handtrackjs.js

/*! For license information please see handtrack.min.js.LICENSE.txt */
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Buffer shape=${this.shape}`;throw new Error(t)}t++}let n=e[e.length-1];for(let t=0;t<e.length-1;++t)n+=this.strides[t]*e[t];return this.values[n]}locToIndex(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(0===this.rank)return[];if(1===this.rank)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return As().makeTensor(this.values,this.shape,this.dtype)}}let As=null,Rs=null,Fs=null;class Ds{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=D(e),this.strides=Z(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return Rs.buffer(this.shape,this.dtype,e)}bufferSync(){return Rs.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return ee(this.shape,e)}arraySync(){return ee(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=As().read(this.dataId);if("string"===this.dtype){const t=await e;try{return t.map((e=>ws(e)))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=As().readSync(this.dataId);if("string"===this.dtype)try{return e.map((e=>ws(e)))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await As().read(this.dataId);return"string"===this.dtype?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(As().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Rs.print(this,e)}clone(){return this.throwIfDisposed(),Rs.clone(this)}toString(e=!1){return Is(this.dataSync(),this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Rs.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),As().makeVariable(this,e,t,n)}}function _s(){return de("Tensor",(()=>Ds))}Object.defineProperty(Ds,Symbol.hasInstance,{value:e=>!!e&&null!=e.data&&null!=e.dataSync&&null!=e.throwIfDisposed}),_s();class Os extends Ds{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 (${this.dtype}) must match`);if(!_(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);As().disposeTensor(this),this.dataId=e.dataId,As().incRef(this,null)}dispose(){As().disposeVariable(this),this.isDisposedInternal=!0}}var Ms,Ls,zs,Ps,Bs;Object.defineProperty(Os,Symbol.hasInstance,{value:e=>e instanceof Ds&&null!=e.assign&&e.assign instanceof Function}),function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"}(Ms||(Ms={})),function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"}(Ls||(Ls={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"}(zs||(zs={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"}(Ps||(Ps={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"}(Bs||(Bs={}));const Ws={float32:Ps,int32:Ls,bool:zs,complex64:Bs};function Vs(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Ws[e][t]}function Us(e){return Vs(e,"int32")}function Gs(e,t){if(e.dtype===t.dtype)return[e,t];const n=Vs(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Hs(e,t){return t.some((t=>t.id===e.id))}function js(e){const t=[];return Ks(e,t,new Set),t}function Ks(e,t,n){if(null==e)return;if(e instanceof Ds)return void t.push(e);if(s=e,!Array.isArray(s)&&"object"!=typeof s)return;var s;const a=e;for(const e in a){const s=a[e];n.has(s)||(n.add(s),Ks(s,t,n))}}function qs(e){return null!=e.kernelName}class Xs{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map((e=>e.name))))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class Ys{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Xs}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then((()=>{}));if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${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 ks(this.backendInstance),!0}setupRegisteredKernels(){ps(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){ps(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 n=t.factory();if(!n||n instanceof I||"function"!=typeof n.then)return this.registry[e]=n,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,s=n.then((n=>!(t<this.pendingBackendInitId||(this.registry[e]=n,this.pendingBackendInit=null,0)))).catch((n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message)),!1)));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}}catch(t){return console.warn(`Initialization of backend ${e} failed`),console.warn(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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:a}=this.initializeBackend(n);if(a||s)return{name:n,asyncInit:a}}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,a=this.readSync(t),r=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,r),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 Ys.nextTensorId++}nextVariableId(){return Ys.nextVariableId++}clone(e){const t=Zs.runKernel(It,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{const t={x:e};return Zs.runKernel(Oe,t,{dtype:"float32"})}})),[],{}),t}runKernel(e,t,n){if(null==cs(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 a=0;n.forEach((e=>{a+="complex64"===e.dtype?3:1}));const r=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-a-r;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const s=this.isTapeOn(),a=this.state.numBytes,r=this.state.numTensors;let i,o;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const l=qs(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(qs(e)){const{kernelName:t,inputs:a,attrs:r}=e;null==this.backendName&&this.backend;const l=cs(t,this.backendName);E(null!=l,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),i=()=>{const e=this.backend.numDataIds();o=l.kernelFunc({inputs:a,attrs:r,backend:this.backend});const i=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,i);const u=i.map((e=>{if(null!=e.rank)return e;const{dataId:t,shape:n,dtype:s}=e;return this.makeTensorFromDataId(t,n,s)}));if(s){const e=this.getTensorsForGradient(t,a,u);n=this.saveTensorsForBackwardMode(e)}return u}}else{const{forwardFunc:t}=e,a=e=>{s&&(n=e.map((e=>this.keep(this.clone(e)))))};i=()=>{const e=this.backend.numDataIds();o=this.tidy((()=>t(this.backend,a)));const n=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,e,n),n}}const{inputs:u,attrs:c}=e,h=qs(e)?null:e.backwardsFunc;let p;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(p=this.profiler.profileKernel(l,u,(()=>i())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs):t=i()})),s&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-r,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map((e=>null!=u[e]?u[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map((e=>this.keep(this.clone(e))))}getTensorsForGradient(e,t,n){const s=hs(e);if(null!=s){const e=s.inputsToSave||[],a=s.outputsToSave||[];let r;s.saveAllInputs?(E(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),r=Object.keys(t).map((e=>t[e]))):r=e.map((e=>t[e]));const i=n.filter(((e,t)=>a[t]));return r.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 a=e;"string"===n&&K(e[0])&&(a=e.map((e=>xs(e))));const r=s.write(a,t,n),i=new Ds(t,n,r,this.nextTensorId());if(this.trackTensor(i,s),"string"===n){const e=this.state.tensorInfo.get(r),t=function(e){if(null==e)return 0;let t=0;return e.forEach((e=>t+=e.length)),t}(a);this.state.numBytes+=t-e.bytes,e.bytes=t}return i}makeTensorFromDataId(e,t,n,s){const a=new Ds(t,n=n||"float32",e,this.nextTensorId());return this.trackTensor(a,s),a}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),null!=s&&s!==e.dtype&&(e=e.cast(s));const a=new Os(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[a.name])throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*j(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 Os||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*j(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,a,r){const i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},o=hs(e);null!=o&&(s=o.gradFunc),null!=s&&(i.gradient=e=>(e=e.map(((e,t)=>{if(null==e){const e=n[t],s=ne(e.size,e.dtype);return this.makeTensor(s,e.shape,e.dtype)}return e})),s(e.length>1?e:e[0],a,r))),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=js(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(E(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 a=this.scopedRun((()=>this.startTape()),(()=>this.endTape()),(()=>this.tidy("forward",e)));E(a instanceof Ds,(()=>"The result y returned by f() must be a tensor."));const r=function(e,t,n){const s={},a={};for(let e=0;e<t.length;e++)s[t[e].id]=!0;for(let n=0;n<e.length;n++){const r=e[n],i=r.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]){r.outputs.forEach((e=>s[e.id]=!0)),o=!0,a[r.id]=!0;break}if(o)break}}const r={};r[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(r[n.outputs[e].id]){for(const e in s)r[s[e].id]=!0,i[n.id]=!0;break}}const o=[];for(let t=0;t<e.length;t++){const n=e[t];if(a[n.id]&&i[n.id]){const e={};for(const t in n.inputs){const a=n.inputs[t];s[a.id]&&(e[t]=a)}const t=Object.assign({},n);t.inputs=e,t.outputs=n.outputs,o.push(t)}}return o}(this.state.activeTape,t,a);if(!s&&0===r.