]),n.apply([2*s])])}}).className="CustomInit",t)}else e=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.filters],null,e,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return Ni((()=>{if(3!==e.length)throw new Mm(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],r=e[1],a=e[2];0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=vw({ones:()=>oc(s),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,o=(e,t,n)=>t&&t[n]?Co(t[n],e):e;let l=o(s,i,0),u=o(s,i,1),c=o(s,i,2),h=o(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=vw({ones:()=>oc(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const p=this.recurrentDropoutMask;let d=o(r,p,0),f=o(r,p,1),m=o(r,p,2),g=o(r,p,3);const[y,b,x,w]=ch(this.kernel.read(),4,3),[v,k,N,I]=this.useBias?ch(this.bias.read(),4):[null,null,null,null];l=this.inputConv(l,y,v,this.padding),u=this.inputConv(u,b,k,this.padding),c=this.inputConv(c,x,N,this.padding),h=this.inputConv(h,w,I,this.padding);const[S,T,C,$]=ch(this.recurrentKernel.read(),4,3);d=this.recurrentConv(d,S),f=this.recurrentConv(f,T),m=this.recurrentConv(m,C),g=this.recurrentConv(g,$);const E=this.recurrentActivation.apply(Io(l,d)),A=this.recurrentActivation.apply(Io(u,f)),R=Io(Co(A,a),Co(E,this.activation.apply(Io(c,m)))),_=Co(this.recurrentActivation.apply(Io(h,g)),this.activation.apply(R));return[_,_,R]}))}getConfig(){const e=super.getConfig(),{units:t}=e,n=kw(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),s)}inputConv(e,t,n,s){const r=Il(e,t,this.strides,s||"valid","channelsFirst"===this.dataFormat?"NCHW":"NHWC",this.dilationRate);return n?Ug(r,n,this.dataFormat):r}recurrentConv(e,t){return Il(e,t,1,"same","channelsFirst"===this.dataFormat?"NCHW":"NHWC")}}Iw.className="ConvLSTM2DCell",go(Iw);class Sw extends Nw{constructor(e){const t=new Iw(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}}Sw.className="ConvLSTM2D",go(Sw);class Tw extends $y{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(null==this.noiseShape)return this.noiseShape;const t=e.shape,n=[];for(let e=0;e<this.noiseShape.length;++e)n.push(null==this.noiseShape[e]?t[e]:this.noiseShape[e]);return n}call(e,t){return Ni((()=>{this.invokeCallHook(e,t);const n=gy(e);if(0<this.rate&&this.rate<1){const e=null!=t.training&&t.training,s=this.getNoiseShape(n);return Hg((()=>Gg(n,this.rate,s,this.seed)),(()=>n),e)}return e}))}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}Tw.className="Dropout",go(Tw);class Cw extends Tw{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}Cw.className="SpatialDropout1D",go(Cw);class $w extends $y{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",null==e.batchInputShape&&null==e.inputShape&&null!=e.inputDim){let t=null;null!=e.batchSize&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,ng(this.units,"units"),this.activation=Ax(e.activation),null!=e.useBias&&(this.useBias=e.useBias),this.kernelInitializer=dy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=dy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ky(e.kernelConstraint),this.biasConstraint=Ky(e.biasConstraint),this.kernelRegularizer=Lx(e.kernelRegularizer),this.biasRegularizer=Lx(e.biasRegularizer),this.activityRegularizer=Lx(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){const t=(e=yy(e))[e.length-1];null==this.kernel&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){const t=(e=yy(e)).slice();return t[t.length-1]=this.units,t}call(e,t){return Ni((()=>{this.invokeCallHook(e,t);const n=gy(e),s=rg(this.activation.getClassName());let r;return null!=s?r=Pg(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Pg(n,this.kernel.read()),null!=this.bias&&(r=Ug(r,this.bias.read())),null!=this.activation&&(r=this.activation.apply(r))),r}))}getConfig(){const e={units:this.units,activation:$x(this.activation),useBias:this.useBias,kernelInitializer:py(this.kernelInitializer),biasInitializer:py(this.biasInitializer),kernelRegularizer:Ox(this.kernelRegularizer),biasRegularizer:Ox(this.biasRegularizer),activityRegularizer:Ox(this.activityRegularizer),kernelConstraint:jy(this.kernelConstraint),biasConstraint:jy(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}$w.className="Dense",go($w);class Ew extends $y{constructor(e){super(e=e||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=yy(e);for(const t of e.slice(1))if(null==t)throw new Mm(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ig(e,1)]}call(e,t){return Ni((()=>{this.invokeCallHook(e,t);let n=gy(e);if("channelsFirst"===this.dataFormat&&n.rank>1){const e=[0];for(let t=2;t<n.rank;++t)e.push(t);e.push(1),n=_i(n,e)}return function(e){if(e.rank<=1)throw new Mm(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);const t=[e.shape[0],Ig(e.shape,1)];return tl(e,t)}(n)}))}getConfig(){const e={};null!=this.dataFormat&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}Ew.className="Flatten",go(Ew);class Aw extends $y{constructor(e){super(e),this.supportsMasking=!0,this.activation=Ax(e.activation)}call(e,t){return Ni((()=>{this.invokeCallHook(e,t);const n=gy(e);return this.activation.apply(n)}))}getConfig(){const e={activation:$x(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}Aw.className="Activation",go(Aw);class Rw extends $y{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return Ni((()=>{return e=gy(e),t=e,n=this.n,Ni((()=>{if(2!==t.shape.length)throw new Mm(`repeat() expects a rank-2 tensor, but received a rank-${t.shape.length} tensor.`);return Lg(Rg(t,1),[1,n,1])}));var t,n}))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}Rw.className="RepeatVector",go(Rw);class _w extends $y{constructor(e){super(e),this.targetShape=e.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(e){return e<0||null==e}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",s=t.slice();let r=1,a=null;for(let e=0;e<s.length;++e){const t=s[e];if(this.isUnknown(t)){if(null!==a)throw new Mm("Can only specifiy one unknown dimension.");a=e}else r*=t}const i=Ig(e);if(null!==a){if(0===r||i%r!=0)throw new Mm(n);s[a]=i/r}else if(i!==r)throw new Mm(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return Ni((()=>{this.invokeCallHook(e,t);const n=gy(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return tl(n,r)}))}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}_w.className="Reshape",go(_w);class Fw extends $y{constructor(e){if(super(e),null==e.dims)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=Cg(1,e.dims.length+1);if(!f(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ny({ndim:this.dims.length+1})]}computeOutputShape(e){const t=(e=yy(e)).slice();return this.dims.forEach(((n,s)=>{t[s+1]=e[n]})),t}call(e,t){return _i(gy(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}Fw.className="Permute",go(Fw);class Dw extends $y{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,this.maskValue=null!=e?null==e.maskValue?0:e.maskValue:0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=gy(e);return Fo(ic(n,this.maskValue),-1)}call(e,t){return Ni((()=>{this.invokeCallHook(e,t);const n=gy(e),s=Fo(ic(n,this.maskValue),-1,!0);return Co(n,Ja(s,n.dtype))}))}}Dw.className="Masking",go(Dw);class Ow extends $y{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==e.batchInputShape&&null==e.inputShape){let t=null;null!=e.batchSize&&(t=e.batchSize),null==e.inputLength?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Gm(e.inputLength))}this.inputDim=e.inputDim,ng(this.inputDim,"inputDim"),this.outputDim=e.outputDim,ng(this.outputDim,"outputDim"),this.embeddingsInitializer=dy(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Lx(e.embeddingsRegularizer),this.activityRegularizer=Lx(e.activityRegularizer),this.embeddingsConstraint=Ky(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return Ni((()=>this.maskZero?(e=gy(e),ic(e,Vl(e))):null))}computeOutputShape(e){if(e=yy(e),null==this.inputLength)return[...e,this.outputDim];const t=Gm(this.inputLength);if(t.length!==e.length-1)throw new Mm(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const r=t[s],a=e[s+1];if(null!=r&&null!=a&&r!==a)throw new Mm(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);null==r&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return Ni((()=>{this.invokeCallHook(e,t);let n=gy(e);"int32"!==n.dtype&&(n=Ag(n,"int32"));const s=Bg(this.embeddings.read(),tl(n,[n.size]));return tl(s,yy(this.computeOutputShape(n.shape)))}))}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:py(this.embeddingsInitializer),embeddingsRegularizer:Ox(this.embeddingsRegularizer),activityRegularizer:Ox(this.activityRegularizer),embeddingsConstraint:jy(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}Ow.className="Embedding",go(Ow);class Mw extends $y{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Lm}computeElementwiseOpOutputShape(e,t){if(null==e||null==t)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(0===t.length)return e;const n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const r=e[e.length-t.length+s],a=t[s];if(null==r||null==a||r<0||a<0)n.push(null);else if(1===r)n.push(a);else if(1===a)n.push(r);else{if(r!==a)throw new Mm("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[yy(e)]),e.length<2)throw new Mm(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const n of e)null!=n&&null!==n[0]&&t.push(n[0]);if(t=Jm(t),t.length>1)throw new Mm(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=null==e[0]?null:e[0].slice(1);for(let t=1;t<e.length;++t){const s=null==e[t]?null:e[t].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}const s=e.map((e=>e.length));-1===e.indexOf(null)&&1===Jm(s).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return Ni((()=>{if(this.reshapeRequired){const t=[],n=e.map((e=>e.rank));if(-1===n.indexOf(null)){const s=Tg(n);for(let n of e){const e=n.rank;for(let t=0;t<s-e;++t)n=Rg(n,1);t.push(n)}return this.mergeFunction(t)}{let n=!1;for(const s of e){const e=s.rank;if(null==e){const e=s.shape,r=e[0],a=e.slice(1).concat([r]);let i=tl(s,[r].concat(Ig(e.slice(1))));i=_i(i,[1,0]),i=tl(i,a),t.push(i),n=!0}else if(e>1){const r=Cg(1,e).concat([0]);t.push(_i(s,r)),n=!0}else t.push(s)}let s=this.mergeFunction(t);const r=s.rank;if(n)if(null==r){const e=s.shape,t=e[e.length-1],n=[t].concat(e.slice(0,e.length-1));s=tl(_i(tl(s,[-1,t]),[1,0]),n)}else if(r>1){const e=[r-1].concat(Cg(0,r-1));s=_i(s,e)}return s}}return this.mergeFunction(e)}))}computeOutputShape(e){let t;t=null==e[0]?null:e[0].slice(1);for(let n=1;n<e.length;++n){const s=null==e[n]?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let n=[];for(const t of e)null!=t&&null!==t[0]&&n.push(t[0]);return n=Jm(n),t=1===n.length?n.concat(t):[null].concat(t),t}computeMask(e,t){return Ni((()=>{if(null==t)return null;if(!Array.isArray(t))throw new Mm("`mask` should be an Array");if(!Array.isArray(e))throw new Mm("`inputs` should be an Array");if(t.length!==e.length)throw new Mm(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every((e=>null==e)))return null;let n=(t=t.map((e=>null==e?e:du(e,0))))[0];for(let e=1;e<t.length-1;++e)n=Pu(n,t[e]);return n}))}}class Lw extends Mw{constructor(e){super(e)}mergeFunction(e){return Ni((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Io(t,e[n]);return t}))}}Lw.className="Add",go(Lw);class zw extends Mw{constructor(e){super(e)}mergeFunction(e){return Ni((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Co(t,e[n]);return t}))}}zw.className="Multiply",go(zw);class Pw extends Mw{constructor(e){super(e)}mergeFunction(e){return Ni((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Io(t,e[n]);return Co(1/e.length,t)}))}}Pw.className="Average",go(Pw);class Bw extends Mw{constructor(e){super(e)}mergeFunction(e){return Ni((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Xu(t,e[n]);return t}))}}Bw.className="Maximum",go(Bw);class Ww extends Mw{constructor(e){super(e)}mergeFunction(e){return Ni((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ec(t,e[n]);return t}))}}Ww.className="Minimum",go(Ww);class Vw extends Mw{constructor(e){super(e),this.DEFAULT_AXIS=-1,null==e&&(e={}),this.axis=null==e.axis?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!Array.isArray(e)||!Array.isArray(e[0])||1===e.length)throw new Mm("A `Concatenate` layer should be called on a list of at least 2 inputs");let t=!0;for(const n of e)if(null!=n){t=!1;break}if(t)return;const n=[];for(let t=0;t<e.length;++t){const s=e[t].slice();s.splice(this.axis,1);let r=!1;for(const e of n)if(f(e,s)){r=!0;break}r||n.push(s)}if(n.length>1)throw new Mm("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return Ni((()=>Og(e,this.axis)))}computeOutputShape(e){if(!Array.isArray(e)||!Array.isArray(e[0]))throw new Mm("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const e of t.slice(1)){if(null==n[s]||null==e[s]){n[s]=null;break}n[s]+=e[s]}return n}computeMask(e,t){if(null==t)return null;if(!Array.isArray(t))throw new Mm("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new Mm("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new Mm(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return Ni((()=>{let n=!0;if(t.forEach((e=>{null==e||(n=!1)})),n)return null;const s=[];for(let n=0;n<e.length;++n)null==t[n]?s.push(Ja(oc(e[n]),"bool")):t[n].rank<e[n].rank?s.push(du(t[n],-1)):s.push(t[n]);const r=rl(s,this.axis);return _o(r,-1,!1)}))}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}function Uw(e,t){for(;e<0;)e+=t;return e}Vw.className="Concatenate",go(Vw);class Gw extends Mw{constructor(e){super(e),this.axes=e.axes,this.normalize=null!=e.normalize&&e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){u(Array.isArray(e)&&2===e.length&&Array.isArray(e[0])&&Array.isArray(e[1]),(()=>"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Lm("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new Mm(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(2!==e.length)throw new Mm(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t,n=e[0],s=e[1];return t=Array.isArray(this.axes)?this.axes.map(((t,n)=>Uw(t,e[n].shape.length))):[Uw(this.axes,n.shape.length),Uw(this.axes,s.shape.length)],this.normalize&&(n=ub(n,t[0]),s=ub(s,t[1])),function(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Lm("batchDot is not implemented for tensors of 4D or higher rank yet");if(u(e.shape.length>=2,(()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`)),u(e.shape.length>=2,(()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`)),"number"==typeof n&&(n=[n,n]),"complex64"===e.dtype||"complex64"===t.dtype)throw new Lm("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,r=t.shape.length;null==n&&(n=[s-1,r-2]);const a=n;return Ni((()=>{let n,i;if(s>r){n=s-r;const e=[];for(let t=0;t<n;++t)e.push(1);t=tl(t,t.shape.concat(e))}else if(r>s){n=r-s;const t=[];for(let e=0;e<n;++e)t.push(1);e=tl(e,e.shape.concat(t))}else n=0;if(2===e.shape.length&&2===t.shape.length)i=a[0]===a[1]?lu(Co(e,t),a[0]):lu(Co(_i(e,[1,0]),t),a[1]);else{const n=a[0]!==e.shape.length-1,s=a[1]===t.shape.length-1;i=bi(e,t,n,s)}if(n>0){let e;e=s>r?s+r-3:s-1;const t=[];for(let s=e;s<e+n;++s)t.push(s);i=dh(i,t)}return 1===i.shape.length&&(i=du(i,1)),i}))}(n,s,t)}interpretAxes(e,t){let n;return n=Array.isArray(this.axes)?this.axes:[Uw(this.axes,e.length),Uw(this.axes,t.length)],n}computeOutputShape(e){u(Array.isArray(e)&&2===e.length&&Array.isArray(e[0])&&Array.isArray(e[1]),(()=>"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Lm("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);const r=t.concat(n);return 1===r.length&&r.push(1),r}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}Gw.className="Dot",go(Gw);class Hw extends $y{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return Ni((()=>{this.invokeCallHook(e,t);const n=gy(e);return Hg((()=>Io(zg(n.shape,0,this.stddev),n)),(()=>n),t.training||!1)}))}}Hw.className="GaussianNoise",go(Hw);class jw extends $y{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return Ni((()=>{this.invokeCallHook(e,t);const n=gy(e);if(this.rate>0&&this.rate<1){return Hg((()=>{const e=Math.sqrt(this.rate/(1-this.rate));return Co(n,zg(n.shape,1,e))}),(()=>n),t.training||!1)}return n}))}}jw.className="GaussianDropout",go(jw);class qw extends $y{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||gy(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return Ni((()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const t=gy(e),s=-1.7580993408473766;let r=wu(zc(n),this.rate);r=Ag(r,"float32");const a=((1-this.rate)*(1+this.rate*s**2))**-.5,i=-a*s*this.rate,o=Io(Co(t,r),Co(Io(r,-1),s));return Io(Co(o,a),i)};return Hg(s,(()=>gy(e)),t.training||!1)}return e}))}}function Kw(e,t,n,s,r,a=.001){let i;if(2===e.rank)i=hl(e,t,n,s,r,a);else if(3===e.rank)i=pl(e,t,n,s,r,a);else{if(4!==e.rank)throw new Lm(`batchNormalization is not implemented for array of rank ${e.rank} yet`);i=dl(e,t,n,s,r,a)}return i}function Xw(e,t,n,s,r=.001){return f(s.slice().sort(),Cg(0,e.rank-1))?function(e,t,n,s,r=.001){return Ni((()=>{const a=sc(e,s),i=a.mean,o=a.variance;return[Kw(e,i,o,n,t,r),i,o]}))}(e,t,n,s,r):function(e,t,n,s,r=.001){return Ni((()=>{const a=sc(e,s),i=a.mean,o=a.variance,l=[];for(const t of Cg(0,e.rank))-1!==s.indexOf(t)?l.push(1):l.push(e.shape[t]);const u=tl(i,l),c=tl(o,l),h=null==t?null:tl(t,l),p=null==n?null:tl(n,l);return[Kw(e,u,c,p,h,r),i,o]}))}(e,t,n,s,r)}qw.className="AlphaDropout",go(qw);class Yw extends $y{constructor(e){null==e&&(e={}),super(e),this.supportsMasking=!0,this.axis=null==e.axis?-1:e.axis,this.momentum=null==e.momentum?.99:e.momentum,this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=dy(e.betaInitializer||"zeros"),this.gammaInitializer=dy(e.gammaInitializer||"ones"),this.movingMeanInitializer=dy(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=dy(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ky(e.betaConstraint),this.gammaConstraint=Ky(e.gammaConstraint),this.betaRegularizer=Lx(e.betaRegularizer),this.gammaRegularizer=Lx(e.gammaRegularizer)}build(e){e=yy(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(null==n)throw new Mm(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ny({ndim:e.length,axes:{[t]:n}})];const s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return Ni((()=>{const n=null!=t.training&&t.training,s=gy(e),r=s.shape,a=r.length,i=Cg(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);const l=Bm(1,a);l[o]=r[o];const u=i.slice();u.sort();const c=!f(u,Cg(0,a).slice(0,a-1));if(!n)return(()=>{if(c){const e=tl(this.movingMean.read(),l),t=tl(this.movingVariance.read(),l),n=this.center?tl(this.beta.read(),l):null,r=this.scale?tl(this.gamma.read(),l):null;return Kw(s,e,t,n,r,this.epsilon)}return Kw(s,this.movingMean.read(),this.movingVariance.read(),null==this.beta?null:this.beta.read(),null==this.gamma?null:this.gamma.read(),this.epsilon)})();const[h,p,d]=Xw(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(e,t,n)=>{Ni((()=>{const s=1-n,r=e.read(),a=Co(Mu(r,t),s);e.write(Mu(r,a))}))};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,d,this.momentum)})(),h}))}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:py(this.betaInitializer),gammaInitializer:py(this.gammaInitializer),movingMeanInitializer:py(this.movingMeanInitializer),movingVarianceInitializer:py(this.movingVarianceInitializer),betaRegularizer:Ox(this.betaRegularizer),gammaRegularizer:Ox(this.gammaRegularizer),betaConstraint:jy(this.betaConstraint),gammaConstraint:jy(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}Yw.className="BatchNormalization",go(Yw);class Zw extends $y{constructor(e){if(null==e&&(e={}),super(e),this.axis=null==e.axis?-1:e.axis,"number"==typeof this.axis){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else{if(!Array.isArray(this.axis))throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);for(const e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=dy(e.betaInitializer||"zeros"),this.gammaInitializer=dy(e.gammaInitializer||"ones"),this.betaRegularizer=Lx(e.betaRegularizer),this.gammaRegularizer=Lx(e.gammaRegularizer),this.supportsMasking=!0}build(e){const t=(e=yy(e)).length;"number"==typeof this.axis&&(this.axis=[this.axis]);for(let e=0;e<this.axis.length;++e)this.axis[e]<0&&(this.axis[e]+=t);for(const e of this.axis)if(e<0||e>=t)throw new Error(`Invalid axis: ${e}`);if(this.axis.length!==Jm(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map((t=>e[t]));this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,true):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,true):this.beta=null,this.built=!0}call(e,t){const n=gy(e),s=n.shape,r=s.length;return Ni((()=>{let{mean:e,variance:t}=sc(n,this.axis,!0);const a=Bm(1,r);for(const e of this.axis)a[e]=s[e];const i=e=>null!=e&&e.shape.length!==r?tl(e,a):e;let o=this.scale?i(this.gamma.read()):null,l=this.center?i(this.beta.read()):null;const u=[],c=[];for(let e=0;e<r;++e)-1!==this.axis.indexOf(e)?(u.push(s[e]),c.push(1)):(u.push(1),c.push(s[e]));return e=mu(e,u),t=mu(t,u),null!=o&&(o=mu(o,c)),null!=l&&(l=mu(l,c)),Kw(n,e,t,l,o,this.epsilon)}))}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:py(this.betaInitializer),gammaInitializer:py(this.gammaInitializer),betaRegularizer:Ox(this.betaRegularizer),gammaRegularizer:Ox(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}Zw.className="LayerNormalization",go(Zw);class Jw extends $y{constructor(e){if(null==e&&(e={}),super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,null==e.padding)this.padding=[[1,1],[1,1]];else if("number"==typeof e.padding)this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,2!==e.padding.length)throw new Mm(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if("number"==typeof e.padding[0])t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,2!==e.padding[0].length)throw new Mm(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],2!==e.padding[1].length)throw new Mm(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ny({ndim:4})]}computeOutputShape(e){let t,n;return e=yy(e),"channelsFirst"===this.dataFormat?(t=null!=e[2]&&e[2]>=0?e[2]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[3]&&e[3]>=0?e[3]+this.padding[1][0]+this.padding[1][1]:null,[e[0],e[1],t,n]):(t=null!=e[1]&&e[1]>=0?e[1]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[2]&&e[2]>=0?e[2]+this.padding[1][0]+this.padding[1][1]:null,[e[0],t,n,e[3]])}call(e,t){return Ni((()=>{return t=gy(e),n=this.padding,s=this.dataFormat,Ni((()=>{if(4!==t.rank)throw new Mm(`temporalPadding expects input tensor to be 4-D, but received a ${t.rank}-D tensor.`);if(null==n&&(n=[[1,1],[1,1]]),2!==n.length||2!==n[0].length||2!==n[1].length)throw new Mm("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(null==s&&(s="channelsLast"),"channelsLast"!==s&&"channelsFirst"!==s)throw new Mm(`Unknown data format: ${s}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let e;return e="channelsFirst"===s?[[0,0],[0,0],n[0],n[1]]:[[0,0],n[0],n[1],[0,0]],uc(t,e)}));var t,n,s}))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}function Qw(e,t,n,s,r,a){return Ni((()=>{let i;mg(r),yg(a),gg(s),null==n&&(n=[1,1]),null==s&&(s="valid"),null==r&&(r="channelsLast"),null==a&&(a="max"),e=qx(e,r);const o="same"===s?"same":"valid";return i="max"===a?ju(e,t,n,o):nl(e,t,n,o),"channelsFirst"===r&&(i=_i(i,[0,3,1,2])),i}))}function ev(e,t,n,s,r,a){return Ni((()=>{let i;mg(r),yg(a),gg(s),null==n&&(n=[1,1,1]),null==s&&(s="valid"),null==r&&(r="channelsLast"),null==a&&(a="max"),e=Kx(e,r);const o="same"===s?"same":"valid";return i="max"===a?qu(e,t,n,o):sl(e,t,n,o),"channelsFirst"===r&&(i=_i(i,[0,4,1,2,3])),i}))}Jw.className="ZeroPadding2D",go(Jw);class tv extends $y{constructor(e){if(null==e.poolSize&&(e.poolSize=2),super(e),"number"==typeof e.poolSize)this.poolSize=[e.poolSize];else{if(!Array.isArray(e.poolSize)||1!==e.poolSize.length||"number"!=typeof e.poolSize[0])throw new Mm(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);this.poolSize=e.poolSize}if(ng(this.poolSize,"poolSize"),null==e.strides)this.strides=this.poolSize;else if("number"==typeof e.strides)this.strides=[e.strides];else{if(!Array.isArray(e.strides)||1!==e.strides.length||"number"!=typeof e.strides[0])throw new Mm(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);this.strides=e.strides}ng(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,gg(this.padding),this.inputSpec=[new Ny({ndim:3})]}computeOutputShape(e){const t=Hx((e=yy(e))[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return Ni((()=>{this.invokeCallHook(e,t),e=Rg(gy(e),2);const n=this.poolingFunction(gy(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return dh(n,[2])}))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class nv extends tv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return mg(r),gg(s),Qw(e,t,n,s,r,"max")}}nv.className="MaxPooling1D",go(nv);class sv extends tv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return mg(r),gg(s),Qw(e,t,n,s,r,"avg")}}sv.className="AveragePooling1D",go(sv);class rv extends $y{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(2!==e.strides.length)throw new Mm(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];ng(this.poolSize,"poolSize"),ng(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,mg(this.dataFormat),gg(this.padding),this.inputSpec=[new Ny({ndim:4})]}computeOutputShape(e){e=yy(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2];return t=Hx(t,this.poolSize[0],this.padding,this.strides[0]),n=Hx(n,this.poolSize[1],this.padding,this.strides[1]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return Ni((()=>(this.invokeCallHook(e,t),this.poolingFunction(gy(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class av extends rv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return mg(r),gg(s),Qw(e,t,n,s,r,"max")}}av.className="MaxPooling2D",go(av);class iv extends rv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return mg(r),gg(s),Qw(e,t,n,s,r,"avg")}}iv.className="AveragePooling2D",go(iv);class ov extends $y{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(3!==e.strides.length)throw new Mm(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];ng(this.poolSize,"poolSize"),ng(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,mg(this.dataFormat),gg(this.padding),this.inputSpec=[new Ny({ndim:5})]}computeOutputShape(e){e=yy(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],s="channelsFirst"===this.dataFormat?e[4]:e[3];return t=Hx(t,this.poolSize[0],this.padding,this.strides[0]),n=Hx(n,this.poolSize[1],this.padding,this.strides[1]),s=Hx(s,this.poolSize[2],this.padding,this.strides[2]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return Ni((()=>(this.invokeCallHook(e,t),this.poolingFunction(gy(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class lv extends ov{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return mg(r),gg(s),ev(e,t,n,s,r,"max")}}lv.className="MaxPooling3D",go(lv);class uv extends ov{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return mg(r),gg(s),ev(e,t,n,s,r,"avg")}}uv.className="AveragePooling3D",go(uv);class cv extends $y{constructor(e){super(e),this.inputSpec=[new Ny({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Lm}}class hv extends cv{constructor(e){super(e||{})}call(e,t){return Ni((()=>{const t=gy(e);return Yu(t,1)}))}}hv.className="GlobalAveragePooling1D",go(hv);class pv extends cv{constructor(e){super(e||{})}call(e,t){return Ni((()=>{const t=gy(e);return nu(t,1)}))}}pv.className="GlobalMaxPooling1D",go(pv);class dv extends $y{constructor(e){super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,mg(this.dataFormat),this.inputSpec=[new Ny({ndim:4})]}computeOutputShape(e){return"channelsLast"===this.dataFormat?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Lm}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class fv extends dv{call(e,t){return Ni((()=>{const t=gy(e);return"channelsLast"===this.dataFormat?Yu(t,[1,2]):Yu(t,[2,3])}))}}fv.className="GlobalAveragePooling2D",go(fv);class mv extends dv{call(e,t){return Ni((()=>{const t=gy(e);return"channelsLast"===this.dataFormat?nu(t,[1,2]):nu(t,[2,3])}))}}mv.className="GlobalMaxPooling2D",go(mv);class gv extends $y{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return null!=this.layer&&this.layer.trainable}set trainable(e){null!=this.layer&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.layer&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const s=lb(t.layer,n);delete t.layer;const r={layer:s};return Object.assign(r,t),new e(r)}}class yv extends gv{constructor(e){super(e),this.supportsMasking=!0}build(e){if((e=yy(e)).length<3)throw new Mm(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){const t=[(e=yy(e))[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return Ni((()=>hw(((e,n)=>[gy(this.layer.call(e,t)),[]]),e=gy(e),[],!1,null,null,!1,!0)[1]))}}yv.className="TimeDistributed",go(yv);class bv extends gv{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=lb(n),t.goBackwards=!0!==t.goBackwards;const s={};var r;if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=lb(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=void 0===e.mergeMode?"concat":e.mergeMode,r=this.mergeMode,eg(dg,"BidirectionalMergeMode",r),e.weights)throw new Lm("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,null!=this.forwardLayer&&(this.forwardLayer.trainable=e),null!=this.backwardLayer&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t,n,s,r=this.forwardLayer.computeOutputShape(e);return Array.isArray(r)&&Array.isArray(r[0])||(r=[r]),this.returnState?(s=r.slice(1),t=r[0]):t=r[0],"concat"===this.mergeMode?(t[t.length-1]*=2,n=[t]):n=null==this.mergeMode?[t,t.slice()]:[t],this.returnState?null==this.mergeMode?n.concat(s).concat(s.slice()):[t].concat(s).concat(s.slice()):Um(n)}apply(e,t){let n=null==t?null:t.initialState,s=null==t?null:t.constants;null==t&&(t={});const r=cw(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(null==n||0===n.length)&&null==s)return super.apply(e,t);const a=[],i=[];if(null!=n){const e=n.length;if(e%2>0)throw new Mm("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);const s=n.map((e=>new Ny({shape:e.shape})));this.forwardLayer.stateSpec=s.slice(0,e/2),this.backwardLayer.stateSpec=s.slice(e/2),i.push(...s)}if(null!=s)throw new Lm("Support for constants in Bidirectional layers is not implemented yet.");const o=a[0]instanceof Iy;for(const e of a)if(e instanceof Iy!==o)throw new Mm("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){const n=[e].concat(a),s=this.inputSpec.concat(i),r=this.inputSpec;this.inputSpec=s;const o=super.apply(n,t);return this.inputSpec=r,o}return super.apply(e,t)}call(e,t){return Ni((()=>{const n=t.initialState;let s,r,a,i;if(null==n)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{const a=n.slice(0,n.length/2),i=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:a})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:i}))}return this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Uc(r,1)),"concat"===this.mergeMode?i=Og([s,r]):"sum"===this.mergeMode?i=Io(s,r):"ave"===this.mergeMode?i=Co(.5,Io(s,r)):"mul"===this.mergeMode?i=Co(s,r):null==this.mergeMode&&(i=[s,r]),this.returnState?null==this.mergeMode?i.concat(a):[i].concat(a):i}))}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){xg(this.forwardLayer.name,(()=>{this.forwardLayer.build(e)})),xg(this.backwardLayer.name,(()=>{this.backwardLayer.build(e)})),this.built=!0}computeMask(e,t){let n;if(Array.isArray(t)&&(t=t[0]),n=this.returnSequences?null==this.mergeMode?[t,t]:t:null==this.mergeMode?[null,null]:null,this.returnState){const e=this.forwardLayer.states.map((e=>null));return Array.isArray(n)?n.concat(e).concat(e):[n].concat(e).concat(e)}return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.forwardLayer&&this.forwardLayer.setFastWeightInitDuringBuild(e),null!=this.backwardLayer&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=lb(t.layer);if(delete t.layer,null!=t.numConstants)throw new Lm("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}bv.className="Bidirectional",go(bv);class xv extends $y{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){const e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return Ni((()=>("float32"!==(e=gy(e)).dtype&&(e=Ag(e,"float32")),Io(Co(e,this.scale),this.offset))))}}xv.className="Rescaling",go(xv);const{resizeBilinear:wv,cropAndResize:vv}=Xp;class kv extends $y{constructor(e){super(e),this.height=e.height,this.width=e.width}centerCrop(e,t,n,s,r,a,i,o){return Ni((()=>{let l,u=!1;const c=[t/a,n/i,(s+t)/a,(r+n)/i],h=[];3===e.rank?(u=!0,l=fh([e])):l=e;for(let e=0;e<l.shape[0];e++)h.push(c);const p=ra(h,[h.length,4]),d=Pc(0,h.length,1,"int32"),f=vv(l,p,d,[s,r],"nearest");return Ag(u?gy(Ch(f)):f,o)}))}upsize(e,t,n,s){return Ni((()=>Ag(wv(e,[t,n]),s)))}call(e,t){return Ni((()=>{const t=gy(e),n=t.dtype,s=t.shape,r=s[s.length-3],a=s[s.length-2];let i=0;r!==this.height&&(i=Math.floor((r-this.height)/2));let o=0;return a!==this.width&&(o=Math.floor((a-this.width)/2),0===o&&(o=1)),i>=0&&o>=0?this.centerCrop(t,i,o,this.height,this.width,r,a,n):this.upsize(e,this.height,this.width,n)}))}getConfig(){const e={height:this.height,width:this.width},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){const t=(e=yy(e)).length-3,n=e.length-2;return e[t]=this.height,e[n]=this.width,e}}kv.className="CenterCrop",go(kv);class Nv extends $y{constructor(e){super(e),this.numTokens=e.numTokens,e.outputMode?this.outputMode=e.outputMode:this.outputMode="multiHot"}getConfig(){const e={numTokens:this.numTokens,outputMode:this.outputMode},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){return null==(e=yy(e))?[this.numTokens]:"oneHot"===this.outputMode&&1!==e[e.length-1]?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return Ni((()=>{let n;if("int32"!==(e=gy(e)).dtype&&(e=Ag(e,"int32")),void 0!==t.countWeights){if("count"!==this.outputMode)throw new Mm(`countWeights is not used when outputMode !== count.\n              Received countWeights=${t.countWeights}`);n=gy(t.countWeights)}const s=nu(e),r=su(e),a=xu(this.numTokens,s).bufferSync().get(0),i=wu(r,0).bufferSync().get(0);if(!a||!i)throw new Mm(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return function(e,t,n,s){let r=gy(e);if("int32"!==r.dtype&&(r=Ag(r,"int32")),"int"===t)return r;const a=r.shape;if(0===r.rank&&(r=du(r,-1)),"oneHot"===t&&1!==r.shape[r.shape.length-1]&&(r=du(r,-1)),r.rank>2)throw new Mm(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${a} which would result in output rank ${r.rank}.`);const i=["multiHot","oneHot"].includes(t);let o;if(o=Ol(r,void 0!==s&&"count"===t?s:[],n,i),"tfIdf"!==t)return o;if(s)return Co(o,s);throw new Mm("When outputMode is 'tfIdf', weights must be provided.")}(e,this.outputMode,this.numTokens,n)}))}}Nv.className="CategoryEncoding",go(Nv);const Iv=new Set(["bilinear","nearest"]);class Sv extends $y{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation){if(!Iv.has(e.interpolation))throw new Mm(`Invalid interpolation parameter: ${e.interpolation} is not implemented`);this.interpolation=e.interpolation}else this.interpolation="bilinear";this.cropToAspectRatio=Boolean(e.cropToAspectRatio)}computeOutputShape(e){const t=(e=yy(e))[2];return[this.height,this.width,t]}getConfig(){const e={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return Ni((()=>{const t=[this.height,this.width];if("bilinear"===this.interpolation)return Xp.resizeBilinear(e,t,!this.cropToAspectRatio);if("nearest"===this.interpolation)return Xp.resizeNearestNeighbor(e,t,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...Iv]} are supported`)}))}}function Tv(e){return new sv(e)}function Cv(e){return new iv(e)}function $v(e){return new uv(e)}function Ev(e){return new pv(e)}function Av(e){return new mv(e)}function Rv(e){return new nv(e)}function _v(e){return new av(e)}Sv.className="Resizing",go(Sv);const Fv=Ev,Dv=Av,Ov=Rv,Mv=_v;var Lv=Object.freeze({__proto__:null,inputLayer:function(e){return new Ay(e)},elu:function(e){return new Wx(e)},reLU:function(e){return new zx(e)},leakyReLU:function(e){return new Px(e)},prelu:function(e){return new Bx(e)},softmax:function(e){return new Ux(e)},thresholdedReLU:function(e){return new Vx(e)},conv1d:function(e){return new iw(e)},conv2d:function(e){return new ew(e)},conv2dTranspose:function(e){return new nw(e)},conv3d:function(e){return new tw(e)},conv3dTranspose:function(e){return new sw(e)},separableConv2d:function(e){return new aw(e)},cropping2D:function(e){return new ow(e)},upSampling2d:function(e){return new lw(e)},depthwiseConv2d:function(e){return new uw(e)},activation:function(e){return new Aw(e)},dense:function(e){return new $w(e)},dropout:function(e){return new Tw(e)},spatialDropout1d:function(e){return new Cw(e)},flatten:function(e){return new Ew(e)},repeatVector:function(e){return new Rw(e)},reshape:function(e){return new _w(e)},permute:function(e){return new Fw(e)},embedding:function(e){return new Ow(e)},add:function(e){return new Lw(e)},average:function(e){return new Pw(e)},concatenate:function(e){return new Vw(e)},maximum:function(e){return new Bw(e)},minimum:function(e){return new Ww(e)},multiply:function(e){return new zw(e)},dot:function(e){return new Gw(e)},batchNormalization:function(e){return new Yw(e)},layerNormalization:function(e){return new Zw(e)},zeroPadding2d:function(e){return new Jw(e)},averagePooling1d:Tv,avgPool1d:function(e){return Tv(e)},avgPooling1d:function(e){return Tv(e)},averagePooling2d:Cv,avgPool2d:function(e){return Cv(e)},avgPooling2d:function(e){return Cv(e)},averagePooling3d:$v,avgPool3d:function(e){return $v(e)},avgPooling3d:function(e){return $v(e)},globalAveragePooling1d:function(e){return new hv(e)},globalAveragePooling2d:function(e){return new fv(e)},globalMaxPooling1d:Ev,globalMaxPooling2d:Av,maxPooling1d:Rv,maxPooling2d:_v,maxPooling3d:function(e){return new lv(e)},gru:function(e){return new yw(e)},gruCell:function(e){return new gw(e)},lstm:function(e){return new xw(e)},lstmCell:function(e){return new bw(e)},simpleRNN:function(e){return new mw(e)},simpleRNNCell:function(e){return new fw(e)},convLstm2d:function(e){return new Sw(e)},convLstm2dCell:function(e){return new Iw(e)},rnn:function(e){return new pw(e)},stackedRNNCells:function(e){return new ww(e)},bidirectional:function(e){return new bv(e)},timeDistributed:function(e){return new yv(e)},globalMaxPool1d:Fv,globalMaxPool2d:Dv,maxPool1d:Ov,maxPool2d:Mv,Layer:$y,RNN:pw,RNNCell:dw,input:px,gaussianNoise:function(e){return new Hw(e)},gaussianDropout:function(e){return new jw(e)},alphaDropout:function(e){return new qw(e)},masking:function(e){return new 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Sk(this.node.rawAttrs,e,t);if(null!