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[a.id]=null==n?function(e){const t=te(D(e),"float32");return Zs.makeTensor(t,e,"float32")}(a.shape):n,function(e,t,n,s){for(let a=t.length-1;a>=0;a--){const r=t[a],i=[];if(r.outputs.forEach((t=>{const n=e[t.id];null!=n?i.push(n):i.push(null)})),null==r.gradient)throw new Error(`Cannot compute gradient: gradient function not found for ${r.kernelName}.`);const o=r.gradient(i);for(const t in r.inputs){if(!(t in o))throw new Error(`Cannot backprop through input ${t}. Available gradients found: ${Object.keys(o)}.`);const a=n((()=>o[t]()));if("float32"!==a.dtype)throw new Error(`Error in gradient for op ${r.kernelName}. The gradient of input ${t} must have 'float32' dtype, but has '${a.dtype}'`);const i=r.inputs[t];if(!_(a.shape,i.shape))throw new Error(`Error in gradient for op ${r.kernelName}. The gradient of input '${t}' has shape '${a.shape}', which does not match the shape of the input '${i.shape}'`);if(null==e[i.id])e[i.id]=a;else{const t=e[i.id];e[i.id]=s(t,a),t.dispose()}}}}(e,r,(e=>this.tidy(e)),Qs);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:a,grads:s}}))}customGrad(e){return E(Y(e),(()=>"The f passed in customGrad(f) must be a function.")),(...t)=>{let n;E(t.every((e=>e instanceof Ds)),(()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors"));const s={};return t.forEach(((e,t)=>{s[t]=e})),this.runKernelFunc({forwardFunc:(s,a)=>(n=e(...t,a),E(n.value instanceof Ds,(()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor")),E(Y(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 a=n.gradFunc(e,s),r=Array.isArray(a)?a:[a];E(r.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(...).")),E(r.every((e=>e instanceof Ds)),(()=>"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 r.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)}async time(e){const t=bs(),n=await this.backend.time(e);return n.wallMs=bs()-t,n}track(e){return null!=this.state.activeScope&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Xs;for(const e in 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window?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error("The current browser does not appear to support IndexedDB.");return t}function Ca(e){const t=e.result;t.createObjectStore(Na,{keyPath:"modelPath"}),t.createObjectStore(Ia,{keyPath:"modelPath"})}class Sa{constructor(e){if(this.indexedDB=$a(),null==e||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise(((e,n)=>{const s=this.indexedDB.open(va,1);s.onupgradeneeded=()=>Ca(s),s.onsuccess=()=>{const a=s.result;if(null==t){const t=a.transaction(Na,"readonly"),s=t.objectStore(Na).get(this.modelPath);s.onsuccess=()=>{if(null==s.result)return a.close(),n(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));e(s.result.modelArtifacts)},s.onerror=e=>(a.close(),n(s.error)),t.oncomplete=()=>a.close()}else{const s=ba(t),r=a.transaction(Ia,"readwrite");let i=r.objectStore(Ia);const o=i.put({modelPath:this.modelPath,modelArtifactsInfo:s});let l;o.onsuccess=()=>{l=a.transaction(Na,"readwrite");const o=l.objectStore(Na).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:s});o.onsuccess=()=>e({modelArtifactsInfo:s}),o.onerror=e=>{i=r.objectStore(Ia);const t=i.delete(this.modelPath);t.onsuccess=()=>(a.close(),n(o.error)),t.onerror=e=>(a.close(),n(o.error))}},o.onerror=e=>(a.close(),n(o.error)),r.oncomplete=()=>{null==l?a.close():l.oncomplete=()=>a.close()}}},s.onerror=e=>n(s.error)}))}}Sa.URL_SCHEME="indexeddb://";const Ta=e=>{return ue().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Sa.URL_SCHEME)?(t=e.slice(Sa.URL_SCHEME.length),new Sa(t)):null;var t};wa.registerSaveRouter(Ta),wa.registerLoadRouter(Ta);class Ea{constructor(){this.indexedDB=$a()}async listModels(){return new Promise(((e,t)=>{const n=this.indexedDB.open(va,1);n.onupgradeneeded=()=>Ca(n),n.onsuccess=()=>{const s=n.result,a=s.transaction(Ia,"readonly"),r=a.objectStore(Ia).getAll();r.onsuccess=()=>{const t={};for(const e of r.result)t[e.modelPath]=e.modelArtifactsInfo;e(t)},r.onerror=e=>(s.close(),t(r.error)),a.oncomplete=()=>s.close()},n.onerror=e=>t(n.error)}))}async removeModel(e){var t;return e=(t=e).startsWith(Sa.URL_SCHEME)?t.slice(Sa.URL_SCHEME.length):t,new Promise(((t,n)=>{const s=this.indexedDB.open(va,1);s.onupgradeneeded=()=>Ca(s),s.onsuccess=()=>{const a=s.result,r=a.transaction(Ia,"readwrite"),i=r.objectStore(Ia),o=i.get(e);let l;o.onsuccess=()=>{if(null==o.result)return a.close(),n(new Error(`Cannot find model with path '${e}' 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Gi(r);{const e=s.size/a.size,t=bi(zi(r),zi(a));return e>1?bi(t,Fr(e)):t}}if(n===Pl.SUM_BY_NONZERO_WEIGHTS){if(null==a)return bi(zi(r),Fr(s.size));{const e=Mi(a,oo(s.shape)),t=ja(zi(ao(e,Fr(0))),"float32");return bi(zi(r),t)}}throw Error(`Unknown reduction: ${n}`)}});la({absoluteDifference_:function(e,t,n,s=Pl.SUM_BY_NONZERO_WEIGHTS){const a=ia(e,"labels","absoluteDifference"),r=ia(t,"predictions","absoluteDifference");let i=null;null!=n&&(i=ia(n,"weights","absoluteDifference")),A(a.shape,r.shape,"Error in absoluteDifference: ");const o=_r(Li(a,r));return Bl(o,i,s)}}),la({cosineDistance_:function(e,t,n,s,a=Pl.SUM_BY_NONZERO_WEIGHTS){const r=ia(e,"labels","cosineDistance"),i=ia(t,"predictions","cosineDistance");let o=null;null!=s&&(o=ia(s,"weights","cosineDistance")),A(r.shape,i.shape,"Error in cosineDistance: ");const l=Fr(1),u=Li(l,zi(Mi(r,i),n,!0));return Bl(u,o,a)}}),la({hingeLoss_:function(e,t,n,s=Pl.SUM_BY_NONZERO_WEIGHTS){let a=ia(e,"labels","hingeLoss");const r=ia(t,"predictions","hingeLoss");let i=null;null!=n&&(i=ia(n,"weights","hingeLoss")),A(a.shape,r.shape,"Error in hingeLoss: ");const o=Fr(1);a=Li(Mi(Fr(2),a),o);const l=yo(Li(o,Mi(a,r)));return Bl(l,i,s)}}),la({huberLoss_:function(e,t,n,s=1,a=Pl.SUM_BY_NONZERO_WEIGHTS){const r=ia(e,"labels","huberLoss"),i=ia(t,"predictions","huberLoss");let o=null;null!=n&&(o=ia(n,"weights","huberLoss")),A(r.shape,i.shape,"Error in huberLoss: ");const l=Fr(s),u=_r(Li(i,r)),c=ji(u,l),h=Li(u,c),p=Or(Mi(Fr(.5),to(c)),Mi(l,h));return Bl(p,o,a)}}),la({logLoss_:function(e,t,n,s=1e-7,a=Pl.SUM_BY_NONZERO_WEIGHTS){const r=ia(e,"labels","logLoss"),i=ia(t,"predictions","logLoss");let o=null;null!=n&&(o=ia(n,"weights","logLoss")),A(r.shape,i.shape,"Error in logLoss: ");const l=Fr(1),u=Fr(s),c=so(Mi(r,Di(Or(i,u)))),h=Mi(Li(l,r),Di(Or(Li(l,i),u))),p=Li(c,h);return Bl(p,o,a)}});const Wl=la({squaredDifference_:function(e,t){let n=ia(e,"a","squaredDifference"),s=ia(t,"b","squaredDifference");[n,s]=Gs(n,s),vi(n.shape,s.shape);const a={a:n,b:s};return Zs.runKernel(Wn,a,{})}});la({meanSquaredError_:function(e,t,n,s=Pl.SUM_BY_NONZERO_WEIGHTS){const a=ia(e,"labels","meanSquaredError"),r=ia(t,"predictions","meanSquaredError");let i=null;null!=n&&(i=ia(n,"weights","meanSquaredError")),A(a.shape,r.shape,"Error in meanSquaredError: ");const o=Wl(a,r);return Bl(o,i,s)}});const Vl=la({log1p_:function(e){const t={x:ia(e,"x","log1p")};return Zs.runKernel(Ot,t)}});la({sigmoidCrossEntropy_:function(e,t,n,s=0,a=Pl.SUM_BY_NONZERO_WEIGHTS){let r=ia(e,"multiClassLabels","sigmoidCrossEntropy");const i=ia(t,"logits","sigmoidCrossEntropy");let o=null;if(null!=n&&(o=ia(n,"weights","sigmoidCrossEntropy")),A(r.shape,i.shape,"Error in sigmoidCrossEntropy: "),s>0){const e=Fr(s),t=Fr(1),n=Fr(.5);r=Or(Mi(r,Li(t,e)),Mi(n,e))}const l=function(e,t){const n=ia(e,"labels","sigmoidCrossEntropyWithLogits"),s=ia(t,"logits","sigmoidCrossEntropyWithLogits");A(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const a=yo(s),r=Mi(s,n),i=Vl(_i(so(_r(s))));return Or(Li(a,r),i)}(r,i);return Bl(l,o,a)}});const Ul=la({logSumExp_:function(e,t=null,n=!1){const s=ia(e,"x","logSumExp"),a=B(t,s.shape),r=Oi(s,a,!0),i=Li(s,r),o=_i(i),l=zi(o,a),u=Di(l),c=Or(Zr(r,u.shape),u);if(n){const e=Yi(c.shape,a);return Zr(c,e)}return c}});la({softmaxCrossEntropy_:function(e,t,n,s=0,a=Pl.SUM_BY_NONZERO_WEIGHTS){let r=ia(e,"onehotLabels","softmaxCrossEntropy");const i=ia(t,"logits","softmaxCrossEntropy");let o=null;if(null!=n&&(o=ia(n,"weights","softmaxCrossEntropy")),A(r.shape,i.shape,"Error in softmaxCrossEntropy: "),s>0){const e=Fr(s),t=Fr(1),n=Fr(r.shape[1]);r=Or(Mi(r,Li(t,e)),bi(e,n))}const l=function(e,t,n=-1){if(-1===n&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return Rr(((e,t,s)=>{const a=Ul(t,[n],!0),r=Li(ja(t,"float32"),a);s([e,r]);const i=so(Mi(r,e));return{value:zi(i,[n]),gradFunc:(e,t)=>{const[s,a]=t,r=Yi(e.shape,[n]);return[Mi(Zr(e,r),Li(ja(s,"float32"),_i(a))),Mi(Zr(e,r),Li(_i(a),ja(s,"float32")))]}}}))(e,t)}(r,i);return Bl(l,o,a)}});const Gl={flipLeftRight:pl,resizeNearestNeighbor:Tl,resizeBilinear:Sl,rotateWithOffset:dl,cropAndResize:hl,nonMaxSuppression:ml,nonMaxSuppressionAsync:async function(e,t,n,s=.5,a=Number.NEGATIVE_INFINITY){const r=ia(e,"boxes","nonMaxSuppressionAsync"),i=ia(t,"scores","nonMaxSuppressionAsync"),o=fl(r,i,n,s,a);n=o.maxOutputSize,s=o.iouThreshold,a=o.scoreThreshold;const l=await Promise.all([r.data(),i.data()]),u=l[0],c=l[1],{selectedIndices:h}=bl(u,c,n,s,a);return r!==e&&r.dispose(),i!==t&&i.dispose(),_o(h,"int32")},nonMaxSuppressionWithScore:$l,nonMaxSuppressionWithScoreAsync:async function(e,t,n,s=.5,a=Number.NEGATIVE_INFINITY,r=0){const i=ia(e,"boxes","nonMaxSuppressionAsync"),o=ia(t,"scores","nonMaxSuppressionAsync"),l=fl(i,o,n,s,a,r);n=l.maxOutputSize,s=l.iouThreshold,a=l.scoreThreshold,r=l.softNmsSigma;const u=await Promise.all([i.data(),o.data()]),c=u[0],h=u[1],{selectedIndices:p,selectedScores:d}=wl(c,h,n,s,a,r);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:_o(p,"int32"),selectedScores:_o(d)}},nonMaxSuppressionPadded:Cl,nonMaxSuppressionPaddedAsync:async function(e,t,n,s=.5,a=Number.NEGATIVE_INFINITY,r=!1){const i=ia(e,"boxes","nonMaxSuppressionAsync"),o=ia(t,"scores","nonMaxSuppressionAsync"),l=fl(i,o,n,s,a,null),u=l.maxOutputSize,c=l.iouThreshold,h=l.scoreThreshold,[p,d]=await Promise.all([i.data(),o.data()]),{selectedIndices:f,validOutputs:m}=xl(p,d,u,c,h,r);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:_o(f,"int32"),validOutputs:Fr(m,"int32")}},transform:El},Hl={bandPart:Fl,gramSchmidt:Ml,qr:zl};class jl extends Dr{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=Zs.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const s=Zs.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:Cr((()=>Bo(s).variable(!1)))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:Cr((()=>Bo(s).variable(!1)))});const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const r=this.accumulatedGrads[n].variable,i=this.accumulatedUpdates[n].variable;Cr((()=>{const e=Or(Mi(r,this.rho),Mi(to(a),1-this.rho)),t=Mi(bi(Ao(Or(i,this.epsilon)),Ao(Or(r,this.epsilon))),a),n=Or(Mi(i,this.rho),Mi(to(t),1-this.rho));r.assign(e),i.assign(n);const o=Or(Mi(t,-this.learningRate),s);s.assign(o)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&(Sr(this.accumulatedGrads.map((e=>e.variable))),Sr(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(!1)}))),this.accumulatedUpdates=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}jl.className="Adadelta",Nr(jl);class Kl extends Dr{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=Zs.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:Cr((()=>Si(s.shape,this.initialAccumulatorValue).variable(e)))}}const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const r=this.accumulatedGrads[n].variable;Cr((()=>{const e=Or(r,to(a));r.assign(e);const t=Or(Mi(bi(a,Ao(Or(e,Zs.backend.epsilon()))),-this.learningRate),s);s.assign(t)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&Sr(this.accumulatedGrads.map((e=>e.variable)))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),this.accumulatedGrads=e.map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Kl.className="Adagrad",Nr(Kl);class ql extends Dr{constructor(e,t,n,s=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Cr((()=>{this.accBeta1=Fr(t).variable(),this.accBeta2=Fr(n).variable()})),null==s&&(this.epsilon=Zs.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);Cr((()=>{const n=Li(1,this.accBeta1),s=Li(1,this.accBeta2);t.forEach(((t,a)=>{const r=Zs.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:Cr((()=>Bo(r).variable(!1)))}),null==this.accumulatedSecondMoment[a]&&(this.accumulatedSecondMoment[a]={originalName:`${t}/v`,variable:Cr((()=>Bo(r).variable(!1)))});const i=Array.isArray(e)?e[a].tensor:e[t];if(null==i)return;const o=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,u=Or(Mi(o,this.beta1),Mi(i,1-this.beta1)),c=Or(Mi(l,this.beta2),Mi(to(i),1-this.beta2)),h=bi(u,n),p=bi(c,s);o.assign(u),l.assign(c);const d=Or(Mi(bi(h,Or(Ao(p),this.epsilon)),-this.learningRate),r);r.assign(d)})),this.accBeta1.assign(Mi(this.accBeta1,this.beta1)),this.accBeta2.assign(Mi(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&Sr(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&Sr(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),Cr((()=>{this.accBeta1.assign(Dl(this.beta1,this.iterations_+1)),this.accBeta2.assign(Dl(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(!1)}))),this.accumulatedSecondMoment=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}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)}}ql.className="Adam",Nr(ql);class Xl extends Dr{constructor(e,t,n,s=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Cr((()=>{this.iteration=Fr(0).variable(),this.accBeta1=Fr(t).variable()})),null==s&&(this.epsilon=Zs.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);Cr((()=>{const n=Li(1,this.accBeta1),s=bi(-this.learningRate,Or(Mi(this.iteration,this.decay),1));t.forEach(((t,a)=>{const r=Zs.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:Bo(r).variable(!1)}),null==this.accumulatedWeightedInfNorm[a]&&(this.accumulatedWeightedInfNorm[a]={originalName:`${t}/v`,variable:Bo(r).variable(!1)});const i=Array.isArray(e)?e[a].tensor:e[t];if(null==i)return;const o=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,u=Or(Mi(o,this.beta1),Mi(i,1-this.beta1)),c=Mi(l,this.beta2),h=_r(i),p=Ui(c,h);o.assign(u),l.assign(p);const d=Or(Mi(bi(s,n),bi(u,Or(p,this.epsilon))),r);r.assign(d)})),this.iteration.assign(Or(this.iteration,1)),this.accBeta1.assign(Mi(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&Sr(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&Sr(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)}}Xl.className="Adamax",Nr(Xl);class Yl extends Dr{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 a=Zs.registeredVariables[t];Cr((()=>{const e=Or(Mi(this.c,s),a);a.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=Tr(Fr(-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)}}Yl.className="SGD",Nr(Yl);class Jl extends Yl{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Fr(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const s=Zs.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:Cr((()=>Bo(s).variable(e)))}}const a=this.accumulations[n].variable,r=Array.isArray(e)?e[n].tensor:e[t];null!=r&&Cr((()=>{let e;const t=Or(Mi(this.m,a),r);e=this.useNesterov?Or(Mi(this.c,Or(r,Mi(t,this.m))),s):Or(Mi(this.c,t),s),a.assign(t),s.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&Sr(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(!1)})))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}Jl.className="Momentum",Nr(Jl);class Zl extends Dr{constructor(e,t=.9,n=0,s=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,null==s&&(this.epsilon=Zs.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=Zs.registeredVariables[t],a=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:Cr((()=>Bo(s).variable(a)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:Cr((()=>Bo(s).variable(a)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:Cr((()=>Bo(s).variable(a)))});const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;Cr((()=>{const e=Or(Mi(i,this.decay),Mi(to(r),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,a=Or(Mi(t,this.decay),Mi(r,1-this.decay)),l=bi(Mi(r,this.learningRate),Ao(Li(e,Or(to(a),this.epsilon)))),u=Or(Mi(o,this.momentum),l);i.assign(e),t.assign(a),o.assign(u);const c=Li(s,u);s.assign(c)}else{const e=Or(Mi(i,this.decay),Mi(to(r),1-this.decay)),t=Or(Mi(o,this.momentum),bi(Mi(r,this.learningRate),Ao(Or(e,this.epsilon))));i.assign(e),o.assign(t);const n=Li(s,t);s.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&Sr(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&Sr(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&Sr(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)}}Zl.className="RMSProp",Nr(Zl);class Ql{static sgd(e){return new Yl(e)}static momentum(e,t,n=!1){return new Jl(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,a=!1){return new Zl(e,t,n,s,a)}static adam(e=.001,t=.9,n=.999,s=null){return new ql(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new jl(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,a=0){return new Xl(e,t,n,s,a)}static adagrad(e,t=.1){return new Kl(e,t)}}const eu={sgd:Ql.sgd,momentum:Ql.momentum,adadelta:Ql.adadelta,adagrad:Ql.adagrad,rmsprop:Ql.rmsprop,adamax:Ql.adamax,adam:Ql.adam},tu="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:"undefined"!