=n.shape)return _k(this.node.rawAttrs,e,t);if(null!=n.type)return Ek(this.node.rawAttrs,e,t);if(null!=n.list){if(null!=n.list.i||null!=n.list.f)return Fk(this.node.rawAttrs,e,t);if(null!=n.list.s)return Dk(this.node.rawAttrs,e,t);if(null!=n.list.shape)return Ok(this.node.rawAttrs,e,t);if(null!=n.list.b)return Mk(this.node.rawAttrs,e,t);if(null!=n.list.type)return Ak(this.node.rawAttrs,e,t)}return t}}var zk=Object.freeze({__proto__:null,abs:$o,acos:Eo,acosh:Ao,add:Io,addN:Ro,all:_o,any:Fo,argMax:Do,argMin:Oo,asin:Mo,asinh:Lo,atan:zo,atan2:Po,atanh:Bo,avgPool:nl,avgPool3d:sl,basicLSTMCell:ll,batchToSpaceND:ul,batchNorm:cl,batchNorm2d:hl,batchNorm3d:pl,batchNorm4d:dl,bincount:fl,broadcastArgs:ml,broadcastTo:gl,buffer:Za,cast:Ja,ceil:yl,clipByValue:xl,clone:Qa,complex:na,concat:rl,concat1d:wl,concat2d:vl,concat3d:kl,concat4d:Nl,conv1d:Sl,conv2d:Il,conv2dTranspose:Cl,conv3d:$l,conv3dTranspose:Al,cos:Rl,cosh:_l,cumprod:Fl,cumsum:Dl,denseBincount:Ol,depthToSpace:Ml,depthwiseConv2d:Ll,diag:zl,dilation2d:Pl,div:To,divNoNan:Ul,dot:Gl,einsum:Hl,elu:jl,equal:Bl,erf:ql,euclideanNorm:hu,exp:pu,expandDims:du,expm1:fu,eye:gu,fill:bl,floor:yu,floorDiv:So,gather:bu,greater:xu,greaterEqual:wu,imag:Ei,isFinite:vu,isInf:ku,isNaN:Nu,leakyRelu:Iu,less:Su,lessEqual:Tu,linspace:Cu,localResponseNormalization:$u,log:Eu,log1p:Au,logSigmoid:Ou,logSoftmax:Lu,logSumExp:zu,logicalAnd:Pu,logicalNot:Bu,logicalOr:Wu,logicalXor:Vu,lowerBound:Hu,matMul:bi,max:nu,maxPool:ju,maxPool3d:qu,maxPoolWithArgmax:Ku,maximum:Xu,mean:Yu,meshgrid:Qu,min:su,minimum:ec,mirrorPad:tc,mod:nc,moments:sc,mul:Co,multiRNNCell:rc,multinomial:ac,neg:Ai,notEqual:ic,oneHot:xi,ones:Ju,onesLike:oc,outerProduct:lc,pad:uc,pad1d:cc,pad2d:hc,pad3d:pc,pad4d:dc,pool:mc,pow:ru,prelu:gc,print:ei,prod:yc,raggedGather:bc,raggedRange:xc,raggedTensorToTensor:wc,rand:vc,randomGamma:Oc,randomNormal:Mc,randomStandardNormal:Lc,randomUniform:zc,range:Pc,real:Ri,reciprocal:Bc,relu:Wc,relu6:Vc,reshape:tl,reverse:Uc,reverse1d:Gc,reverse2d:Hc,reverse3d:jc,reverse4d:qc,round:Kc,rsqrt:Xc,scalar:au,selu:Yc,separableConv2d:Zc,setdiff1dAsync:Jc,sigmoid:al,sign:Qc,sin:eh,sinh:th,slice:il,slice1d:nh,slice2d:sh,slice3d:rh,slice4d:ah,softmax:ih,softplus:Du,spaceToBatchND:fc,fft:oh,ifft:lh,irfft:uh,rfft:hh,split:ch,sqrt:iu,square:ou,squaredDifference:ph,squeeze:dh,stack:fh,step:mh,stridedSlice:gh,sub:Mu,sum:lu,tan:yh,tanh:ol,tensor:ra,tensor1d:bh,tensor2d:xh,tensor3d:Pi,tensor4d:wh,tensor5d:vh,tensor6d:kh,tile:mu,topk:Nh,truncatedNormal:Ih,unique:Sh,unsortedSegmentSum:Th,unstack:Ch,upperBound:$h,variable:Eh,where:Wl,whereAsync:Rh,zeros:Zu,zerosLike:Vl,op:ta,OP_SCOPE_SUFFIX:ea,booleanMaskAsync:_h,transpose:_i,norm:cu,movingAverage:Fh,scatterND:Dh,searchSorted:Gu,sparseToDense:Oh,gatherND:Mh,dropout:Lh,enclosingPowerOfTwo:zh,cosineWindow:Ph,inTopKAsync:Bh,image:Xp,linalg:Yp,losses:Zp,spectral:qp,fused:Zh,signal:Kp,sparse:Jp,string:Qp});function Pk(e,t,n=""){if("number"!=typeof e&&"number"!=typeof t){u(e.length===t.length,(()=>n+` Shapes ${e} and ${t} must match`));for(let s=0;s<e.length;s++){const r=e[s],a=t[s];u(r<0||a<0||r===a,(()=>n+` Shapes ${e} and ${t} must match`))}}}function Bk(e){return"number"!=typeof e&&!e.some((e=>e<0))}function Wk(e,t,n){let s=Vk(e,n);const r=!Bk(s);if(r&&0===t.length)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(r&&t.forEach((e=>{s=Vk(e.shape,s)})),!Bk(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function Vk(e,t){if("number"==typeof e)return t;if("number"==typeof t)return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);const n=[];for(let s=0;s<e.length;++s){const r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}class Uk{constructor(e,t,n,s,r,a,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=au(0),Si(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach((t=>{null!=e&&e.has(t.tensor.id)||t.tensor.dispose()})),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map((e=>this.read(e)))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);const n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},\n          because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(0!==this.size()||null!=this.elementShape&&0!==this.elementShape.length||(this.elementShape=t.shape),Pk(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,Si(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach(((e,n)=>this.write(e,t[n])))}gather(e,t){if(t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let t=0;t<this.size();t++)e.push(t)}if(0===e.length)return ra([],[0].concat(this.elementShape));const n=this.readMany(e);return Pk(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),fh(n,0)}concat(e){if(e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(0===this.size())return ra([],[0].concat(this.elementShape));const t=[];for(let e=0;e<this.size();e++)t.push(e);const n=this.readMany(t);return Pk(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),rl(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);const n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n}  vs. ${this.maxSize})`);this.writeMany(e,Ch(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0;const s=e.map((e=>(n+=e,n)));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const r=0===n?0:t.size/n,a=[];Ni((()=>{t=tl(t,[1,n,r]);for(let n=0;n<e.length;++n){const i=[0,0===n?0:s[n-1],0],o=[1,e[n],r];a[n]=tl(il(t,i,o),this.elementShape)}return a}));const i=[];for(let t=0;t<e.length;t++)i[t]=t;this.writeMany(i,a)}}class Gk{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,null!=e&&e.forEach((e=>{if(n!==e.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${e.dtype}`);Pk(t,e.shape,"TensorList shape mismatch: "),Si(e)})),this.idTensor=au(0),this.maxNumElements=s,Si(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Gk([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach((t=>{null!=e&&e.has(t.id)||t.dispose()})),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(-1!==n&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);Pk(e,this.elementShape,"TensorList shape mismatch: ");const s=Wk(this.elementShape,this.tensors,e);return Ni((()=>{const e=this.tensors.map((e=>tl(e,s)));return fh(e,0)}))}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(0===this.size())throw new Error("Trying to pop from an empty list.");const n=Wk(this.elementShape,this.tensors,e),s=this.tensors.pop();return s.kept=!1,Pk(s.shape,e,"TensorList shape mismatch: "),tl(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Pk(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Si(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(-1!==this.maxNumElements&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);const t=new Gk([],this.elementShape,this.elementDtype,this.maxNumElements);t.tensors.length=e;for(let n=0;n<Math.min(this.tensors.length,e);++n)t.tensors[n]=this.tensors[n];return t}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(null==this.tensors[e])throw new Error(`element at index ${e} is null.`);Pk(this.tensors[e].shape,t,"TensorList shape mismatch: ");const s=Wk(this.elementShape,this.tensors,t);return tl(this.tensors[e],s)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||-1!==this.maxNumElements&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Pk(this.elementShape,t.shape,"TensorList shape mismatch: "),Si(t),null!=this.tensors[e]&&(this.tensors[e].kept=!1),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);Pk(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());const s=Wk(this.elementShape,this.tensors,n);return 0===e.length?ra([],[0].concat(s)):Ni((()=>{const t=e.map((e=>tl(this.tensors[e],s)));return fh(t,0)}))}concat(e,t){if(e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Pk(this.elementShape,t,"TensorList shape mismatch: ");const n=Wk(this.elementShape,this.tensors,t);return 0===this.size()?ra([],[0].concat(n)):Ni((()=>{const e=this.tensors.map((e=>tl(e,n)));return rl(e,0)}))}}const Hk=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{const s=Yv("thenBranch",e,t,n),r=Yv("elseBranch",e,t,n),a=Yv("cond",e,t,n),i=Yv("args",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{const s=Yv("body",e,t,n),r=Yv("cond",e,t,n),a=Yv("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),o=a.map((e=>e.id));let l=await i[0].data();i.forEach((e=>{e.kept||-1!==o.indexOf(e.id)||e.dispose()}));let u=a;for(;l[0];){const e=u;u=await n.functionMap[s].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);const t=u.map((e=>e.id));e.forEach((e=>{e.kept||-1!==o.indexOf(e.id)||-1!==t.indexOf(e.id)||e.dispose()}));const a=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await a[0].data(),a.forEach((e=>{e.kept||-1!==o.indexOf(e.id)||-1!==t.indexOf(e.id)||e.dispose()}))}return u}case"LoopCond":return[nk(Yv("pred",e,t,n))];case"Switch":{const s=Yv("pred",e,t,n);let r=Yv("data",e,t,n);return r.kept||(r=nk(r)),(await s.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{const s=e.inputNames.find((e=>void 0!==Zv(e,t,n)));if(s){return[nk(Zv(s,t,n))]}return}case"Enter":{const s=Yv("frameName",e,t,n),r=Yv("tensor",e,t,n);return n.enterFrame(s),[nk(r)]}case"Exit":{const s=Yv("tensor",e,t,n);return n.exitFrame(),[nk(s)]}case"NextIteration":{const s=Yv("tensor",e,t,n);return n.nextIteration(),[nk(s)]}case"TensorArrayV3":{const s=Yv("size",e,t,n),r=Yv("dtype",e,t,n),a=Yv("elementShape",e,t,n),i=Yv("dynamicSize",e,t,n),o=Yv("clearAfterRead",e,t,n),l=Yv("identicalElementShapes",e,t,n),u=Yv("name",e,t,n),c=new Uk(u,r,s,a,l,i,o);return n.addTensorArray(c),[c.idTensor,au(1)]}case"TensorArrayWriteV3":{const s=Yv("tensorArrayId",e,t,n),r=Yv("index",e,t,n),a=Yv("tensor",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case"TensorArrayReadV3":{const s=Yv("tensorArrayId",e,t,n),r=Yv("index",e,t,n);return[n.getTensorArray(s.id).read(r)]}case"TensorArrayGatherV3":{const s=Yv("tensorArrayId",e,t,n),r=Yv("indices",e,t,n),a=Yv("dtype",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case"TensorArrayScatterV3":{const s=Yv("tensorArrayId",e,t,n),r=Yv("indices",e,t,n),a=Yv("tensor",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case"TensorArrayConcatV3":{const s=Yv("tensorArrayId",e,t,n),r=n.getTensorArray(s.id),a=Yv("dtype",e,t,n);return[r.concat(a)]}case"TensorArraySplitV3":{const s=Yv("tensorArrayId",e,t,n),r=Yv("tensor",e,t,n),a=Yv("lengths",e,t,n),i=n.getTensorArray(s.id);return i.split(a,r),[i.idTensor]}case"TensorArraySizeV3":{const s=Yv("tensorArrayId",e,t,n);return[au(n.getTensorArray(s.id).size(),"int32")]}case"TensorArrayCloseV3":{const 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a=Vk(e.shape.slice(1),n),i=0===s?0:e.size/s,o=Ni((()=>{const n=[];e=tl(e,[1,s,i]);for(let s=0;s<t.length;++s){const o=[0,0===s?0:r[s-1],0],l=[1,t[s],i];n[s]=tl(il(e,o,l),a)}return e.dispose(),n})),l=new Gk([],n,e.dtype,t.length);for(let e=0;e<o.length;e++)l.setItem(e,o[e]);return l}(s,Yv("lengths",e,t,n),r);return n.addTensorList(a),[a.idTensor]}case"TensorListLength":{const s=Yv("tensorListId",e,t,n);return[au(n.getTensorList(s.id).size(),"int32")]}case"TensorListResize":{const s=Yv("tensorListId",e,t,n),r=Yv("size",e,t,n),a=n.getTensorList(s.id).resize(r);return n.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function jk(e,t,n){const[s,r]=Yv("fusedOps",e,t,n),a="biasadd"===s,i=!a,o="prelu"===r,l="fusedbatchnorm"===s,u=Yv("numArgs",e,t,n);if(a){if(o&&2!==u)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&a&&1!==u)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");const c=Yv("strides",e,t,n),h=tk(e,t,n),p=Yv("dataFormat",e,t,n).toUpperCase(),d=Yv("dilations",e,t,n);let[f,m]=Yv("args",e,t,n);i&&(m=f,f=void 0);return{stride:c,pad:h,dataFormat:p,dilations:d,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:Yv("leakyreluAlpha",e,t,n)}}function qk(e,t,n){return{boxes:Yv("boxes",e,t,n),scores:Yv("scores",e,t,n),maxOutputSize:Yv("maxOutputSize",e,t,n),iouThreshold:Yv("iouThreshold",e,t,n),scoreThreshold:Yv("scoreThreshold",e,t,n),softNmsSigma:Yv("softNmsSigma",e,t,n)}}class Kk{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=au(0),this.tensorMap=new Map,Si(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return au(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),Ni((()=>{const e=Ch(t),s=n.length,r=e.length;u(s===r,(()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${r} elements.`));for(let t=0;t<s;t++){const s=n[t],r=e[t];Si(r),this.tensorMap.set(s,r)}return this.handle}))}async find(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return Ni((()=>{const e=[];for(let s=0;s<n.length;s++){const r=n[s],a=this.findWithDefault(r,t);e.push(a)}return fh(e)}))}findWithDefault(e,t){const n=this.tensorMap.get(e);return null!=n?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}}function Xk(e,t,n,s,r=Ni){const 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r((()=>((e,t,n,s=zk)=>{switch(e.op){case"Abs":case"ComplexAbs":return[s.abs(Yv("x",e,t,n))];case"Acos":return[s.acos(Yv("x",e,t,n))];case"Acosh":return[s.acosh(Yv("x",e,t,n))];case"Asin":return[s.asin(Yv("x",e,t,n))];case"Asinh":return[s.asinh(Yv("x",e,t,n))];case"Atan":return[s.atan(Yv("x",e,t,n))];case"Atan2":return[s.atan2(Yv("x",e,t,n),Yv("y",e,t,n))];case"Atanh":return[s.atanh(Yv("x",e,t,n))];case"Ceil":return[s.ceil(Yv("x",e,t,n))];case"Complex":return[s.complex(Yv("real",e,t,n),Yv("imag",e,t,n))];case"Cos":return[s.cos(Yv("x",e,t,n))];case"Cosh":return[s.cosh(Yv("x",e,t,n))];case"Elu":return[s.elu(Yv("x",e,t,n))];case"Erf":return[s.erf(Yv("x",e,t,n))];case"Exp":return[s.exp(Yv("x",e,t,n))];case"Expm1":return[s.expm1(Yv("x",e,t,n))];case"Floor":return[s.floor(Yv("x",e,t,n))];case"Log":return[s.log(Yv("x",e,t,n))];case"Log1p":return[s.log1p(Yv("x",e,t,n))];case"Imag":return[s.imag(Yv("x",e,t,n))];case"Neg":return[s.neg(Yv("x",e,t,n))];case"Reciprocal":return[s.reciprocal(Yv("x",e,t,n))];case"Real":return[s.real(Yv("x",e,t,n))];case"Relu":return[s.relu(Yv("x",e,t,n))];case"Round":return[s.round(Yv("x",e,t,n))];case"Selu":return[s.selu(Yv("x",e,t,n))];case"Sigmoid":return[s.sigmoid(Yv("x",e,t,n))];case"Sin":return[s.sin(Yv("x",e,t,n))];case"Sign":return[s.sign(Yv("x",e,t,n))];case"Sinh":return[s.sinh(Yv("x",e,t,n))];case"Softplus":return[s.softplus(Yv("x",e,t,n))];case"Sqrt":return[s.sqrt(Yv("x",e,t,n))];case"Square":return[s.square(Yv("x",e,t,n))];case"Tanh":return[s.tanh(Yv("x",e,t,n))];case"Tan":return[s.tan(Yv("x",e,t,n))];case"ClipByValue":return[s.clipByValue(Yv("x",e,t,n),Yv("clipValueMin",e,t,n),Yv("clipValueMax",e,t,n))];case"Relu6":return[s.relu6(Yv("x",e,t,n))];case"Rsqrt":return[s.rsqrt(Zv(e.inputNames[0],t,n))];case"Prod":return[s.prod(Yv("x",e,t,n),Yv("axes",e,t,n))];case"LeakyRelu":return[s.leakyRelu(Yv("x",e,t,n),Yv("alpha",e,t,n))];case"Prelu":return[s.prelu(Yv("x",e,t,n),Yv("alpha",e,t,n))];case"IsNan":return[s.isNaN(Zv(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"control":return Hk(e,t,n);case"convolution":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"Conv1D":{const r=Yv("stride",e,t,n),a=Yv("pad",e,t,n),i=Yv("dataFormat",e,t,n).toUpperCase(),o=Yv("dilation",e,t,n);return[s.conv1d(Yv("x",e,t,n),Yv("filter",e,t,n),r,a,i,o)]}case"Conv2D":{const r=Yv("strides",e,t,n),a=tk(e,t,n),i=Yv("dataFormat",e,t,n).toUpperCase(),o=Yv("dilations",e,t,n);return[s.conv2d(Yv("x",e,t,n),Yv("filter",e,t,n),[r[1],r[2]],a,i,[o[1],o[2]])]}case"_FusedConv2D":{const{stride:r,pad:a,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:c,leakyreluAlpha:h}=jk(e,t,n);return[s.fused.conv2d({x:Yv("x",e,t,n),filter:Yv("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:c,preluActivationWeights:u,leakyreluAlpha:h})]}case"FusedDepthwiseConv2dNative":{const{stride:r,pad:a,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:c,leakyreluAlpha:h}=jk(e,t,n);return[s.fused.depthwiseConv2d({x:Yv("x",e,t,n),filter:Yv("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:c,preluActivationWeights:u,leakyreluAlpha:h})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const r=Yv("outputShape",e,t,n),a=Yv("strides",e,t,n),i=tk(e,t,n);return[s.conv2dTranspose(Yv("x",e,t,n),Yv("filter",e,t,n),r,[a[1],a[2]],i)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const r=Yv("strides",e,t,n),a=tk(e,t,n),i=Yv("dilations",e,t,n),o=Yv("dataFormat",e,t,n).toUpperCase();return[s.depthwiseConv2d(Yv("input",e,t,n),Yv("filter",e,t,n),[r[1],r[2]],a,o,[i[1],i[2]])]}case"Conv3D":{const r=Yv("strides",e,t,n),a=Yv("pad",e,t,n),i=Yv("dataFormat",e,t,n).toUpperCase(),o=Yv("dilations",e,t,n);return[s.conv3d(Yv("x",e,t,n),Yv("filter",e,t,n),[r[1],r[2],r[3]],a,i,[o[1],o[2],o[3]])]}case"AvgPool":{const r=Yv("strides",e,t,n),a=Yv("pad",e,t,n),i=Yv("kernelSize",e,t,n);return[s.avgPool(Yv("x",e,t,n),[i[1],i[2]],[r[1],r[2]],a)]}case"MaxPool":{const r=Yv("strides",e,t,n),a=Yv("pad",e,t,n),i=Yv("kernelSize",e,t,n);return[s.maxPool(Yv("x",e,t,n),[i[1],i[2]],[r[1],r[2]],a)]}case"MaxPoolWithArgmax":{const r=Yv("strides",e,t,n),a=Yv("pad",e,t,n),i=Yv("kernelSize",e,t,n),o=Yv("includeBatchInIndex",e,t,n),{result:l,indexes:u}=s.maxPoolWithArgmax(Yv("x",e,t,n),[i[1],i[2]],[r[1],r[2]],a,o);return[l,u]}case"AvgPool3D":{const r=Yv("strides",e,t,n),a=Yv("pad",e,t,n),i=Yv("kernelSize",e,t,n);return[s.avgPool3d(Yv("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],a)]}case"MaxPool3D":{const r=Yv("strides",e,t,n),a=Yv("pad",e,t,n),i=Yv("kernelSize",e,t,n);return[s.maxPool3d(Yv("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{const r=Yv("strides",e,t,n),a=Yv("pad",e,t,n),i=Yv("dilations",e,t,n),o=r[1],l=r[2],u=i[1],c=i[2];return[s.dilation2d(Yv("x",e,t,n),Yv("filter",e,t,n),[o,l],a,[u,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"creation":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"Fill":{const r=Yv("shape",e,t,n),a=Yv("dtype",e,t,n),i=Yv("value",e,t,n);return[s.fill(r,i,a)]}case"LinSpace":{const 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r.image.nonMaxSuppressionAsync(s,a,i,o,l)]}case"Where":{const s=r.cast(Yv("condition",e,t,n),"bool"),a=[await r.whereAsync(s)];return s.dispose(),a}case"ListDiff":return r.setdiff1dAsync(Yv("x",e,t,n),Yv("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n);case"evaluation":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"LowerBound":{const r=Yv("sortedSequence",e,t,n),a=Yv("values",e,t,n);return[s.lowerBound(r,a)]}case"TopKV2":{const r=Yv("x",e,t,n),a=Yv("k",e,t,n),i=Yv("sorted",e,t,n),o=s.topk(r,a,i);return[o.values,o.indices]}case"UpperBound":{const r=Yv("sortedSequence",e,t,n),a=Yv("values",e,t,n);return[s.upperBound(r,a)]}case"Unique":{const r=Yv("x",e,t,n),a=s.unique(r);return[a.values,a.indices]}case"UniqueV2":{const r=Yv("x",e,t,n),a=Yv("axis",e,t,n),i=s.unique(r,a);return[i.values,i.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"image":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"ResizeBilinear":{const r=Yv("images",e,t,n),a=Yv("size",e,t,n),i=Yv("alignCorners",e,t,n),o=Yv("halfPixelCenters",e,t,n);return[s.image.resizeBilinear(r,[a[0],a[1]],i,o)]}case"ResizeNearestNeighbor":{const r=Yv("images",e,t,n),a=Yv("size",e,t,n),i=Yv("alignCorners",e,t,n),o=Yv("halfPixelCenters",e,t,n);return[s.image.resizeNearestNeighbor(r,[a[0],a[1]],i,o)]}case"CropAndResize":{const r=Yv("image",e,t,n),a=Yv("boxes",e,t,n),i=Yv("boxInd",e,t,n),o=Yv("cropSize",e,t,n),l=Yv("method",e,t,n),u=Yv("extrapolationValue",e,t,n);return[s.image.cropAndResize(r,a,i,o,l,u)]}case"ImageProjectiveTransformV3":{const r=Yv("images",e,t,n),a=Yv("transforms",e,t,n),i=Yv("outputShape",e,t,n),o=Yv("fillValue",e,t,n),l=Yv("interpolation",e,t,n),u=Yv("fillMode",e,t,n);return[s.image.transform(r,a,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"graph":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const r=Yv("default",e,t,n);return[Zv(e.name,t,n)||r];case"Placeholder":return[Zv(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":case"Snapshot":return[nk(Yv("x",e,t,n))];case"IdentityN":return Yv("x",e,t,n).map((e=>nk(e)));case"Shape":return[s.tensor1d(Yv("x",e,t,n).shape,"int32")];case"ShapeN":return Yv("x",e,t,n).map((e=>s.tensor1d(e.shape)));case"Size":return[s.scalar(Yv("x",e,t,n).size,"int32")];case"Rank":return[s.scalar(Yv("x",e,t,n).rank,"int32")];case"NoOp":return[s.scalar(1)];case"Print":const a=Yv("x",e,t,n),i=Yv("data",e,t,n),o=Yv("message",e,t,n),l=Yv("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let e=0;e<i.length;e++)console.log(Array.prototype.slice.call(i[e].dataSync()).slice(0,l));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"logical":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"Equal":return[s.equal(Yv("a",e,t,n),Yv("b",e,t,n))];case"NotEqual":return[s.notEqual(Yv("a",e,t,n),Yv("b",e,t,n))];case"Greater":return[s.greater(Yv("a",e,t,n),Yv("b",e,t,n))];case"GreaterEqual":return[s.greaterEqual(Yv("a",e,t,n),Yv("b",e,t,n))];case"Less":return[s.less(Yv("a",e,t,n),Yv("b",e,t,n))];case"LessEqual":return[s.lessEqual(Yv("a",e,t,n),Yv("b",e,t,n))];case"LogicalAnd":return[s.logicalAnd(Yv("a",e,t,n),Yv("b",e,t,n))];case"LogicalNot":return[s.logicalNot(Yv("a",e,t,n))];case"LogicalOr":return[s.logicalOr(Yv("a",e,t,n),Yv("b",e,t,n))];case"Select":case"SelectV2":return[s.where(Yv("condition",e,t,n),Yv("a",e,t,n),Yv("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"matrices":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[s.matMul(Yv("a",e,t,n),Yv("b",e,t,n),Yv("transposeA",e,t,n),Yv("transposeB",e,t,n))];case"Einsum":return[s.einsum(Yv("equation",e,t,n),...Yv("tensors",e,t,n))];case"Transpose":return[s.transpose(Yv("x",e,t,n),Yv("perm",e,t,n))];case"_FusedMatMul":const[r,a]=Yv("fusedOps",e,t,n),i="biasadd"===r,o="prelu"===a,l=Yv("numArgs",e,t,n),u=Yv("leakyreluAlpha",e,t,n);if(i){if(o&&2!==l)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&1!==l)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[c,h]=Yv("args",e,t,n);return[s.fused.matMul({a:Yv("a",e,t,n),b:Yv("b",e,t,n),transposeA:Yv("transposeA",e,t,n),transposeB:Yv("transposeB",e,t,n),bias:c,activation:a,preluActivationWeights:h,leakyreluAlpha:u})];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"normalization":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"EuclideanNorm":return[s.euclideanNorm(Yv("x",e,t,n),Yv("axis",e,t,n),Yv("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[s.batchNorm(Yv("x",e,t,n),Yv("mean",e,t,n),Yv("variance",e,t,n),Yv("offset",e,t,n),Yv("scale",e,t,n),Yv("epsilon",e,t,n))];case"LRN":return[s.localResponseNormalization(Yv("x",e,t,n),Yv("radius",e,t,n),Yv("bias",e,t,n),Yv("alpha",e,t,n),Yv("beta",e,t,n))];case"Softmax":return[s.softmax(Yv("x",e,t,n))];case"LogSoftmax":return[s.logSoftmax(Yv("x",e,t,n))];case"SparseToDense":return[s.sparseToDense(Yv("sparseIndices",e,t,n),Yv("outputShape",e,t,n),Yv("sparseValues",e,t,n),Yv("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"ragged":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"RaggedGather":{const{outputNestedSplits:r,outputDenseValues:a}=s.raggedGather(Yv("paramsNestedSplits",e,t,n),Yv("paramsDenseValues",e,t,n),Yv("indices",e,t,n),Yv("outputRaggedRank",e,t,n));return r.concat(a)}case"RaggedRange":{const{rtNestedSplits:r,rtDenseValues:a}=s.raggedRange(Yv("starts",e,t,n),Yv("limits",e,t,n),Yv("splits",e,t,n));return[r,a]}case"RaggedTensorToTensor":return[s.raggedTensorToTensor(Yv("shape",e,t,n),Yv("values",e,t,n),Yv("defaultValue",e,t,n),Yv("rowPartitionTensors",e,t,n),Yv("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"reduction":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"Max":{const r=Yv("axis",e,t,n),a=Yv("keepDims",e,t,n);return[s.max(Yv("x",e,t,n),r,a)]}case"Mean":{const r=Yv("axis",e,t,n),a=Yv("keepDims",e,t,n);return[s.mean(Yv("x",e,t,n),r,a)]}case"Min":{const r=Yv("axis",e,t,n),a=Yv("keepDims",e,t,n);return[s.min(Yv("x",e,t,n),r,a)]}case"Sum":{const r=Yv("axis",e,t,n),a=Yv("keepDims",e,t,n);return[s.sum(Yv("x",e,t,n),r,a)]}case"All":{const r=Yv("axis",e,t,n),a=Yv("keepDims",e,t,n);return[s.all(Yv("x",e,t,n),r,a)]}case"Any":{const r=Yv("axis",e,t,n),a=Yv("keepDims",e,t,n);return[s.any(Yv("x",e,t,n),r,a)]}case"ArgMax":{const r=Yv("axis",e,t,n);return[s.argMax(Yv("x",e,t,n),r)]}case"ArgMin":{const r=Yv("axis",e,t,n);return[s.argMin(Yv("x",e,t,n),r)]}case"Prod":{const r=Yv("axis",e,t,n),a=Yv("keepDims",e,t,n);return[s.prod(Yv("x",e,t,n),r,a)]}case"Cumprod":{const r=Yv("axis",e,t,n),a=Yv("exclusive",e,t,n),i=Yv("reverse",e,t,n);return[s.cumprod(Yv("x",e,t,n),r,a,i)]}case"Cumsum":{const r=Yv("axis",e,t,n),a=Yv("exclusive",e,t,n),i=Yv("reverse",e,t,n);return[s.cumsum(Yv("x",e,t,n),r,a,i)]}case"Bincount":const r=Yv("x",e,t,n),a=Yv("weights",e,t,n),i=Yv("size",e,t,n);return[s.bincount(r,a,i)];case"DenseBincount":{const r=Yv("x",e,t,n),a=Yv("weights",e,t,n),i=Yv("size",e,t,n),o=Yv("binaryOutput",e,t,n);return[s.denseBincount(r,a,i,o)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"slice_join":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"ConcatV2":case"Concat":{const r=Yv("n",e,t,n),a=Yv("axis",e,t,n);let i=Yv("tensors",e,t,n);return i=i.slice(0,r),[s.concat(i,a)]}case"Gather":{const r=Yv("x",e,t,n),a=Yv("indices",e,t,n);return[s.gather(r,s.cast(a,"int32"),0)]}case"GatherV2":{const r=Yv("axis",e,t,n),a=Yv("batchDims",e,t,n),i=Yv("x",e,t,n),o=Yv("indices",e,t,n);return[s.gather(i,s.cast(o,"int32"),r,a)]}case"Reverse":{const r=Yv("dims",e,t,n),a=[];for(let e=0;e<r.length;e++)r[e]&&a.push(e);const i=Yv("x",e,t,n);return[s.reverse(i,a)]}case"ReverseV2":{const r=Yv("axis",e,t,n),a=Yv("x",e,t,n);return[s.reverse(a,r)]}case"Slice":{const r=Yv("begin",e,t,n),a=Yv("size",e,t,n);return[s.slice(Yv("x",e,t,n),r,a)]}case"StridedSlice":{const r=Yv("begin",e,t,n),a=Yv("end",e,t,n),i=Yv("strides",e,t,n),o=Yv("beginMask",e,t,n),l=Yv("endMask",e,t,n),u=Yv("ellipsisMask",e,t,n),c=Yv("newAxisMask",e,t,n),h=Yv("shrinkAxisMask",e,t,n),p=Yv("x",e,t,n);return[s.stridedSlice(p,r,a,i,o,l,u,c,h)]}case"Pack":return Ni((()=>{const r=Yv("axis",e,t,n),a=Yv("tensors",e,t,n),i=a[0].shape,o=s.squeeze(a[0]).shape,l=a.map((e=>{const t=f(e.shape,i);if(!t&&!f(s.squeeze(e).shape,o))throw new Error("the input tensors shape does not match");return t?e:s.reshape(e,i)}));return[s.stack(l,r)]}));case"Unpack":{const r=Yv("axis",e,t,n),a=Yv("tensor",e,t,n);return s.unstack(a,r)}case"Tile":{const r=Yv("reps",e,t,n);return[s.tile(Yv("x",e,t,n),r)]}case"Split":case"SplitV":{const r=Yv("axis",e,t,n),a=Yv("numOrSizeSplits",e,t,n),i=Yv("x",e,t,n);return s.split(i,a,r)}case"ScatterNd":{const r=Yv("indices",e,t,n),a=Yv("values",e,t,n),i=Yv("shape",e,t,n);return[s.scatterND(r,a,i)]}case"GatherNd":{const r=Yv("x",e,t,n),a=Yv("indices",e,t,n);return[s.gatherND(r,a)]}case"SparseToDense":{const r=Yv("sparseIndices",e,t,n),a=Yv("outputShape",e,t,n),i=Yv("sparseValues",e,t,n),o=Yv("defaultValue",e,t,n);return[s.sparseToDense(r,i,a,i.dtype===o.dtype?o:s.cast(o,i.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"sparse":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"SparseFillEmptyRows":{const{outputIndices:r,outputValues:a,emptyRowIndicator:i,reverseIndexMap:o}=s.sparse.sparseFillEmptyRows(Yv("indices",e,t,n),Yv("values",e,t,n),Yv("denseShape",e,t,n),Yv("defaultValue",e,t,n));return[r,a,i,o]}case"SparseReshape":{const{outputIndices:r,outputShape:a}=s.sparse.sparseReshape(Yv("inputIndices",e,t,n),Yv("inputShape",e,t,n),Yv("newShape",e,t,n));return[r,a]}case"SparseSegmentMean":return[s.sparse.sparseSegmentMean(Yv("data",e,t,n),Yv("indices",e,t,n),Yv("segmentIds",e,t,n))];case"SparseSegmentSum":return[s.sparse.sparseSegmentSum(Yv("data",e,t,n),Yv("indices",e,t,n),Yv("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"spectral":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"FFT":return[s.fft(Yv("x",e,t,n))];case"IFFT":return[s.ifft(Yv("x",e,t,n))];case"RFFT":return[s.rfft(Yv("x",e,t,n))];case"IRFFT":return[s.irfft(Yv("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"string":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"StringNGrams":{const{nGrams:r,nGramsSplits:a}=s.string.stringNGrams(Yv("data",e,t,n),Yv("dataSplits",e,t,n),Yv("separator",e,t,n),Yv("nGramWidths",e,t,n),Yv("leftPad",e,t,n),Yv("rightPad",e,t,n),Yv("padWidth",e,t,n),Yv("preserveShortSequences",e,t,n));return[r,a]}case"StringSplit":{const{indices:r,values:a,shape:i}=s.string.stringSplit(Yv("input",e,t,n),Yv("delimiter",e,t,n),Yv("skipEmpty",e,t,n));return[r,a,i]}case"StringToHashBucketFast":return[s.string.stringToHashBucketFast(Yv("input",e,t,n),Yv("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"transformation":return r((()=>((e,t,n,s=zk)=>{switch(e.op){case"Cast":return[s.cast(Yv("x",e,t,n),Yv("dtype",e,t,n))];case"ExpandDims":{const r=Yv("axis",e,t,n);return[s.expandDims(Yv("x",e,t,n),r)]}case"Squeeze":{const r=Yv("axis",e,t,n);return[s.squeeze(Yv("x",e,t,n),r)]}case"Reshape":return[s.reshape(Yv("x",e,t,n),Yv("shape",e,t,n))];case"MirrorPad":return[s.mirrorPad(Yv("x",e,t,n),Yv("padding",e,t,n),Yv("mode",e,t,n))];case"PadV2":case"Pad":return[s.pad(Yv("x",e,t,n),Yv("padding",e,t,n),Yv("constantValue",e,t,n))];case"SpaceToBatchND":{const r=Yv("blockShape",e,t,n),a=Yv("paddings",e,t,n);return[s.spaceToBatchND(Yv("x",e,t,n),r,a)]}case"BatchToSpaceND":{const r=Yv("blockShape",e,t,n),a=Yv("crops",e,t,n);return[s.batchToSpaceND(Yv("x",e,t,n),r,a)]}case"DepthToSpace":{const r=Yv("blockSize",e,t,n),a=Yv("dataFormat",e,t,n).toUpperCase();return[s.depthToSpace(Yv("x",e,t,n),r,a)]}case"BroadcastTo":return[s.broadcastTo(Yv("x",e,t,n),Yv("shape",e,t,n))];case"BroadcastArgs":return[s.broadcastArgs(Yv("s0",e,t,n),Yv("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"hash_table":return(async(e,t,n,s)=>{switch(e.op){case"HashTable":case"HashTableV2":{const r=s.getHashTableHandleByName(e.name);if(null!=r)return[r];{const r=Yv("keyDType",e,t,n),a=Yv("valueDType",e,t,n),i=new Kk(r,a);return s.addHashTable(e.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{const r=Yv("tableHandle",e,t,n,s),a=Yv("keys",e,t,n),i=Yv("values",e,t,n),o=s.getHashTableById(r.id);return[await o.import(a,i)]}case"LookupTableFind":case"LookupTableFindV2":{const r=Yv("tableHandle",e,t,n,s),a=Yv("keys",e,t,n),i=Yv("defaultValue",e,t,n),o=s.getHashTableById(r.id);return[await o.find(a,i)]}case"LookupTableSize":case"LookupTableSizeV2":{const r=Yv("tableHandle",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n,s);case"custom":const a=Xv(e.op);if(a&&a.customExecutor)return a.customExecutor(new Lk(e,t,n));throw TypeError(`Custom op ${e.op} is not registered.`);default:throw TypeError(`Unknown op '${e.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return H(a)?a.then((e=>[].concat(e))):[].concat(a)}class Yk{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map((e=>0===e.id&&0===e.iterationId?"":`${e.frameName}-${e.iterationId}`)).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(!(this.contexts&&this.contexts.length>1))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()}nextIteration(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");{this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function Zk(e,t,n,s){const r=new Set,a=[];let i=null,o=null;const l=new Set,u=Object.keys(e).map((e=>ek(e)[0]));let c=[];null!=s&&(c=s.map((e=>ek(e.name)[0])));const h=[...t];for(;h.length>0;){const e=h.pop();(tN(e)||nN(e)||sN(e))&&null==i&&(i=e,o=i.children.map((e=>e.name)).filter((e=>r.has(e)))),r.add(e.name),null==n[e.name]&&(-1===u.indexOf(e.name)&&-1===c.indexOf(e.name)&&(0!==e.inputs.length?e.inputs.forEach((e=>{l.has(e.name)||(l.add(e.name),h.push(e))})):a.push(e.name)))}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}const Jk=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Qk=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],eN=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function tN(e){return Jk.indexOf(e.op)>=0}function nN(e){return Qk.indexOf(e.op)>=0}function sN(e){return eN.indexOf(e.op)>=0}class rN{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach((t=>{this._functionExecutorMap[t]=new rN(e.functions[t],this)}))}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map((t=>e[t].map((e=>e.id))));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get outputs(){return this._outputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get inputNodes(){return this._inputs.map((e=>e.signatureKey||e.name))}get outputNodes(){return this._outputs.map((e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t}))}get functions(){return Object.keys(this._functions).reduce(((e,t)=>(e[t]=this._functions[t].signature,e)),{})}getCompilationKey(e,t){const n=e.map((e=>e.name)).sort(),s=t.map((e=>e.name)).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){const n=Zk(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(null!=r)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){const n=t.map((e=>e.name)),r=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${r}]. Missing the following inputs: [${s}]`)}return function(e,t,n){const{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map((e=>ek(e)[0])).map((t=>e.nodes[t])),o=e.initNodes;i.forEach((e=>{s.has(e.name)&&a.push(e)})),e.weights.forEach((e=>{s.has(e.name)&&a.push(e)})),null!=o&&o.forEach((e=>{s.has(e.name)&&a.push(e)}));const l=new Set,u=[];for(;a.length>0;){const e=a.pop();l.add(e.name),t[e.name]||u.push(e),e.children.forEach((e=>{!l.has(e.name)&&s.has(e.name)&&e.inputs.every((e=>l.has(e.name)))&&a.push(e)}))}return u}(this.graph,this.weightMap,n)}cloneAndKeepTensor(e){if(null==e)return null;const t=e.clone();return Si(t),t}cloneTensorList(e){if(!e)return null;const t=e.map((e=>this.cloneAndKeepTensor(e)));return t}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map((([e,t])=>[e,this.cloneTensorList(t)])))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const s=n.map((e=>this.graph.nodes[ek(e)[0]])),r=t.map((e=>ek(e)[0]));let a=r.map((e=>this.graph.nodes[e]));0===a.length&&(a=this._outputs);const i=this.getCompilationKey(s,a);let o=this.compiledMap.get(i);null==o&&(o=this.compile(e,a),this.compiledMap.set(i,o));try{this.keepIntermediateTensors=X().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){this.keepIntermediateTensors=!1,console.warn(e.message)}const l={},u={};return Ni((()=>{const n=new Yk(this.weightMap,l,u,this.functionExecutorMap),s=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach((t=>{const[n,r]=ek(t),a=[];a[r]=e[t],s[n]=a,this.keepIntermediateTensors&&(this.clonedTensorsMap[n]=this.cloneTensorList(a))}));const a=this.getFrozenTensorIds(s),i={};for(let e=0;e<o.length;e++){const t=o[e];if(!s[t.name]){const e=Xk(t,s,n,this._resourceManager);if(H(e))throw new Error(`The execution of the op '${t.op}' returned a promise. Please use model.executeAsync() instead.`);s[t.name]=e,this.keepIntermediateTensors&&(this.clonedTensorsMap[t.name]=this.cloneTensorList(e)),this.checkTensorForDisposal(t.name,t,s,n,a,r,i)}}return null==this.parent&&n.dispose(a),t.map((e=>Zv(e,s,n)))}))}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map((t=>e[t])).map((e=>e.map((e=>e.id)))));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,i){"control"!==t.category&&-1===a.indexOf(e)&&(n[e].forEach((e=>{null!=e&&(i[e.id]=(i[e.id]||0)+t.children.length)})),t.inputs.forEach((e=>{if("control"!==e.category){const t=function(e,t,n){return t[Qv(e,n.currentContextId)]}(e.name,n,s);null!=t&&t.forEach((e=>{if(e&&!e.kept&&!r.has(e.id)){const t=i[e.id];1===t?(e.dispose(),delete i[e.id]):null!=t&&i[e.id]--}}))}})))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach((e=>{for(const t of e)t&&!t.isDisposed&&t.dispose()})),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,s={},r={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=X().