=typeof setImmediate?setImmediate:e=>e();function nu(){return new Promise((e=>tu((()=>e()))))}function su(e,t){const n=e[0].length;e.forEach(((e,t)=>{E(e.length===n,(()=>`Error in concat${n}D: rank of tensors[${t}] must be the same as the rank of the rest (${n})`))})),E(t>=0&&t<n,(()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`));const s=e[0];e.forEach(((e,a)=>{for(let r=0;r<n;r++)E(r===t||e[r]===s[r],(()=>`Error in concat${n}D: Shape of tensors[${a}] (${e}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`))}))}function au(e,t){const n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}const ru=30;function iu(e){return e<=ru?e:J(e,Math.floor(Math.sqrt(e)))}function 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this.throwIfDisposed(),bo(this,e)},_s().prototype.rfft=function(){return this.throwIfDisposed(),jo(this)};const gp=la({round_:function(e){const t={x:ia(e,"x","round")};return Zs.runKernel($n,t)}});_s().prototype.round=function(){return this.throwIfDisposed(),gp(this)},_s().prototype.rsqrt=function(){return this.throwIfDisposed(),Dc(this)},_s().prototype.selu=function(){return this.throwIfDisposed(),xo(this)},_s().prototype.separableConv2d=function(e,t,n,s,a,r){return this.throwIfDisposed(),wo(this,e,t,n,s,a,r)},_s().prototype.sigmoid=function(){return this.throwIfDisposed(),ko(this)};const yp=la({sign_:function(e){const t={x:ia(e,"x","sign")};return Zs.runKernel(Dn,t)}});_s().prototype.sign=function(){return this.throwIfDisposed(),yp(this)},_s().prototype.sin=function(){return this.throwIfDisposed(),bc(this)},_s().prototype.sinh=function(){return this.throwIfDisposed(),wc(this)},_s().prototype.slice=function(e,t){return this.throwIfDisposed(),vo(this,e,t)},_s().prototype.softmax=function(e){return this.throwIfDisposed(),So(this,e)},_s().prototype.softplus=function(){return this.throwIfDisposed(),To(this)},_s().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),rc(this,e,t)},_s().prototype.split=function(e,t){return this.throwIfDisposed(),Eo(this,e,t)},_s().prototype.sqrt=function(){return this.throwIfDisposed(),Ao(this)},_s().prototype.square=function(){return this.throwIfDisposed(),to(this)},_s().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Wl(this,e)},_s().prototype.squeeze=function(e){return this.throwIfDisposed(),Ro(this,e)},_s().prototype.stack=function(e,t){this.throwIfDisposed();const n=e instanceof Ds?[this,e]:[this,...e];return Fo(n,t)},_s().prototype.step=function(e){return this.throwIfDisposed(),Jo(this,e)};const bp=la({stridedSlice_:function(e,t,n,s,a=0,r=0,i=0,o=0,l=0){const u={x:ia(e,"x","stridedSlice")},c={begin:t,end:n,strides:s,beginMask:a,endMask:r,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return Zs.runKernel(Hn,u,c)}});_s().prototype.stridedSlice=function(e,t,n,s,a,r,i,o){return this.throwIfDisposed(),bp(this,e,t,n,s,a,r,i,o)},_s().prototype.sub=function(e){return this.throwIfDisposed(),Li(this,e)},_s().prototype.sum=function(e,t){return this.throwIfDisposed(),zi(this,e,t)};const xp=la({tan_:function(e){const t={x:ia(e,"x","tan")};return Zs.runKernel(jn,t)}});_s().prototype.tan=function(){return this.throwIfDisposed(),xp(this)},_s().prototype.tanh=function(){return this.throwIfDisposed(),Do(this)},_s().prototype.tile=function(e){return this.throwIfDisposed(),$i(this,e)},_s().prototype.toBool=function(){return this.throwIfDisposed(),ja(this,"bool")},_s().prototype.toFloat=function(){return this.throwIfDisposed(),ja(this,"float32")},_s().prototype.toInt=function(){return this.throwIfDisposed(),ja(this,"int32")};const wp=la({topk_:function(e,t=1,n=!0){const s=ia(e,"x","topk");if(0===s.rank)throw new Error("topk() expects the input to be of rank 1 or higher");const a=s.shape[s.shape.length-1];if(t>a)throw new Error(`'k' passed to topk() must be <= the last dimension (${a}) but got ${t}`);const r={x:s},i={k:t,sorted:n},[o,l]=Zs.runKernel(Xn,r,i);return{values:o,indices:l}}});_s().prototype.topk=function(e,t){return this.throwIfDisposed(),wp(this,e,t)},_s().prototype.transpose=function(e){return this.throwIfDisposed(),Wo(this,e)};const kp=la({unique_:function(e,t=0){const n=ia(e,"x","unique","string_or_numeric");E(n.rank>0,(()=>"The input tensor must be at least 1D"));const s={x:n},a={axis:t},[r,i]=Zs.runKernel(Zn,s,a);return{values:r,indices:i}}});let vp;function Np(){return null==vp&&(vp=Zs.backend.epsilon()),vp}_s().prototype.unique=function(e){return this.throwIfDisposed(),kp(this,e)},_s().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),Oc(this,e,t)},_s().prototype.unstack=function(e){return this.throwIfDisposed(),Lo(this,e)},_s().prototype.where=function(e,t){return this.throwIfDisposed(),Po(e,this,t)},_s().prototype.zerosLike=function(){return this.throwIfDisposed(),Bo(this)};class Ip extends Error{constructor(e){super(e),Object.setPrototypeOf(this,Ip.prototype)}}class $p extends Error{constructor(e){super(e),Object.setPrototypeOf(this,$p.prototype)}}class Cp extends Error{constructor(e){super(e),Object.setPrototypeOf(this,Cp.prototype)}}class Sp extends Error{constructor(e){super(e),Object.setPrototypeOf(this,Sp.prototype)}}class Tp extends Error{constructor(e){super(e),Object.setPrototypeOf(this,Tp.prototype)}}function Ep(e,t){if(Array.isArray(e)){let n=[];for(let s=0;s<t;s++)n=n.concat(e);return n}{const n=new Array(t);return n.fill(e),n}}function Ap(e,t){if(!e)throw new Tp(t)}function Rp(e,t){let n=0;for(const s of e)s===t&&n++;return n}function Fp(e){return 1===e.length?e[0]:e}function 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Mi(e,bi(n,Or(Np(),t)))}))}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}}Yp.className="MaxNorm",Nr(Yp);class Jp extends Xp{constructor(e){super(),this.defaultAxis=0,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return Cr((()=>bi(e,Or(Np(),qp(e,this.axis)))))}getConfig(){return{axis:this.axis}}}Jp.className="UnitNorm",Nr(Jp);class Zp extends Xp{apply(e){return yo(e)}}Zp.className="NonNeg",Nr(Zp);class Qp extends Xp{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 Cr((()=>{const t=qp(e,this.axis),n=Or(Mi(this.rate,ri(t,this.minValue,this.maxValue)),Mi(1-this.rate,t));return 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Co(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return vo(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return vo(e,[t,0,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new Cp(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}))}function Sd(e,t,n){return Cr((()=>{switch(e.rank){case 1:return No(e,t,n);case 2:return Io(e,[0,t],[e.shape[0],n]);case 3:return $o(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return Co(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new Cp(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}}))}function Td(e,t,n,s){return Cr((()=>{switch(e.rank){case 1:return No(e,t,n);case 2:switch(s){case 1:return Cd(e,t,n);case 2:return Sd(e,t,n);default:throw new Cp(`The axis is not within the rank of the tensor ${s}`)}case 3:switch(s){case 1:return Cd(e,t,n);case 2:return $o(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return Sd(e,t,n);default:throw new Cp(`The axis is not within the rank of the tensor ${s}`)}case 4:switch(s){case 1:return Cd(e,t,n);case 2:return Co(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return Co(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return Sd(e,t,n);default:throw new Cp(`The axis is not within the rank of the tensor ${s}`)}default:throw new Cp(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}}))}function Ed(e,t=-1){let n;return t<0&&(n=e[0].rank,t=0!==n?n:0),t===e[0].rank&&(t=-1),ii(e,t)}function Ad(e,t){switch(e.rank){case 1:return oi([e,t]);case 2:return li([e,t],0);case 3:return ui([e,t],0);case 4:return ci([e,t],0);default:throw new Cp(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function Rd(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new Cp(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return $i(e,t)}function Fd(e,t=0,n=1,s,a){return mo(e,t,n,s,a)}function Dd(e,t,n,s){if(e.rank<2||t.rank<2)throw new Sp(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3&&e.shape.slice(-1)[0]!==t.shape.slice(-2)[0])throw new Sp(`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 ol({a:e,b:t,transposeA:!1,transposeB:!1,bias:s?Md(e.rank,s,"channelsLast"):null,activation:n});{const a=e.shape.slice(),r=a.pop();e=e.reshape([-1,r]);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=t.transpose(c).reshape([l,-1]);const h=[...a,...u];return ol({a:e,b:t,transposeA:!1,transposeB:!1,bias:s?Md(e.rank,s,"channelsLast"):null,activation:n}).reshape(h)}}function _d(e,t,n){return Cr((()=>(t=Array.isArray(t)?_o(t,"int32"):t.toInt(),Ei(e,t,n))))}function Od(e){return Mi(e,e)}function Md(e,t,n){const s=t.shape;if(1!==t.rank&&t.rank!==e)throw new Cp(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(5===e){if("channelsFirst"===n)return 1===s.length?t.reshape([1,s[0],1,1,1]):t.reshape([1,s[3],s[0],s[1],s[2]]);if("channelsLast"===n)return 1===s.length?t.reshape([1,1,1,1,s[0]]):t.reshape([1].concat(s))}else if(4===e){if("channelsFirst"===n)return 1===s.length?t.reshape([1,s[0],1,1]):t.reshape([1,s[2],s[0],s[1]]);if("channelsLast"===n)return 1===s.length?t.reshape([1,1,1,s[0]]):t.reshape([1].concat(s))}else if(3===e){if("channelsFirst"===n)return 1===s.length?t.reshape([1,s[0],1]):t.reshape([1,s[1],s[0]]);if("channelsLast"===n)return 1===s.length?t.reshape([1,1,s[0]]):t.reshape([1].concat(s))}else if(e<3)return t;throw new Cp(`Unsupported input rank by biasAdd: ${t.rank}`)}function Ld(e,t,n){return Cr((()=>(null==n&&(n="channelsLast"),cd(n),e.add(Md(e.rank,t,n)))))}function zd(e,t,n,s){return Cr((()=>Vo(e,t,n,s)))}function Pd(e,t,n=!1){return n?e():t()}const Bd=["fanIn","fanOut","fanAvg"],Wd=["normal","uniform","truncatedNormal"];class Vd extends kr{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}}class Ud extends Vd{apply(e,t){return io(e,t)}}Ud.className="Zeros",Nr(Ud);class Gd extends Vd{apply(e,t){return oo(e,t)}}Gd.className="Ones",Nr(Gd);class Hd extends Vd{constructor(e){if(super(),"object"!=typeof e)throw new Cp(`Expected argument of type ConstantConfig but got ${e}`);if(void 0===e.value)throw new Cp(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return Cr((()=>Mi(Fr(this.value),oo(e,t))))}getConfig(){return{value:this.value}}}Hd.className="Constant",Nr(Hd);class jd extends Vd{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 go(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}}jd.className="RandomUniform",Nr(jd);class Kd extends Vd{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 Sp(`randomNormal does not support dType ${t}.`);return Fd(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}Kd.className="RandomNormal",Nr(Kd);class qd extends Vd{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 Sp(`truncatedNormal does not support dType ${t}.`);return Mo(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}qd.className="TruncatedNormal",Nr(qd);class Xd extends Vd{constructor(e){super(),this.gain=null!=e.gain?e.gain:1}apply(e,t){return Cr((()=>{if(2!==e.length||e[0]!==e[1])throw new Cp("Identity matrix initializer can only be used for 2D square matrices.");return Mi(this.gain,Ci(e[0]))}))}getConfig(){return{gain:this.gain}}}Xd.className="Identity",Nr(Xd);class Yd extends Vd{constructor(e){if(super(),e.scale<0)throw new Cp(`scale must be a positive float. Got: ${e.scale}`);var t;this.scale=null==e.scale?1:e.scale,this.mode=null==e.mode?"fanIn":e.mode,t=this.mode,Up(Bd,"FanMode",t),this.distribution=null==e.distribution?"normal":e.distribution,function(e){Up(Wd,"Distribution",e)}(this.distribution),this.seed=e.seed}apply(e,t){const n=function(e,t="channelsLast"){let n,s;if(cd(t),2===e.length)n=e[0],s=e[1];else if(-1!==[3,4,5].indexOf(e.length)){if("channelsFirst"===t){const t=xd(e,2);n=e[1]*t,s=e[0]*t}else if("channelsLast"===t){const t=xd(e,0,e.length-2);n=e[e.length-2]*t,s=e[e.length-1]*t}}else{const t=xd(e);n=Math.sqrt(t),s=Math.sqrt(t)}return[n,s]}(e),s=n[0],a=n[1];let r=this.scale;if("fanIn"===this.mode?r/=Math.max(1,s):"fanOut"===this.mode?r/=Math.max(1,a):r/=Math.max(1,(s+a)/2),"normal"===this.distribution){const n=Math.sqrt(r);if("float32"!==(t=t||"float32")&&"int32"!==t)throw new Sp(`${this.getClassName()} does not support dType ${t}.`);return Mo(e,0,n,t,this.seed)}{const n=Math.sqrt(3*r);return go(e,-n,n,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}}Yd.className="VarianceScaling",Nr(Yd);class Jd extends Yd{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Yd.className}}Jd.className="GlorotUniform",Nr(Jd);class Zd extends Yd{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Yd.className}}Zd.className="GlorotNormal",Nr(Zd);class Qd extends Yd{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Yd.className}}Qd.className="HeNormal",Nr(Qd);class ef extends Yd{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Yd.className}}ef.className="HeUniform",Nr(ef);class tf extends Yd{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Yd.className}}tf.className="LeCunNormal",Nr(tf);class nf extends Yd{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Yd.className}}nf.className="LeCunNormal",Nr(nf);class sf extends Vd{constructor(e){if(super(),this.DEFAULT_GAIN=1,this.gain=null==e.gain?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,null!=this.seed)throw new Sp("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return Cr((()=>{if(e.length<2)throw new Sp("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const t=Fd(e[0]>e[1]?[e[1],e[0]]:e,0,1,"float32");let n=Hl.gramSchmidt(t);return e[0]>e[1]&&(n=n.transpose()),Mi(this.gain,n)}))}getConfig(){return{gain:this.gain,seed:this.seed}}}sf.className="Orthogonal",Nr(sf);const af={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 rf(e,t={}){return Pp(e,vr.getMap().classNameMap,t,"initializer")}function of(e){return Lp(e)}function lf(e){if("string"==typeof e){const t=e in af?af[e]:e;if("GlorotNormal"===t)return new Zd;if("GlorotUniform"===t)return new Jd;if("HeNormal"===t)return new Qd;if("HeUniform"===t)return new ef;if("LeCunNormal"===t)return new tf;if("LeCunUniform"===t)return new nf;{const e={};return e.className=t,e.config={},rf(e)}}return e instanceof Vd?e:rf(e)}let uf=0;function cf(){return uf++}const hf={};function pf(e=""){return e in hf||(hf[e]=0),hf[e]+=1,e+hf[e].toString()}function df(e){return Array.isArray(e)&&Array.isArray(e[0])}function ff(e){return 0===e.length?[]:Array.isArray(e[0])?e:[e]}function mf(e){let t;if(Array.isArray(e)){if(1!==e.length)throw new Cp(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function gf(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(1===e.length)return(e=e)[0];throw new Cp(`Expected exactly 1 Shape; got ${e.length}`)}return e}function yf(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}class bf{constructor(e,t="float32",n="Variable",s=!0,a=null){this.dtype=null==t?"float32":t,this.shape=e.shape,this.id=cf(),n=null==n?"Variable":n,this.originalName=md(n),this.name=gd(this.originalName),this.trainable_=s,this.constraint=a,this.val=function(e,t=!0,n,s){return Zs.makeVariable(e,t,n,s)}(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 xf(e){return e.map((e=>e.read()))}function wf(e){e.forEach((e=>{e[0].write(e[1])}))}class kf{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 vf{constructor(e,t,n,s,a,r,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=a,this.outputTensorIndex=i,this.id=cf(),null!=r&&(this.originalName=md(r),this.name=gd(this.originalName)),this.rank=t.length}}let Nf=0;class If{constructor(e,t){this.callArgs=t,this.id=Nf++,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 $f=0;class Cf extends kr{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=$f++,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=_p(e)+"_"+pf(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 $p(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new Cp(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Fp(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Fp(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Ip(`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 Ip(`Layer ${this.name} is not connected, no input to return.`);return Fp(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(0===this.inboundNodes.length)throw new Ip(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Ip(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Fp(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=Dp(e),null==this.inputSpec||0===this.inputSpec.length)return;const t=Dp(this.inputSpec);if(e.length!==t.length)throw new Cp(`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],a=t[n];if(null==a)continue;const r=s.rank;if(null!=a.ndim&&r!==a.ndim)throw new Cp(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${r}`);if(null!=a.maxNDim&&r>a.maxNDim)throw new Cp(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${r}`);if(null!=a.minNDim&&r<a.minNDim)throw new Cp(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${r}.`);if(null!=a.dtype&&s.dtype!==a.dtype)throw new Cp(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${s.dtype}.`);if(a.axes){const e=s.shape;for(const t in a.axes){const s=Number(t),r=a.axes[t],i=s>=0?e[s]:e[e.length+s];if(null!=r&&-1===[r,null].indexOf(i))throw new Cp(`Input ${n} is incompatible with layer ${this.name}: expected axis ${s} of input shape to have value ${r} but got shape ${e}.