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){this.keepIntermediateTensors=!1,console.warn(e.message)}const a=new Yk(this.weightMap,s,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));const i=await this.executeWithControlFlow(e,a,t,n),o=t.map((e=>Zv(e,i,a))),l=o.map((e=>e.id)),u=Object.keys(e).map((t=>e[t].id)),c=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach((e=>{e.forEach((e=>{!e||e.isDisposed||c.has(e.id)||e.dispose()}))})),null==this.parent&&a.dispose(c),o}async executeFunctionAsync(e,t,n){const s=e.reduce(((e,t,n)=>(e[this.inputs[n].name]=t,e)),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){const r=Object.keys(e),a=r.map((e=>this.graph.nodes[ek(e)[0]])),i=n.map((e=>ek(e)[0]));let o=i.map((e=>this.graph.nodes[e]));0===o.length&&(o=this._outputs);const{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=Zk(e,o,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),d=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,s]=ek(t),r=[];r[s]=e[t],d[n]=r}));const f={},m=this.getFrozenTensorIds(d),g={};for(;p.length>0;){const e=this.processStack(a,p,t,d,g,m,i,f,l);await Promise.all(e)}null!=c||s||console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const y=o.filter((e=>!tN(e)&&!Zv(e.name,d,t))).map((e=>e.name));if(y.length>0){let e="";throw null!=c&&(e=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${e}`)}return d}processStack(e,t,n,s,r,a,i,o,l){const u=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&Yv("isConstant",e.node,s,n)&&([c]=Jv(e.node.name,n)),null==s[e.node.name]){const h=Xk(e.node,s,n,this._resourceManager);c||([c]=Jv(e.node.name,n));const p=n.currentContext;H(h)?u.push(h.then((u=>(s[c]=u,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(u)),n.currentContext=p,this.checkTensorForDisposal(c,e.node,s,n,a,i,o),this.processChildNodes(e.node,t,n,s,r,l),u)))):(s[c]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(h)),this.checkTensorForDisposal(c,e.node,s,n,a,i,o),this.processChildNodes(e.node,t,n,s,r,l))}else this.processChildNodes(e.node,t,n,s,r,l)}return u}processChildNodes(e,t,n,s,r,a){e.children.forEach((e=>{const[i]=Jv(e.name,n);!r[i]&&a.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!Zv(e,s,n)))&&(r[i]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!Zv(e,s,n)))&&(r[i]=!0,t.push({contexts:n.currentContext,node:e})))}))}dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((t=>{const n=e[t],[s]=ek(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){const e=r.attrParams.shape.value,t=e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t));u(t,(()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`))}r.attrParams.dtype&&r.attrParams.dtype.value&&u(n.dtype===r.attrParams.dtype.value,(()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`))}))}mapInputs(e){var t,n;const s={};for(const r in e){const a=null===(n=null===(t=this._signature)||void 0===t?void 0:t.inputs)||void 0===n?void 0:n[r];null!=a?s[a.name]=e[r]:s[r]=e[r]}return s}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=ek(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>{var t,n;const s=null===(n=null===(t=this._signature)||void 0===t?void 0:t.outputs)||void 0===n?void 0:n[e];return null!=s?s.name:e}),{})}checkOutputs(e){e.forEach((e=>{const[t]=ek(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}}class aN{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}class iN{constructor(e,t={},n=yi){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,null==t&&(this.loadOptions={}),this.resourceManager=new aN}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{const t=this.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=this.handler.load();return H(e)?e.then((e=>this.loadSync(e))):this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n=this.artifacts.signature;if(null!=this.artifacts.userDefinedMetadata){const e=this.artifacts.userDefinedMetadata;null!=e.signature&&(n=e.signature),null!=e.structuredOutputKeys&&(this.structuredOutputKeys=e.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new rN(kk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=kk.Instance.transformGraph(e.modelInitializer);this.initializer=new rN(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if("string"==typeof e){const t=this.io.getSaveHandlers(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Error(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){const t={};return(e instanceof vr?[e]:e).forEach(((e,n)=>t[this.structuredOutputKeys[n]]=e)),t}return e}predict(e,t){const n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){const n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof vr||Array.isArray(e))){const n=null===(t=this.signature)||void 0===t?void 0:t.inputs;if(null!=n)for(const t in n){const s=n[t];null!=s.resourceId&&(e[t]=this.resourceIdToCapturedInput[s.resourceId])}return e}e=Array.isArray(e)?e:[e];const n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let s=0;return this.inputNodes.reduce(((t,n)=>{var r,a,i;const o=null===(i=null===(a=null===(r=this.signature)||void 0===r?void 0:r.inputs)||void 0===a?void 0:a[n])||void 0===i?void 0:i.resourceId;return t[n]=null!=o?this.resourceIdToCapturedInput[o]:e[s++],t}),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){const t=this.initializerSignature.outputs,n=Object.keys(t);for(let s=0;s<n.length;s++){const r=t[n[s]];this.resourceIdToCapturedInput[r.resourceId]=e[s]}}}execute(e,t){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ii(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}}const oN="4.1.0";function lN(e,t,n=new Map,s=new Set){if(null==e)return null;if("function"==typeof Blob&&e instanceof Blob)return e.slice();if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const r=t(e);if(r.recurse&&null!==r.value)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse){if(dN(e)){const r=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const i=lN(e[a],t,n,s);r[a]=i}return s.delete(e),e.__proto__&&(r.__proto__=e.__proto__),r}throw new Error(`Can't recurse into non-iterable type: ${e}`)}return n.set(e,r.value),r.value}function uN(e,t=hN){return cN(e,t)}function cN(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const r=t(e);if(r.recurse&&null!==r.value)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse){if(dN(s)){const r=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const s=cN(e.map((e=>e[a])),t,n);r[a]=s}return n.delete(s),r}throw new Error(`Can't recurse into non-iterable type: ${s}`)}return r.value}function hN(e){return null===e?null:dN(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function pN(e,t){const n=new Map;lN(e,t,n);for(const e of Array.from(n.keys())){const t=n.get(e);if(H(t)){const s=await t;n.set(e,s)}}return lN(e,t,n)}function dN(e){let t=!1;if(X().get("IS_BROWSER"))t=e instanceof TextDecoder;else{const{StringDecoder:n}=require("string_decoder");t=e instanceof n}return null!=e&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||"object"==typeof e&&!(e instanceof vr)&&!(e instanceof Promise)&&!t)}function fN(e){return function(e,t){return lN(e,t)}(e,mN)}function mN(e){return e instanceof vr?{value:e.clone(),recurse:!1}:dN(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class gN{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,null==e)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return 0===this.length()}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class yN extends gN{constructor(){super(yN.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=2*this.capacity,t=new Array(e),n=this.length();for(let e=0;e<n;e++)t[e]=this.get(this.wrap(this.begin+e));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}function bN(e){return new kN(e)}function xN(e){return new NN(e)}function wN(e,t){return new DN(e,t)}yN.INITIAL_CAPACITY=32;class vN{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new AN(this,e)}filter(e){return new $N(this,e)}map(e){return new EN(this,e)}mapAsync(e){return new RN(this,e)}serialMapAsync(e){return new RN(this,e).serial()}flatmap(e){return new FN(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile((e=>!0===e))}rowMajorBatch(e,t=!0){return new CN(this,e,t)}columnMajorBatch(e,t=!0,n=hN){return this.rowMajorBatch(e,t).map((e=>uN(e,n)))}concatenate(e,t){return new DN(bN([this,e]),t)}take(e){return e<0||null==e?this:new TN(this,e)}skip(e){return e<0||null==e?this:new SN(this,e)}prefetch(e){return new LN(this,e)}shuffle(e,t){return new zN(this,e,t)}serial(){return new IN(this)}}class kN extends vN{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:fN(e),done:!1}}}class NN extends vN{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class IN extends vN{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){return this.upstream.next()}}class SN extends vN{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;Ii(e.value)}return this.upstream.next()}}class TN extends vN{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class CN extends vN{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class $N extends vN{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ii(e.value)}}}class EN extends vN{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Or(e.value),n=this.transform(e.value),s=Or(n);for(const e of t)Dr(e,s)||e.dispose();return{value:n,done:!1}}}class AN extends vN{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class RN extends vN{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Or(e.value),n=await this.transform(e.value),s=Or(n);for(const e of t)Dr(e,s)||e.dispose();return{value:n,done:!1}}}class _N extends vN{constructor(){super(),this.outputQueue=new yN,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;0===this.outputQueue.length();)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class FN extends _N{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=Or(e.value),n=this.transform(e.value),s=Or(n);this.outputQueue.pushAll(n);for(const e of t)Dr(e,s)||e.dispose();return!0}}class DN extends vN{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,null==this.iterator){const e=await this.moreIterators.next();if(e.done)return{value:null,done:!0};this.iterator=e.value,null!=this.baseErrorHandler&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var ON;!function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"}(ON||(ON={}));class MN extends vN{constructor(e,t=ON.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;const s=await pN(this.iterators,(function(e){if(e instanceof vN){return{value:e.next().then((e=>(t++,e.done&&n++,e.value))),recurse:!1}}return{value:null,recurse:!0}}));if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ON.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ON.SHORTEST:return{value:null,done:!0};case ON.LONGEST:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class LN extends vN{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new gN(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class zN extends LN{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Rc(n||rr().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(!t.done)return this.refill(),t;this.upstreamExhausted=!0}return{value:null,done:!0}}}class PN{constructor(){this.size=null}batch(e,t=!0){const n=this;let s;return u(e>0,(()=>`batchSize needs to be positive, but it is\n      ${e}`)),s=this.size===1/0||null==this.size?this.size:t?Math.ceil(this.size/e):Math.floor(this.size/e),BN((async()=>(await n.iterator()).columnMajorBatch(e,t,WN)),s)}concatenate(e){const t=this;let n;return n=this.size===1/0||e.size===1/0?1/0:null!=this.size&&null!=e.size?this.size+e.size:null,BN((async()=>(await t.iterator()).concatenate(await e.iterator())),n)}filter(e){const t=this;let n;return n=this.size===1/0?1/0:null,BN((async()=>(await t.iterator()).filter((t=>Ni((()=>e(t)))))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return BN((async()=>(await t.iterator()).map((t=>Ni((()=>e(t)))))),this.size)}mapAsync(e){const t=this;return BN((async()=>(await t.iterator()).mapAsync(e)),this.size)}prefetch(e){if(null==e)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return BN((async()=>(await t.iterator()).prefetch(e)),this.size)}repeat(e){const t=this;let n;return n=null!=this.size&&e>0?this.size*e:0===e?0:null!=this.size&&(void 0===e||e<0)?1/0:null,BN((async()=>wN(xN((async()=>({value:await t.iterator(),done:!1}))).take(e))),n)}skip(e){const t=this;let n;return n=null!=this.size&&e>=0&&this.size>=e?this.size-e:null!=this.size&&(this.size<e||void 0===e||e<0)?0:null,BN((async()=>(await t.iterator()).skip(e)),n)}shuffle(e,t,n=!0){if(null==e||e<0)throw null==this.size?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,r=Rc(t||rr().toString());return BN((async()=>{let t=r.int32();return n&&(t+=r.int32()),(await s.iterator()).shuffle(e,t.toString())}),this.size)}take(e){const t=this;let n;return n=null!=this.size&&this.size>e?e:null!=this.size&&this.size<=e?this.size:null,BN((async()=>(await t.iterator()).take(e)),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}function BN(e,t=null){return new class extends PN{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function WN(e){if(null===e)return null;const t=e[0];if(null==(n=t)||null===(s=n)||"object"!=typeof s&&"function"!=typeof s||Array.isArray(n)||"object"==typeof n&&n instanceof vr||C(n)){return{value:function(e){if(0===e.length)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof vr?fh(e):ra(e)}(e),recurse:!1}}var n,s;return{value:null,recurse:!0}}PN.MAX_BUFFER_SIZE=1e4;class VN extends PN{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split("\n").map((e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e)))}}const UN='"',GN=Symbol("out"),HN=Symbol("field"),jN=Symbol("quote"),qN=Symbol("quoteafterquote"),KN=Symbol("quoteinquote");class XN extends PN{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new VN(e),t||(t={}),this.hasHeader=!1!==t.hasHeader,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(u(null==t.delimiter,(()=>"Delimiter should not be provided when delimWhitespace is true.")),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&u(e.length===this.fullColumnNames.length,(()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+").")),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce(((e,t)=>(e[t]=e[t]+1||1,e)),{}),n=Object.keys(t).filter((e=>t[e]>1));if(u(0===n.length,(()=>"Duplicate column names found: "+n.toString())),this.columnConfigs)for(const e of Object.keys(this.columnConfigs)){if(-1===this.fullColumnNames.indexOf(e))throw new Error('The key "'+e+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value;return this.parseRow(n,!1)}return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map((e=>this.makeDataElement(e)))}makeDataElement(e){const t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){const a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!this.configuredColumnsOnly||i){const o=t[r];let l=null;if(""===o)if(i&&void 0!==i.default)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{const e=Number(o);if(isNaN(e))l=i&&"bool"===i.dtype?this.getBoolean(o):o;else if(i&&i.dtype)switch(i.dtype){case"float32":default:l=e;break;case"int32":l=Math.floor(e);break;case"bool":l=this.getBoolean(o)}else l=e}i&&i.isLabel?s[a]=l:n[a]=l}}return 0===Object.keys(s).length?n:{xs:n,ys:s}}getBoolean(e){return"1"===e||"true"===e.toLowerCase()?1:0}parseRow(e,t=!0){const n=[];let s=0;const r=e.length;let a=GN;for(let t=0;t<r;t++)switch(a){case GN:switch(e.charAt(t)){case UN:s=t+1,a=jN;break;case this.delimiter:if(s=t+1," "===this.delimiter&&this.delimWhitespace)break;n.push(""),a=GN;break;default:a=HN,s=t}break;case HN:if(e.charAt(t)===this.delimiter)n.push(e.substring(s,t)),a=GN,s=t+1;break;case jN:if(e.charAt(t)===UN)a=qN;break;case qN:switch(e.charAt(t)){case this.delimiter:n.push(e.substring(s,t-1)),a=GN,s=t+1;break;case UN:a=jN;break;default:a=KN}break;case KN:if(e.charAt(t)===UN)a=jN}if(a===qN?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class YN extends vN{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!1!==e.includeSpectrogram,this.includeWaveform=!0===e.includeWaveform,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!X().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");const t=new YN(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:null==this.audioTrackConstraints||this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,this.sampleRateHz){if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`)}else this.sampleRateHz=this.audioContext.sampleRate;const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=2*this.fftSize,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const t=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(t,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const e=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(e,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise((s=>{const r=setInterval((()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))}),this.fftSize/this.sampleRateHz*1e3)}))}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),null!=this.stream&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach(((e,s)=>n.set(e,s*t))),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(d(t));return n.set(e,n.length-e.length),ra(n,t)}}class ZN extends vN{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=bh([0],"int32"),this.webcamConfig.centerCrop){const e=1*this.webcamConfig.resizeWidth/this.webcamVideoElement.width,t=1*this.webcamConfig.resizeHeight/this.webcamVideoElement.height,n=(1-e)/2,s=(1-t)/2,r=n+e,a=t+s;this.cropBox=xh([s,n,a,r],[1,4])}else this.cropBox=xh([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!X().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new ZN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&u("user"===this.webcamConfig.facingMode||"environment"===this.webcamConfig.facingMode,(()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`));try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise((e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}}))}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ui(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(!this.resize)return{value:e,done:!1};try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{e.dispose()}}needToResize(){return!(!this.webcamConfig.resizeWidth||!this.webcamConfig.resizeHeight||this.webcamVideoElement.width===this.webcamConfig.resizeWidth&&this.webcamVideoElement.height===this.webcamConfig.resizeHeight)}cropAndResizeFrame(e){return Ni((()=>{const t=du(Ja(e,"float32"),0);let n;n=Xp.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return tl(n,s.slice(1))}))}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach((e=>e.stop()));try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class JN{}class QN extends vN{split(e){return new eI(this,e)}}class eI extends QN{constructor(e,t){super(),this.upstream=e,this.impl=new tI(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class tI extends _N{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return""!==this.carryover&&(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const e of t.slice(0,-1))this.outputQueue.push(e);return this.carryover=t[t.length-1],!0}}class nI extends vN{decodeUTF8(){return new sI(this)}}class sI extends QN{constructor(e){super(),this.upstream=e,this.impl=new rI(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class rI extends _N{constructor(e){if(super(),this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:e}=require("string_decoder");this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t,n;return!e.done&&(t=e.value,n=X().get("IS_BROWSER")?this.decoder.decode(t,{stream:!0}):this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0)}}class aI extends nI{constructor(e,t={}){super(),this.file=e,this.options=t,u(e instanceof Uint8Array||!!X().get("IS_BROWSER")&&(e instanceof File||e instanceof Blob),(()=>"FileChunkIterator only supports File, Blob and Uint8Array right now.")),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1048576}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise(((e,t)=>{const n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{const s=new FileReader;s.onload=n=>{let r=s.result;if(r instanceof ArrayBuffer&&(r=new Uint8Array(r)),!(r instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(r)},s.onabort=e=>t(new Error("Aborted")),s.onerror=e=>t(new Error(e.type));const r=this.file.slice(this.offset,n);s.readAsArrayBuffer(r)}this.offset=n}));return{value:await e,done:!1}}}const iI=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function oI(e){return"string"==typeof e&&"file://"===e.slice(0,7)}class lI extends JN{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(oI(this.input)&&X().get("IS_NODE")){const e=require("fs");this.input=e.readFileSync(this.input.slice(7))}return new aI(this.input,this.options)}}class uI extends JN{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return oI(this.url)?new lI(this.url,this.fileOptions).iterator():async function(e,t={},n){let s,r;"string"==typeof e?s=e:(s=e.url,r=iI(e));const a=await(n||ar)(s,r);if(a.ok){const e=new Uint8Array(await a.arrayBuffer());return new aI(e,t)}throw new Error(a.statusText)}(this.url,this.fileOptions)}}const cI="4.1.0";var hI=Object.freeze({__proto__:null,array:function(e){return BN((async()=>bN(e)),e.length)},Dataset:PN,zip:function(e){if(!dN(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=null==t?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=null==t?e[n].size:Math.min(t,e[n].size);return BN((async()=>function(e,t=ON.FAIL){return new MN(e,t)}(await pN(e,(e=>{if(e instanceof PN)return{value:e.iterator(),recurse:!1};if(dN(e))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")})),ON.SHORTEST)),t)},CSVDataset:XN,TextLineDataset:VN,csv:function(e,t={}){return new XN(new uI(e),t)},func:function(e){const t=xN(e);return BN((async()=>t))},generator:function(e){return BN((async()=>{const t=await e();return xN((()=>t.next()))}))},microphone:async function(e){return YN.create(e)},webcam:async function(e,t){return ZN.create(e,t)},FileDataSource:lI,URLDataSource:uI,version_data:cI});function pI(e,t){Array.isArray(e)||(e=[e]),e.forEach((e=>{null!=e&&u("complex64"!==e.dtype,(()=>`${t} does not support complex64 tensors in the CPU backend.`))}))}const dI=Ah;class fI extends n{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new t(this,vi())}nextDataId(){return fI.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().get("IS_NODE")&&us("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. 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bI(e){const{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,o=n.makeTensorInfo(s.shape,"complex64");return n.data.get(o.dataId).complexTensorInfos={real:n.makeTensorInfo(s.shape,"float32",a),imag:n.makeTensorInfo(r.shape,"float32",i)},o}const xI={kernelName:Se,backendName:"cpu",kernelFunc:bI};function wI(e,t,n="float32"){if("complex64"===n){return bI({inputs:{real:wI(e,t,"float32"),imag:wI(e,t,"float32")},backend:e})}const s=B(d(t),n);return e.makeTensorInfo(t,n,s)}function vI(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const kI={kernelName:dt,backendName:"cpu",kernelFunc:vI};function NI(e){const{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.real,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}const II={kernelName:ln,backendName:"cpu",kernelFunc:NI};function SI(e,t,n,s){if("int32"===s){return[t,"int32",Int32Array.from(e)]}if("bool"===s){const s=sr([0],n),[r,a]=yI(((e,t)=>e!==t?1:0))(t,[],e,s,"bool");return[a,"bool",r]}throw new Error(`Error in Cast: failed to cast ${n} to ${s}`)}function TI(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if("complex64"===a){if("complex64"===r.dtype)return vI({inputs:{x:r},backend:n});const e=wI(n,r.shape,r.dtype),t=TI({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),s=bI({inputs:{real:t,imag:e},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),s}if("complex64"===r.dtype){const e=NI({inputs:{input:r},backend:n}),t=TI({inputs:{x:e},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(e),t}if(!T(r.dtype,a)){const e=vI({inputs:{x:r},backend:n});return{dataId:e.dataId,shape:e.shape,dtype:a}}const i=n.data.get(r.dataId).values,[o,l,u]=SI(i,r.shape,r.dtype,a);return n.makeTensorInfo(o,l,u)}const CI={kernelName:ke,backendName:"cpu",kernelFunc:TI};function $I(e,t,n,s){return null==n?({inputs:n,backend:r})=>{const{a:a,b:i}=n,o=r;pI([a,i],e);const l=o.data.get(a.dataId).values,u=o.data.get(i.dataId).values,c="string"===a.dtype?ff(l):l,h="string"===a.dtype?ff(u):u,p=s||a.dtype,[d,f]=t(a.shape,i.shape,c,h,p);return o.makeTensorInfo(f,p,d)}:({inputs:e,backend:r})=>{const{a:a,b:i}=e,o=r;if("complex64"===a.dtype||"complex64"===i.dtype){const e=TI({inputs:{x:a},backend:o,attrs:{dtype:"complex64"}}),t=o.data.get(e.dataId),s=t.complexTensorInfos.real,r=t.complexTensorInfos.imag,l=o.data.get(s.dataId).values,u=o.data.get(r.dataId).values,c=TI({inputs:{x:i},backend:o,attrs:{dtype:"complex64"}}),h=o.data.get(c.dataId),p=h.complexTensorInfos.real,d=h.complexTensorInfos.imag,f=o.data.get(p.dataId).values,m=o.data.get(d.dataId).values,[g,y,b]=n(a.shape,i.shape,l,u,f,m),x=o.makeTensorInfo(b,"float32",g),w=o.makeTensorInfo(b,"float32",y),v=bI({inputs:{real:x,imag:w},backend:o});return 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zI(e,t,n){return({inputs:s,attrs:r,backend:a})=>{const{x:i}=s;if(pI(i,e),"string"===i.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const o=a,l=o.data.get(i.dataId).values,u=n||i.dtype,c=t(l,u,r);return o.makeTensorInfo(i.shape,u,c)}}const PI=MI((e=>Math.ceil(e))),BI=zI(Ne,PI),WI={kernelName:Ne,backendName:"cpu",kernelFunc:BI};function VI(e,t,n,s){const r=N(n,d(t));if(s&&"string"!==n){let t=0;e.forEach((e=>{const n=d(e.shape);r.set(e.vals,t),t+=n}))}else{let s=0;e.forEach((e=>{const a="string"===n?ff(e.vals):e.vals;let i=0;for(let n=0;n<e.shape[0];++n){const o=n*t[1]+s;for(let t=0;t<e.shape[1];++t)r[o+t]=a[i++]}s+=e.shape[1]}))}return r}const 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sS=yI(((e,t)=>e>t?1:0)),rS=$I(ht,sS,null,"bool"),aS={kernelName:ht,backendName:"cpu",kernelFunc:rS},iS=yI(((e,t)=>e>=t?1:0)),oS=$I(pt,iS,null,"bool"),lS={kernelName:pt,backendName:"cpu",kernelFunc:oS},uS=yI(((e,t)=>e<t?1:0)),cS=$I(wt,uS,null,"bool"),hS={kernelName:wt,backendName:"cpu",kernelFunc:cS},pS=yI(((e,t)=>e<=t?1:0)),dS=$I(vt,pS,null,"bool"),fS={kernelName:vt,backendName:"cpu",kernelFunc:dS};function mS(e,t,n){const s=(t-e)/(n-1),r=B(n,"float32");r[0]=e;for(let e=1;e<r.length;e++)r[e]=r[e-1]+s;return r}const gS=MI((e=>Math.log(e))),yS=zI(Nt,gS),bS={kernelName:Nt,backendName:"cpu",kernelFunc:yS};function xS(e,t,n,s){const r=k(s,d(n));for(let n=0;n<r.length;++n){const s=n*t;let a=e[s];for(let n=0;n<t;++n){const t=e[s+n];(Number.isNaN(t)||t>a)&&(a=t)}r[n]=a}return r}const wS=yI(((e,t)=>Math.max(e,t))),vS=$I(_t,wS),kS={kernelName:_t,backendName:"cpu",kernelFunc:vS},NS=yI(((e,t)=>Math.min(e,t))),IS=$I(Bt,NS),SS={kernelName:Bt,backendName:"cpu",kernelFunc:IS},TS=yI(((e,t)=>e*t)),CS=EI(((e,t,n,s)=>({real:e*n-t*s,imag:e*s+t*n}))),$S=$I(Gt,TS,CS),ES={kernelName:Gt,backendName:"cpu",kernelFunc:$S};function AS(e,t,n){const s=nr(-1,n);return TS([],t,s,e,n)}const RS={kernelName:Ht,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s}=t;pI(s,"neg");const r=n.data.get(s.dataId).values,[a,i]=AS(r,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,a)}},_S=yI(((e,t)=>e!==t?1:0)),FS=$I(jt,_S,null,"bool"),DS={kernelName:jt,backendName:"cpu",kernelFunc:FS};function OS(e,t,n,s,r){const a=t.length,i=d(t),o=M(t),l=M(r),u=k(n,d(r));for(let t=0;t<i;++t){const n=G(t,a,o),r=new Array(n.length);for(let e=0;e<r.length;e++)r[e]=n[s[e]];u[U(r,a,l)]=e[t]}return u}function MS(e){const{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{perm:a}=n;pI(r,"transpose");const i=r.shape.length,o=new Array(i);for(let e=0;e<o.length;e++)o[e]=r.shape[a[e]];const l=OS(s.data.get(r.dataId).values,r.shape,r.dtype,a,o);return{dataId:s.write(l,o,r.dtype),shape:o,dtype:r.dtype}}const LS={kernelName:Jn,backendName:"cpu",kernelFunc:MS};function zS(e,t,n,s){const[r,a]=Yl(e,s),i=Ar(t,"int32"),o=B(d(r),i),l=d(a);for(let e=0;e<o.length;++e){const t=e*l;let s=1;for(let e=0;e<l;++e)s*=n[t+e];o[e]=s}return{outVals:o,outShape:r,outDtype:i}}const PS={kernelName:nn,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;pI(r,"prod");const o=r.shape.length,l=w(a,r.shape),u=Ql(l,o);let c=l,h=r;const p=[];null!=u&&(h=MS({inputs:{x:r},backend:n,attrs:{perm:u}}),p.push(h),c=tu(c.length,o));const d=n.data.get(h.dataId).values,{outVals:f,outShape:m,outDtype:g}=zS(h.shape,h.dtype,d,c);let y=m;return i&&(y=Zl(m,l)),p.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(y,g,f)}};function BS(e,t,n,s){const r=[];let a=0;const i=t.length-1+n.length,o=new Array(i).fill(null).map((()=>[0]));!function(e,t){for(let n=0;n<e.length;++n){const s=e[n],r=n===e.length-1?t:e[n+1].length;if(0===s.length)throw new Error("Ragged splits may not be empty");if(s[0]<0)throw new Error("Ragged splits must be non-negative");if(s[s.length-1]>r)throw new Error("Ragged splits must not point past values");for(let e=1;e<s.length;++e)if(s[e-1]>s[e])throw new Error("Ragged splits must be sorted in ascending order")}}(n,s);let l=1;for(let e=0;e<t.length-1;++e){l*=t[e];const n=t[e+1];for(let t=1;t<l+1;++t)o[e].push(t*n)}for(let s=0;s<e.length;++s){let i=e[s],l=e[s]+1;for(let e=0;e<n.length;++e){const s=n[e],r=e+t.length-1;if(r>=0){const e=o[r],t=e[e.length-1]-s[i];for(let e=i;e<l;++e)o[r].push(s[e+1]+t)}i=s[i],l=s[l]}l!==i&&(r.push([i,l]),a+=l-i)}return{outSplits:o,valueSlices:r,numValues:a}}function WS(e,t){const n=e.slice(0,t);for(;n.length<t;)n.push(1);for(let s=t;s<e.length;s++)n[t-1]*=e[s];return n}function VS(e,t,n,s,r){const a=t.slice();a[0]=r;const i=N(n,d(a)),o=e.length;return function(e,t,n,s,r,a){const i=WS(t,2)[1],o=WS(a,2)[1];let l=0;for(const t of n)for(let n=t[0];n<t[1];++n){for(let t=0;t<s;++t)r[l*o+t]=e[n*i+t];++l}}(e,t,s,0===o?0:o/t[0],i,a),[i,a]}function US(e,t,n,s,r,a,i,o){if(0===e.length)throw new Error("paramsNestedSplits must be non empty");if(0===t[0].length)throw new Error("Split tensors must not be scalars");if(function(e,t,n){e.forEach(((e,s)=>{if(e<0||e>=n){const r=G(s,t.length,M(t)).join(",");throw new Error(`indices[${r}] = ${e} is not in [0, ${n})`)}}))}(a,i,t[0][0]-1),0===s.length)throw new Error("params.rank must be nonzero");const l=s[0],{outSplits:u,valueSlices:c,numValues:h}=BS(a,i,e,l),p=function(e){const t=[];for(let n=0;n<e.length;++n){const s=N("int32",e[n].length);t.push(s),e[n].forEach(((e,t)=>s[t]=e))}return t}(u),d=VS(n,s,r,c,h);return[p,d[0],d[1]]}const GS=2147483647;function HS(e,t,n,s,r,a,i){if(t.length>1)throw new Error("starts must be a scalar or vector");if(r.length>1)throw new Error("limits must be a scalar or vector");if(i.length>1)throw new Error("deltas must be a scalar or vector");const o=0===t.length,l=0===r.length,u=0===i.length,c=[];o||c.push(t[0]),l||c.push(r[0]),u||c.push(i[0]);for(let e=1;e<c.length;++e)if(c[e]!==c[e-1])throw new Error("starts, limits, and deltas must have the same shape");const h=0===c.length?1:c[0],p=N("int32",h+1);p[0]=0;for(let t=0;t<h;++t){const n=o?e[0]:e[t],r=l?s[0]:s[t],i=u?a[0]:a[t];if(0===i)throw new Error("Requires delta != 0");let c;if(i>0&&r<n||i<0&&r>n)c=0;else if(c=Math.ceil(Math.abs((r-n)/i)),c>GS)throw new Error("Requires ((limit - start) / delta) <= 2147483647");p[t+1]=p[t]+c}const d=N(n,p[h]);let f=0;for(let t=0;t<h;++t){const n=p[t+1]-p[t];let s=o?e[0]:e[t];const r=u?a[0]:a[t];for(let e=0;e<n;++e)d[f++]=s,s+=r}return[p,d]}var jS=fd;class qS{constructor(e,t,n,s,r,a,i,o,l,u){this.shape=e,this.shapeShape=t,this.values=n,this.valuesShape=s,this.valuesDType=r,this.defaultValue=a,this.defaultValueShape=i,this.rowPartitionValues=o,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=gd(u),this.raggedRank=yd(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===jS.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===jS.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){const t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case jS.VALUE_ROWIDS:return qS.getMaxWidthValueRowID(t);case jS.ROW_SPLITS:return qS.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${jS[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){const t=e.length;if(0===t||1===t)return 0;let n=0;for(let s=0;s<t-1;++s){const t=e[s+1]-e[s];t>n&&(n=t)}return n}static getMaxWidthValueRowID(e){const t=e.length;if(0===t)return 0;let n=0,s=e[0],r=0;for(let a=1;a<t;++a){const t=e[a];t!==s&&(s=t,r=Math.max(a-n,r),n=a)}return Math.max(t-n,r)}tensorShapeFromTensor(e,t,n=!0){if(0===t.length){if(-1===e[0])return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return XS(e,n)}calculateOutputSize(e){const t=this.valuesShape;bd(this.defaultValueShape,t);const n=this.tensorShapeFromTensor(this.shape,this.shapeShape),s=md(this.raggedRank,n,t);s[0]<0&&(s[0]=e);for(let e=1;e<=this.raggedRank;++e)s[e]<0&&(s[e]=this.getMaxWidth(e));return s}calculateFirstParentOutputIndex(e,t,n){const s=Math.min(e,n),r=[];let a=0;for(let e=0;e<s;++e,a+=t)r.push(a);for(let t=s;t<e;++t)r.push(-1);return u(r.length===e,(()=>"Final length of result must be equal to firstDimension.")),r}calculateOutputIndexRowSplit(e,t,n,s){const r=e.length,a=[];for(let i=0;i<r-1;++i){const r=e[i+1]-e[i];let o=Math.min(s,r),l=t[i];-1===l&&(o=0);for(let e=0;e<o;++e)a.push(l),l+=n;for(let e=0;e<r-o;++e)a.push(-1)}if(r>0&&a.length!==e[r-1])throw new Error("Invalid row split size.");return a}calculateOutputIndexValueRowID(e,t,n,s){const r=e.length,a=[];if(0===r)return[];let i=0,o=e[0];if(o>=t.length)throw new Error(`Got currentValueRowId=${o}, which is not less than ${t.length}`);let l=t[o];a.push(l);for(let u=1;u<r;++u){const r=e[u];if(r===o)l>=0&&(++i,i<s?l+=n:l=-1);else{if(i=0,o=r,r>=t.length)throw new Error(`Got nextValueRowId=${r} which is not less than ${t.length}`);l=t[r]}a.push(l)}if(a.length!==e.length)throw new Error("Invalid row ids.");return a}calculateOutputIndex(e,t,n,s){const r=this.getRowPartitionTensor(e),a=this.getRowPartitionTypeByDimension(e);switch(a){case jS.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(r,t,n,s);case jS.ROW_SPLITS:if(r.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${r.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(r,t,n,s);default:throw new Error(`Unsupported partition type: ${jS[a]}`)}}getFirstDimensionSize(){const e=this.rowPartitionValues[0];if(0===this.rowPartitionTypes.length)throw new Error("No row_partition_types given.");const t=this.rowPartitionTypes[0];switch(t){case jS.FIRST_DIM_SIZE:return e[0];case jS.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case jS.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${jS[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");const e=this.getFirstDimensionSize(),t=this.calculateOutputSize(e),n=new Array(this.raggedRank+1);n[n.length-1]=1;for(let e=n.length-2;e>=0;--e)n[e]=n[e+1]*t[e+1];const s=XS(t,!1),r=N(this.valuesDType,d(s));if(n[0]*t[0]>0){let a=this.calculateFirstParentOutputIndex(e,n[0],t[0]);for(let e=1;e<=this.raggedRank;++e){a=this.calculateOutputIndex(e-1,a,n[e],t[e])}this.setOutput(this.raggedRank,a,r,s)}return[s,r]}setOutput(e,t,n,s){if(0===n.length)return;const r=this.values,a=n;let i=s.slice();i=i.slice(e+1);const o=d(i),l=t.length;let u=this.defaultValue;if(u.length!==o&&1!==u.length){const e=this.defaultValueShape;Ni((()=>{const t=tl(u,e),n=gl(t,i);u=n.dataSync()}))}let c=0,h=0,p=0;for(let e=0;e<=l;++e){let s=e<l?t[e]:-1;if(s!==p){if(h<p){const e=r.subarray(c*o);KS(a.subarray(h*o),e,(p-h)*o)}if(e>=l){const e=n.length;s=Math.floor(e/o)}if(s>p)if(1===this.defaultValue.length)a.subarray(p*o,s*o).fill(this.defaultValue[0]),p=s;else for(;s>p;){KS(a.slice(p*o),u,o),++p}s<0?