`)}}if(null!=a.shape)for(let e=0;e<a.shape.length;++e){const t=a.shape[e],r=s.shape[e];if(null!=t&&null!=r&&t!==r)throw new Cp(`Input ${n} is incompatible with layer ${this.name}: expected shape=${a.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=Dp(e);let s=!0;for(const e of n)if(!(e instanceof vf)){s=!1;break}let a=!0;for(const e of n)if(e instanceof vf){a=!1;break}if(s===a)throw new Cp("Arguments to apply() must be all SymbolicTensors or all Tensors");return fd(this.name,(()=>{if(!this.built){this.assertInputCompatibility(e);const t=[];for(const n of Dp(e))t.push(n.shape);this.build(Fp(t)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t);const a=Dp(s),r=[];for(let e of a)-1!==n.indexOf(e)&&(e=e.clone()),r.push(e);if(s=Fp(r),null!=this.activityRegularizer)throw new Sp("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}{const n=function(e){e=Dp(e);const t=[];for(const n of e)t.push(n.shape);return Fp(t)}(e),s=this.computeOutputShape(n);let a;const r="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(e)?n[0]:n),a=null!=s&&s.length>0&&Array.isArray(s[0])?s.map(((n,s)=>new vf(r,n,this,Dp(e),t,this.name,s))):new vf(r,s,this,Dp(e),t,this.name),this.addInboundNode(e,a,null,null,n,s,t),this._refCount++,null!=this.activityRegularizer)throw new Sp("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}}))}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 Ip(`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 Ip(`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 $p(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return yf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return xf(e?this.trainableWeights:this.weights)}setWeights(e){Cr((()=>{const t=this.weights;if(t.length!==e.length)throw new Cp(`You called setWeights(weights) on layer "${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=xf(t);for(let a=0;a<s.length;++a){const r=s[a],i=t[a],o=e[a];if(!_(r.shape,o.shape))throw new Cp(`Layer weight shape ${r.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}wf(n)}))}addWeight(e,t,n,s,a,r,i){if(-1!==this._addedWeightNames.indexOf(e))throw new Cp(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),null==n&&(n="float32"),this.fastWeightInitDuringBuild&&(s=lf("zeros"));const o=s.apply(t,n),l=new bf(o,n,e,r,i);return o.dispose(),null!=a&&this.addLoss((()=>a.apply(l.read()))),null==r&&(r=!0),r?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){null==e||Array.isArray(e)&&0===e.length||(e=Dp(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 ${this.name} does not support masking, but was passed an inputMask.`)}))}return null}return t}addInboundNode(e,t,n,s,a,r,i=null){const o=Dp(e);t=Dp(t),n=Dp(n),s=Dp(s),a=ff(a),r=ff(r);const l=[],u=[],c=[];for(const e of o)l.push(e.sourceLayer),u.push(e.nodeIndex),c.push(e.tensorIndex);new If({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:a,outputShapes:r},i);for(let e=0;e<t.length;e++)t[e].sourceLayer=this,t[e].nodeIndex=this.inboundNodes.length-1,t[e].tensorIndex=e}getConfig(){const e={name:this.name,trainable:this.trainable};return null!=this.batchInputShape&&(e.batchInputShape=this.batchInputShape),null!=this.dtype&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach((e=>e.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error(`Layer '${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 Sf(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=Sf(e.inputTensors[n],e.inboundLayers[n],e.nodeIndices[n]);for(const e of s)-1===t.indexOf(e)&&t.push(e)}return t}}}class Tf extends Cf{constructor(e){if(super({dtype:e.dtype,name:null!=e.name?e.name:pf("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 Cp("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 Cp("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(null!=e.batchSize)throw new Cp("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 vf(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new If({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new Cp(`Cannot pass any input to an InputLayer's apply() method. 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vf){if(null==this.id2Value[e.id])throw new Cp(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}{const t=this.name2Id[e];if(null==t)throw new Cp(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof vf){if(null==this.id2Value[e.id])throw new Cp(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}{const t=this.name2Id[e];if(null==t)throw new Cp(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){null!=this.id2Mask&&Sr(this.id2Mask)}}const mm={},gm={};function ym(e,t,n,s){const a=null!=n&&n.training,r=Array.isArray(e),i=r?e:[e],o=i.map((e=>e.name)),l=[],u=t.names();for(const e of o)-1!==u.indexOf(e)?l.push(t.getValue(e)):l.push(null);null!=s&&(s.maxNumTensors=-1/0,s.minNumTensors=1/0);const c=o.join(",")+"|"+t.names().join(",");let h,p;if(null==mm[c]){const e=function(e,t){E(null!=e&&e.length>0,(()=>"Expected at least one fetch, got none"));let n=[],s={};if(1===e.length){const a=xm(e[0],t);n=a.sorted,s=a.recipientMap}else{const a=new Set;for(const r of e){const{sorted:e,recipientMap:i}=xm(r,t);for(const t of e)a.has(t.name)||(n.push(t),a.add(t.name));for(const e in i)null==s[e]&&(s[e]=new Set),i[e].forEach((t=>s[e].add(t)))}}return{sorted:n,recipientCounts:bm(s)}}(i,t);h=e.sorted,p=e.recipientCounts,mm[c]=h,gm[c]=p}h=mm[c],p={},a||Object.assign(p,gm[c]);const d=new fm(t);for(let e=0;e<h.length;++e){if(null!=s){const e=$r().numTensors;e>s.maxNumTensors&&(s.maxNumTensors=e),e<s.minNumTensors&&(s.minNumTensors=e)}const r=h[e],i=r.sourceLayer;if(i instanceof Tf)continue;const u=[],c=[],f=[];let m=!1;for(const e of r.inputs){const n=d.getValue(e),s=d.getMask(e);u.push(n),c.push(s),null!=s&&(m=!0),a||(p[e.name]--,0!==p[e.name]||t.hasKey(e)||-1!==o.indexOf(e.name)||n.isDisposed||!0===e.sourceLayer.stateful||f.push(n))}m&&((n=n||{}).mask=c[0]);const g=Dp(i.apply(u,n));let y=null;i.supportsMasking&&(y=i.computeMask(u,c));const b=wm(r),x=Array.isArray(b)?b:[b];for(let e=0;e<x.length;++e){d.hasKey(x[e])||d.add(x[e],g[e],Array.isArray(y)?y[0]:y);const t=o.indexOf(x[e].name);-1!==t&&(l[t]=g[e])}a||Sr(f)}return d.disposeMasks(),r?l:l[0]}function bm(e){const t={};for(const n in e)t[n]=e[n].size;return t}function xm(e,t){const n=new Set,s=[],a={};for(const e of t.names())n.add(e);const r=[],i=[];for(r.push(e);r.length>0;){const e=r[r.length-1];if(n.has(e.name)){r.pop();continue}const t=i[i.length-1]===r.length-1;if(0===e.inputs.length||t)r.pop(),s.push(e),n.add(e.name),t&&i.pop();else{i.push(r.length-1);for(const t of e.inputs)null==a[t.name]&&(a[t.name]=new Set),a[t.name].add(e.name),n.has(t.name)||r.push(t)}}return{sorted:s,recipientMap:a}}function wm(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}class km extends Cf{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,null==this.name){const e=this.getClassName().toLowerCase();this.name=pf(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],Wp(this.inputs).length!==this.inputs.length)throw new Cp(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((e=>e.name))}`);Wp(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;Ap(0===n,"input layer has >1 nodes"),Ap(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 Tf))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={},a={},r={},i=[],o=(e,t,n,s,a,l)=>{null!=s&&null!=a&&null!=l||(s=e.sourceLayer,a=e.nodeIndex,l=e.tensorIndex);const u=s.inboundNodes[a];if(-1!==n.indexOf(u))throw new $p(`The tensor ${e.name} at layer "${s.name}" is part of a cycle.`);if(-1!==t.indexOf(u))return;this.containerNodes.add(km.nodeKey(s,a)),s.id in r||(r[s.id]=Object.keys(r).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],a=u.inboundLayers[e],r=u.nodeIndices[e],i=u.tensorIndices[e];o(s,t,n,a,r,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 r=t[e.id];const i=null==s[e.outboundLayer.id]?0:s[e.outboundLayer.id];r=Math.max(r,i),s[e.outboundLayer.id]=r,a[e.outboundLayer.id]=e.outboundLayer,t[e.id]=r;for(let s=0;s<e.inboundLayers.length;s++){const a=e.inboundLayers[s],i=e.nodeIndices[s],o=a.inboundNodes[i],l=null==t[o.id]?0:t[o.id];t[o.id]=Math.max(r+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(a[e])}let d=Object.keys(p).map((e=>parseInt(e,10))).sort(Bp);this.layers=[];for(const e of d){const t=p[e];t.sort(((e,t)=>{const n=r[e.id],s=r[t.id];return n<s?