(c=e+1,h=p):(c=e,h=p,p=h+1)}else++p}}}function KS(e,t,n){for(let s=0;s<n;s++)e[s]=t[s]}function XS(e,t){const n=[];for(let s of e){if(s<0){if(!t)throw new Error(`Dimension ${s} must be >= 0`);if(s<-1)throw new Error(`Dimension ${s} must be >= -1`);s=-1}n.push(s)}return n}function YS(e,t,n,s,r,a,i,o,l,u){return new qS(e,t,n,s,r,a,i,o,l,u).compute()}function ZS(e,t,n,s){if(e===t||e<t&&n<0||t<e&&n>1)return B(0,s);const r=B(Math.abs(Math.ceil((t-e)/n)),s);t<e&&1===n&&(n=-1),r[0]=e;for(let e=1;e<r.length;e++)r[e]=r[e-1]+n;return r}const JS=MI((e=>1/Math.sqrt(e))),QS=zI(xn,JS),eT={kernelName:xn,backendName:"cpu",kernelFunc:QS};function tT(e,t,n,s,r,a,i,o,l,u){const c=[s/r,r],h=e.values,p=t.values;if(0===s)return Za(n,t.dtype);const d=Za(c,t.dtype);"string"==typeof l||"number"==typeof l?d.values.fill(l):"boolean"==typeof l&&d.values.fill(+l);for(let e=0;e<a;e++){const a=[];let l=0;for(let t=0;t<i;t++){const n=h[e*i+t];a.push(n),l+=n*o[t]}if(l<0||l>=s/r)throw new Error(`Invalid indices: ${a} does not index into ${n}`);for(let n=0;n<r;n++)u?d.values[l*r+n]+=p[e*r+n]:d.values[l*r+n]=0===t.rank?p[0]:p[e*r+n]}return d}const nT=MI((e=>1/(1+Math.exp(-e)))),sT=LI($n,(e=>1/(1+Math.exp(-e)))),rT={kernelName:$n,backendName:"cpu",kernelFunc:sT};function aT(e,t,n,s,r){const a=oo(s,t,n),i=d(n),o=M(s);if(a){const n=lo(t,o);return"string"===r?e.slice(n,n+i):e.subarray(n,n+i)}const l=Za(s,r,"string"===r?ff(e):e),u=Za(n,r);for(let e=0;e<u.size;++e){const n=u.indexToLoc(e),s=n.map(((e,n)=>e+t[n]));u.set(l.get(...s),...n)}return"string"===r?mf(u.values):u.values}function iT(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s;pI(r,"slice");const[o,l]=uo(r,a,i);Zi(r,o,l);const u=aT(n.data.get(r.dataId).values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,u)}const oT={kernelName:In,backendName:"cpu",kernelFunc:iT};function lT(e,t,n,s,r,a,i){const o=t[0],l=a[0],u=new Array(l),c=new 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w=y*i-p,v=Math.max(0,w),k=Math.min(r.inHeight,c+w),N=n+y*b;for(let n=0;n<r.outWidth;++n){const i=n*o-d,c=Math.max(0,i),p=Math.min(r.inWidth,h+i);let y=f,b=0,w=0;for(let n=v;n<k;n+=l){const r=m+n*s[1];for(let n=c;n<p;n+=u){const i=e[r+n*s[2]+t];"max"===a&&i>y?y=i:"avg"===a&&(b+=i,w++)}if(isNaN(y))break}g[N+n*x+t]="avg"===a?b/w:y}}}return m}function yC(e,t,n,s,r=!1,a=!1){const i=Za(s.outShape,"int32"),o=s.strideHeight,l=s.strideWidth,u=s.dilationHeight,c=s.dilationWidth,h=s.effectiveFilterHeight,p=s.effectiveFilterWidth,d=s.padInfo.top,f=s.padInfo.left,m=Za(t,n,e);for(let e=0;e<s.batchSize;++e)for(let t=0;t<s.inChannels;++t)for(let n=0;n<s.outHeight;++n){const g=n*o-d;let y=g;for(;y<0;)y+=u;const b=Math.min(s.inHeight,h+g);for(let o=0;o<s.outWidth;++o){const h=o*l-f;let d=h;for(;d<0;)d+=c;const x=Math.min(s.inWidth,p+h);let w=Number.NEGATIVE_INFINITY,v=-1;for(let n=y;n<b;n+=u){const i=n-g;for(let o=d;o<x;o+=c){const 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o=i;for(;o<0;)o+=h;const d=Math.min(r.inWidth,f+i),g=v+n*I;let k=b,N=0,S=0;for(let n=T;n<C;n+=u){const r=x+n*s[1];for(let n=p;n<m;n+=c){const i=r+n*s[2];for(let n=o;n<d;n+=h){const r=e[i+n*s[3]+t];if("max"===a&&r>k?k=r:"avg"===a&&(N+=r,S++),isNaN(k))break}if(isNaN(k))break}if(isNaN(k))break}w[g+t]="avg"===a?N/S:k}}}}return x}const xC={kernelName:de,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t;pI(r,"avgPool");const{filterSize:a,strides:i,pad:o,dimRoundingMode:l}=s;u(Jo(i,1),(()=>`Error in avgPool: Either strides or dilations must be 1. 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c=Uo(a.shape,i,o,1,l,u),h=c.strideDepth,p=c.strideHeight,d=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,b=c.dilationHeight,x=c.dilationWidth,w=c.effectiveFilterDepth,v=c.effectiveFilterHeight,k=c.effectiveFilterWidth,N=w-1-c.padInfo.front,I=k-1-c.padInfo.left,S=v-1-c.padInfo.top,T=Za(a.shape,"float32"),C=1/(f*m*g),$=n.bufferSync(r);for(let e=0;e<c.batchSize;++e)for(let t=0;t<c.inChannels;++t)for(let n=0;n<c.inDepth;++n)for(let s=0;s<c.inHeight;++s)for(let r=0;r<c.inWidth;++r){const a=n-N,i=s-S,o=r-I;let l=0;for(let n=0;n<w;n+=y){const s=(a+n)/h;if(!(s<0||s>=c.outDepth||Math.floor(s)!==s))for(let n=0;n<v;n+=b){const r=(i+n)/p;if(!(r<0||r>=c.outHeight||Math.floor(r)!==r))for(let n=0;n<k;n+=x){const a=(o+n)/d;if(a<0||a>=c.outWidth||Math.floor(a)!==a)continue;l+=$.get(e,s,r,a,t)}}}T.set(l*C,e,n,s,r,t)}return n.makeTensorInfo(T.shape,T.dtype,T.values)}};const kC={kernelName:fe,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;pI([r,a],"avgPoolGrad");const{filterSize:o,strides:l,pad:u}=s,c=Vo(i.shape,o,l,1,u),h=c.strideHeight,p=c.strideWidth,d=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,y=c.effectiveFilterHeight,b=c.effectiveFilterWidth,x=b-1-c.padInfo.left,w=y-1-c.padInfo.top,v=Za(i.shape,"float32"),k=1/(d*f),N=n.data.get(r.dataId).values,I=Za(r.shape,"float32",N);for(let e=0;e<c.batchSize;++e)for(let t=0;t<c.inChannels;++t)for(let n=0;n<c.inHeight;++n)for(let s=0;s<c.inWidth;++s){const r=n-w,a=s-x;let i=0;for(let n=0;n<y;n+=m){const s=(r+n)/h;if(!(s<0||s>=c.outHeight||Math.floor(s)!==s))for(let n=0;n<b;n+=g){const r=(a+n)/p;if(r<0||r>=c.outWidth||Math.floor(r)!==r)continue;i+=I.get(e,s,r,t)}}v.set(i*k,e,n,s,t)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}};const NC={kernelName:lt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:i,mean:o,variance:l}=t;u(o.shape.length===l.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),u(null==i||o.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),u(null==a||o.shape.length===a.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks.")),pI([r,o,l,a,i],"batchNorm");let{varianceEpsilon:c}=s;null==c&&(c=.001);const h=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,f=a?n.data.get(a.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),g=new Float32Array(h.length),y=m.length,b=f.length,x=d.length,w=p.length;let v=0,k=0,N=0,I=0;for(let 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n.makeTensorInfo([i],a.dtype,o)}};const TC={kernelName:ve,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,o=Li(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}},CC=LI(Ie,((e,t)=>{const n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e})),$C={kernelName:Ie,backendName:"cpu",kernelFunc:CC},EC={kernelName:Te,backendName:"cpu",kernelFunc:e=>{const{x:t}=e.inputs,n=e.backend,s=new Float32Array(d(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(a.dataId).values,l=n.data.get(i.dataId).values;for(let e=0;e<o.length;e++){const t=o[e],n=l[e];s[e]=Math.hypot(t,n)}return n.makeOutput(s,t.shape,"float32")}};function AC(e){const{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}const RC={kernelName:mt,backendName:"cpu",kernelFunc:AC};function _C(e){const{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w(r,t[0].shape)[0];pd(t.map((e=>e.shape)),a);let i=dd(t.map((e=>e.shape)),a);if(0===d(i))return n.makeTensorInfo(i,t[0].dtype,[]);const o=t.filter((e=>d(e.shape)>0));if(1===o.length)return vI({inputs:{x:o[0]},backend:n});if("complex64"===o[0].dtype){const e=o.map((e=>NI({inputs:{input:e},backend:n}))),t=o.map((e=>AC({inputs:{input:e},backend:n}))),s=_C({inputs:e,backend:n,attrs:{axis:a}}),r=_C({inputs:t,backend:n,attrs:{axis:a}}),i=bI({inputs:{real:s,imag:r},backend:n});return e.forEach((e=>n.disposeIntermediateTensorInfo(e))),t.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(r),i}const l=o.map((e=>{const t=d(e.shape.slice(a));return HT({inputs:{x:e},backend:n,attrs:{shape:[-1,t]}})})),u=l.map((e=>({vals:n.data.get(e.dataId).values,shape:e.shape})));i=dd(l.map((e=>e.shape)),1);const c=1===l[0].shape[0],h=VI(u,i,t[0].dtype,c),p=dd(o.map((e=>e.shape)),a),f=n.makeTensorInfo(p,t[0].dtype,h);return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),f}const FC={kernelName:Ce,backendName:"cpu",kernelFunc:_C};function DC(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=s;pI([r,a],"conv2d");const h=Qo(l),p=Go(r.shape,a.shape,i,u,o,c,!1,h),d=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,b=p.padInfo.top,x="channelsLast"===p.dataFormat,w=new yr(p.outShape,r.dtype),v=M(r.shape),k=M(a.shape),N=v[0],I=x?v[1]:v[2],S=x?v[2]:1,T=x?1:v[1],C=w.strides[0],$=x?w.strides[1]:w.strides[2],E=x?w.strides[2]:1,A=x?1:w.strides[1],R=n.data.get(r.dataId).values,_=n.data.get(a.dataId).values,F=w.values;for(let e=0;e<p.batchSize;++e){const t=e*N,n=e*C;for(let e=0;e<p.outHeight;++e){const s=n+e*$,r=e*p.strideHeight-b;for(let e=0;e<d;++e){const n=r+e*m;if(n<0||n>=p.inHeight)continue;const a=e*k[0],i=t+n*I;for(let e=0;e<p.outWidth;++e){const t=s+e*E,n=e*p.strideWidth-y;for(let e=0;e<f;++e){const s=n+e*g;if(s<0||s>=p.inWidth)continue;const r=i+s*S;let o=a+e*k[1];for(let e=0;e<p.inChannels;++e){const n=R[r+e*T];for(let e=0;e<p.outChannels;++e)F[t+e*A]+=n*_[o+e];o+=p.outChannels}}}}}}return n.makeTensorInfo(w.shape,w.dtype,F)}const OC={kernelName:$e,backendName:"cpu",kernelFunc:DC};const MC={kernelName:Ee,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=s;pI([r,a],"conv2dBackpropFilter");const h=Qo(l),p=Go(r.shape,c,i,1,o,u,!1,h),{strideHeight:d,strideWidth:f,filterHeight:m,filterWidth:g}=p,y="channelsLast"===p.dataFormat,b=new yr(p.filterShape,"float32"),x=p.padInfo.left,w=p.padInfo.top,v=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,N=new yr(r.shape,r.dtype,v),I=new yr(a.shape,a.dtype,k);for(let e=0;e<m;++e){const t=Math.max(0,Math.ceil((w-e)/d)),n=Math.min(p.outHeight,(p.inHeight+w-e)/d);for(let s=0;s<g;++s){const r=Math.max(0,Math.ceil((x-s)/f)),a=Math.min(p.outWidth,(p.inWidth+x-s)/f);for(let i=0;i<p.inChannels;++i)for(let o=0;o<p.outChannels;++o){let l=0;for(let u=0;u<p.batchSize;++u)for(let c=t;c<n;++c){const t=e+c*d-w;for(let e=r;e<a;++e){const n=s+e*f-x;l+=y?N.get(u,t,n,i)*I.get(u,c,e,o):N.get(u,i,t,n)*I.get(u,o,c,e)}}b.set(l,e,s,i,o)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}};const LC={kernelName:Ae,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=s;pI([r,a],"conv2dBackpropInput");const h=M(a.shape),p=M(r.shape);let d=Qo(u);const f=Go(i,a.shape,o,1,l,c,!1,d),m=new 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JC={kernelName:Ve,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=s;pI([r,a],"depthwiseConv2dNativeBackpropFilter");const h=Go(r.shape,c,i,o,l,u,!0),{strideHeight:p,strideWidth:d,filterHeight:f,filterWidth:m}=h,g=new yr(h.filterShape,"float32"),y=h.padInfo.left,b=h.padInfo.top,x=h.outChannels/h.inChannels,w=n.data.get(r.dataId).values,v=new yr(r.shape,r.dtype,w),k=n.data.get(a.dataId).values,N=new yr(a.shape,a.dtype,k);for(let e=0;e<f;++e){const t=Math.max(0,Math.ceil((b-e)/p)),n=Math.min(h.outHeight,(h.inHeight+b-e)/p);for(let s=0;s<m;++s){const r=Math.max(0,Math.ceil((y-s)/d)),a=Math.min(h.outWidth,(h.inWidth+y-s)/d);for(let i=0;i<h.outChannels;++i){const o=Math.trunc(i/x),l=i%x;let u=0;for(let l=0;l<h.batchSize;++l)for(let c=t;c<n;++c){const t=e+c*p-b;for(let e=r;e<a;++e){const n=s+e*d-y;u+=v.get(l,t,n,o)*N.get(l,c,e,i)}}g.set(u,e,s,o,l)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}};const QC={kernelName:Ue,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=s;pI([r,a],"depthwiseConv2DNativeBackpropInput");const h=M(r.shape),p=M(a.shape),d=Go(c,a.shape,i,o,l,u,!0),f=new yr(d.inShape,"float32"),m=f.values,[g,y,b]=f.strides,x=n.data.get(r.dataId).values,[w,v,k]=h,N=n.data.get(a.dataId).values,[I,S,T]=p,{batchSize:C,filterHeight:$,filterWidth:E,inChannels:A,inHeight:R,inWidth:_,outChannels:F,outHeight:D,outWidth:O,strideHeight:L,strideWidth:z}=d,P=$-1-d.padInfo.top,B=E-1-d.padInfo.left,W=F/A;for(let e=0;e<C;++e)for(let t=0;t<A;++t)for(let n=0;n<R;++n){const s=n-P,r=Math.max(0,Math.ceil(s/L)),a=Math.min(D,($+s)/L);for(let i=0;i<_;++i){const o=i-B,l=Math.max(0,Math.ceil(o/z)),u=Math.min(O,(E+o)/z);let c=0;for(let n=r;n<a;++n){const r=n*L-s;for(let s=l;s<u;++s){const a=w*e+v*n+k*s,i=I*($-1-r)+S*(E-1-(s*z-o))+T*t;for(let 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-= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${NR}\n    ${IR}\n    ${SR}\n  `}(o);t.isPacked?(u=function(e,t,n){switch(e.length){case 0:return CR();case 1:return function(e,t,n){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(1===s[0])return n?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    ":`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${s[1]}.0);\n      }\n    `;if(1===s[1])return n?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    ":`\n      int getOutputCoords() {\n        return 2 * 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ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}(e,t,n);default:return function(e,t,n){if(n)return"\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  ";const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2);let i=a,o="",l="b, r, c";for(let t=2;t<e.length-1;t++)i*=e[e.length-t-1],o=`\n      int b${t} = index / ${i};\n      index -= b${t} * ${i};\n    `+o,l=`b${t}, `+l;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      ${o}\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec${e.length}(${l});\n    }\n  `}(e,t,n)}}(t.logicalShape,i,n.enableShapeUniforms),c=function(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}(o)):(u=function(e,t,n){switch(e.length){case 0:return CR();case 1:return function(e,t,n){if(1===t[0])return n?"\n      int getOutputCoords() {\n     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getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `;if(1===e[1])return n?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ":`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;if(1===e[0])return n?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ":`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;if(n)return"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  ";return`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}(e,t,n);case 3:return function(e,t,n){if(n){return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${mR(["r","c","d"],e)}\n    return ivec3(r, c, d);\n  }\n`}const s=fR(["r","c","d"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec3(r, c, d);\n    }\n  `}(e,t,n);case 4:return function(e,t,n){if(n){return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${mR(["r","c","d","d2"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `}const s=fR(["r","c","d","d2"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec4(r, c, d, d2);\n    }\n  `}(e,t,n);case 5:return function(e,t){const n=fR(["r","c","d","d2","d3"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}(e,t);case 6:return function(e,t){const n=fR(["r","c","d","d2","d3","d4"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}(t.logicalShape,i,n.enableShapeUniforms),c=function(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}(o)),n.packedInputs&&(h+=TR);return[h,l,c,r,u,a,n.userCode].join("\n")}function vR(e,t=!1){const n=e.shapeInfo.logicalShape;switch(n.length){case 0:return function(e,t){const n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;const[r,a]=e.shapeInfo.texShape;if(1===r&&1===a)return`\n      float ${s}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;const i=$R(n);if(t)return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;const[o,l]=e.shapeInfo.texShape;return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${o}, ${l}, ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}(e,t);case 1:return function(e,t){const n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${s}(int index) {\n        ${ER(e)}\n      }\n    `;const r=e.shapeInfo.texShape,a=r[0],i=r[1];if(1===i&&1===a)return`\n      float ${s}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;const o=$R(n);if(1===i)return t?`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(1===a)return t?`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(t)return`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `;return`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}(e,t);case 2:return function(e,t){const n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(null!=a&&f(n,a)){if(t)return`\n      float ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `;const e=a[0];return`\n    float ${r}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${a[1]}.0, ${e}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `}const{newShape:i,keptDims:o}=v(n),l=i;if(l.length<n.length){const n=["row","col"];return`\n      ${vR(_R(e,l),t)}\n      float ${r}(int row, int col) {\n        return ${r}(${FR(n,o)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${ER(e)}\n      }\n    `;const u=a[0],c=a[1],h=$R(s);if(1===c)return t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${h}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${h}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;if(1===u)return t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${h}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${h}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${s}, uv);\n    }\n  `;if(t)return`\n      float ${r}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s}Shape[1] + col + ${h};\n        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n        return sampleTexture(${s}, uv);\n      }\n    `;return`\n  float ${r}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${h};\n    vec2 uv = uvFromFlat(${u}, ${c}, index);\n    return sampleTexture(${s}, uv);\n  }\n`}(e,t);case 3:return function(e,t){const n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=v(n),u=o;if(u.length<n.length){const n=["row","col","depth"];return`\n        ${vR(_R(e,u),t)}\n        float ${r}(int row, int col, int depth) {\n          return ${r}(${FR(n,l)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${i}, 1)));\n        ${ER(e)}\n      }\n    `;const c=e.shapeInfo.texShape,h=c[0],p=c[1],d=e.shapeInfo.flatOffset;if(p===a&&null==d)return t?`\n      float ${r}(int row, int col, int depth) {\n        int stride1 = ${s}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n        float ${r}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${i}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${p}.0, ${h}.0);\n          return sampleTexture(${s}, uv);\n        }\n      `;if(p===i&&null==d)return t?`\n      float ${r}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${h}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;const f=$R(s);if(t)return`\n    float ${r}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${s}Shape[1] * ${s}Shape[2];\n      int stride1 = ${s}Shape[2];\n      int index = row * stride0 + col * stride1 + depth + ${f};\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n      return sampleTexture(${s}, uv);\n    }\n    `;return`\n      float ${r}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${i} + depth + ${f};\n        vec2 uv = uvFromFlat(${h}, ${p}, index);\n        return sampleTexture(${s}, uv);\n      }\n  `}(e,t);case 4:return function(e,t){const n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:l,keptDims:u}=v(n);if(l.length<n.length){const n=["row","col","depth","depth2"];return`\n      ${vR(_R(e,l),t)}\n      float ${r}(int row, int col, int depth, int depth2) {\n        return ${r}(${FR(n,u)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${o}, ${i}, ${a}, 1)));\n        ${ER(e)}\n      }\n    `;const c=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],d=h[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(d===o&&null==c)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${i}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${d}.0, ${p}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;if(d===a&&null==c)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${d}.0, ${p}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;const y=$R(s);if(t)return`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${y});\n      return sampleTexture(${s}, uv);\n    }\n  `;return`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${p}, ${d}, index + ${y});\n      return sampleTexture(${s}, uv);\n    }\n  `}(e,t);case 5:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:l,keptDims:u}=v(t);if(l.length<t.length){const t=["row","col","depth","depth2","depth3"];return`\n      ${vR(_R(e,l))}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${FR(t,u)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${o}, ${i}, ${a}, ${r})) +\n          depth3;\n        ${ER(e)}\n      }\n    `;const c=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],d=h[1];if(d===o&&null==c)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${i}, ${a}, ${r}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${d}.0, ${p}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(d===r&&null==c)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${d}.0, ${p}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;const f=$R(n);return`\n    float ${s}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} + depth * ${a} +\n          depth2 * ${r} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${p}, ${d}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}(e);case 6:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v(t);if(r.length<t.length){const t=["row","col","depth","depth2","depth3","depth4"];return`\n      ${vR(_R(e,r))}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${FR(t,a)});\n      }\n    `}const i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${u}, ${l}, ${o})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${i}, 1)));\n        ${ER(e)}\n      }\n    `;const h=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],f=p[1];if(f===c&&null==h)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${u}, ${l}, ${o}, ${i})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===i&&null==h)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;const m=$R(n);return`\n    float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${u} + depth * ${l} +\n          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${d}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function kR(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return function(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=dR();return`\n    vec4 ${n}() {\n      return ${s.texture2D}(${t}, halfCR);\n    }\n  `}(e);case 1:return function(e,t){const n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=dR();if(t)return`\n    vec4 ${s}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `;const i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`\n    vec4 ${s}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${i[0]}, ${i[1]}, index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}(e,t);case 2:return function(e,t){const n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],l=dR();if(null!=a&&f(n,a))return t?`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n\n        return ${l.texture2D}(${s}, uv);\n      }\n    `:`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);\n\n        return ${l.texture2D}(${s}, uv);\n      }\n    `;if(t)return`\n    vec4 ${r}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${l.texture2D}(${s}, uv);\n    }\n  `;const u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${r}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);\n      return ${l.texture2D}(${s}, uv);\n    }\n  `}(e,t);case 3:return function(e,t){const n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(1===n[0]){const s=[1,2],a=["b","row","col"];return`\n        ${kR(_R(e,n.slice(1)),t)}\n        vec4 ${r}(int b, int row, int col) {\n          return ${r}(${FR(a,s)});\n        }\n      `}const o=dR();if(t)return`\n    vec4 ${r}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `;const l=i[0],u=i[1],c=Math.ceil(n[2]/2),h=c*Math.ceil(n[1]/2);return`\n    vec4 ${r}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${l}, ${u}, ${h}, ${c}, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `}(e,t);default:return function(e,t){const n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=dR();if(t)return`\n    vec4 ${s}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);\n    }\n  `;const a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=l[0],c=l[1],h=Math.ceil(a[i-1]/2);let p=h*Math.ceil(a[i-2]/2),d="int b, int row, int col",f=`b * ${p} + (row / 2) * ${h} + (col / 2)`;for(let e=2;e<i-1;e++)d=`int b${e}, `+d,p*=a[i-e-1],f=`b${e} * ${p} + `+f;return`\n    vec4 ${s}(${d}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});\n      return ${r.texture2D}(${n}, uv);\n    }\n  `}(e,t)}}const NR="\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",IR="\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SR="\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",TR="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function CR(){return"\n    int getOutputCoords() {\n      return 0;\n    }\n  "}function $R(e){return`offset${e}`}function ER(e){const t=e.name,n=d(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function AR(e){if(e<=1)return"int";if(2===e)return"ivec2";if(3===e)return"ivec3";if(4===e)return"ivec4";if(5===e)return"ivec5";if(6===e)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function RR(e,t,n){const{newShape:s,keptDims:r}=v(t),a=t.length,i=e&&3===a&&1===t[0],o=i?t.slice(1):s,l=!e&&a>1&&!f(t,n)&&s.length<a||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function _R(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function FR(e,t){return t.map((t=>e[t])).join(", ")}function DR(e,t,n,s){const r=n.map(((e,n)=>{const s={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked,flatOffset:null};return null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0&&(s.flatOffset=e.texData.slice.flatOffset),{name:t.variableNames[n],shapeInfo:s}})),a=r.map((e=>e.shapeInfo)),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=wR(r,i,t),l=function(e,t){const n=JA(e,(()=>e.createShader(e.FRAGMENT_SHADER)),"Unable to create fragment WebGLShader.");if(WA(e,(()=>e.shaderSource(n,t))),WA(e,(()=>e.compileShader(n))),X().get("ENGINE_COMPILE_ONLY"))return n;if(!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw HA(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}(e.gl,o),u=e.createProgram(l);return X().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:a,outShapeInfo:i},OR(e,t,u))}function OR(e,t,n){const s={},r={},a={},i=[];let o,l,u,c=null,h=null;h=e.getUniformLocation(n,"NAN",!1),1===X().getNumber("WEBGL_VERSION")&&(c=e.getUniformLocation(n,"INFINITY",!1));const p=!1;for(let i=0;i<t.variableNames.length;i++){const o=t.variableNames[i];s[o]=e.getUniformLocation(n,o,p),s[`offset${o}`]=e.getUniformLocation(n,`offset${o}`,p),t.enableShapeUniforms&&(r[`${o}Shape`]=e.getUniformLocation(n,`${o}Shape`,p),a[`${o}TexShape`]=e.getUniformLocation(n,`${o}TexShape`,p))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,"outShape",p),u=e.getUniformLocation(n,"outShapeStrides",p),l=e.getUniformLocation(n,"outTexShape",p)),t.customUniforms&&t.customUniforms.forEach(((t,s)=>{i[s]=e.getUniformLocation(n,t.name,p)})),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:h,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function MR(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach(((e,n)=>{const s=e.logicalShape,r=t[n],a=r.shape;if(!f(s,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${s} and ${a} must match`);if(e.isUniform&&r.isUniform)return;const i=e.texShape,o=r.isUniform?null:r.texData.texShape;if(!f(i,o))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${o} must match`)}))}function LR(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}class zR{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=DA.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=dR();this.outputShape=e,this.enableShapeUniforms=LR(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?mR(["r","c","d"],e):fR(["r","c","d"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${t.output} = result;\n      }\n    `}}class PR{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=DA.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=dR();this.outputShape=e,this.enableShapeUniforms=LR(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?mR(["r","c","d"],e):fR(["r","c","d"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${t.output} = result;\n      }\n    `}}class BR{constructor(e){this.variableNames=["A"],this.outTexUsage=OA.DOWNLOAD;const t=dR();this.outputShape=e,this.userCode=`\n      ${bR}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}}class WR{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=OA.DOWNLOAD;const t=dR();this.outputShape=e,this.userCode=`\n      ${bR}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${t.output} = encode_float(x);\n      }\n    `}}const VR={R:0,G:1,B:2,A:3};class UR{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];const s=dR();this.outputShape=e,this.enableShapeUniforms=LR(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let a="";for(let e=0;e<n.length;e++){const t=n[e];a+=`\n          if(offset == ${e}) {\n            result = values[${VR[t]}];\n          }`}this.userCode=`\n      ${this.enableShapeUniforms?"\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n":yR(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int flatIndex = getFlatIndex(coords);\n        float result = 0.;\n        int offset = imod(flatIndex, ${n.length});\n\n        flatIndex = idiv(flatIndex, ${n.length}, 1.);\n\n        int r = flatIndex / texShape[1];\n        if (r < texShape[0]) {\n          int c = imod(flatIndex, texShape[1]);\n          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n          vec4 values = ${s.texture2D}(A, uv);\n          ${a}\n        }\n        ${s.output} = vec4(${r}, 0., 0., 0.);\n      }\n    `}}class GR{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];const n=dR();this.outputShape=e,this.enableShapeUniforms=LR(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let t=0;t<=1;t++)for(let r=0;r<=1;r++){const a=2*t+r;s+=`\n          localCoords = coords;\n          if(localCoords[2] + ${r} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {\n          localCoords[2] += ${r};\n          if (localCoords[1] + ${t} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {\n            localCoords[1] += ${t};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${a}] = values[0];\n            } else if (offset == 1) {\n              result[${a}] = values[1];\n            } else if (offset == 2) {\n              result[${a}] = values[2];\n            } else {\n              result[${a}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?"\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n":yR(e)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${s}\n\n          ${n.output} = ${r};\n        }\n    `}}function HR(e){const t=dR();return function(e,t){const n=JA(e,(()=>e.createShader(e.VERTEX_SHADER)),"Unable to create vertex WebGLShader.");if(WA(e,(()=>e.shaderSource(n,t))),WA(e,(()=>e.compileShader(n))),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}(e,`${t.version}\n    precision highp float;\n    ${t.attribute} vec3 clipSpacePos;\n    ${t.attribute} vec2 uv;\n    ${t.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`)}function jR(e){return function(e,t){const n=JA(e,(()=>e.createBuffer()),"Unable to create WebGLBuffer");return WA(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,n))),WA(e,(()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW))),n}(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function qR(e){return function(e,t){const n=JA(e,(()=>e.createBuffer()),"Unable to create WebGLBuffer");return WA(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n))),WA(e,(()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW))),n}(e,new Uint16Array([0,1,2,2,1,3]))}function KR(e,t,n,s,r,a){!function(e,t){const n=X().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0)throw new Error(`Requested texture size [${e}x${t}] is invalid.`);if(e>n||t>n)throw new Error(`Requested texture size [${e}x${t}] greater than WebGL maximum on this browser / GPU [${n}x${n}].`)}(t,n);const i=function(e){return JA(e,(()=>e.createTexture()),"Unable to create WebGLTexture.")}(e),o=e.TEXTURE_2D;return WA(e,(()=>e.bindTexture(o,i))),WA(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE))),WA(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE))),WA(e,(()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST))),WA(e,(()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST))),1===X().getNumber("WEBGL_VERSION")?WA(e,(()=>e.texImage2D(o,0,s,t,n,0,r,a,null))):WA(e,(()=>e.texStorage2D(o,1,s,t,n))),WA(e,(()=>e.bindTexture(e.TEXTURE_2D,null))),{texture:i,texShape:[n,t]}}function XR(e){return e.internalFormatFloat}function YR(e){return e.internalFormatHalfFloat}function ZR(e){return e.downloadTextureFormat}function JR(e){return e.internalFormatPackedFloat}function QR(e){return e.internalFormatPackedHalfFloat}function e_(e,t,n,s,r,a,i,o){const l=e,u=new Float32Array(function(e,t){const[n,s]=PA(e,t);return n*s*4}(a,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}class t_{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];const t=X().getNumber("WEBGL_VERSION");if(null!=e?(this.gl=e,function(e,t){RA[e]=t}(t,e)):this.gl=FA(t),e=this.gl,2===X().getNumber("WEBGL_VERSION")){const t=e;this.createVertexArray=()=>WA(t,(()=>t.createVertexArray())),this.bindVertexArray=e=>WA(t,(()=>t.bindVertexArray(e))),this.deleteVertexArray=e=>WA(t,(()=>t.deleteVertexArray(e))),this.getVertexArray=()=>WA(t,(()=>t.getParameter(t.VERTEX_ARRAY_BINDING)))}else if(null!=e){const t=e.getExtension("OES_vertex_array_object");if(null==t)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>WA(e,(()=>t.createVertexArrayOES())),this.bindVertexArray=n=>WA(e,(()=>t.bindVertexArrayOES(n))),this.deleteVertexArray=n=>WA(e,(()=>t.deleteVertexArrayOES(n))),this.getVertexArray=()=>WA(e,(()=>e.getParameter(t.VERTEX_ARRAY_BINDING_OES)))}let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),1===X().getNumber("WEBGL_VERSION")){const e="OES_texture_float",t="OES_texture_half_float";if(this.textureFloatExtension=UA(this.gl,e),oR(this.gl,t))this.textureHalfFloatExtension=UA(this.gl,t);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),oR(this.gl,s))this.colorBufferHalfFloatExtension=UA(this.gl,s);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",oR(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else{if(!oR(this.gl,s))throw new Error("GL context does not support color renderable floats");this.colorBufferHalfFloatExtension=this.gl.getExtension(s)}this.vertexBuffer=jR(this.gl),this.indexBuffer=qR(this.gl),this.framebuffer=function(e){return JA(e,(()=>e.createFramebuffer()),"Unable to create WebGLFramebuffer.")}(this.gl),this.textureConfig=BA(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool("DEBUG")}dispose(){if(this.disposed)return;null!=this.program&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;WA(e,(()=>e.finish())),WA(e,(()=>e.bindFramebuffer(e.FRAMEBUFFER,null))),WA(e,(()=>e.deleteFramebuffer(this.framebuffer))),WA(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,null))),WA(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null))),WA(e,(()=>e.deleteBuffer(this.indexBuffer))),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,s){const[r,a]=LA(t,n);return KR(e,r,a,XR(s),s.textureFormatFloat,e.FLOAT)}(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,s){const[r,a]=LA(t,n);return KR(e,r,a,YR(s),s.textureFormatFloat,s.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,s){const[r,a]=LA(t,n);return KR(e,r,a,ZR(s),e.RGBA,e.UNSIGNED_BYTE)}(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),function(e,t,n){WA(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),n.data instanceof Uint8Array?2===X().getNumber("WEBGL_VERSION")?WA(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data))):WA(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data))):2===X().getNumber("WEBGL_VERSION")?WA(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n))):WA(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n))),WA(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),function(e,t,n,s,r,a){let i,o,l;WA(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),r instanceof Uint8Array?(i=new Uint8Array(n*s*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*s*4),o=e.FLOAT,l=a.internalFormatPackedFloat),i.set(r),2===X().getNumber("WEBGL_VERSION")?WA(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,o,i))):WA(e,(()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,o,i))),WA(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,s){const[r,a]=PA(t,n);return KR(e,r,a,QR(s),e.RGBA,s.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,s){const[r,a]=PA(t,n);return KR(e,r,a,JR(s),e.RGBA,e.FLOAT)}(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(YA(this.gl,this.framebuffer),this.outputTexture=null),WA(this.gl,(()=>this.gl.deleteTexture(e)))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n,s){const[r,a]=LA(t,n),i=new Uint8Array(t*n*4);return WA(e,(()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i))),new Float32Array(i.buffer)}(this.gl,t,n,this.textureConfig)))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return e_(this.gl,e,0,0,0,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return function(e,t,n){const s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=function(e,t,n,s){const r=e.createBuffer();WA(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r)));const a=16*t*n;return WA(e,(()=>e.bufferData(e.PIXEL_PACK_BUFFER,a,e.STREAM_READ))),WA(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0))),WA(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null))),r}(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(X().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const e=s.clientWaitSync(r,0,0);return e===s.ALREADY_SIGNALED||e===s.CONDITION_SATISFIED},t=r}else X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n){const s=new Float32Array(t*n*4);return WA(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s))),s}(this.gl,t,n)))}createProgram(e){this.throwIfDisposed();const t=this.gl;null==this.vertexShader&&(this.vertexShader=HR(t));const n=function(e){return JA(e,(()=>e.createProgram()),"Unable to create WebGLProgram.")}(t);let s;return WA(t,(()=>t.attachShader(n,this.vertexShader))),WA(t,(()=>t.attachShader(n,e))),function(e,t){if(WA(e,(()=>e.linkProgram(t))),!X().get("ENGINE_COMPILE_ONLY")&&!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}(t,n),s=Object.assign(n,{vao:this.createVertexArray()}),this.bindVertexArray(s.vao),WA(t,(()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer))),console.assert(function(e,t,n){return WA(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,n))),qA(e,t,"clipSpacePos",n,3,20,0)&&qA(e,t,"uv",n,2,20,12)}(t,s,this.vertexBuffer),"gpgpu_util.bindVertexProgramAttributeStreams not fully successful."),this.debug&&jA(t,s),this.setProgram(s),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&(WA(this.gl,(()=>this.gl.deleteProgram(e))),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,null!=this.program&&(this.bindVertexArray(this.program.vao),this.debug&&jA(this.gl,this.program)),WA(this.gl,(()=>this.gl.useProgram(e)))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?function(e,t,n){return JA(e,(()=>e.getUniformLocation(t,n)),'uniform "'+n+'" not present in program.')}(this.gl,e,t):function(e,t,n){return e.getUniformLocation(t,n)}(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),WA(this.gl,(()=>this.gl.getAttribLocation(e,t)))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),KA(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,r]=PA(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){null!=this.program&&jA(this.gl,this.program),ZA(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;if(this.debug){const e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}WA(e,(()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),WA(this.gl,(()=>this.gl.finish()))}getQueryTimerExtension(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=UA(this.gl,2===X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(2===X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(2===X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2();return void e.endQuery(t.TIME_ELAPSED_EXT)}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await b((()=>this.disposed||this.isQueryAvailable(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")))),this.getQueryTime(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(0===t)return null;if(2===t){const t=this.gl;return t.getQueryParameter(e,t.QUERY_RESULT)/1e6}{const t=this.getQueryTimerExtensionWebGL1();return t.getQueryObjectEXT(e,t.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(0===t)return!0;if(2===t){const t=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=t.getQueryParameter(e,t.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}{const t=this.getQueryTimerExtensionWebGL1(),n=t.getQueryObjectEXT(e,t.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(t.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise((t=>{this.addItemToPoll((()=>e.isFencePassed()),(()=>t()))}))}pollItems(){const e=function(e){let t=0;for(;t<e.length;++t){if(!e[t]())break}return t-1}(this.itemsToPoll.map((e=>e.isDoneFn)));for(let t=0;t<=e;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in X().platform&&(n=X().platform.setTimeoutCustom.bind(X().platform)),b((()=>(this.pollItems(),0===this.itemsToPoll.length)),(()=>0),null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),XA(this.gl,e,this.framebuffer),this.debug&&ZA(this.gl)}unbindTextureToFrameBuffer(){null!=this.outputTexture?(XA(this.gl,this.outputTexture,this.framebuffer),this.debug&&ZA(this.gl)):YA(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const s=this.gl;XA(s,e,this.framebuffer),this.debug&&ZA(s),this.outputTexture=e,WA(s,(()=>s.viewport(0,0,t,n))),WA(s,(()=>s.scissor(0,0,t,n)))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),WA(this.gl,(()=>this.gl.scissor(e,t,n,s)))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(null==this.program)throw new Error("No GPU program is currently set.")}}const{addImpl:n_,bincountImpl:s_,bincountReduceImpl:r_,castImpl:a_,ceilImpl:i_,concatImpl:o_,equalImpl:l_,expImpl:u_,expm1Impl:c_,floorImpl:h_,gatherNdImpl:p_,gatherV2Impl:d_,greaterImpl:f_,greaterEqualImpl:m_,lessImpl:g_,lessEqualImpl:y_,linSpaceImpl:b_,logImpl:x_,maxImpl:w_,maximumImpl:v_,minimumImpl:k_,multiplyImpl:N_,negImpl:I_,notEqualImpl:S_,prodImpl:T_,raggedGatherImpl:C_,raggedRangeImpl:$_,raggedTensorToTensorImpl:E_,rangeImpl:A_,rsqrtImpl:R_,scatterImpl:__,sigmoidImpl:F_,simpleAbsImpl:D_,sliceImpl:O_,sparseFillEmptyRowsImpl:M_,sparseReshapeImpl:L_,sparseSegmentReductionImpl:z_,sqrtImpl:P_,stridedSliceImpl:B_,stringNGramsImpl:W_,stringSplitImpl:V_,stringToHashBucketFastImpl:U_,subImpl:G_,tileImpl:H_,topKImpl:j_,transposeImpl:q_,uniqueImpl:K_}=_T;function X_(e,t){return["x","y","z","w","u","v"].slice(0,t).map((t=>`${e}.${t}`))}function Y_(e,t){return 1===t?[e]:X_(e,t)}class Z_{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=LR(this.outputShape.length),0===this.rank)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{const e=Y_("rc",this.rank),t=AR(this.rank),n=this.getOutOfBoundsCondition(e),s=this.getSetup(e),r=this.getOutput(e);this.userCode=`\n        void main() {\n          ${t} rc = getOutputCoords();\n\n          if(${n}) {\n            setOutput(vec4(0));\n          } else {\n            ${s}\n\n            setOutput(vec4(${r}));\n          }\n        }\n      `}}getSourceCoordsArr(e){const t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${0===n?"r":"rp1"}, ${0===s?"c":"cp1"}`;for(let t=2;t<this.rank;t++)r=`${e[e.length-1-t]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(1===this.rank)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(1===this.rank)return"";const t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${s};\n    `}getOutput(e){const t=this.getSourceCoordsArr(e);if(1===this.rank){return`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`}return`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}}class J_{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=LR(this.outputShape.length);let n="";for(let e=0;e<4;e++){let t="thisRC = rc;";e%2==1&&(t+="thisRC.z += 1;"),e>1&&(t+="thisRC.y += 1;"),n+=`\n        ${t}\n        ${e>0?"if(thisRC.y < rows && thisRC.z < cols){":""}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${e}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${e>0?"}":""}\n      `}var s,r;this.userCode=`\n      ${s=t,r=this.enableShapeUniforms,`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${r?gR(["r","c","d"],"inputShape"):fR(["r","c","d"],s)}\n      return ivec3(r, c, d);\n    }\n  `}\n      ${this.enableShapeUniforms?"\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n":yR(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};\n        int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}}class Q_{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=tF(t,n),r=nF(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);const a=eF(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();const e=this.freeTextures[r].shift();return this.usedTextures[r].push(e),e}let i;return s===MA.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===MA.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===MA.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===MA.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===MA.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(null==this.freeTextures)return;const r=tF(n,s),a=nF(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);const i=eF(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");-1!==o&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;const l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(null!=this.freeTextures){for(const e in this.freeTextures)this.freeTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));for(const e in this.usedTextures)this.usedTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}}function eF(e,t,n,s,r){const a=function(e,t){switch(e){case MA.PACKED_2X2_FLOAT32:return JR(t);case MA.PACKED_2X2_FLOAT16:return QR(t);case MA.UNPACKED_FLOAT32:return XR(t);case MA.UNPACKED_FLOAT16:return YR(t);case MA.PACKED_4X1_UNSIGNED_BYTE:return ZR(t);default:throw new Error(`Unknown physical texture type ${e}`)}}(t,s);let i;if(r){const[t,n]=PA(e[0],e[1]);i=t*n}else{const[t,n]=LA(e[0],e[1]);i=t*n}const o=function(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}(n,a);return i*o}function tF(e,t){if(e===OA.UPLOAD)return MA.PACKED_2X2_FLOAT32;if(e===OA.RENDER||null==e)return function(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?MA.PACKED_2X2_FLOAT32:MA.UNPACKED_FLOAT32:e?MA.PACKED_2X2_FLOAT16:MA.UNPACKED_FLOAT16}(t);if(e===OA.DOWNLOAD||e===OA.PIXELS)return MA.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function nF(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class sF{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=LR(this.outputShape.length),this.userCode=`\n      float unaryOperation(float x) {\n        ${t}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}}const rF="return abs(x);";const aF="return x;";class iF{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=LR(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}}class oF{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=LR(this.outputShape.length);const t=e.length,n=Y_("rc",t),s=AR(t),r=function(e,t){if(1===e)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${r});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}}const lF=Ah,uF={};const cF=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");class hF extends n{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(null!=e){if(e instanceof t_)n=e;else{const t=FA(X().getNumber("WEBGL_VERSION"),e);n=new t_(t)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{const e=FA(X().getNumber("WEBGL_VERSION"));n=new t_(e),this.binaryCache=((s=X().getNumber("WEBGL_VERSION"))in uF||(uF[s]={}),uF[s]),this.gpgpuCreatedLocally=!0}var s;this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Q_(this.gpgpu),this.numMBBeforeWarning=null==X().global.screen?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*600/1024/1024,this.texData=new t(this,vi())}nextDataId(){return hF.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,s,r,a){const i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[s,r]},o.texShape=[s,r];const l=nR(t),u=new UR(l,!1,a),c=this.runWebGLProgram(u,[i],n,[[s,r]]);return c.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),c.dataId}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().getBool("DEBUG"))&&this.checkNumericalProblems(e),"complex64"===n&&null!=e)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:OA.UPLOAD,refCount:1}),s}refCount(e){if(this.texData.has(e)){return this.texData.get(e).refCount}return 0}incRef(e){this.texData.get(e).refCount++}decRef(e){if(this.texData.has(e)){this.texData.get(e).refCount--}}move(e,t,n,s,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),"complex64"===s)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:OA.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(null!=a){let t;t=o?new iF(i,aF):new sF(i,aF);const n=this.runWebGLProgram(t,[{dataId:e,shape:i,dtype:s}],s),r=this.readSync(n.dataId);return this.disposeIntermediateTensorInfo(n),r}if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===s)return n;const l=null!=this.activeTimers;let u,c;if(l&&(u=rr()),"complex64"===s){c=Dd(this.readSync(r.real.dataId),this.readSync(r.imag.dataId))}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=rr()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){const t=this.pendingRead.get(e);return new Promise((e=>t.push(e)))}const t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(null!=r){let t;t=o?new iF(s,aF):new sF(s,aF);const n=this.runWebGLProgram(t,[{dataId:e,shape:s,dtype:a}],a),r=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),r}if(null!=n)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===X().getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l,u,c=null;if("complex64"!==a&&X().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);const t=this.texData.get(l.dataId);c=this.gpgpu.createBufferFromTexture(t.texture.texture,...zA(s))}if(this.pendingRead.set(e,[]),"complex64"!==a&&await this.gpgpu.createAndWaitForFence(),"complex64"===a){const e=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]);u=Dd(e[0],e[1])}else if(null==c)u=this.getValuesFromTexture(e);else{const e=d(s);u=this.gpgpu.downloadFloat32MatrixFromBuffer(c,e)}if(null!=l&&this.disposeIntermediateTensorInfo(l),null!=c){const e=this.gpgpu.gl;WA(e,(()=>e.deleteBuffer(c)))}const h=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach((e=>e(h))),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&vi().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){const n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:l}=n;if("complex64"===i)throw new Error("Does not support reading texture for complex64 dtype.");if(null!=a){let n;n=o?new iF(r,aF):new sF(r,aF);const s=this.runWebGLProgram(n,[{dataId:e,shape:r,dtype:i}],i),a=this.readToGPU(s,t);return this.disposeIntermediateTensorInfo(s),a}if(null==l)throw null!=s?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");const u=this.decode(e,t.customTexShape),c=vi().makeTensorFromTensorInfo(u),h=this.texData.get(u.dataId);return Object.assign({tensorRef:c},h.texture)}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map((e=>or(e)));return Za(e.shape,e.dtype,n)}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return Za(e.shape,e.dtype,t)}checkNumericalProblems(e){if(null!=e)for(let t=0;t<e.length;t++){const n=e[t];if(!VA(n)){if(X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE"))throw Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);throw Error(`The value ${n} cannot be represented on this device.`)}}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=d(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const n=this.decode(e),s=this.texData.get(n.dataId),a=this.gpgpu.downloadMatrixFromPackedTexture(s.texture.texture,...zA(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(n),a}const a=X().getBool("WEBGL_PACK")&&!0===s,i=a?nR(t):t,o=a?new WR(i):new BR(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){const t=this.activeTimers,n=[];let s=!1;null==this.programTimersStack?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const r=p(this.activeTimers.map((e=>e.query))).filter((e=>null!=e)),a=p(this.activeTimers.map((e=>e.name))).filter((e=>null!=e));this.activeTimers=t,s&&(this.programTimersStack=null);const i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const e=await Promise.all(r);i.kernelMs=l(e),i.getExtraProfileInfo=()=>e.map(((e,t)=>({name:a[t],ms:e}))).map((e=>`${e.name}: ${e.ms}`)).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:rr(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=rr(),e)}async getQueryTime(e){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);const{complexTensorInfos:n}=this.texData.get(e);return null!=n&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){const{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),null!=t&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));const u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=cF){return X().getBool("WEBGL_CPU_FORWARD")&&e.every((e=>null==this.texData.get(e.dataId).texture&&d(e.shape)<t))}getGPGPUContext(){return this.gpgpu}where(e){us("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return lF(e.shape,t)}packedUnaryOp(e,t,n){const s=new iF(e.shape,t),r=this.compileAndRun(s,[e],n);return vi().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&"complex64"!==e.dtype){const t=D_(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,t)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,rF,e.dtype);const t=new sF(e.shape,rF),n=this.compileAndRun(t,[e]);return vi().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let s;if("string"===t&&null!=n&&n.length>0&&A(n[0])){const r=n.map((e=>ir(e)));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){return vi().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){const t=new oF(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new Z_(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const n=[eR(e.shape),...tR(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[eR(t),...tR(t)],a=new J_(r,n),i=[n],o=this.runWebGLProgram(a,[s],e.dtype,i,!0);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e,t){const n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(null!=t){u(d(r)<=t[0]*t[1]*4,(()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."))}const i=nR(r);let o;o=s?new PR(i):new zR(i);const l=[null!=t?t:zA(i)];return{dtype:a,shape:r,dataId:this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,!0,t).dataId}}runWebGLProgram(e,t,n,s,r=!1,a){const i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===DA.DENSE){const t=null!=a?a:zA(e.outputShape);o.texShape=t.map((e=>2*e))}if(null!=e.outTexUsage&&(o.usage=e.outTexUsage),0===d(i.shape))return o.values=k(i.dtype,0),i;const l=[],u=t.map((t=>{if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let n=this.texData.get(t.dataId);if(null==n.texture){if(!e.packedInputs&&d(t.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:n.values};e.packedInputs&&(n.isPacked=!0,n.shape=t.shape)}if(this.uploadToGPU(t.dataId),!!n.isPacked!=!!e.packedInputs)t=n.isPacked?this.unpackTensor(t):this.packTensor(t),l.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!rR(n.shape,t.shape)){const e=t,s=t.shape;t.shape=n.shape,t=this.packedReshape(t,s),l.push(t),n=this.texData.get(t.dataId),e.shape=s}return{shape:t.shape,texData:n,isUniform:!1}}));this.uploadToGPU(i.dataId);const c={shape:i.shape,texData:o,isUniform:!1},h=function(e,t,n){let s="";t.concat(n).forEach((t=>{const r=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!t.isUniform){const a=t.texData.texShape,{useSqueezeShape:i,uniformShape:o,keptDims:l}=RR(e.packedInputs,t.shape,a);let u="",c="",h="";if(1===o.length&&e.packedInputs){const e=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];u=`${e[0]>1}_${e[1]>1}`}else if(2!==o.length||e.packedInputs){if(o.length>2&&!e.packedInputs){const e=M(o);h=`${e[0]===a[1]}_${e[e.length-1]===a[1]}`}}else c=`${o[0]>1}_${o[1]>1}`;const p=t.shape.length,m=2===o.length&&f(t.shape,a),g=1===d(t.shape),y=Oi(t.shape,n.shape),b=!e.packedInputs&&p===n.shape.length&&f(a,n.texData.texShape),x=e.packedInputs||o.length>2?"":`${a[0]>1}_${a[1]>1}`;s+=`${p}_${b}_${i?l:""}_${o.length}_${g}_${y}_${m}_${u}_${c}_${h}_${x}_${r}`}else{const e=t.isUniform?"uniform":t.texData.texShape;s+=`${t.shape}_${e}_${r}`}}));const r=e.userCode;let a=e.constructor.name;return a+="_"+s+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,a}(e,u,c),p=this.getAndSaveBinary(h,(()=>DR(this.gpgpu,e,u,c))),m=null!=this.activeTimers;let g;m&&(g=this.startTimer()),X().get("ENGINE_COMPILE_ONLY")||function(e,t,n,s,r){t.program.enableShapeUniforms||(MR(t.inShapeInfos,n),MR([t.outShapeInfo],[s]));const a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),1===X().getNumber("WEBGL_VERSION")&&null!==t.infLoc&&e.gl.uniform1f(t.infLoc,1/0),null!==t.nanLoc&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach(((n,s)=>{const r=t.program.variableNames[s],a=t.uniformLocations[r],i=t.uniformLocations[`offset${r}`],o=t.inShapesLocations[`${r}Shape`],l=t.inTexShapesLocations[`${r}TexShape`];if(o){const{uniformShape:s}=RR(t.program.packedInputs,n.shape,n.texData.texShape);switch(s.length){case 1:e.gl.uniform1iv(o,new Int32Array(s));break;case 2:e.gl.uniform2iv(o,new Int32Array(s));break;case 3:e.gl.uniform3iv(o,new Int32Array(s));break;case 4:e.gl.uniform4iv(o,new Int32Array(s))}}if(l&&e.gl.uniform2i(l,n.texData.texShape[0],n.texData.texShape[1]),null!=a)if(n.isUniform)if(d(n.shape)<2)e.gl.uniform1f(a,n.uniformValues[0]);else{let t=n.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(a,t)}else null!=n.texData.slice&&null!=i&&e.gl.uniform1i(i,n.texData.slice.flatOffset),e.setInputMatrixTexture(n.texData.texture.texture,a,s)}));const o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape))}if(t.outShapeStridesLocation){const n=M(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(n));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(n));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(n))}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach(((n,s)=>{const a=t.customUniformLocations[s],i=r[s];if("float"===n.type)e.gl.uniform1fv(a,i);else if("vec2"===n.type)e.gl.uniform2fv(a,i);else if("vec3"===n.type)e.gl.uniform3fv(a,i);else if("vec4"===n.type)e.gl.uniform4fv(a,i);else if("int"===n.type)e.gl.uniform1iv(a,i);else if("ivec2"===n.type)e.gl.uniform2iv(a,i);else if("ivec3"===n.type)e.gl.uniform3iv(a,i);else{if("ivec4"!==n.type)throw Error(`uniform type ${n.type} is not supported yet.`);e.gl.uniform4iv(a,i)}})),e.executeProgram()}(this.gpgpu,p,u,c,s),l.forEach((e=>this.disposeIntermediateTensorInfo(e))),m&&(g=this.endTimer(g),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(g)}));const y=X().get("WEBGL_FLUSH_THRESHOLD");if(y>0){const e=rr();e-this.lastGlFlushTime>y&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&!1===r){const e=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),e}return i}compileAndRun(e,t,n,s,r=!1){n=n||t[0].dtype;return this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(!this.disposed){if(!X().getBool("IS_TEST")){Object.keys(this.binaryCache).forEach((e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}))}this.textureManager.dispose(),null!=this.canvas&&"undefined"!=typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}}floatPrecision(){return null==this.floatPrecisionValue&&(this.floatPrecisionValue=Ni((()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=X().getBool("DEBUG");X().set("DEBUG",!1);const t=this.abs(au(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16}))),this.floatPrecisionValue}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:l}=t;if(null!=a)return;const u=null!=this.activeTimers;let c;u&&(c=rr());let h=t.texShape;if(null==h&&(h=function(e,t=!1){let n=X().getNumber("WEBGL_MAX_TEXTURE_SIZE"),s=X().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");if(s===1/0&&X().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")&&(s=n/2),t&&(n*=2,s*=2,1===(e=e.map(((t,n)=>n>=e.length-2?i(e[n]):e[n]))).length&&(e=[2,e[0]])),2!==e.length){const t=v(e);e=t.newShape}let r=d(e),a=null;e.length<=1&&r<=n?a=[1,r]:2===e.length&&e[0]<=n&&e[1]<=n?a=e:3===e.length&&e[0]*e[1]<=n&&e[2]<=n?a=[e[0]*e[1],e[2]]:3===e.length&&e[0]<=n&&e[1]*e[2]<=n?a=[e[0],e[1]*e[2]]:4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n?a=[e[0]*e[1]*e[2],e[3]]:4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n&&(a=[e[0],e[1]*e[2]*e[3]]);const o=null!=a&&Math.max(...a)>s&&Math.min(...a)<=(t?2:1)&&Math.min(...a)>0;if(null==a||o)if(t){const t=eR(e);let n=2,s=2;e.length&&([n,s]=tR(e)),r=t*(n/2)*(s/2),a=g(r).map((e=>2*e))}else a=g(r);return a}(n,l),t.texShape=h),null!=r){const e=nR(n);let a,i=h[1],o=h[0];const p=r instanceof Uint8Array||r instanceof Uint8ClampedArray;!l&&p||([i,o]=PA(h[0],h[1])),a=l?new GR(e,p):new UR(e,p);const d=p?[o,i]:h,f=this.makeTensorInfo(d,s),m=this.texData.get(f.dataId);m.usage=p?OA.PIXELS:OA.UPLOAD,m.texShape=d,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(f.dataId),i,o,r);const g=[[o,i]],y=!0,b=this.runWebGLProgram(a,[f],s,g,y),x=this.texData.get(b.dataId);t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,X().get("ENGINE_COMPILE_ONLY")?this.disposeData(b.dataId):(t.texture=x.texture,t.values=null,this.texData.delete(b.dataId)),this.disposeIntermediateTensorInfo(f),u&&(this.uploadWaitMs+=rr()-c)}else{const e=this.acquireTexture(h,o,s,l);t.texture=e}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return null!=t&&(n.values=function(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){const n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length);for(let t=0;t<n.length;++t)n[t]=Math.round(e[t]);return n}throw new Error(`Unknown dtype ${t}`)}(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>1024*this.numMBBeforeWarning*1024){const e=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${e} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*$(t)}checkCompileCompletion(){for(const[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){const e=[];if(this.gpgpu.parallelCompilationExtension){for(const[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}for(const[,t]of Object.entries(this.binaryCache)){const n=new Promise((e=>{try{this.checkCompletion_(t),e(!0)}catch(e){throw e}}));e.push(n)}return Promise.all(e)}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await hd(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(!1===this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)){if(console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),!1===this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS))throw HA(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.");throw new Error("Failed to link vertex and fragment shaders.")}return!0}getUniformLocations(){for(const[,e]of Object.entries(this.binaryCache)){const{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=OR(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromTexture(e,t,n){const{texture:s,height:r,width:a,channels:i}=e,o=vi().backend;if(!o.gpgpu.gl.isTexture(s))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");const l=o.writeTexture(s,t,n,r,a,i);return vi().makeTensorFromDataId(l,t,n,o)}}hF.nextDataId=0;jr()&&Ci("webgl",(()=>new hF),2);class pF{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=Li(t,n),this.enableShapeUniforms=LR(this.outputShape.length),this.userCode=`\n      float binaryOperation(float a, float b) {\n        ${e}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `}}class dF{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=Li(t,n);const r=this.outputShape.length;this.enableShapeUniforms=LR(r);let a="";if(s)if(0===r||1===d(this.outputShape))a="\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        ";else{if(a=`\n          ${AR(r)} coords = getOutputCoords();\n        `,1===r)this.enableShapeUniforms?a+="\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          ":a+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{const e=Y_("coords",r);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${e[r-2]} + 1) >= outShape[${r} - 2];\n            bool nextColOutOfBounds =\n              (${e[r-1]} + 1) >= outShape[${r} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${e[r-2]} + 1) >= ${this.outputShape[r-2]};\n            bool nextColOutOfBounds =\n              (${e[r-1]} + 1) >= ${this.outputShape[r-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${a}\n\n        setOutput(result);\n      }\n    `}}function fF(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const mF={kernelName:dt,backendName:"webgl",kernelFunc:fF};function gF(e){const{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(a.dataId),o=fF({inputs:{x:s},backend:n}),l=fF({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},a}const yF={kernelName:Se,backendName:"webgl",kernelFunc:gF},bF="return (a < 0.) ? b * a : a;",xF="\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";const wF={kernelName:xt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",nr(a,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new dF(xF,r.shape,i.shape):new pF(bF,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}},vF="return (a < 0.) ? b * a : a;",kF="\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";const NF={kernelName:tn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new dF(kF,s.shape,r.shape):new pF(vF,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}};function IF({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{const{x:i}=r,o=a,l=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&null!=n){const e=o.texData.get(i.dataId),t=n(e.values,l);return o.makeTensorInfo(i.shape,l,t)}let u;return u=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&null!=t?new iF(i.shape,t):new sF(i.shape,e),o.runWebGLProgram(u,[i],l)}}function SF({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{const{a:l,b:u}=i,c=o;if(s&&"complex64"===l.dtype){const t=c.texData.get(l.dataId),n=c.texData.get(u.dataId),[s,r]=[[t.complexTensorInfos.real,n.complexTensorInfos.real],[t.complexTensorInfos.imag,n.complexTensorInfos.imag]].map((t=>{const[n,s]=t,r={dataId:n.dataId,dtype:n.dtype,shape:l.shape},a={dataId:s.dataId,dtype:s.dtype,shape:u.shape},i=new pF(e,l.shape,u.shape);return c.runWebGLProgram(i,[r,a],Ar(n.dtype,s.dtype))})),a=gF({inputs:{real:s,imag:r},backend:c});return c.disposeIntermediateTensorInfo(s),c.disposeIntermediateTensorInfo(r),a}const h=a||Ar(l.dtype,u.dtype);if(("string"===l.dtype||"string"===u.dtype||c.shouldExecuteOnCPU([l,u]))&&null!=r){const e=c.texData.get(l.dataId).values,t=c.texData.get(u.dataId).values,n="string"===l.dtype?ff(e):e,s="string"===l.dtype?ff(t):t,[a,i]=r(l.shape,u.shape,n,s,h),o=c.makeTensorInfo(i,h);return c.texData.get(o.dataId).values=a,o}let p;return p=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&null!=t?new dF(t,l.shape,u.shape,n):new pF(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function TF(e,t=!1){if("linear"===e)return"return x;";if("relu"===e)return t?"\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n":"if (isnan(x)) return x;\n  return (x < 0.0) ? 0.0 : x;\n";if("elu"===e)return t?"\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n":"return (x >= 0.0) ? x : (exp(x) - 1.0);";if("relu6"===e)return t?"\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n":"if (isnan(x)) return x;\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n";if("prelu"===e)return t?kF:vF;if("leakyrelu"===e)return t?xF:bF;if("sigmoid"===e)return"return 1.0 / (1.0 + exp(-1.0 * x));";throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}class CF{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=LR(this.outputShape.length);const u=s?e[1]:e[2],c=Math.ceil(u/2),h=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",d=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let m="",g="";i&&(m=o?`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:l?`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:`vec4 activation(vec4 x) {\n          ${i}\n        }`,g="result = activation(result);");const y=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${c}; i++) {\n          int batchA = ${b};\n          int batchB = ${x};\n          vec4 a = getMatrixA(batchA, ${h});\n          vec4 b = getMatrixB(batchB, ${p});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${d[0]} * ${f[0]});\n          result += (${d[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${y}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}}const $F="return areal * breal - aimag * bimag;",EF="return areal * bimag + aimag * breal;";class AF{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=Li(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}}const RF="return a * b;";function _F(e){const{inputs:t,backend:n}=e,{a:s,b:r}=t,a=Ar(s.dtype,r.dtype);if("complex64"===s.dtype){const e=n.texData.get(s.dataId),t=n.texData.get(r.dataId),a=new AF($F,s.shape,r.shape),i=new AF(EF,s.shape,r.shape),o=[{dataId:e.complexTensorInfos.real.dataId,dtype:e.complexTensorInfos.real.dtype,shape:s.shape},{dataId:e.complexTensorInfos.imag.dataId,dtype:e.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:t.complexTensorInfos.real.dataId,dtype:t.complexTensorInfos.real.dtype,shape:r.shape},{dataId:t.complexTensorInfos.imag.dataId,dtype:t.complexTensorInfos.imag.dtype,shape:r.shape}],l=n.runWebGLProgram(a,o,"float32"),u=n.runWebGLProgram(i,o,"float32"),c=gF({inputs:{real:l,imag:u},backend:n});return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(u),c}if(n.shouldExecuteOnCPU([s,r])){const e=n.texData.get(s.dataId),t=n.texData.get(r.dataId),[i,o]=N_(s.shape,r.shape,e.values,t.values,a),l=n.makeTensorInfo(o,a);return n.texData.get(l.dataId).values=i,l}let i;return i=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new dF(RF,s.shape,r.shape):new pF(RF,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}const FF={kernelName:Gt,backendName:"webgl",kernelFunc:_F};function DF(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=d(r.shape),l=x(a,o),c=d(l);u(o===c,(()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`));const h=i.texData.get(r.dataId);return!h.isPacked||rR(r.shape,l)||null!==h.texture&&rR(h.shape,l)?(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype}):function(e,t,n){const s=[eR(e.shape),...tR(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[eR(t),...tR(t)],i=new J_(a,s),o=[s],l=n.runWebGLProgram(i,[r],e.dtype,o,!0);return{dataId:l.dataId,shape:t,dtype:l.dtype}}(r,l,i)}const OF={kernelName:hn,backendName:"webgl",kernelFunc:DF};class MF{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];const i=4*Math.floor(n/4),o=n%4;let l="sumValue += dot(values, ones);";if(null!=t){const e=1/t;l=`sumValue += dot(values * ${m(e)?e.toPrecision(2):e}, ones);`}let u="";r%n>0&&(u=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${u}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${l}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${1===o}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${l}\n        } else if (${2===o}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${l}\n        } else if (${3===o}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${l}\n        }\n        setOutput(sumValue);\n      }\n    `}}class LF{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i="0.0",o="";"prod"===t?i="1.0":"min"===t?(i="1.0 / 1e-20",o="min"):"max"===t&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"sum"===t?l="sumValue":"prod"===t?l="prodValue":"all"===t?l="allValue":"any"===t&&(l="anyValue");const u=4*Math.floor(n/4),c=n%4;let h=`\n      if (${"sum"===t}) {\n        sumValue += dot(values, ones);\n      } else if (${"prod"===t}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${o}(values, minMaxValue);\n        if (${"min"===t} || ${"max"===t}) {\n          minMaxValue = ${o}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,p="vec4";"all"===t?(i="1.0",h="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",p="bvec4"):"any"===t&&(i="0.0",h="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",p="bvec4");let d="";r%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${i});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${u}; i += 4) {\n          int inIdx = inOffset + i;\n          ${p} values = ${p}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${h}\n        }\n\n        int inIdx = inOffset + ${u};\n        if (${1===c}) {\n          ${p} values = ${p}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${h}\n        } else if (${2===c}) {\n          ${p} values = ${p}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${h}\n        } else if (${3===c}) {\n          ${p} values = ${p}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${h}\n        }\n        setOutput(${l});\n      }\n    `}}function zF(e,t,n,s){const r=function(e){const t=[];for(;0===t.length||1!==t[t.length-1].outSize;){const n=t.length?t[t.length-1].outSize:e[1],s=xd(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}(e.shape);let a=e;for(let i=0;i<r.length;i++){const{inSize:o,windowSize:l,outSize:u}=r[i];let c,h;c="mean"===n?0===i?new MF({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new MF({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):new LF({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),h=a,a=s.runWebGLProgram(c,[a],t),h.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(h)}return a}class PF{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;const s=AR(this.rank),r=function(e){const t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let t=0;t<e.length;t++)s[e[t]]=n[t];return s.join()}(t);this.userCode=`\n    void main() {\n      ${s} resRC = getOutputCoords();\n      setOutput(getA(${r}));\n    }\n    `}}class BF{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const s=AR(this.rank),r=X_("rc",this.rank),a=new Array(this.rank);for(let e=0;e<t.length;e++)a[t[e]]=r[e];const i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${s} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${l};\n      if(${o}) {\n        result[1] = ${l};\n      }\n      --${r[this.rank-1]};\n      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${l};\n        if(${o}) {\n          result[3] = ${l};\n        }\n      }\n      setOutput(result);\n    }\n    `}}function WF(e,t,n){const s=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new BF(e.shape,t):new PF(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function VF(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return function(e,t,n,s){const r=t,a=e.shape.length,i=w(r,e.shape);let o=i;const l=Ql(o,a),u=null!=l;let c=e;u&&(c=WF(e,l,s),o=tu(o.length,a)),Jl("sum",o,a);const[h,p]=Yl(c.shape,o);let f=h;n&&(f=Zl(h,i));const m=d(p),g=DF({inputs:{x:c},attrs:{shape:[d(e.shape)/m,m]},backend:s}),y=zF(g,Rr(e.dtype),"sum",s),b=DF({inputs:{x:y},attrs:{shape:f},backend:s});return s.disposeIntermediateTensorInfo(g),s.disposeIntermediateTensorInfo(y),u&&s.disposeIntermediateTensorInfo(c),b}(r,a,i,n)}const UF={kernelName:Rn,backendName:"webgl",kernelFunc:VF};function GF(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,l=new Array(o);for(let e=0;e<l.length;e++)l[e]=r.shape[a[e]];let u;if(i.shouldExecuteOnCPU([r])){const e=i.texData.get(r.dataId).values,t=q_(e,r.shape,r.dtype,a,l);u=i.makeTensorInfo(l,r.dtype);i.texData.get(u.dataId).values=t}else u=WF(r,a,i);return u}const HF={kernelName:Jn,backendName:"webgl",kernelFunc:GF};function jF({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){const c=e.shape.length,h=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],f=s?t.shape[h-1]:t.shape[h-2],m=n?e.shape[c-1]:e.shape[c-2],g=s?t.shape[h-2]:t.shape[h-1],y=e.shape.slice(0,-2),b=t.shape.slice(0,-2),x=d(y),w=d(b),v=Li(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([m,g]);u(p===f,(()=>`Error in matMul: inner shapes (${p}) and (${f}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`));const k=n?