-1:n>s?1:0}));for(const e of t)e instanceof km&&this.internalContainerRefs.push(e),this.layers.push(e)}this.layersByDepth=p,d=Object.keys(h).map((e=>parseInt(e,10))).sort(Bp);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 $p(`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 $p(`The name "${e}" is used ${t} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new If({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map((e=>null)),outputMasks:this.outputs.map((e=>null)),inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs.map((e=>e.shape))}),this.built=!0,this._refCount=1}assertNotDisposed(){if(0===this._refCount)throw new Error(`Container '${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 Cp("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 Cp(`Duplicate weight name: ${t.originalName}`);n[t.originalName]=t,s++}const a=[];for(const s in e){let r=s;if(null==n[s]){const e=s.split("/");r=e.slice(0,-2).concat([e[e.length-1]]).join("/")}if(null!=n[r])a.push([n[r],e[s]]);else if(t)throw new Cp(`Provided weight data has no target variable: ${s}`);delete n[r]}if(t){const e=[];for(const t in n)e.push(t);if(e.length>0)throw new Cp(`${e.length} of ${s} weights are not set: ${e}`)}wf(a)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion="tfjs-layers 3.3.0",t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=dm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return Cr((()=>{e=Dp(e);const n=new fm;for(let t=0;t<this.inputs.length;++t)n.add(this.inputs[t],e[t]);return ym(this.outputs,n,t)}))}computeMask(e,t){return Cr((()=>{let n;return e=Dp(e),n=null==t?Ep(null,e.length):Dp(t),this.runInternalGraph(e,n)[1]}))}computeOutputShape(e){const t=ff(e);if(t.length!==this.inputLayers.length)throw new Cp(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let e=0;e<t.length;e++){const s=this.inputLayers[e],a=t[e];n[s.name+"_0_0"]=a}const s=Object.keys(this.nodesByDepth).map((e=>parseInt(e,10))).sort(Bp);if(s.length>1)for(const e of s){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer;if(-1!==this.inputLayers.map((e=>e.id)).indexOf(t.id))continue;const s=[];for(let t=0;t<e.inboundLayers.length;t++){const a=e.inboundLayers[t],r=e.nodeIndices[t],i=e.tensorIndices[t],o=n[`${a.name}_${r}_${i}`];s.push(o)}const a=ff(t.computeOutputShape(Fp(s))),r=t.inboundNodes.indexOf(e);for(let e=0;e<a.length;e++)n[`${t.name}_${r}_${e}`]=a[e]}}const a=[],r=[];for(let e=0;e<this.outputLayers.length;e++){const t=this.outputLayers[e],n=this.outputLayersNodeIndices[e],s=this.outputLayersTensorIndices[e],a=`${t.name}_${n}_${s}`;r.push(a)}for(let e=0;e<r.length;e++){const t=r[e];Ap(t in n),a.push(n[t])}return Fp(a)}runInternalGraph(e,t){null==t&&(t=Ep(null,e.length));const n={};for(let s=0;s<this.inputs.length;++s){const a=this.inputs[s],r=e[s],i=t[s];n[a.id]=[r,i]}const s=Object.keys(this.nodesByDepth).map((e=>parseInt(e,10))).sort(Bp);for(const e of s){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer,s=e.inputTensors,a=e.outputTensors,r=new Array;for(const e of s)e.id in n&&r.push(n[e.id]);if(r.length===s.length){let s,i,o,l,u={};if(null!=e.callArgs&&(u=e.callArgs),1===r.length){const[e,n]=r[0];null==u.mask&&(u.mask=n),o=Dp(t.call(e,u)),l=Dp(t.computeMask(e,n)),s=[e],i=[n]}else s=r.map((e=>e[0])),i=r.map((e=>e[1])),null==u.mask&&(u.mask=i),o=Dp(t.call(s,u)),l=Dp(t.computeMask(s,i));if(t.activityRegularizer)throw new Sp("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let e=0;e<a.length;++e){const t=a[e],s=o[e],r=l[e];n[t.id]=[s,r]}}}}const a=[],r=[],i=[];for(const e of this.outputs){Ap(e.id in n,`Could not compute output ${e.name} : ${e.id}`);const[t,s]=n[e.id];i.push(t.shape),a.push(t),r.push(s)}return[a,r,i]}buildNodeConversionMap(e){const t={};let n;for(const e of this.layers){n=e instanceof km?1:0;for(let s=0;s<e.inboundNodes.length;s++){const a=km.nodeKey(e,s);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(null!=t){if(this.layers.length<=t)throw new Cp(`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 Cp("Provide either a layer name or layer index");for(const t of this.layers)if(t.name===e)return t;throw new Cp(`No such layer: ${e}`)}calculateLosses(){return Cr((()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=km.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(),a=e.getConfig(),r=[];for(let n=0;n<e.inboundNodes.length;n++){const s=e.inboundNodes[n],a=km.nodeKey(e,n);let i={};if(this.containerNodes.has(a)){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 a=s.inboundLayers[n],r=s.nodeIndices[n],o=s.tensorIndices[n];let l=t[km.nodeKey(a,r)];null==l&&(l=0),e.push([a.name,l,o,i])}r.push(e)}}}const i={};i.name=e.name,i.className=s,i.config=a,i.inboundNodes=r,n.push(i)}e.layers=n;const s=[];for(let e=0;e<this.inputLayers.length;e++){const n=this.inputLayers[e],a=this.inputLayersNodeIndices[e],r=km.nodeKey(n,a);if(!this.containerNodes.has(r))continue;let i=t[r];null==i&&(i=0);const o=this.inputLayersTensorIndices[e];s.push([n.name,i,o])}e.inputLayers=s;const a=[];for(let e=0;e<this.outputLayers.length;e++){const n=this.outputLayers[e],s=this.outputLayersNodeIndices[e],r=km.nodeKey(n,s);if(!this.containerNodes.has(r))continue;let i=t[r];null==i&&(i=0);const o=this.outputLayersTensorIndices[e];a.push([n.name,i,o])}return e.outputLayers=a,e}static fromConfig(e,t,n={},s=!1){const a={},r={};function i(e,t){e.name in r?r[e.name].push(t):r[e.name]=[t]}function o(e,t){const n=[];let s;for(const r of t){const o=r[0],l=r[1],u=r[2];if(s=null==r[3]?{}:r[3],!(o in a))return void i(e,t);const c=a[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(Fp(n),s)}function l(e){const n=e.name,r=Bf(e,null!=t.customObjects?t.customObjects:{});r.setFastWeightInitDuringBuild(s),a[n]=r,e.inboundNodes.forEach((e=>{if(!(e instanceof Array))throw new Cp(`Corrupted configuration, expected array for nodeData: ${e}`);i(r,e)}))}const u=t.name,c=t.layers;for(const e of c)l(e);for(;!Vp(r);)for(const e of c){const t=a[e.name];if(t.name in r){const e=r[t.name];delete r[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];Ap(t in a);const r=a[t].inboundNodes[n].outputTensors;h.push(r[s])}const f=t.outputLayers;for(const e of f){const t=e[0],n=e[1],s=e[2];Ap(t in a);const r=a[t].inboundNodes[n].outputTensors;p.push(r[s])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new Cp("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(){Cr((()=>{this.layers.forEach((e=>{e.stateful&&e.resetStates()}))}))}}function vm(e,t){return function(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: ${JSON.stringify(e)}`)}(e,t,"classWeight")}async function Nm(e,t,n,s){if(null!=t||null!=s)throw new Error("Support sampleWeight is not implemented yet");if(null!=n){const t=Cr((()=>{if(1===e.shape.length)return e.clone();if(2===e.shape.length){if(e.shape[1]>1){const t=1;return e.argMax(t)}if(1===e.shape[1])return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());Sr(t);const a=[];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`);a.push(n[e])})),_o(a,"float32")}return null}function Im(e,t){return Mi(e,t)}function $m(e,t){let n,s;const a=t;n=a.xs,s=a.ys,E(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 r=Cm("input",e.inputNames,n),i=Cm("output",e.outputNames,s),o=r[0].shape[0];E(r.length===e.inputs.length,(()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${r.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`)),E(i.length===e.outputs.length,(()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  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The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);const n=e.loss;t=n.map((e=>Yf(e)))}else{const n=Yf(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 Cp(`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(Yf(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=[],fd("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. 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Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){const s=null==n.batchSize?32:n.batchSize;Tm(s);const a=this.standardizeUserDataXY(e,t,!0,s);try{const r=a[0].concat(a[1]);this.makeTestFunction();const i=this.testFunction;return Fp(this.testLoop(i,r,s,n.verbose,n.steps))}finally{Dm(a[0],e),Dm(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),async function(e,t,n){const s=null!=(n=n||{}).batches,a=e.testFunction;let r=[];if(n.verbose>0)throw new Sp("Verbose mode is not implemented yet.");E(!s||n.batches>0&&Number.isInteger(n.batches),(()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`));const i="function"==typeof t.next?t:await t.iterator();let o=0,l=0;for(;!s||l<n.batches;){const t=await i.next();if(r=Cr((()=>{if(t.value){const{xs:n,ys:s}=$m(e,t.value),i=n.concat(s),u=Cr((()=>a(i)));if(Sr(i),0===l)for(let e=0;e<u.length;++e)r.push(Fr(0));const c=i[0].shape[0];for(let e=0;e<u.length;++e){const t=u[e],n=r[e];r[e]=Cr((()=>Or(r[e],Mi(c,t)))),l>0&&Sr(n)}Sr(u),o+=c,++l}return r})),t.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). 