[x,p,m]:[x,m,p],N=s?[w,g,f]:[w,f,g],I=DF({inputs:{x:e},backend:r,attrs:{shape:k}}),S=DF({inputs:{x:t},backend:r,attrs:{shape:N}}),T=[I,S],C=Math.max(x,w),$=n?I.shape[1]:I.shape[2],E=null!=a,A=null!=i,R="leakyrelu"===l,_=null!=l?TF(l,!0):null;let F;if((1===m||1===g)&&$>1e3&&!1===(E||A||R||null!=_)){let e=I,t=S;n&&(e=GF({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),T.push(e)),s&&(t=GF({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),T.push(t));const a=1===g;let i=e;1!==g&&(i=DF({inputs:{x:e},backend:r,attrs:{shape:[C,$,1]}}),T.push(i));const o=1===g?2:1;let l=t;a&&(l=DF({inputs:{x:t},backend:r,attrs:{shape:[C,1,$]}}),T.push(l));const u=_F({inputs:{a:i,b:l},backend:r});F=VF({inputs:{x:u},backend:r,attrs:{axis:o,keepDims:!0}}),T.push(u)}else{const l=Ar(e.dtype,t.dtype),u=new CF(k,N,[C,m,g],n,s,E,_,A,R),c=[I,S];if(null!=a&&c.push(a),A&&c.push(i),R){const e=r.makeTensorInfo([],"float32",nr(o,"float32"));c.push(e),T.push(e)}F=r.runWebGLProgram(u,c,l)}const D=DF({inputs:{x:F},backend:r,attrs:{shape:v}});T.push(F);for(const e of T)r.disposeIntermediateTensorInfo(e);return D}const qF={kernelName:is,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=s;return jF({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}},KF="return abs(x);";const XF={kernelName:Q,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&"complex64"!==s.dtype){const e=n.texData.get(s.dataId),t=D_(e.values);return n.makeTensorInfo(s.shape,s.dtype,t)}let r;return r=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new iF(s.shape,KF):new sF(s.shape,KF),n.runWebGLProgram(r,[s],s.dtype)}},YF=IF({opSnippet:"if (isnan(x)) return x;\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n"}),ZF={kernelName:ee,backendName:"webgl",kernelFunc:YF},JF=IF({opSnippet:"if (isnan(x)) return x;\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));"}),QF={kernelName:te,backendName:"webgl",kernelFunc:JF},eD="return a + b;",tD=SF({opSnippet:eD,packedOpSnippet:eD,supportsComplex:!0,cpuKernelImpl:n_}),nD={kernelName:ne,backendName:"webgl",kernelFunc:tD};class sD{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`float v${e} = get${e}AtOutCoords();`)}));const s=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n      void main() {\n        ${n.join("\n        ")}\n\n        float result = ${s};\n        setOutput(result);\n      }\n    `}}class rD{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`vec4 v${e} = get${e}AtOutCoords();`)}));const s=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n      void main() {\n        ${n.join("\n        ")}\n\n        vec4 result = ${s};\n        setOutput(result);\n      }\n    `}}const aD={kernelName:se,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:s}=t,r=n;if(1===r.length)return fF({inputs:{x:r[0]},backend:s});if(r.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const t=Math.floor(r.length/2),n=e({inputs:r.slice(0,t),backend:s}),a=e({inputs:r.slice(t),backend:s});return e({inputs:[n,a],backend:s})}const a=r.map((e=>e.dtype)).reduce(((e,t)=>Ar(e,t))),i=r.map((e=>e.shape)),o=X().getBool("WEBGL_PACK")?new rD(r[0].shape,i):new sD(r[0].shape,i);return s.runWebGLProgram(o,r,a)}};const iD={kernelName:re,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,l=w(a,r.shape);let u=l;const c=Ql(u,o);let h=r;null!=c&&(h=GF({inputs:{x:r},backend:n,attrs:{perm:c}}),u=tu(u.length,o)),Jl("all",u,o);const[p,f]=Yl(h.shape,u),m=DF({inputs:{x:h},backend:n,attrs:{shape:[-1,d(f)]}}),g=zF(m,m.dtype,"all",n);let y;if(i){y=DF({inputs:{x:g},backend:n,attrs:{shape:Zl(p,l)}})}else y=DF({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=c&&n.disposeIntermediateTensorInfo(h),y}};const oD={kernelName:ae,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,l=w(a,r.shape);let u=l;const c=Ql(u,o);let h=r;null!=c&&(h=GF({inputs:{x:r},backend:n,attrs:{perm:c}}),u=tu(u.length,o)),Jl("any",u,o);const[p,f]=Yl(h.shape,u),m=DF({inputs:{x:h},backend:n,attrs:{shape:[-1,d(f)]}}),g=zF(m,m.dtype,"any",n);let y;if(i){y=DF({inputs:{x:g},backend:n,attrs:{shape:Zl(p,l)}})}else y=DF({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=c&&n.disposeIntermediateTensorInfo(h),y}};class lD{constructor(e,t,n){this.variableNames=["A"];const{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];const i="max"===t?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${s};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${s}; i++) {\n          int inIdx = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}}class uD{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,u(e.length>2,(()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`));const r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");const i=this.outputShape,o=i.length,l=AR(o),c=Y_("coords",o);let h,p;if(1===a){p=o+1;const e=AR(p);h=`\n        ${e} sourceLocR = ${e}(${c.join()}, 0);\n        ++${c[o-1]};\n        ${e} sourceLocG = ${e}(${c.join()}, 0);\n        ++${c[o-2]};\n        ${e} sourceLocA = ${e}(${c.join()}, 0);\n        --${c[o-1]};\n        ${e} sourceLocB = ${e}(${c.join()}, 0);\n        --${c[o-2]};`}else p=o,h=`\n        ${l} sourceLocR = coords;\n        ++${c[o-1]};\n        ${l} sourceLocG = coords;\n        ++${c[o-2]};\n        ${l} sourceLocA = coords;\n        --${c[o-1]};\n        ${l} sourceLocB = coords;\n        --${c[o-2]};`;const d=["x","y","z","w","u","v"].slice(0,p),f="."+d[p-1],m=d.map((e=>"int "+e)),g=Y_("sourceLocR",p-1).concat("inIdx.r"),y=Y_("sourceLocG",p-1).concat("inIdx.g"),b=Y_("sourceLocB",p-1).concat("inIdx.b"),x=Y_("sourceLocA",p-1).concat("inIdx.a"),w="max"===n?"greaterThan":"lessThan",v=s?"":`\n          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${y.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${x.join()})));`,k=`vec4(\n            getAChannel(${g.join()}),\n            hasNextCol ? getAChannel(${y.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,N=s?"":`\n      float getBestIndicesAChannel(${m.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${m.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${N}\n      void main() {\n        ${l} coords = getOutputCoords();\n        bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};\n        bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};\n        ${h}\n        ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n          sourceLocB${f}, sourceLocA${f}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${v}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${w}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}}function cD(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];null!=s&&(r=s.shape[0],a=s.shape[1]);const i=xd(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},l=new lD(o,n,null==s),u=[t];null!=s&&u.push(s);const c=e.runWebGLProgram(l,u,"int32");if(1===c.shape[1])return c;const h=cD(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function hD(e,t,n,s=null){const r=null!=s?s.shape:t.shape,a=xd(r[r.length-1]),i=new uD(r,a,n,null==s),o=null==s?[t]:[t,s],l=e.runWebGLProgram(i,o,"int32");if(l.shape.length===t.shape.length){const s=hD(e,t,n,l);return e.disposeIntermediateTensorInfo(l),s}return l}function pD(e,t,n,s){const r=[n];if(Jl("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){const n=[],a=e.texData.get(t.dataId);let i=t;null!==a&&a.isPacked&&(i=e.unpackTensor(t),n.push(i));const[o,l]=Yl(i.shape,r),u=d(l),c=DF({inputs:{x:i},backend:e,attrs:{shape:[-1,u]}});n.push(c);const h=cD(e,c,s);n.push(h);const p=DF({inputs:{x:h},backend:e,attrs:{shape:o}});return n.forEach((t=>e.disposeIntermediateTensorInfo(t))),p}return hD(e,t,s)}const dD={kernelName:ie,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;let i=w(a,r.shape);const o=Ql(i,r.shape.length);let l=r;const u=[];null!=o&&(l=GF({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=tu(i.length,l.shape.length)),Jl("argMax",[i[0]],l.shape.length);const c=pD(n,l,i[0],"max");return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}};const fD={kernelName:oe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;let i=w(a,r.shape);const o=Ql(i,r.shape.length);let l=r;const u=[];null!=o&&(l=GF({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=tu(i.length,l.shape.length)),Jl("argMin",[i[0]],l.shape.length);const c=pD(n,l,i[0],"min");return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}},mD=IF({opSnippet:"if (isnan(x)) return x;\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n"}),gD={kernelName:le,backendName:"webgl",kernelFunc:mD},yD=IF({opSnippet:"if (isnan(x)) return x;return log(x + sqrt(x * x + 1.0));"}),bD={kernelName:ue,backendName:"webgl",kernelFunc:yD},xD=IF({opSnippet:"if (isnan(x)) return x;\n  return atan(x);\n"}),wD={kernelName:ce,backendName:"webgl",kernelFunc:xD},vD=SF({opSnippet:"\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n\n  return atan(a, b);\n",packedOpSnippet:"\n  vec4 result = atan(a, b);\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  \n  result.r = isNaN.r ? NAN : result.r;\n  result.g = isNaN.g ? NAN : result.g;\n  result.b = isNaN.b ? NAN : result.b;\n  result.a = isNaN.a ? NAN : result.a;\n\n  return result;\n"}),kD={kernelName:pe,backendName:"webgl",kernelFunc:vD},ND=IF({opSnippet:"if (isnan(x)) return x;\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;"}),ID={kernelName:he,backendName:"webgl",kernelFunc:ND};class SD{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;const f="avg"===t,m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let y="0.0";if(f||(y="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n        const ivec2 strides = ivec2(${i}, ${o});\n        const ivec2 pads = ivec2(${p}, ${d});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${l}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${h};\n                wC += ${u}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${t} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${s?r?m:g:`wR * ${h} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `)}let b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(b="avgValue / count");const x=4*Math.floor(a/4),w=a%4,v=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${o});\n      const ivec2 pads = ivec2(${p}, ${d});\n      const float initializationValue = ${y};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${y});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${l}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${u};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${u}, d),\n              getValue(batch, xR, xC + 2 * ${u}, d),\n              getValue(batch, xR, xC + 3 * ${u}, d)\n            );\n\n            ${v}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${1===w}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${v}\n          } else if (${2===w}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${u}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${v}\n          } else if (${3===w}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${u}, d),\n              getValue(batch, xR, xC + 2 * ${u}, d),\n              initializationValue\n            );\n\n            ${v}\n          }\n        }\n        setOutput(${b});\n      }\n    `}}class TD{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;const b="avg"===t;let x="0.0";if(b||(x="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${o}, ${l});\n        const ivec3 pads = ivec3(${m}, ${g}, ${y});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${p};\n              wD += ${u}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${d};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${h}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${t} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `)}let w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(w="avgValue / count");const v=4*Math.floor(a/4),k=a%4,N=`\n      if (${b}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${o}, ${l});\n      const ivec3 pads = ivec3(${m}, ${g}, ${y});\n      const float initializationValue = ${x};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${x});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${p};\n            wD += ${u}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${d};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${v}; wC += 4) {\n              int xC = xCCorner + wC * ${h};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${h}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${h}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${h}, ch)\n              );\n\n              ${N}\n            }\n\n            int xC = xCCorner + ${v};\n            if (${1===k}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${N}\n            } else if (${2===k}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${h}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${N}\n            } else if (${3===k}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${h}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${h}, ch),\n                initializationValue\n              );\n\n              ${N}\n            }\n          }\n          setOutput(${w});\n        }\n      }\n    `}}const CD={kernelName:de,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t;hR(r,"avgPool");const{filterSize:a,strides:i,pad:o,dimRoundingMode:l}=s;u(Jo(i,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));const c=Vo(r.shape,a,i,1,o,l);if(1===c.filterWidth&&1===c.filterHeight&&f(c.inShape,c.outShape))return fF({inputs:{x:r},backend:n});const h=new SD(c,"avg",!1);return n.runWebGLProgram(h,[r],"float32")}};const $D={kernelName:me,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=s,c=Uo(r.shape,a,i,[1,1,1],o,l,u),h=new TD(c,"avg",!1);return n.runWebGLProgram(h,[r],"float32")}};class ED{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${u}, ${c});\n      const float avgMultiplier = float(${h});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${o};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${l};\n            wC+= ${i}) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class AD{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${d}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${o}) {\n          float dyD = float(dyDCorner + wD) / ${r}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${h};\n              wR += ${l}) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${p};\n                wC += ${u}) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const RD={kernelName:ge,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=s,h=Uo(i.shape,o,l,[1,1,1],u,c),p=new AD(h);return n.runWebGLProgram(p,[r],i.dtype)}};const _D={kernelName:fe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;hR([r,a],"avgPoolGrad");const{filterSize:o,strides:l,pad:u}=s,c=Vo(i.shape,o,l,1,u),h=new ED(c);return n.runWebGLProgram(h,[r],i.dtype)}};const FD={kernelName:ye,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return jF({a:r,b:a,transposeA:i,transposeB:o,backend:n})}};class DD{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],Li(e,t),Li(e,n);let i="0.0";null!=s&&(Li(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";null!=r&&(Li(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${i};\n        float scale = ${o};\n        float inv = scale * inversesqrt(variance + float(${a}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `}}class OD{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],Li(e,t),Li(e,n);let i="vec4(0.0)";null!=s&&(Li(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";null!=r&&(Li(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 offset = ${i};\n        vec4 scale = ${o};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `}}const MD={kernelName:lt,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,mean:r,variance:a,offset:i,scale:o}=e;u(r.shape.length===a.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),u(null==i||r.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),u(null==o||r.shape.length===o.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));let{varianceEpsilon:l}=n;null==l&&(l=.001);const c=[s,r,a];let h=null;null!=i&&(h=i.shape,c.push(i));let p=null;null!=o&&(p=o.shape,c.push(o));const d=X().getBool("WEBGL_PACK_NORMALIZATION")?new OD(s.shape,r.shape,a.shape,h,p,l):new DD(s.shape,r.shape,a.shape,h,p,l);return t.runWebGLProgram(d,c,c[0].dtype)}};class LD{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=AR(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const n=function(e){if(1===e)return"sourceLoc";if(e<=6)return zD.slice(0,e).map((e=>"sourceLoc."+e)).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}(this.rank);let s;s=`\n        ${t} sourceLoc;\n        ${t} coords = getOutputCoords();\n        ${e.map(((e,t)=>`sourceLoc.${zD[t]} = start[${t}] + coords.${zD[t]};`)).join("\n")}\n      `,this.userCode=`\n      void main() {\n        ${s}\n        setOutput(getSource(${n}));\n      }\n    `}}const zD=["x","y","z","w","u","v"];class PD{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const t=AR(this.rank),n=Y_("coords",this.rank),s=Y_("sourceLoc",this.rank),r=1===this.rank?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,i=`\n      result.x = ${a};\n      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n        ++${s[this.rank-1]};\n        result.y = ${a};\n        --${s[this.rank-1]};\n      }\n    `,o=1===this.rank?"":`\n      --${n[this.rank-1]};\n      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {\n        ++${s[this.rank-2]};\n        result.z = ${a};\n        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n          ++${s[this.rank-1]};\n          result.w = ${a};\n        }\n      }\n    `,l=this.rank<=4?`sourceLoc = coords +\n            ${t}(${e.map(((e,t)=>`start[${t}]`)).join()});`:e.map(((e,t)=>`${s[t]} = ${n[t]} + start[${t}];`)).join("\n");this.userCode=`\n      void main() {\n        ${t} coords = getOutputCoords();\n        ${t} sourceLoc;\n        ${l}\n        vec4 result = vec4(0.);\n        ${i}\n        ${o}\n        setOutput(result);\n      }\n    `}}function BD(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,l]=uo(r,a,i);if(Zi(r,o,l),0===d(l))return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||"string"===r.dtype){const e=n.texData.get(r.dataId),t=O_(e.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,t)}const{isPacked:u}=n.texData.get(r.dataId),c=oo(r.shape,o,l);if(u||!c){const e=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PD(l):new LD(l),t=[o];return n.runWebGLProgram(e,[r],r.dtype,t)}return n.uploadToGPU(r.dataId),function(e,t,n,s){const r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=lo(t,M(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};const l=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,l+1),a}(r,o,l,n)}const WD={kernelName:In,backendName:"webgl",kernelFunc:BD},VD={kernelName:be,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;u(r.shape.length<=4,(()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"));const o=a.reduce(((e,t)=>e*t)),l=vd(r.shape,a,o),c=kd(l.length,a.length),h=Nd(r.shape,a,o),p=Id(i,a.length),d=Sd(h,i,a.length),f=[],m=DF({inputs:{x:r},backend:n,attrs:{shape:l}}),g=GF({inputs:{x:m},backend:n,attrs:{perm:c}}),y=DF({inputs:{x:g},backend:n,attrs:{shape:h}}),b=BD({inputs:{x:y},backend:n,attrs:{begin:p,size:d}});return f.push(m),f.push(g),f.push(y),f.forEach((e=>n.disposeIntermediateTensorInfo(e))),b}};const UD={kernelName:xe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),l=n.readSync(a.dataId),u=s_(o,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,u)}};const GD={kernelName:ve,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=Li(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}},HD=SF({opSnippet:"return float(a != b);",cpuKernelImpl:S_,dtype:"bool"}),jD={kernelName:jt,backendName:"webgl",kernelFunc:HD};function qD(e){const{inputs:t,backend:n}=e,{input:s}=t;return fF({inputs:{x:n.texData.get(s.dataId).complexTensorInfos.real},backend:n})}const KD={kernelName:ln,backendName:"webgl",kernelFunc:qD};const XD={kernelName:ke,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:s,attrs:r}=t,{x:a}=n,{dtype:i}=r;if("complex64"===i){if("complex64"===a.dtype)return fF({inputs:{x:a},backend:s});const t=Zu(a.shape),n=e({inputs:{x:a},backend:s,attrs:{dtype:"float32"}}),r=gF({inputs:{real:n,imag:t},backend:s});return t.dispose(),s.disposeIntermediateTensorInfo(n),r}if("complex64"===a.dtype){const t=qD({inputs:{input:a},backend:s}),n=e({inputs:{x:t},backend:s,attrs:{dtype:i}});return s.disposeIntermediateTensorInfo(t),n}if(!T(a.dtype,i)){const e=fF({inputs:{x:a},backend:s});return{dataId:e.dataId,shape:e.shape,dtype:i}}if(s.shouldExecuteOnCPU([a])){const e=s.texData.get(a.dataId).values,[t,n,r]=a_(e,a.shape,a.dtype,i);return s.makeTensorInfo(t,n,r)}if("int32"===i)return function(e,t){const n=new sF(e.shape,"return float(int(x));"),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}(a,s);if("bool"===i){const e=s.makeTensorInfo([],"bool",k("bool",1)),t=HD({inputs:{a:a,b:e},backend:s});return s.disposeIntermediateTensorInfo(e),t}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${i}`)}},YD="return ceil(x);",ZD=IF({opSnippet:YD,packedOpSnippet:YD,cpuKernelImpl:i_}),JD={kernelName:Ne,backendName:"webgl",kernelFunc:ZD};class QD{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    "}}class eO{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    "}}const tO={kernelName:Ie,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s;let o;o=X().getBool("WEBGL_PACK_CLIP")?new eO(r.shape):new QD(r.shape);const l=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}};class nO{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    "}}function sO(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}const rO={kernelName:Te,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new nO(s.shape),i=[sO(s,r.complexTensorInfos.real),sO(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}};class aO{constructor(e){this.outputShape=[],this.outputShape=dd(e,1),this.variableNames=e.map(((e,t)=>`T${t}`));const t=new Array(e.length-1);t[0]=e[0][1];for(let n=1;n<t.length;n++)t[n]=t[n-1]+e[n][1];const n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let e=1;e<t.length;e++){const s=t[e-1];n.push(`else if (yC < ${t[e]}) setOutput(getT${e}(yR, yC-${s}));`)}const s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join("\n        ")}\n      }\n    `}}class iO{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=dd(e,t);const n=this.outputShape,s=n.length,r=AR(s),a=Y_("coords",s),i=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map(((e,t)=>`T${t}`));const o=new Array(e.length-1);o[0]=e[0][t];for(let n=1;n<o.length;n++)o[n]=o[n-1]+e[n][t];const l=i[t],u=i.slice(-2),c=i.join();let h=`if (${l} < ${o[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${u.join()}));\n        }`;for(let e=1;e<o.length;e++){const t=o[e-1];h+=`\n        if (${l} < ${o[e]}  && ${l} >= ${o[e-1]}) {\n          return getChannel(\n            getT${e}(${oO(i,l,t)}),\n            vec2(${oO(u,l,t)}));\n        }`}const p=o.length,d=o[o.length-1];h+=`\n        return getChannel(\n          getT${p}(${oO(i,l,d)}),\n          vec2(${oO(u,l,d)}));`,this.userCode=`\n      float getValue(${i.map((e=>"int "+e))}) {\n        ${h}\n      }\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[s-1]} = ${a[s-1]} + 1;\n        if (${a[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[s-2]} = ${a[s-2]} + 1;\n        if (${a[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[s-1]} = ${a[s-1]} - 1;\n        if (${a[s-2]} < ${n[s-2]} &&\n            ${a[s-1]} < ${n[s-1]}) {\n          result.b = getValue(${a});\n        }\n        setOutput(result);\n      }\n    `}}function oO(e,t,n){const s=e.indexOf(t);return e.map(((e,t)=>t===s?`${e} - ${n}`:e)).join()}function lO(e){const{inputs:t,backend:n}=e,{input:s}=t;return fF({inputs:{x:n.texData.get(s.dataId).complexTensorInfos.imag},backend:n})}const uO={kernelName:mt,backendName:"webgl",kernelFunc:lO};function cO(e,t,n){const s=e[0].dtype;if("complex64"===s){const s=e.map((e=>qD({inputs:{input:e},backend:n}))),r=e.map((e=>lO({inputs:{input:e},backend:n}))),a=cO(s,t,n),i=cO(r,t,n),o=gF({inputs:{real:a,imag:i},backend:n});return s.forEach((e=>n.disposeIntermediateTensorInfo(e))),r.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),o}let r=n.shouldExecuteOnCPU(e);if("string"===s&&(r=!0),r){const r=e.map((e=>{const s=d(e.shape.slice(t));return DF({inputs:{x:e},backend:n,attrs:{shape:[-1,s]}})})),a=r.map((e=>({vals:n.readSync(e.dataId),shape:e.shape}))),i=dd(r.map((e=>e.shape)),1),o=1===r[0].shape[0],l=o_(a,i,s,o),u=dd(e.map((e=>e.shape)),t),c=n.makeTensorInfo(u,s,l);return r.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}const a=e.filter((e=>d(e.shape)>0)),i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&a[0].shape.length>1;if(1===a.length){const t=i?new sF(e[0].shape,aF):new iF(e[0].shape,aF);return n.runWebGLProgram(t,e,s)}const o=X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(a.length>o){const e=[];for(let s=0;s<a.length;s+=o){const r=a.slice(s,s+o);e.push(cO(r,t,n))}const s=cO(e,t,n);for(const t of e)n.disposeIntermediateTensorInfo(t);return s}if(i){const e=new iO(a.map((e=>e.shape)),t);return n.runWebGLProgram(e,a,s)}const{tensors2D:l,outShape:u}=function(e,t,n){const s=dd(e.map((e=>e.shape)),t);return{tensors2D:e.map((e=>DF({inputs:{x:e},attrs:{shape:[-1,d(e.shape.slice(t))]},backend:n}))),outShape:s}}(a,t,n),c=new aO(l.map((e=>e.shape))),h=n.runWebGLProgram(c,l,s);l.forEach((e=>n.disposeIntermediateTensorInfo(e)));const p=DF({inputs:{x:h},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(h),p}function hO(e){const{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w(r,t[0].shape)[0];pd(t.map((e=>e.shape)),a);const i=dd(t.map((e=>e.shape)),a);if(0===d(i))return n.makeTensorInfo(i,t[0].dtype,[]);const o=t.filter((e=>d(e.shape)>0));return 1===o.length?fF({inputs:{x:o[0]},backend:n}):cO(o,a,n)}const pO={kernelName:Ce,backendName:"webgl",kernelFunc:hO};class dO{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,d=4*Math.floor(e.inChannels/4),f=e.inChannels%4,m="channelsLast"===e.dataFormat,g=m?1:2,y=m?2:3,b=m?3:1;let x="",w="";n&&(x=s?`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`\n          float activation(float x) {\n            ${n}\n          }\n        `,w="result = activation(result);");const v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${x}\n\n      const ivec2 strides = ivec2(${o}, ${l});\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${b}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${g}], coords[${y}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${h}; wR++) {\n          int xR = xRCorner + wR * ${u};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${p}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${d}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${1===f}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${d}) *\n                    getW(wR, wC, ${d}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${d}, xR, xC) *\n                    getW(wR, wC, ${d}, d2);\n              }\n\n            } else if (${2===f}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${d}, d2),\n                getW(wR, wC, ${d} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${d}),\n                  getX(batch, xR, xC, ${d} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${d}, xR, xC),\n                  getX(batch, ${d} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${3===f}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${d}, d2),\n                getW(wR, wC, ${d} + 1, d2),\n                getW(wR, wC, ${d} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${d}),\n                  getX(batch, xR, xC, ${d} + 1),\n                  getX(batch, xR, xC, ${d} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${d}, xR, xC),\n                  getX(batch, ${d} + 1, xR, xC),\n                  getX(batch, ${d} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${v}\n        ${w}\n        setOutput(result);\n      }\n    `}}class fO{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,p=e.filterWidth,d=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${r}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${n}, ${s});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${o};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h}; wR++) {\n            int xR = xRCorner + wR * ${l};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p}; wC++) {\n              int xC = xCCorner + wC * ${u};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${d}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${1===f}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${d}) *\n                  getW(wF, wR, wC, ${d}, d2);\n              } else if (${2===f}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${d}),\n                  getX(batch, xF, xR, xC, ${d} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${d}, d2),\n                  getW(wF, wR, wC, ${d} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${3===f}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${d}),\n                  getX(batch, xF, xR, xC, ${d} + 1),\n                  getX(batch, xF, xR, xC, ${d} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${d}, d2),\n                  getW(wF, wR, wC, ${d} + 1, d2),\n                  getW(wF, wR, wC, ${d} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class mO{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=LR(this.outputShape.length);const a=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,h=c;let p="\n       int xR; int xC; int xCOffset;\n       vec4 wTexel; vec4 previous; vec4 final;";for(let e=0;e<c;e++)p+=`\n           vec4 xTexelC${2*e};\n           int xTexelC${2*e}Ready;\n           vec4 xTexelC${2*e+1};\n           int xTexelC${2*e+1}Ready;\n           vec4 xC${e};`;p+=`\n     for (int r = 0; r < ${u}; r++) {\n      for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {\n       `;for(let e=0;e<c;e++)p+=`\n           xTexelC${2*e} = vec4(0.0);\n           xTexelC${2*e}Ready = 0;\n           xTexelC${2*e+1} = vec4(0.0);\n           xTexelC${2*e+1}Ready = 0;\n           xC${e} = vec4(0.0);`;p+="\n         xR = xRCorner + r * dilations[0];\n         if (xR >=0 && xR < inDims[0]) {\n       ";for(let t=0;t<(h+1)/2;t++){const n=2*t;if(p+=`\n           xC = xCCorner + ${n*l};\n           `,1===o){if(n<c&&(a%2==1?(p+=`\n                 xCOffset = xC + 1;\n                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n}Ready == 0) {\n                   xTexelC${n} = getX(batch, xR, xCOffset, d1);\n\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${n}.zw = vec2(0.0);\n                   }\n                   xTexelC${n}Ready = 1;\n                 }\n               `,p+=1===l&&n>0?`\n                 xC${n} = vec4(xTexelC${n-2}.zw, xTexelC${n}.xy);\n                 `:`\n                   xCOffset = xC + 1 - 2;\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       previous.zw = vec2(0.0);\n                     }\n\n                     xC${n} = vec4(previous.zw, xTexelC${n}.xy);\n                   } else {\n                     xC${n} = vec4(0.0, 0.0, xTexelC${n}.xy);\n                   }\n                   `):p+=`\n                 if (xC >= 0 && xC < inDims[1] && xTexelC${n}Ready == 0) {\n                   xTexelC${n} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${n}.zw = vec2(0.0);\n                   }\n                   xTexelC${n}Ready = 1;\n                 }\n\n                 xC${n} = xTexelC${n};\n                 `,n+1<c)){const e=a%2==0?i(l):l;l%2==0&&a%2==1||l%2!=0&&a%2!=1?(p+=`\n                   xCOffset = xC + imod(pads[1], 2) + ${e};\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n+1}Ready == 0) {\n                     xTexelC${n+1} = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       xTexelC${n+1}.zw = vec2(0.0);\n                     }\n                     xTexelC${n+1}Ready = 1;\n                   }\n                   `,p+=l>1?`\n                     xCOffset -= 2;\n                     if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                      previous = getX(batch, xR, xCOffset, d1);\n                      xC${n+1} = vec4(previous.zw, xTexelC${n+1}.xy);\n                     } else {\n                      xC${n+1} = vec4(0.0, 0.0, xTexelC${n+1}.xy);\n                     }\n                     `:`\n                     xC${n+1} = vec4(xTexelC${n}.zw, xTexelC${n+1}.xy);\n                     `):p+=1===e?`\n                     xC${n+1} = xTexelC${n};\n                     `:`\n                     xCOffset = xC + ${e};\n\n                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n+1}Ready == 0) {\n                       xTexelC${n+1} = getX(batch, xR, xCOffset, d1);\n                       if (xCOffset + 1 >= inDims[1]) {\n                         xTexelC${n+1}.zw = vec2(0.0);\n                       }\n                       xTexelC${n+1}Ready = 1;\n                     }\n\n                     xC${n+1} = xTexelC${n+1};\n                     `}}else n<c&&(a%2==1?(p+=`\n                 xCOffset = xC + 1 - strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n}Ready == 0) {\n                   xTexelC${n} = getX(batch, xR, xCOffset, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${n}.zw = vec2(0.0);\n                   }\n                   xTexelC${n}Ready = 1;\n                 }\n\n                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${n+1}Ready == 0) {\n                   xTexelC${n+1} = getX(batch, xR, xC + 1, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xC + 2 >= inDims[1]) {\n                     xTexelC${n+1}.zw = vec2(0.0);\n                   }\n                   xTexelC${n+1}Ready = 1;\n                 }\n\n                 xC${n} = vec4(xTexelC${n}.zw, xTexelC${n+1}.zw);\n               `,n+1<c&&(p+=`\n                   final = vec4(0.0);\n                   xCOffset = xC + 1 + strides[1];\n                   if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                     final = getX(batch, xR, xCOffset, d1);\n                   }\n                   xC${n+1} = vec4(xTexelC${n+1}.xy, final.xy);\n                 `)):(p+=`\n                 if(xC >= 0 && xC < inDims[1] && xTexelC${n}Ready == 0) {\n                   xTexelC${n} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${n}.zw = vec2(0.0);\n                   }\n                   xTexelC${n}Ready = 1;\n                 }\n\n                 xCOffset = xC + strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${n+1}Ready == 0) {\n                   xTexelC${n+1} = getX(batch, xR, xCOffset, d1);\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${n+1}.zw = vec2(0.);\n                   }\n                   xTexelC${n+1}Ready = 1;\n                 }\n\n                 xC${n} = vec4(\n                   xTexelC${n}.xy, xTexelC${n+1}.xy);\n               `,n+1<c&&(p+=`\n                   xC${n+1} = vec4(xTexelC${n}.zw, xTexelC${n+1}.zw);\n                 `)));n<c&&(p+=`\n             wTexel = getW(r, ${n}, d1, d2);\n             dotProd += xC${n}.xxzz * vec4(wTexel.xy, wTexel.xy);\n             if(d1 + 1 < ${e.inChannels}) {\n               dotProd += xC${n}.yyww * vec4(wTexel.zw, wTexel.zw);\n             }\n           `,n+1<c&&(p+=`\n               wTexel = getW(r, ${n+1}, d1, d2);\n               dotProd += xC${n+1}.xxzz * vec4(wTexel.xy, wTexel.xy);\n               if(d1 + 1 < ${e.inChannels}) {\n                 dotProd += xC${n+1}.yyww * vec4(wTexel.zw, wTexel.zw);\n               }\n             `))}p+="\n     }\n   ",p+="\n     }\n   ",p+="\n     }\n   ";let d="",f="";n&&(d=s?`vec4 activation(vec4 a) {\n           vec4 b = getPreluActivationWeightsAtOutCoords();\n           ${n}\n         }`:r?`vec4 activation(vec4 a) {\n           vec4 b = getLeakyreluAlphaAtOutCoords();\n           ${n}\n         }`:`vec4 activation(vec4 x) {\n           ${n}\n         }`,f="result = activation(result);");const m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n       ${d}\n\n       void main() {\n         ivec4 coords = getOutputCoords();\n         int batch = coords.x;\n         ivec2 xRCCorner = coords.yz * strides - pads;\n         int d2 = coords.w;\n         int xRCorner = xRCCorner.x;\n         int xCCorner = xRCCorner.y;\n\n         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n         vec4 dotProd = vec4(0.000000000000001);\n\n         ${p}\n\n         vec4 result = dotProd - vec4(0.000000000000001);\n         ${m}\n         ${f}\n         setOutput(result);\n       }\n     `}}class gO{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=LR(this.outputShape.length);const{dataFormat:n}=t,s=dR(),r="channelsLast"===n,a=r?1:2,i=r?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`;let l="";for(let e=0;e<=1;e++)for(let t=0;t<=1;t++)l+=`\n          blockIndex = rc.z + ${t};\n          pos = rc.y + ${e};\n\n          ${o}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${a}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${r}) {\n                  innerDims = vec2(d1, ch);\n                  result[${2*e+t}] = getChannel(\n                    getA(rc.x, d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${2*e+t}] = getChannel(\n                    getA(rc.x, ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${l}\n\n        ${s.output} = result;\n      }\n    `}}function yO(e,t){const n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&1===n&&e[0]>1?[e[0],1]:null}function bO({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){const l=e.shape,c=s.texData.get(e.dataId),h=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,m="channelsLast"===n.dataFormat;let g;const y=[];if(null!=a){const e=yO(a.shape,m);null!=e&&(a=DF({inputs:{x:a},backend:s,attrs:{shape:e}}),y.push(a))}if(null!=r){const e=yO(r.shape,m);null!=e&&(r=DF({inputs:{x:r},backend:s,attrs:{shape:e}}),y.push(r))}if(!((1===p||1===d)&&h>1e3)&&c.isPacked&&m&&null!=c.texture&&l[2]%2!=0&&f(c.shape.slice(-3),l.slice(-3))){const h=l[0]*l[1]*(l[2]+1),p={dataId:e.dataId,shape:[1,h,n.inChannels],dtype:e.dtype},d=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,u(rR(c.shape,p.shape),(()=>`packed reshape ${c.shape} to ${p.shape} isn't free`));const f=DF({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(f);const m=jF({a:p,b:f,backend:s,transposeA:false,transposeB:false,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),b=s.texData.get(m.dataId);u(b.isPacked,(()=>"batchMatMul result is expected to be packed")),c.shape=d,b.shape=n.outShape,g=fF({inputs:{x:m},backend:s}),g.shape=n.outShape,y.push(m)}else{const l=n.outHeight*n.outWidth,u=DF({inputs:{x:e},backend:s,attrs:{shape:m?[n.batchSize,l,n.inChannels]:[n.batchSize,n.inChannels,l]}}),c=DF({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),h=jF({a:m?u:c,b:m?c:u,transposeA:!m,transposeB:false,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=DF({inputs:{x:h},backend:s,attrs:{shape:n.outShape}}),y.push(u),y.push(c),y.push(h)}for(const e of y)s.disposeIntermediateTensorInfo(e);return g}function xO({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){const{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:p,dataFormat:f}=n,m="channelsLast"===f,g=l*u*c,y=p*h,b=[n.batchSize,g,y],x=[];if(null!=a){const e=yO(a.shape,m);null!=e&&(a=DF({inputs:{x:a},backend:s,attrs:{shape:e}}),x.push(a))}if(null!=r){const e=yO(r.shape,m);null!=e&&(r=DF({inputs:{x:r},backend:s,attrs:{shape:e}}),x.push(r))}const w=DF({inputs:{x:t},backend:s,attrs:{shape:[1,g,d(t.shape)/g]}});x.push(w);const v=new gO(b,n),k=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],N=s.runWebGLProgram(v,[e],"float32",k),I=DF({inputs:{x:N},backend:s,attrs:{shape:b}});x.push(N),x.push(I);const S=null!=r,T=null!=a,C="leakyrelu"===o,$=o?TF(o,!0):null,E=new CF(m?I.shape:w.shape,m?w.shape:I.shape,m?[n.batchSize,y,n.outChannels]:[n.batchSize,n.outChannels,y],!0,!1,S,$,T,C),A=m?[I,w]:[w,I];if(r&&A.push(r),T&&A.push(a),C){const e=s.makeTensorInfo([],"float32",nr(i,"float32"));A.push(e),x.push(e)}const R=s.runWebGLProgram(E,A,"float32"),_=DF({inputs:{x:R},backend:s,attrs:{shape:n.outShape}});x.push(R);for(const e of x)s.disposeIntermediateTensorInfo(e);return _}const wO={kernelName:$e,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=s,h=Qo(l),p=Go(r.shape,a.shape,i,u,o,c,!1,h);let d;if(1!==p.filterHeight||1!==p.filterWidth||1!==p.dilationHeight||1!==p.dilationWidth||1!==p.strideHeight||1!==p.strideWidth||"SAME"!==p.padInfo.type&&"VALID"!==p.padInfo.type)if(p.strideWidth<=2&&"channelsLast"===h&&X().getBool("WEBGL_EXP_CONV")){const e=new mO(p),t=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];d=n.runWebGLProgram(e,[r,a],"float32",t)}else if(X().getBool("WEBGL_CONV_IM2COL"))d=xO({x:r,filter:a,convInfo:p,backend:n});else{const e=new dO(p);d=n.runWebGLProgram(e,[r,a],"float32")}else d=bO({x:r,filter:a,convInfo:p,backend:n});const f=DF({inputs:{x:d},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(d),f}};class vO{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a="channelsLast"===e.dataFormat;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${a}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class kO{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a="channelsLast"===e.dataFormat,i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${a}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class NO{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${r};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${a};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${s} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class IO{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${o}, ${l}, ${u});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${r}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${s}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${s} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const SO={kernelName:Ee,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,h=Qo(l),p=Go(r.shape,c,i,1,o,u,!1,h),d=new vO(p);return n.runWebGLProgram(d,[r,a],"float32")}};const TO={kernelName:Ae,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=s,h=Qo(u),p=Go(i,a.shape,o,1,l,c,!1,h),d=new kO(p);return n.runWebGLProgram(d,[r,a],"float32")}};const CO={kernelName:Re,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:l}=s,u=Ho(r.shape,a.shape,i,l,o),c=new fO(u);return n.runWebGLProgram(c,[r,a],"float32")}};const $O={kernelName:_e,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:l}=s,u=Ho(r.shape,l,i,1,o),c=new NO(u);return n.runWebGLProgram(c,[r,a],"float32")}};const EO={kernelName:Fe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:l}=s,u=Ho(l,a.shape,o,1,i),c=new IO(u);return n.runWebGLProgram(c,[r,a],"float32")}},AO=IF({opSnippet:"if (isnan(x)) return x;\n  return cos(x);\n"}),RO={kernelName:De,backendName:"webgl",kernelFunc:AO},_O=IF({opSnippet:"\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n"}),FO={kernelName:Oe,backendName:"webgl",kernelFunc:_O};class DO{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[a,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];const p="bilinear"===s?1:0,[d,f]=[i-1+".0",o-1+".0"],[m,g,y]=c>1?[""+(i-1)/(c-1),"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[b,x,w]=h>1?[""+(o-1)/(h-1),"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${b});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${a}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${x};\n\n        float in_y = ${y};\n        if( in_y < 0.0 || in_y > ${d} ) {\n          setOutput(float(${r}));\n          return;\n        }\n        float in_x = ${w};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${r}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${p} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}}const OO={kernelName:ze,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=s,c=new DO(r.shape,a.shape,o,l,u);return n.runWebGLProgram(c,[r,a,i],"float32")}};var MO;!function(e){e.Prod="*",e.Sum="+"}(MO||(MO={}));class LO{constructor(e,t,n,s){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];const r=this.outputShape.length,a=this.op===MO.Prod?"1.0":"0.0",i=n?a:`getX(${zO(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1];let l="",u="";n?(l=s?"end != "+(o-1):"end != 0",u=s?"end + 1":"end - 1"):(l=s?`end + pow2 < ${o}`:"end >= pow2",u=s?"end + pow2":"end - pow2"),this.userCode=`\n      void main() {\n        ${AR(r)} coords = getOutputCoords();\n        int end = ${PO(r,"coords",this.op)};\n        float val = ${i};\n        int pow2 = int(pow(2.0, index));\n        if (${l}) {\n          int idx = ${u};\n          ${PO(r,"coords",this.op)} = idx;\n          val ${this.op}= getX(${zO(r,"coords",this.op)});\n        }\n        setOutput(val);\n      }\n    `}}function zO(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.x, ${t}.y`;if(3===e)return`${t}.x, ${t}.y, ${t}.z`;if(4===e)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function PO(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.y`;if(3===e)return`${t}.z`;if(4===e)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function BO(e,t,n,s,r,a){const i=t.shape.length,o=Ql([s],i);let l=t;null!=o&&(l=GF({inputs:{x:t},backend:n,attrs:{perm:o}}));const u=tu(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);const c=l.shape[u];let h=fF({inputs:{x:l},backend:n});for(let t=0;t<=Math.ceil(Math.log2(c))-1;t++){const s=new LO(e,l.shape,!1,a),r=[[t]],i=h;h=n.runWebGLProgram(s,[h],h.dtype,r),n.disposeIntermediateTensorInfo(i)}if(r){const t=new LO(e,l.shape,r,a),s=h;h=n.runWebGLProgram(t,[h],h.dtype),n.disposeIntermediateTensorInfo(s)}if(null!=o){const e=GF({inputs:{x:h},backend:n,attrs:{perm:eu(o)}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(l),e}return h}const WO={kernelName:Me,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return BO(MO.Prod,r,n,a,i,o)}};const VO={kernelName:Le,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return BO(MO.Sum,r,n,a,i,o)}};const UO={kernelName:Pe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(1===r.shape.length){const e=n.readSync(r.dataId),t=n.readSync(a.dataId),s=s_(e,t,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,s)}if(2===r.shape.length){const e=n.bufferSync(r),t=n.bufferSync(a),s=r_(e,t,i,o);return n.makeTensorInfo(s.shape,a.dtype,s.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}};class GO{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}getHeightCoordString(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"}getWidthCoordString(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"}getDepthCoordString(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"}getOutputDepthSize(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}const HO={kernelName:Be,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],l=("NHWC"===i?r.shape[1]:r.shape[2])*a,u=("NHWC"===i?r.shape[2]:r.shape[3])*a,c=("NHWC"===i?r.shape[3]:r.shape[1])/(a*a),h=new GO("NHWC"===i?[o,l,u,c]:[o,c,l,u],a,i);return n.runWebGLProgram(h,[r],r.dtype)}};class jO{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=LR(this.outputShape.length);const a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels;let l="",u="";n&&(l=s?`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`\n          float activation(float x) {\n            ${n}\n          }\n        `,u="result = activation(result);");const c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${l}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${o};\n        int q = d2 - d1 * ${o};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${a}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${i}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${u}\n        setOutput(result);\n      }\n    `}}class qO{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=LR(this.outputShape.length);const a=e.outChannels/e.inChannels,o=e.padInfo.left,l=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,h=e.filterWidth,p=h;let d="\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;";for(let e=0;e<h;e++)d+=`\n          vec4 xTexelC${2*e};\n          int xTexelC${2*e}Ready;\n          vec4 xTexelC${2*e+1};\n          int xTexelC${2*e+1}Ready;\n          vec4 xC${e};`;d+=`\n    for (int r = 0; r < ${c}; r++) {\n      `;for(let e=0;e<h;e++)d+=`\n          xTexelC${2*e} = vec4(0.0);\n          xTexelC${2*e}Ready = 0;\n          xTexelC${2*e+1} = vec4(0.0);\n          xTexelC${2*e+1}Ready = 0;\n          xC${e} = vec4(0.0);`;d+="\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      ";for(let e=0;e<(p+1)/2;e++){const t=2*e;if(d+=`\n          xC = xCCorner + ${t*u};\n          `,1===l){if(t<h&&(o%2==1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n                  xTexelC${t} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${t}.zw = vec2(0.0);\n                  }\n                  xTexelC${t}Ready = 1;\n                }\n              `,d+=1===u&&t>0?`\n                xC${t} = vec4(xTexelC${t-2}.zw, xTexelC${t}.xy);\n                `:`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${t} = vec4(previous.zw, xTexelC${t}.xy);\n                  } else {\n                    xC${t} = vec4(0.0, 0.0, xTexelC${t}.xy);\n                  }\n                  `):d+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n                  xTexelC${t} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${t}.zw = vec2(0.0);\n                  }\n                  xTexelC${t}Ready = 1;\n                }\n\n                xC${t} = xTexelC${t};\n                `,t+1<h)){const e=o%2==0?i(u):u;u%2==0&&o%2==1||u%2!=0&&o%2!=1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${e};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n                    xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${t+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${t+1}Ready = 1;\n                  }\n                  `,d+=u>1?`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n                     xC${t+1} = vec4(previous.zw, xTexelC${t+1}.xy);\n                    } else {\n                     xC${t+1} = vec4(0.0, 0.0, xTexelC${t+1}.xy);\n                    }\n                    `:`\n                    xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.xy);\n                    `):d+=1===e?`\n                    xC${t+1} = xTexelC${t};\n                    `:`\n                    xCOffset = xC + ${e};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n                      xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${t+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${t+1}Ready = 1;\n                    }\n\n                    xC${t+1} = xTexelC${t+1};\n                    `}}else t<h&&(o%2==1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n                  xTexelC${t} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${t}.zw = vec2(0.0);\n                  }\n                  xTexelC${t}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${t+1}Ready == 0) {\n                  xTexelC${t+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${t+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${t+1}Ready = 1;\n                }\n\n                xC${t} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n              `,t+1<h&&(d+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${t+1} = vec4(xTexelC${t+1}.xy, final.xy);\n                `)):(d+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n                  xTexelC${t} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${t}.zw = vec2(0.0);\n                  }\n                  xTexelC${t}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n                  xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${t+1}.zw = vec2(0.);\n                  }\n                  xTexelC${t+1}Ready = 1;\n                }\n\n                xC${t} = vec4(\n                  xTexelC${t}.xy, xTexelC${t+1}.xy);\n              `,t+1<h&&(d+=`\n                  xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n                `)));t<h&&(d+=`\n            wTexel = getW(r, ${t}, d1, q);\n            dotProd += xC${t} * vec4(wTexel.xz, wTexel.xz);\n          `,t+1<h&&(d+=`\n              wTexel = getW(r, ${t+1}, d1, q);\n              dotProd += xC${t+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}d+="\n    }\n  ",d+="\n      }\n    ";let f="",m="";n&&(f=s?`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`vec4 activation(vec4 x) {\n          ${n}\n        }`,m="result = activation(result);");const g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${f}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${a};\n        int q = d2 - d1 * ${a};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${d}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${g}\n        ${m}\n        setOutput(result);\n      }\n    `}}const KO={kernelName:We,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=s;let h=l;null==h&&(h=[1,1]),u(Jo(i,h),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${h}'`));const p=Go(r.shape,a.shape,i,h,o,c,!0);let d;d=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?new qO(p):new jO(p);const f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",f)}};class XO{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${a} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class YO{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const ZO={kernelName:Ve,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=s,h=Go(r.shape,c,i,o,l,u,!0),p=new XO(h);return n.runWebGLProgram(p,[r,a],"float32")}};const JO={kernelName:Ue,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=s,h=Go(c,a.shape,i,o,l,u,!0),p=new YO(h);return n.runWebGLProgram(p,[r,a],"float32")}};class QO{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode="\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    "}}const eM={kernelName:Ge,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=d(s.shape),i=DF({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new QO(a),l=n.runWebGLProgram(o,[i],i.dtype),u=DF({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}};class tM{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=s;this.userCode=`\n      const ivec2 strides = ivec2(${r}, ${a});\n      const ivec2 pads = ivec2(${c}, ${h});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${i}; h++) {\n          int hIn = hBeg + h * ${l};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${o}; w++) {\n              int wIn = wBeg + w * ${u};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}}const nM={kernelName:He,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:l}=s,u=Wo(r.shape,a.shape,i,o,"NHWC",l);let c;const h=new tM(u);c=n.runWebGLProgram(h,[r,a],"float32");const p=DF({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),p}};const sM={kernelName:Xe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:l}=Gd(r,a.length);jd(i.length,l,a);const{path:u,steps:c}=qd(o,l),h=c.length;let p=null,d=i.length;const m=[];for(let e=0;e<h;++e){for(const t of c[e]){const{permutationIndices:e,expandDims:s}=Hd(d,l[t]);let r;Kd(e)?r=a[t]:(r=GF({inputs:{x:a[t]},backend:n,attrs:{perm:e}}),m.push(r));const i=r.shape.slice();for(let e=0;e<s.length;++e)i.splice(s[e],0,1);f(r.shape,i)||(r=DF({inputs:{x:r},backend:n,attrs:{shape:i}}),m.push(r)),null===p?p=r:(p=_F({inputs:{a:r,b:p},backend:n}),m.push(p))}e<h-1&&(u[e]>=0&&(p=VF({inputs:{x:p},backend:n,attrs:{axis:u[e]-(i.length-d),keepDims:!1}}),m.push(p)),d--)}for(const e of m)e!==p&&n.disposeIntermediateTensorInfo(e);return p}},rM=IF({opSnippet:"return (x >= 0.0) ? x : (exp(x) - 1.0);",packedOpSnippet:"\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n"}),aM={kernelName:Ye,backendName:"webgl",kernelFunc:rM},iM={kernelName:Ze,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new dF("\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",s.shape,r.shape):new pF("return (b >= 1.0) ? a : a * (b + 1.0);",s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)}},oM=SF({opSnippet:"return float(a == b);",packedOpSnippet:"\n  return vec4(equal(a, b));\n",dtype:"bool",cpuKernelImpl:l_}),lM={kernelName:Qe,backendName:"webgl",kernelFunc:oM},uM=IF({opSnippet:`\n  // Error function is calculated approximately with elementary function.\n  // See "Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n  float p = ${$d};\n  float a1 = ${Ed};\n  float a2 = ${Ad};\n  float a3 = ${Rd};\n  float a4 = ${_d};\n  float a5 = ${Fd};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`}),cM={kernelName:Je,backendName:"webgl",kernelFunc:uM},hM=IF({opSnippet:"if (isnan(x)) return x;\n  return exp(x);\n",packedOpSnippet:"\n  vec4 result = exp(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n",cpuKernelImpl:u_,dtype:"float32"}),pM={kernelName:et,backendName:"webgl",kernelFunc:hM};function dM(e){const{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice();let l=r;return r<0&&(u(-(i+1)<=r,(()=>`Axis must be in the interval [${-(i+1)}, ${i}]`)),l=i+r+1),o.splice(l,0,1),DF({inputs:{x:a},backend:s,attrs:{shape:o}})}const fM={kernelName:tt,backendName:"webgl",kernelFunc:dM},mM="return exp(x) - 1.0;",gM=IF({opSnippet:mM,packedOpSnippet:mM,cpuKernelImpl:c_}),yM={kernelName:nt,backendName:"webgl",kernelFunc:gM};class bM{constructor(e,t,n){this.variableNames=["real","imag"];const s=t[1];this.outputShape=t;const r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0";let i;if("real"===e)i="return real * expR - imag * expI;";else{if("imag"!==e)throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);i="return real * expI + imag * expR;"}this.userCode=`\n      const float exponentMultiplier = ${r};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${i}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${s});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${s}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${a};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `}}function xM(e,t,n){const s=n.texData.get(e.dataId),r=d(e.shape),a=e.shape[e.shape.length-1],i=DF({inputs:{x:e},backend:n,attrs:{shape:[r/a,a]}}),o=i.shape,l=new bM("real",o,t),u=new bM("imag",o,t),c=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:o},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:o}],h=n.runWebGLProgram(l,c,"float32"),p=n.runWebGLProgram(u,c,"float32"),f=gF({inputs:{real:h,imag:p},backend:n});n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p);const m=DF({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}const wM={kernelName:st,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:s}=t;return xM(s,!1,n)}};class vM{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode="\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    "}}function kM(e){const{backend:t,attrs:n}=e,{shape:s,value:r}=n;let{dtype:a}=n;if(a=a||F(r),"string"===a){const e=N(a,d(s));return e.fill(r),t.makeTensorInfo(s,a,e)}{const e=new vM(s,r),n=[[r]];return t.runWebGLProgram(e,[],a,n)}}const NM={kernelName:rt,backendName:"webgl",kernelFunc:kM};class IM{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${t} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}}const SM={kernelName:at,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,s=t,r=new IM(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},TM="return floor(x);",CM=IF({opSnippet:TM,packedOpSnippet:TM,cpuKernelImpl:h_}),$M={kernelName:it,backendName:"webgl",kernelFunc:CM},EM=SF({opSnippet:"\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n",packedOpSnippet:"\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n",dtype:"int32"}),AM={kernelName:ot,backendName:"webgl",kernelFunc:EM};class RM{constructor(e){this.variableNames=["A"];const t=dR(),[n,s]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);\n\n        vec4 values = ${t.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `}}class _M{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=dR(),[n,s]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${s}.0, ${n}.0);\n            vec4 values = ${t.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${t.output} = result;\n      }\n    `}}const FM={kernelName:rs,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:r}=t;const{numChannels:a}=s,i="undefined"!=typeof HTMLVideoElement&&r instanceof HTMLVideoElement,o="undefined"!=typeof HTMLImageElement&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],h=[u,l,a];if(o||i){const e=X().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");null!=DM&&e===OM||(OM=e,DM=document.createElement("canvas").getContext("2d",{willReadFrequently:OM})),DM.canvas.width=l,DM.canvas.height=u,DM.drawImage(r,0,0,l,u),r=DM.canvas}const p=n.makeTensorInfo(c,"int32");n.texData.get(p.dataId).usage=OA.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);const d=X().getBool("WEBGL_PACK")?new _M(h):new RM(h),f=n.runWebGLProgram(d,[p],"int32");return n.disposeData(p.dataId),f}};let DM,OM=X().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");const MM={kernelName:os,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=s,m=Qo(c),g=Go(r.shape,a.shape,l,h,u,p,!1,m);let y;const b=[],x=null!=i,w=null!=o,v="leakyrelu"===d,k=()=>{const e=[r,a],t=(e,t)=>{if("NCHW"===t&&1===e.shape.length&&1!==e.shape[0]){const t=DF({inputs:{x:e},backend:n,attrs:{shape:[e.shape[0],1,1]}});return b.push(t),t}return e};if(x&&e.push(t(i,c)),w&&e.push(t(o,c)),v){const t=n.makeTensorInfo([],"float32",nr(f,"float32"));e.push(t),b.push(t)}return e};if(1!==g.filterHeight||1!==g.filterWidth||1!==g.dilationHeight||1!==g.dilationWidth||1!==g.strideHeight||1!==g.strideWidth||"SAME"!==g.padInfo.type&&"VALID"!==g.padInfo.type)if(g.strideWidth<=2&&"channelsLast"===m&&X().getBool("WEBGL_EXP_CONV")){const e=d?TF(d,!0):null,t=new mO(g,x,e,w,v),s=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],r=k();y=n.runWebGLProgram(t,r,"float32",s)}else if(X().getBool("WEBGL_CONV_IM2COL"))y=xO({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:f});else{const e=d?TF(d,!1):null,t=new dO(g,x,e,w,v),s=k();y=n.runWebGLProgram(t,s,"float32")}else y=bO({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:f});const N=DF({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach((e=>n.disposeIntermediateTensorInfo(e))),N}};const LM={kernelName:ls,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=s,m=[];let g=h;null==g&&(g=[1,1]),u(Jo(l,g),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${g}'`));const y=Go(r.shape,a.shape,l,g,c,p,!0),b=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels==1,x=d?TF(d,b):null,w=[r,a],v=null!=i,k=null!=o,N="leakyrelu"===d;if(v&&w.push(i),k&&w.push(o),N){const e=n.makeTensorInfo([],"float32",nr(f,"float32"));w.push(e),m.push(e)}let I;I=b?new qO(y,v,x,k,N):new jO(y,v,x,k,N);const S=[[y.padInfo.top,y.padInfo.left],[y.strideHeight,y.strideWidth],[y.dilationHeight,y.dilationWidth],[y.inHeight,y.inWidth]],T=n.runWebGLProgram(I,w,"float32",S);return m.forEach((e=>n.disposeIntermediateTensorInfo(e))),T}};class zM{constructor(e,t,n,s){this.sliceDim=e,this.strides=t,this.paramsShape=s,this.variableNames=["x","indices"],this.outputShape=n;const r=AR(n.length);let a="\n    int index;";for(let e=0;e<this.sliceDim;e++)a+=`\n          index = round(getIndices(coords[0], ${e}));\n          out_of_bounds = out_of_bounds || index < 0;\n          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[e]};\n          flattenIndex += index * ${this.strides[e]};`;this.userCode=`\n         void main() {\n          ${r} coords = getOutputCoords();\n          int flattenIndex = 0;\n          bool out_of_bounds = false;\n\n          ${a}\n\n          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n        }\n      `}}const PM={kernelName:ct,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=d(s.shape),[l,u,c,h]=Hi(s,r),p=DF({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),f=DF({inputs:{x:s},backend:n,attrs:{shape:[d(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||"string"===s.dtype){const e=n.readSync(r.dataId),t=n.bufferSync(s),a=p_(e,t,s.dtype,u,i,c,h,s.shape,o);return n.makeTensorInfo(l,s.dtype,a.values)}const m=new zM(i,h,[u,c],s.shape),g=n.runWebGLProgram(m,[f,p],f.dtype),y=DF({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),y}};class BM{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;const n=AR(this.rank),s=function(e,t){const n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let t=0;t<e.length;t++)2===t?s.push("index"):s.push(`${n[t]}`);return s.join()}(e);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${s}));\n      }\n    `}}function WM(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,l=w(i,r.shape)[0];if(X().get("DEBUG")){const e=n.readSync(a.dataId),t=r.shape[l];for(let n=0;n<e.length;++n){const s=e[n];u(s<=t-1&&s>=0,(()=>`GatherV2: the index value ${s} is not in [0, ${t-1}]`))}}const c=pf(r,a,l,o),h=d(a.shape),p=[],f=DF({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),m=DF({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,h/c.batchSize]}});p.push(f),p.push(m);const g=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,a])||"string"===r.dtype){const e=n.bufferSync(m),t=n.bufferSync(f),s=d_(t,e,g);return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(c.outputShape,s.dtype,s.values)}const y=new BM(f.shape,g),b=n.runWebGLProgram(y,[f,m],f.dtype);p.push(b);const x=DF({inputs:{x:b},backend:n,attrs:{shape:c.outputShape}});return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),x}const VM={kernelName:ut,backendName:"webgl",kernelFunc:WM},UM=SF({opSnippet:"return float(a > b);",packedOpSnippet:"\n  return vec4(greaterThan(a, b));\n",cpuKernelImpl:f_,dtype:"bool"}),GM={kernelName:ht,backendName:"webgl",kernelFunc:UM},HM=SF({opSnippet:"return float(a >= b);",packedOpSnippet:"\n  return vec4(greaterThanEqual(a, b));\n",dtype:"bool",cpuKernelImpl:m_}),jM={kernelName:pt,backendName:"webgl",kernelFunc:HM};const qM={kernelName:ft,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:s}=t;return xM(s,!0,n)}},KM=IF({opSnippet:"return float(!isnan(x) && !isinf(x));",dtype:"bool"}),XM={kernelName:gt,backendName:"webgl",kernelFunc:KM},YM=IF({opSnippet:"return float(isinf(x));",dtype:"bool"}),ZM={kernelName:yt,backendName:"webgl",kernelFunc:YM},JM=IF({opSnippet:"return float(isnan(x));",dtype:"bool"}),QM={kernelName:bt,backendName:"webgl",kernelFunc:JM},eL=SF({opSnippet:"return float(a < b);",packedOpSnippet:"\n  return vec4(lessThan(a, b));\n",cpuKernelImpl:g_,dtype:"bool"}),tL={kernelName:wt,backendName:"webgl",kernelFunc:eL},nL=SF({opSnippet:"return float(a <= b);",packedOpSnippet:"\n  return vec4(lessThanEqual(a, b));\n",cpuKernelImpl:y_,dtype:"bool"}),sL={kernelName:vt,backendName:"webgl",kernelFunc:nL};const rL={kernelName:kt,backendName:"webgl",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=b_(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}},aL=IF({opSnippet:"if (isnan(x)) return x;\n  return x < 0.0 ? 0./0. : log(x);\n",packedOpSnippet:"\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n",cpuKernelImpl:x_}),iL={kernelName:Nt,backendName:"webgl",kernelFunc:aL},oL=IF({opSnippet:"if (isnan(x)) return x;\n  return log(1.0 + x);\n"}),lL={kernelName:It,backendName:"webgl",kernelFunc:oL},uL=SF({opSnippet:"return float(a >= 1.0 && b >= 1.0);",packedOpSnippet:"\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n",dtype:"bool"}),cL={kernelName:St,backendName:"webgl",kernelFunc:uL},hL=IF({opSnippet:"return float(!(x >= 1.0));"}),pL={kernelName:Tt,backendName:"webgl",kernelFunc:hL},dL=SF({opSnippet:"return float(a >= 1.0 || b >= 1.0);",packedOpSnippet:"\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n",dtype:"bool"}),fL={kernelName:Ct,backendName:"webgl",kernelFunc:dL};class mL{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];const a=t,i=e[3]-1;let o;this.outputShape=e;const l=`float(${n}) + float(${s}) * sum`;o=.5===r?`inversesqrt(${l})`:1===r?`1.0/(${l})`:`exp(log(${l}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${a}; j <= ${a}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${i}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${o};\n        setOutput(val);\n      }\n    `}}class gL{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const a=t,i=e[3]-1;let o;this.outputShape=e;const l=`float(${n}) + float(${s}) * sum`;o=.5===r?`inversesqrt(${l})`:1===r?`1.0/(${l})`:`exp(log(${l}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${a};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${a}; j <= ${a}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${o};\n        setOutput(result);\n      }\n    `}}const yL={kernelName:Et,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:l}=s,u=X().getBool("WEBGL_PACK_NORMALIZATION")?new gL(r.shape,a,i,o,l):new mL(r.shape,a,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)}};class bL{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${s}) * norm + float(${n});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${s})\n                * float(${r})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${r});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}}const xL={kernelName:At,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=s,h=new bL(r.shape,o,l,u,c);return n.runWebGLProgram(h,[r,a,i],r.dtype)}};function wL(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,l=w(a,r.shape);let u=l;const c=Ql(u,o),h=null!=c,p=n.shouldExecuteOnCPU([r]);let f=r;if(h){if(p){const e=n.texData.get(f.dataId).values,t=new Array(o);for(let e=0;e<t.length;e++)t[e]=r.shape[c[e]];const s=q_(e,r.shape,r.dtype,c,t);f=n.makeTensorInfo(t,r.dtype);n.texData.get(f.dataId).values=s}else f=WF(r,c,n);u=tu(u.length,o)}Jl("max",u,o);const[m,g]=Yl(f.shape,u);let y,b=m;if(i&&(b=Zl(m,l)),p){const e=n.texData.get(f.dataId).values,t=w_(e,d(g),b,r.dtype);y=n.makeTensorInfo(b,r.dtype);n.texData.get(y.dataId).values=t}else y=function(e,t,n,s){const r=d(t),a=DF({inputs:{x:e},attrs:{shape:[d(e.shape)/r,r]},backend:s}),i=zF(a,e.dtype,"max",s),o=DF({inputs:{x:i},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(a),s.disposeIntermediateTensorInfo(i),o}(f,g,b,n);return h&&n.disposeIntermediateTensorInfo(f),y}const vL={kernelName:Rt,backendName:"webgl",kernelFunc:wL},kL=SF({opSnippet:"\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n\n  return max(a, b);\n",packedOpSnippet:"\n  vec4 result = vec4(max(a, b));\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  \n  result.r = isNaN.r ? NAN : result.r;\n  result.g = isNaN.g ? NAN : result.g;\n  result.b = isNaN.b ? NAN : result.b;\n  result.a = isNaN.a ? NAN : result.a;\n\n  return result;\n",cpuKernelImpl:v_}),NL={kernelName:_t,backendName:"webgl",kernelFunc:kL};const IL={kernelName:Ft,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t;hR(r,"maxPool");const{filterSize:a,strides:i,pad:o,dimRoundingMode:l}=s;u(Jo(i,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));const c=Vo(r.shape,a,i,1,o,l);if(1===c.filterWidth&&1===c.filterHeight&&f(c.inShape,c.outShape))return fF({inputs:{x:r},backend:n});const h=new SD(c,"max",!1);return n.runWebGLProgram(h,[r],r.dtype)}};const SL={kernelName:Ot,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=s,c=Uo(r.shape,a,i,[1,1,1],o,u,l),h=new TD(c,"max",!1);return n.runWebGLProgram(h,[r],r.dtype)}};class TL{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,l=r*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${r};\n          wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${a}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class CL{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,d=o*l*u-1;this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${h}, ${p});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${o};\n           wD += ${r}) {\n          float dyD = float(dyDCorner + wD) / ${t}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${l};\n              wR += ${a}) {\n            float dyR = float(dyRCorner + wR) / ${n}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${u};\n                wC += ${i}) {\n              float dyC = float(dyCCorner + wC) / ${s}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${d} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${l} * ${u} +\n                  wR * ${u} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const $L={kernelName:Mt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=s,h=Uo(i.shape,o,l,[1,1,1],u,c),p=new TD(h,"max",!0),d=n.runWebGLProgram(p,[i],i.dtype),f=new CL(h),m=n.runWebGLProgram(f,[r,d],i.dtype);return n.disposeIntermediateTensorInfo(d),m}};const EL={kernelName:Dt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;hR([a,i],"maxPoolGrad");const{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=s,p=Vo(o.shape,l,u,1,c,h),d=new SD(p,"max",!0),f=n.runWebGLProgram(d,[o],o.dtype),m=new TL(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}};const AL={kernelName:Lt,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,l=n;u(4===s.shape.length,(()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`));const c=[1,1];u(Jo(a,c),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`));const h=Vo(s.shape,r,a,c,i),[p,d]=function(e,t,n,s){let r=new SD(n,"max",!1);const a=s.runWebGLProgram(r,[e],"float32");return r=new SD(n,"max",!0,!0,t),[a,s.runWebGLProgram(r,[e],"float32")]}(s,o,h,l);return[p,d]}};const RL={kernelName:zt,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,l=w(a,s.shape);let u=l;const c=Ql(u,o),h=null!=c,p=i.shouldExecuteOnCPU([s]),f=[];let m=s;if(h){if(p){const e=i.texData.get(m.dataId).values,t=new Array(o);for(let e=0;e<t.length;e++)t[e]=s.shape[c[e]];const n=q_(e,s.shape,s.dtype,c,t);m=i.makeTensorInfo(t,s.dtype);i.texData.get(m.dataId).values=n}else m=WF(s,c,i);f.push(m),u=tu(u.length,o)}Jl("sum",u,o);const[g,y]=Yl(m.shape,u);let b=g;r&&(b=Zl(g,l));const x=function(e,t,n,s){const r=d(t),a=DF({inputs:{x:e},attrs:{shape:[d(e.shape)/r,r]},backend:s}),i=zF(a,"float32","mean",s),o=DF({inputs:{x:i},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(a),s.disposeIntermediateTensorInfo(i),o}(m,y,b,i);for(const e of f)i.disposeIntermediateTensorInfo(e);return x}};const _L={kernelName:Pt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,l=w(a,r.shape);let u=l;const c=Ql(u,o);let h=r;null!=c&&(h=GF({inputs:{x:r},backend:n,attrs:{perm:c}}),u=tu(u.length,r.shape.length)),Jl("min",u,o);const[p,f]=Yl(h.shape,u),m=DF({inputs:{x:h},backend:n,attrs:{shape:[-1,d(f)]}}),g=zF(m,m.dtype,"min",n);let y;if(i){y=DF({inputs:{x:g},backend:n,attrs:{shape:Zl(p,l)}})}else y=DF({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=c&&n.disposeIntermediateTensorInfo(h),y}},FL=SF({opSnippet:"\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n\n  return min(a, b);\n",packedOpSnippet:"\n  vec4 result = vec4(min(a, b));\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  \n  result.r = isNaN.r ? NAN : result.r;\n  result.g = isNaN.g ? NAN : result.g;\n  result.b = isNaN.b ? NAN : result.b;\n  result.a = isNaN.a ? NAN : result.a;\n\n  return result;\n",cpuKernelImpl:k_}),DL={kernelName:Bt,backendName:"webgl",kernelFunc:FL};class OL{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const s=e.length,r=AR(s),a=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l="reflect"===n?0:1;this.userCode=1!==s?`\n      ${r} start = ${r}(${a});\n      ${r} end = ${r}(${i});\n\n      void main() {\n        ${r} outC = getOutputCoords();\n        for (int i = 0; i < ${s}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${l};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};\n          }\n        }\n        ${r} coords = outC - start;\n        setOutput(getX(${o}));\n      }\n    `:`\n        int start = ${a};\n        int end = ${i};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${l};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${l};\n          }\n          setOutput(getX(outC - start));\n        }\n      `}}class ML{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const s=e.length,r=AR(s),a=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=Y_("rc",s),l=Y_("source",s),u=`${o[s-1]} < ${this.outputShape[s-1]}`,c=1===s?"source":`vec2(${l.slice(-2).join()})`,h="reflect"===n?0:1;let p="";if(1===s){const e=`\n        ${r} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${h};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${h};\n        }\n        source -= start;\n      `;p=`\n        ${r} rc = outputLoc;\n        ${e}\n        result[0] = getChannel(getX(${l.join()}), ${c});\n        ${o[s-1]} += 1;\n        if(${u}) {\n          ${e}\n          result[1] = getChannel(getX(${l.join()}), ${c});\n        }\n      `}else{const e=`\n        ${r} source = rc;\n        ${r} lt = ${r}(lessThan(source, start));\n        ${r} gte = ${r}(greaterThanEqual(source, end));\n        ${r} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${h}) +\n                gte * ((end - 1) * 2 - source + ${h});\n        source -= start;\n      `;p=`\n        ${r} rc = outputLoc;\n        ${e}\n        result[0] = getChannel(getX(${l.join()}), ${c});\n        ${o[s-1]} += 1;\n        if(${u}) {\n          ${e}\n          result[1] = getChannel(getX(${l.join()}), ${c});\n        }\n        rc = outputLoc;\n        ${o[s-2]} += 1;\n        if(${o[s-2]} < ${this.outputShape[s-2]}) {\n          ${e}\n          result[2] = getChannel(getX(${l.join()}), ${c});\n          ${o[s-1]} += 1;\n          if(${u}) {\n            ${e}\n            result[3] = getChannel(getX(${l.join()}), ${c});\n          }\n        }\n      `}this.userCode=`\n      const ${r} start = ${r}(${a});\n      const ${r} end = ${r}(${i});\n\n      void main() {\n        ${r} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${p}\n        setOutput(result);\n      }\n    `}}const LL={kernelName:Wt,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s}=e,{paddings:r,mode:a}=n,i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ML(s.shape,r,a):new OL(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)}},zL=SF({opSnippet:"if (b == 0.0) return NAN;\n  return mod(a, b);",packedOpSnippet:"\n  vec4 result = mod(a, b);\n  bvec4 isNaN = equal(b, vec4(0.0));\n  \n  result.r = isNaN.r ? NAN : result.r;\n  result.g = isNaN.g ? NAN : result.g;\n  result.b = isNaN.b ? NAN : result.b;\n  result.a = isNaN.a ? NAN : result.a;\n\n  return result;\n"}),PL={kernelName:Vt,backendName:"webgl",kernelFunc:zL};class BL{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${t-1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${t-1}));\n      }\n    `}}const WL=SF({opSnippet:"\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;",packedOpSnippet:"\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n",checkOutOfBounds:!0}),VL={kernelName:Ke,backendName:"webgl",kernelFunc:WL},UL="return a - b;",GL=SF({opSnippet:UL,packedOpSnippet:UL,supportsComplex:!0,cpuKernelImpl:G_}),HL={kernelName:jn,backendName:"webgl",kernelFunc:GL};function jL(e){const{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w([a],r.shape),o=wL({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=Zl(o.shape,i),u=DF({inputs:{x:o},backend:n,attrs:{shape:l}}),c=GL({inputs:{a:r,b:u},backend:n}),h=hM({inputs:{x:c},backend:n}),p=VF({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),d=DF({inputs:{x:p},backend:n,attrs:{shape:l}}),f=WL({inputs:{a:h,b:d},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),f}const qL={kernelName:Dn,backendName:"webgl",kernelFunc:jL};const KL={kernelName:Ut,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:i,normalized:o}=s,l=o?r:jL({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new BL(u,c,a),p=[[i]],d=n.runWebGLProgram(h,[l],"int32",p);return o||n.disposeIntermediateTensorInfo(l),d}};const XL={kernelName:Ht,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){const e=n.texData.get(s.dataId),[t,r]=I_(e.values,s.shape,s.dtype);return n.makeTensorInfo(r,s.dtype,t)}let r;return r=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new iF(s.shape,"\n  vec4 result = -x;\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n"):new sF(s.shape,"if (isnan(x)) return x;\n  return -x;\n"),n.runWebGLProgram(r,[s],s.dtype)}},YL=cp;const ZL={kernelName:qt,backendName:"webgl",kernelFunc:function(e){us("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:h}=YL(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}},JL=hp;const QL={kernelName:Kt,backendName:"webgl",kernelFunc:function(e){us("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(r.dataId),h=n.readSync(a.dataId),{selectedIndices:p,validOutputs:d}=JL(c,h,i,o,l,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([d]))]}},ez=pp;const tz={kernelName:Xt,backendName:"webgl",kernelFunc:function(e){us("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),h=n.readSync(a.dataId),p=i,d=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=ez(c,h,p,d,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}};class nz{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${s}), float(${n}),\n                      float(index == coords.y)));\n      }\n    `}}const sz={kernelName:Zt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{dtype:a,depth:i,onValue:o,offValue:l}=s,u=d(r.shape),c=new nz(u,i,o,l),h=DF({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(c,[h],a);n.disposeIntermediateTensorInfo(h);const f=DF({inputs:{x:p},backend:n,attrs:{shape:[...r.shape,i]}});return n.disposeIntermediateTensorInfo(p),f}};function rz(e){const{inputs:t,backend:n}=e,{x:s}=t;if("complex64"===s.dtype){const e=qD({inputs:{input:s},backend:n}),t=rz({inputs:{x:e},backend:n}),r=lO({inputs:{input:s},backend:n}),a=rz({inputs:{x:r},backend:n}),i=gF({inputs:{real:t,imag:a},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),i}return kM({attrs:{shape:s.shape,dtype:s.dtype,value:"string"===s.dtype?"":0},backend:n})}const az={kernelName:ns,backendName:"webgl",kernelFunc:rz};const iz={kernelName:Yt,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:s}=t,{x:r}=n;if("string"===r.dtype)throw new Error("onesLike is not supported under string dtype");if("complex64"===r.dtype){const t=qD({inputs:{input:r},backend:s}),n=e({inputs:{x:t},backend:s}),a=lO({inputs:{input:r},backend:s}),i=rz({inputs:{x:a},backend:s}),o=gF({inputs:{real:n,imag:i},backend:s});return s.disposeIntermediateTensorInfo(t),s.disposeIntermediateTensorInfo(n),s.disposeIntermediateTensorInfo(a),s.disposeIntermediateTensorInfo(i),o}return kM({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:s})}};const oz={kernelName:Jt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(1===t.length)return dM({inputs:{input:t[0]},backend:n,attrs:{dim:r}});const a=t[0].shape,i=t[0].dtype;t.forEach((e=>{c(a,e.shape,"All tensors passed to stack must have matching shapes"),u(i===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));const o=[],l=hO({inputs:t.map((e=>{const t=dM({inputs:{input:e},backend:n,attrs:{dim:r}});return o.push(t),t})),backend:n,attrs:{axis:r}});return o.forEach((e=>n.disposeIntermediateTensorInfo(e))),l}};class lz{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const s=e.length,r=AR(s),a=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);this.userCode=1!==s?`\n      ${r} start = ${r}(${a});\n      ${r} end = ${r}(${i});\n\n      void main() {\n        ${r} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          ${r} coords = outC - start;\n          setOutput(getX(${o}));\n        }\n      }\n    `:`\n        int start = ${a};\n        int end = ${i};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `}}class uz{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const s=e.length,r=AR(s),a=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=Y_("rc",s),l=Y_("source",s),u=`${o[s-1]} < ${this.outputShape[s-1]}`,c=1===s?"source":`vec2(${l.slice(-2).join()})`,h=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;\n       if(${u}) {\n      `,1===s?"":`}\n       rc = outputLoc;\n       ${o[s-2]} += 1;\n       if(${o[s-2]} < ${this.outputShape[s-2]}) {`,1===s?"":`  ${o[s-1]} += 1;\n         if(${u}) {`],p=1===s?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let d="";for(let e=0,t=1===s?2:4;e<t;e++)d+=`\n        ${h[e]}\n        if (${p}) {\n          result[${e}] = float(value);\n        } else {\n          ${r} source = rc - start;\n          result[${e}] = getChannel(getX(${l.join()}), ${c});\n        }\n      `;d+=1===s?"} ":"}}",this.userCode=`\n      const ${r} start = ${r}(${a});\n      const ${r} end = ${r}(${i});\n\n      void main() {\n        ${r} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${d}\n        setOutput(result);\n      }\n    `}}const cz=e=>{const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(0===d(r.shape)){const e=a.map(((e,t)=>e[0]+r.shape[t]+e[1]));return kM({backend:n,attrs:{shape:e,value:i,dtype:r.dtype}})}const o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uz(r.shape,a,i):new lz(r.shape,a,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},hz={kernelName:Qt,backendName:"webgl",kernelFunc:cz},pz=SF({opSnippet:"\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",packedOpSnippet:"\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  bvec4 isNaN1 = lessThan(a, vec4(0.0));\n  bvec4 isNaN2 = lessThan(floor(b), b);\n  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n  \n  result.r = isNaN.r ? NAN : result.r;\n  result.g = isNaN.g ? NAN : result.g;\n  result.b = isNaN.b ? NAN : result.b;\n  result.a = isNaN.a ? NAN : result.a;\n\n  return result;\n"}),dz={kernelName:en,backendName:"webgl",kernelFunc:pz};const fz={kernelName:nn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,l=[],u=w(a,r.shape);let c=u;const h=Ql(c,o);let p,f=r;if(null!=h&&(f=GF({inputs:{x:r},backend:n,attrs:{perm:h}}),c=tu(c.length,o),l.push(f)),Jl("prod",c,o),n.shouldExecuteOnCPU([f])){const e=n.texData.get(f.dataId).values,{outVals:t,outShape:s,outDtype:r}=T_(f.shape,f.dtype,e,c);p=n.makeTensorInfo(s,r,t)}else{const[e,t]=Yl(f.shape,c),s=d(t),a=DF({inputs:{x:f},backend:n,attrs:{shape:[-1,s]}}),i=zF(a,Rr(r.dtype),"prod",n);p=DF({inputs:{x:i},backend:n,attrs:{shape:e}}),l.push(a),l.push(i)}if(i){l.push(p);const e=Zl(p.shape,u);p=DF({inputs:{x:p},backend:n,attrs:{shape:e}})}return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),p}};const mz={kernelName:sn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{paramsNestedSplits:r,paramsDenseValues:a,indices:i}=t,{outputRaggedRank:o}=s,l=r.map((e=>n.readSync(e.dataId))),u=r.map((e=>e.shape)),c=n.readSync(a.dataId),h=n.readSync(i.dataId),[p,d,f]=C_(l,u,c,a.shape,a.dtype,h,i.shape,o),m=p.map((e=>n.makeTensorInfo([e.length],"int32",e))),g=n.makeTensorInfo(f,a.dtype,d);return m.concat([g])}};const gz={kernelName:rn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{starts:s,limits:r,deltas:a}=t,i=n.readSync(s.dataId),o=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=$_(i,s.shape,s.dtype,o,r.shape,l,a.shape);return[n.makeTensorInfo([u.length],"int32",u),n.makeTensorInfo([c.length],s.dtype,c)]}};const yz={kernelName:an,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{shape:r,values:a,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),h=n.readSync(i.dataId),p=o.map((e=>n.readSync(e.dataId))),d=o.map((e=>e.shape)),[f,m]=E_(u,r.shape,c,a.shape,a.dtype,h,i.shape,p,d,l);return n.makeTensorInfo(f,a.dtype,m)}},bz=e=>{const{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=A_(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},xz={kernelName:on,backendName:"webgl",kernelFunc:bz},wz=IF({opSnippet:"return 1.0 / x;"}),vz={kernelName:un,backendName:"webgl",kernelFunc:wz},kz=IF({opSnippet:"if (isnan(x)) return x;\n  return (x < 0.0) ? 0.0 : x;\n",packedOpSnippet:"\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n"}),Nz={kernelName:cn,backendName:"webgl",kernelFunc:kz},Iz=IF({opSnippet:"if (isnan(x)) return x;\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n",packedOpSnippet:"\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n"}),Sz={kernelName:gn,backendName:"webgl",kernelFunc:Iz};class Tz{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];const[a,i,o,l]=e;this.outputShape=[a,t,n,l];const u=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n];let h;h=r?"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${u[0]/c[0]},\n          ${u[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${h};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `}}class Cz{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[a,i,o,l]=e;this.outputShape=[a,t,n,l];const u=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n];let h;h=r?"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${u[0]/c[0]},\n          ${u[1]/c[1]},\n          ${u[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${h};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${l-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}}const $z={kernelName:fn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[l,u]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Cz(r.shape,l,u,a,i):new Tz(r.shape,l,u,a,i);return n.runWebGLProgram(c,[r],"float32")}};class Ez{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],l=[n&&a>1?a-1:a,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,p=1/c,d=2*Math.ceil(h)+2,f=2*Math.ceil(p)+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${u});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${h});\n        const float invWidthScale = float(${p});\n\n        const int winHeight = int(${d});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}}const Az={kernelName:mn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new Ez(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}};class Rz{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];const[a,i,o,l]=e;this.outputShape=[a,t,n,l];const u=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],h=s?"0.5":"0.0";let p;p=r?"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${u[0]/c[0]},\n          ${u[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}}class _z{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[a,i,o,l]=e;this.outputShape=[a,t,n,l];const u=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],h=s?"0.5":"0.0";let p;p=r?"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${u[0]/c[0]},\n          ${u[1]/c[1]},\n          ${u[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${p};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${l-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}}const Fz={kernelName:pn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[l,u]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new _z(r.shape,l,u,a,i):new Rz(r.shape,l,u,a,i);return n.runWebGLProgram(c,[r],r.dtype)}};class Dz{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],l=[n&&a>1?a-1:a,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,p=1/c,d=2*Math.ceil(h)+2,f=2*Math.ceil(p)+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${u});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${h});\n        const float invWidthScale = float(${p});\n\n        const int winHeight = int(${d});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${l[0]}));\n\n            float sourceFracCol =\n                float(${o[1]}) *\n                  (float(dyC) / float(${l[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${s}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}}const Oz={kernelName:dn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new Dz(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}};class Mz{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,1===n)return void(this.userCode=`\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${e[0]} - coord - 1));\n        }\n      `);const s=e.map(((n,s)=>(n=>-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - coords[${n}] - 1`:`coords[${n}]`)(s))).join(","),r=AR(n);this.userCode=`\n      void main() {\n        ${r} coords = getOutputCoords();\n        setOutput(getX(${s}));\n      }\n    `}}class Lz{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;const s=Y_("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=AR(n);function o(n){const s=e.map(((s,r)=>function(n,s){return-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - ${s[n]} - 1`:`${s[n]}`}(r,n)));return`getChannel(getX(${s.join(",")}), vec2(${s.slice(-2).join(",")}))`}this.userCode=1===n?`\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${e[0]} - rc - 1),\n            ${e[0]} - rc - 1);\n          if(${r}){\n              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),\n                ${e[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `:`\n        void main() {\n          ${i} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${function(e){return o(e)}(s.slice())};\n          if(${r}){\n            result.g = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",o(e)}(s.slice())};\n          }\n          if(${a}) {\n            result.b = ${function(e){return e[n-2]="("+e[n-2]+" + 1)",o(e)}(s.slice())};\n            if(${r}) {\n              result.a = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",o(e)}(s.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `}}const zz={kernelName:yn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w(a,r.shape);if(0===i)return fF({inputs:{x:r},backend:n});const l=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lz(r.shape,o):new Mz(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}};class Pz{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];const n=e[1],s=e[2];this.outputShape=e;let r="";r="number"==typeof t?`float outputValue = ${t.toFixed(2)};`:`\n        vec3 fill = vec3(${t.join(",")});\n        float outputValue = fill[coords[3]];`,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${r}\n          if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}}const Bz={kernelName:as,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,l=new Pz(s.shape,a),[u,c]=wd(i,s.shape[1],s.shape[2]),h=[[u,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[s],s.dtype,h)}},Wz=IF({opSnippet:"\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n"}),Vz={kernelName:bn,backendName:"webgl",kernelFunc:Wz},Uz=IF({opSnippet:"return inversesqrt(x);",cpuKernelImpl:R_}),Gz={kernelName:xn,backendName:"webgl",kernelFunc:Uz};class Hz{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;const o=AR(r.length),l=AR(a.length);let u="";1===n?u="i":2===n&&(u="i, j");const c=`getIndices(${u})`;let h="";1===s?h="i":2===s&&(h="i, coords[1]");const p=`getUpdates(${h})`,d=t>1?"strides[j]":"strides";this.userCode=`\n        ${o} strides = ${o}(${r});\n\n        void main() {\n          ${l} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${e}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${t}; j++) {\n              int index = round(${c});\n              flattenedIndex += index * ${d};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${p};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `}}const jz={kernelName:wn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=Xi(0,r,i),p=[h/u,u];if(0===h)return n.makeTensorInfo(i,r.dtype);const d=DF({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),f=DF({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new Hz(l,o,d.shape.length,f.shape.length,c,p),y=n.runWebGLProgram(g,[f,d,m],f.dtype),b=DF({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),b}};class qz{constructor(e,t,n,s){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];const r=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,a=2===X().getNumber("WEBGL_VERSION")?"while (left < right) {":r,i="left"===s?"<":"<=";this.userCode=`\n       int findBound(int batch, float value) {\n         int left = 0;\n         int right = numInputs;\n         int mid;\n         ${a}\n           mid = (left + right) / 2;\n           if (getSortedSequence(batch, mid) ${i} value) {\n             left = mid + 1;\n           } else {\n             right = mid;\n           }\n         }\n         return right;\n       }\n\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int valueIndex = coords[1];\n\n         float value = getValues(batch, valueIndex);\n\n         setOutput(float(findBound(batch, value)));\n       }\n     `}}const Kz={kernelName:vn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{sortedSequence:r,values:a}=t,{side:i}=s,o=new qz(r.shape[0],r.shape[1],a.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,a],"int32",l)}};class Xz{constructor(e,t,n){let s,r;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error(`Where for rank ${n} is not yet supported`);if(1===n)r="resRC",s="resRC";else{const n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[],i=[];for(let s=0;s<t.length;s++)i.push(`${n[s]}`),s<e&&a.push(`${n[s]}`);s=a.join(),r=i.join()}const a=AR(n);this.userCode=`\n      void main() {\n        ${a} resRC = getOutputCoords();\n        float cVal = getC(${s});\n        if (cVal >= 1.0) {\n          setOutput(getA(${r}));\n        } else {\n          setOutput(getB(${r}));\n        }\n      }\n    `}}const Yz={kernelName:kn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Xz(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],Ar(r.dtype,a.dtype))}},Zz=IF({opSnippet:`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = 1.7580993408473768;\n  float scale = ${Cd};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`}),Jz={kernelName:Nn,backendName:"webgl",kernelFunc:Zz},Qz=IF({opSnippet:"if (isnan(x)) return x;\n  return 1.0 / (1.0 + exp(-1.0 * x));\n",packedOpSnippet:"\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n",cpuKernelImpl:F_}),eP={kernelName:$n,backendName:"webgl",kernelFunc:Qz},tP=IF({opSnippet:"\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n"}),nP={kernelName:Cn,backendName:"webgl",kernelFunc:tP},sP=IF({opSnippet:"if (isnan(x)) return x;\n  return sin(x);\n"}),rP={kernelName:Sn,backendName:"webgl",kernelFunc:sP},aP=IF({opSnippet:"\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n"}),iP={kernelName:Tn,backendName:"webgl",kernelFunc:aP},oP=IF({opSnippet:"\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n"}),lP={kernelName:En,backendName:"webgl",kernelFunc:oP},uP={kernelName:_n,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;u(r.shape.length<=4,(()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"));const o=a.reduce(((e,t)=>e*t)),l=[[0,0]];l.push(...i);for(let e=1+a.length;e<r.shape.length;++e)l.push([0,0]);const c=[],h=cz({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=vd(h.shape,a,o,!1),d=kd(p.length,a.length,!1),f=Nd(h.shape,a,o,!1),m=DF({inputs:{x:h},backend:n,attrs:{shape:p}}),g=GF({inputs:{x:m},backend:n,attrs:{perm:d}}),y=DF({inputs:{x:g},backend:n,attrs:{shape:f}});return c.push(h),c.push(m),c.push(g),c.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}};const cP={kernelName:On,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(1!==a.shape.length)throw new Error(`Dense shape must be a vector, saw:\n         ${a.shape}`);if(2!==s.shape.length)throw new Error(`Indices must be a matrix, saw:\n         ${s.shape}`);if(1!==r.shape.length)throw new Error(`Values must be a vector, saw:\n         ${r.shape}`);if(0!==i.shape.length)throw new Error(`Default value must be a scalar, saw:\n        ${i.shape}`);const o=n.readSync(s.dataId),l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[h,p,d,f,m]=M_(o,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(p,s.dtype,h),n.makeTensorInfo([p[0]],r.dtype,d),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map((e=>Number(e))))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}};const hP={kernelName:Mn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(2!==s.shape.length)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(1!==r.shape.length)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(1!==a.shape.length)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);const i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,h]=L_(o,s.shape,s.dtype,i,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([h.length],a.dtype,new Int32Array(h))]}};const pP={kernelName:Ln,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==r.shape.length)throw new Error(`Indices should be a vector but received shape\n              ${r.shape}`);if(1!==a.shape.length)throw new Error(`Segment ids should be a vector but received shape\n              ${a.shape}`);const i=n.readSync(s.dataId),o=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=z_(i,s.shape,s.dtype,o,l,!0);return n.makeTensorInfo(c,s.dtype,u)}};const dP={kernelName:zn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==r.shape.length)throw new Error(`Indices should be a vector but received shape\n             ${r.shape}`);if(1!==a.shape.length)throw new Error(`Segment ids should be a vector but received shape\n             ${a.shape}`);const i=n.readSync(s.dataId),o=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=z_(i,s.shape,s.dtype,o,l);return n.makeTensorInfo(c,s.dtype,u)}};const fP={kernelName:Pn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:h,outputSize:p}=Xi(0,r,o);if("string"===a.dtype){const e=n.bufferSync(r),t=n.bufferSync(a),s=or(n.readSync(i.dataId)[0]),d=__(e,t,o,p,c,u,l,h,s,false);return n.makeTensorInfo(o,d.dtype,d.values)}const d=new Hz(u,l,r.shape.length,a.shape.length,h,[p,1],false),f=n.runWebGLProgram(d,[a,r,i],a.dtype),m=DF({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}};const mP={kernelName:Fn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w(i,r.shape)[0],l=Yd(r,a,o),u=r.shape.length,c=new Array(u).fill(0),h=r.shape.slice();return l.map((e=>{const t=[...h];t[o]=e;const s=BD({inputs:{x:r},backend:n,attrs:{begin:c,size:t}});return c[o]+=e,s}))}},gP="return sqrt(x);",yP=IF({opSnippet:gP,packedOpSnippet:gP,cpuKernelImpl:P_}),bP={kernelName:An,backendName:"webgl",kernelFunc:yP},xP={kernelName:Wn,backendName:"webgl",kernelFunc:IF({opSnippet:"return x * x;"})},wP="return (a - b) * (a - b);",vP=SF({opSnippet:wP,packedOpSnippet:wP}),kP={kernelName:Bn,backendName:"webgl",kernelFunc:vP};const NP={kernelName:ss,backendName:"webgl",kernelFunc:function({inputs:e,attrs:t,backend:n}){const{x:s}=e,r=`if (isnan(x)) return x;\n    return x > 0.0 ? 1.0 : float(${t.alpha});\n  `,a=new sF(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}};class IP{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const s=n.length,r=AR(n.length),a=AR(n.length);let i="";if(1===s)i="coords * strides + begin";else{let e=0;i=n.map(((t,s)=>(e++,1===n.length?`coords * strides[${s}] + begin[${s}]`:`coords[${e-1}] * strides[${s}] + begin[${s}]`))).join(",")}this.userCode=`\n      ${r} begin = ${r}(${e});\n      ${r} strides = ${r}(${t});\n\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${i}));\n      }\n    `}}const SP={kernelName:Vn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:h,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:f,finalShape:m,isIdentity:g,sliceDim0:y,isSimpleSlice:b,begin:x,end:w,strides:v}=co(r.shape,a,i,o,l,c,h,p,d);let k;if(g)k=DF({inputs:{x:r},backend:n,attrs:{shape:m}});else if(y||b){u(r.shape.length>=1,(()=>`Input must have rank at least 1, got: ${r.shape.length}`));const e=Ji(x,w,v),t=BD({inputs:{x:r},backend:n,attrs:{begin:x,size:e}});k=DF({inputs:{x:t},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(t)}else{if(n.shouldExecuteOnCPU([r])){const e=n.readSync(r.dataId),t=Za(r.shape,r.dtype,e),s=B_(f,t,v,x);k=n.makeTensorInfo(m,r.dtype,s.values)}else{const e=new IP(x,v,f);k=n.runWebGLProgram(e,[r],r.dtype)}}const N=DF({inputs:{x:k},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(k),N}};const TP={kernelName:Un,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:h}=t,p=n.readSync(c.dataId),d=n.readSync(h.dataId),[f,m]=W_(p,d,r,a,i,o,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(h.shape,"int32",m)]}};const CP={kernelName:Gn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if("string"!==a.dtype)throw new Error("Input must be of datatype string");if(1!==a.shape.length)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(0!==i.shape.length)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);const o=n.readSync(a.dataId),l=n.readSync(i.dataId)[0],[u,c,h]=V_(o,l,r),p=c.length;return[n.makeTensorInfo([p,2],"int32",u),n.makeTensorInfo([p],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(h))]}};const $P={kernelName:Hn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if("string"!==a.dtype)throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");const i=n.readSync(a.dataId),o=U_(i,r);return n.makeTensorInfo(a.shape,"int32",o)}},EP=IF({opSnippet:"return tan(x);"}),AP={kernelName:qn,backendName:"webgl",kernelFunc:EP},RP=IF({opSnippet:"\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n"}),_P={kernelName:Kn,backendName:"webgl",kernelFunc:RP};class FP{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;const s=AR(this.rank),r=function(e){const t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(1===t)return`imod(resRC, ${e[0]})`;const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let t=0;t<e.length;t++)s.push(`imod(${n[t]}, ${e[t]})`);return s.join()}(e);this.userCode=`\n      void main() {\n        ${s} resRC = getOutputCoords();\n        setOutput(getA(${r}));\n      }\n    `}}function DP(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if("string"===r.dtype||r.shape.length>5){const e=n.readSync(r.dataId),t="string"===r.dtype?e.map((e=>or(e))):e,s=Za(r.shape,r.dtype,t),i=H_(s,a);return n.makeTensorInfo(i.shape,i.dtype,i.values)}const i=new FP(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}const OP={kernelName:Xn,backendName:"webgl",kernelFunc:DP};class MP{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode="\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     "}}class LP{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode="\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     "}}function zP(e,t){null!==t&&e.disposeIntermediateTensorInfo(t)}function PP(e){let t=1;for(;t<e;)t*=2;return t}const BP={kernelName:Yn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,c=u[u.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>l){const e=n.readSync(r.dataId),[t,s]=j_(e,u,r.dtype,a,i);return[n.makeTensorInfo(t.shape,t.dtype,t.values),n.makeTensorInfo(s.shape,s.dtype,s.values)]}if(0===a)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(1===c)return[r,kM({attrs:{shape:u,dtype:"int32",value:0},backend:n})];const h=n.texData.get(r.dataId),p=null!==h&&h.isPacked,f=p?n.unpackTensor(r):r,m=d(u)/c,g=DF({inputs:{x:f},attrs:{shape:[m,c]},backend:n});p&&zP(n,f);const y=PP(a),b=PP(c);let x=null;const w=()=>null===x?[g,g]:[g,x],v=(e,t,s)=>{const r=w(),a=new MP(s),i=[[c],[null===x?1:0],[Number.NEGATIVE_INFINITY],[e],[t]],o=x;x=n.runWebGLProgram(a,r,"int32",i),zP(n,o)};for(let e=1;e<y;e*=2){const t=2*e;for(let n=e;n>=1;n/=2)v(t,n,[m,b])}for(let e=b;e>y;e/=2){const t=w(),s=new LP([m,e/2]),r=[[c],[null===x?1:0],[y]],a=x;x=n.runWebGLProgram(s,t,"int32",r),zP(n,a);const i=y/2,o=2*i;for(let e=i;e>=1;e/=2)v(o,e,x.shape)}let k=x;x=BD({inputs:{x:x},backend:n,attrs:{begin:0,size:[m,a]}}),zP(n,k);let N=WM({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});zP(n,g);const I=u.slice(0,-1);I.push(a),k=x,x=DF({inputs:{x:x},attrs:{shape:I},backend:n}),zP(n,k);const S=N;return N=DF({inputs:{x:N},attrs:{shape:I},backend:n}),zP(n,S),[N,x]}};class WP{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;const i="nearest"===n?1:2;let o;switch(s){case"constant":default:o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${o} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${r});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${r});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${i} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}}const VP={kernelName:Zn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=s,[c,h,p,d]=r.shape,[f,m]=null!=u?u:[h,p],g=new WP(h,p,i,o,l,[c,f,m,d]);return n.runWebGLProgram(g,[r,a],"float32")}};const UP={kernelName:Qn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;hR(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const i=s.readSync(a.dataId),{outputValues:o,outputShape:l,indices:u}=K_(i,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,o),s.makeTensorInfo([u.length],"int32",u)]}};const GP={kernelName:es,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{value:r}=t;let{axis:a}=s;a<0&&(a+=r.shape.length);const i=r,o=i.shape.length,l=r.shape[a],u=new Array(o-1);let c=0;for(let e=0;e<o;e++)e!==a&&(u[c++]=i.shape[e]);const h=[],p=new Array(o).fill(0),d=i.shape.slice();d[a]=1;const f=new Array(l);for(let e=0;e<f.length;e++){p[a]=e;const t=BD({inputs:{x:i},backend:n,attrs:{begin:p,size:d}}),s=DF({inputs:{x:t},backend:n,attrs:{shape:u}});f[e]=s,h.push(t)}return h.forEach((e=>n.disposeIntermediateTensorInfo(e))),f}};class HP{constructor(e,t){this.variableNames=["x","segmentIds"];const n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];const o=4*Math.floor(n/4),l=n%4,u="\n        sumValue += dot(values, segFilter);\n    ";let c="";r%n>0&&(c=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `);let h="";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        ${c}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${h}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${a})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${o}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${o};\n        if (${1===l}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${u}\n        } else if (${2===l}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${u}\n        } else if (${3===l}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}}const jP={kernelName:ts,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,l=[];let u=0;const c=Ql([u],o);let h=r;null!=c&&(h=GF({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(h),u=tu(1,o)[0]);const p=hf(h.shape,u,i),f=d([h.shape[u]]),m=DF({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}});l.push(m);const g=Rr(r.dtype),y=(e,t,s,r,a)=>{const i=e.shape[0],o=e.shape[1],u=cf(o,a),c=new HP({windowSize:u,inSize:o,batchSize:i,numSegments:a},t),h=n.compileAndRun(c,[e,s],r);if(l.push(h),h.shape[1]===a)return h;const p=bz({backend:n,attrs:{start:0,stop:a,step:1,dtype:"float32"}}),d=DP({inputs:{x:p},backend:n,attrs:{reps:[o/u]}});l.push(p),l.push(d);return y(h,t,d,r,a)},b=DF({inputs:{x:y(m,"unsortedSegmentSum",a,g,i)},backend:n,attrs:{shape:p}});let x=b;if(null!=c){l.push(b);const e=eu(c);x=GF({inputs:{x:x},backend:n,attrs:{perm:e}})}return 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