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Use LayersModel.compile(modelCompileArgs).");const a=[];for(let e=0;e<this.feedOutputShapes.length;++e){const t=this.feedOutputShapes[e];this.feedLossFns[e]===jf?a.push(t.slice(0,t.length-1).concat([1])):a.push(t)}if(function(e,t,n){const s=Wp(e.map((e=>e.shape[0])));s.sort();const a=Wp(t.map((e=>e.shape[0])));if(a.sort(),s.length>1)throw new Cp(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map((e=>e.shape)))}`);if(a.length>1)throw new Cp(`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&&a.length>0&&!_(s,a))throw new Cp(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${a[0]} target sample(s).`)}(e=Mm(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Mm(t,this.feedOutputNames,a,!1,"target")),function(e,t,n){const s=[Vf,Kf,Hf];for(let a=0;a<e.length;++a){const r=e[a],i=t[a],o=n[a];if(null!=i){if(i===Hf&&1===r.shape[r.shape.length-1])throw new Cp(`You are passing a target array of shape ${r.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=r.shape.slice(1),t=o.slice(1);for(let n=0;n<e.length;++n){const s=e[n],a=t[n];if(null!=a&&s!==a)throw new Cp(`A target Tensor with shape ${r.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 Cp(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,a=!0,r){const[i,o]=this.standardizeUserDataXY(e,t,a,r);if(null!=n)throw new Error("sample weight is not supported yet.");let l=null;if(null!=s){const e=vm(s,this.outputNames);l=[];for(let t=0;t<e.length;++t)l.push(await Nm(o[t],null,e[t]))}return[i,o,l]}testLoop(e,t,n,s=0,a){return Cr((()=>{const r=this.checkNumSamples(t,n,a,"steps"),i=[];if(s>0)throw new Sp("Verbose mode is not implemented yet.");if(null!=a)throw new Sp("steps mode in testLoop() is not implemented yet");{const s=Rm(r,n),a=_o(Nd(0,r));for(let n=0;n<s.length;++n){const r=s[n][0],o=s[n][1],l=Cd(a,r,o-r),u=Am(t,l),c=e(u);if(0===n)for(let e=0;e<c.length;++e)i.push(Fr(0));for(let e=0;e<c.length;++e){const t=c[e];i[e]=Or(i[e],Mi(o-r,t))}}for(let e=0;e<i.length;++e)i[e]=bi(i[e],r)}return i}))}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let a=s;Rp(e,s)>1&&(a+=`_${Rp(e.slice(0,n),s)}`),t.push(a)}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),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+2*this.outputs.length),r=[],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 fm(e),o=ym(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!=a[e]&&(n=Im(n,a[e]));const r=Gi(n);t.push(r),l=0===e?n:Or(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],a=this.metricsTensors[e][1];n=Gi(t(s[a],o[a]))}Tr(n),r.push(n)}return l=Gi(l),this.calculateLosses().forEach((e=>{l=Or(l,e)})),l}),!0,i)].concat(r)}}makeTestFunction(){this.testFunction=e=>Cr((()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=[];for(let e=0;e<this.inputs.length;++e)r.push({key:this.inputs[e],value:s[e]});const i=new fm(r),o=ym(this.outputs,i);for(let e=0;e<this.lossFunctions.length;++e){const s=this.lossFunctions[e],r=Gi(s(a[e],o[e]));n=0===e?r:Or(n,r),t.push(n)}for(let e=0;e<this.metricsTensors.length;++e){const n=this.metricsTensors[e][0],s=this.metricsTensors[e][1],r=Gi(n(a[s],o[s]));t.push(r)}return t}))}async fit(e,t,n={}){return async function(e,t,n,s={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");let a,r,i,o,l,u,c;e.isTraining=!0;try{const h=null==s.batchSize?32:s.batchSize;Tm(h);const p=!1,d=await e.standardizeUserData(t,n,s.sampleWeight,s.classWeight,p,h);a=d[0],r=d[1],c=d[2];let f,m=!1;if(null!=s.validationData&&s.validationData.length>0){if(m=!0,2!==s.validationData.length)throw 3===s.validationData.length?new Sp("validationData including sample weights is not supported yet."):new Cp(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${s.validationData} is invalid.`);i=s.validationData[0],o=s.validationData[1];const t=!0,n=await e.standardizeUserData(i,o,null,null,t,h);l=n[0],u=n[1],f=l.concat(u)}else if(null!=s.validationSplit&&s.validationSplit>0&&s.validationSplit<1){m=!0;const e=Math.floor(a[0].shape[0]*(1-s.validationSplit)),t=a[0].shape[0];l=Em(a,e,t),a=Em(a,0,e),u=Em(r,e,t),r=Em(r,0,e),f=l.concat(u)}else null!=s.validationSteps&&(m=!0);const g=a.concat(r).concat(c);e.checkTrainableWeightsConsistency();const y=e.makeTrainFunction(),b=e.getDedupedMetricsNames();let x,w;m?(e.makeTestFunction(),x=e.testFunction,w=b.slice().concat(b.map((e=>"val_"+e)))):(x=null,f=[],w=b.slice());const k=Lf(s.callbacks,s.yieldEvery);return await async function(e,t,n,s,a,r,i,o,l,u,c,h,p,d,f){null==a&&(a=32),null==r&&(r=1),null==c&&(c=!0),null==p&&(p=0);let m=!1;null!=l&&null!=u&&(m=!0);const g=e.checkNumSamples(n,a,d,"steps_per_epoch");let y;null!=g&&(y=Nd(0,g)),null==i&&(i=1);const{callbackList:b,history:x}=Pf(o,i,r,p,g,d,a,m,h);b.setModel(e),e.history=x,await b.onTrainBegin(),e.stopTraining_=!1;for(let i=p;i<r;++i){await b.onEpochBegin(i);const r={};{if("batch"===c)throw new Sp("batch shuffling is not implemneted yet");c&&C(y);const i=_o(y),o=Rm(g,a);for(let c=0;c<o.length;++c){const h={};if(await b.onBatchBegin(c,h),Cr((()=>{const p=o[c][0],d=o[c][1],f=Cd(i,p,d-p);h.batch=c,h.size=d-p;const g=Am(n,f),y=t(g);for(let e=0;e<s.length;++e){const t=s[e],n=y[e];h[t]=n,Tr(n)}if(c===o.length-1&&m){const t=e.testLoop(l,u,a);for(let e=0;e<s.length;++e){const n=s[e],a=t[e];Tr(a),r["val_"+n]=a}}})),await b.onBatchEnd(c,h),Af(h),e.stopTraining_)break}i.dispose()}if(await b.onEpochEnd(i,r),e.stopTraining_)break}return await b.onTrainEnd(),await e.history.syncData(),e.history}(e,y,g,b,h,s.epochs,s.verbose,k,x,f,s.shuffle,w,s.initialEpoch,null)}finally{e.isTraining=!1,Dm(a,t),Dm(r,n),Dm(l,i),Dm(u,o),null!=c&&Sr(c)}}(this,e,t,n)}async fitDataset(e,t){return async function(e,t,n){const s=null!=n.batchesPerEpoch;if(E(null!=e.optimizer,(()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).")),E(null!=n,(()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.")),E(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}`)),E(!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}`)),E(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 a=null!=n.validationData;let r,i;if(a)if(Sm(n.validationData))E(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 Sp("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}(n.validationData);r=e.xs,i=e.ys}const o=e.makeTrainFunction(),l=e.getDedupedMetricsNames();let u;u=a?l.slice().concat(l.map((e=>"val_"+e))):l.slice();const c=Lf(n.callbacks,n.yieldEvery),h=null==n.verbose?1:n.verbose,{callbackList:p,history:d}=Pf(c,h,n.epochs,null,null,function(e,t){let n=null;return null!=t.batchesPerEpoch?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}(t,n),null,a,u);p.setModel(e),e.history=d,await p.onTrainBegin(),e.stopTraining_=!1;let f=null==n.initialEpoch?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){const u={};await p.onEpochBegin(f);let c=0,h=0;for(s||(m=await t.iterator());!s||c<n.batchesPerEpoch;){const t=await m.next();if(s&&t.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${c} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, `+n.batchesPerEpoch*n.epochs+" batches). You may need to use the repeat() function when building your dataset.");break}if(null!=t.value){const{xs:s,ys:a}=$m(e,t.value),r={};r.batch=h,r.size=s[0].shape[0],await p.onBatchBegin(h,r);const i=[];if(null!=n.classWeight){const t=vm(n.classWeight,e.outputNames);for(let e=0;e<t.length;++e)i.push(await Nm(a[e],null,t[e]))}const u=s.concat(a).concat(i),d=o(u);Sr(u);for(let e=0;e<l.length;++e){const t=l[e],n=d[e];r[t]=n,Tr(n)}await p.onBatchEnd(h,r),Af(r),h++,c++}if(s?c>=n.batchesPerEpoch:t.done){if(a){let t;t=Sm(n.validationData)?Dp(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):Dp(e.evaluate(r,i,{batchSize:null==n.validationBatchSize?32:n.validationBatchSize,verbose:0}));for(let n=0;n<e.metricsNames.length;++n)u[`val_${e.metricsNames[n]}`]=t[n]}break}if(e.stopTraining_)break}if(await p.onEpochEnd(f,u),f++,e.stopTraining_)break}return await p.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],a=n[1],r=this.makeTrainFunction()(s.concat(a)),i=[];for(const e of r){const t=await e.data();i.push(t[0])}return Sr(r),Fp(i)}getNamedWeights(e){const t=[],n=null!=e&&e.trainableOnly,s=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let e=0;e<s.length;++e)n&&!s[e].trainable||t.push({name:s[e].originalName,tensor:a[e]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(0===e.refCountAfterDispose&&null!=this.optimizer&&this.isOptimizerOwned){const t=$r().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-$r().numTensors}return e}getLossIdentifiers(){let e;if("string"==typeof this.loss)e=_p(this.loss);else if(Array.isArray(this.loss)){for(const e of this.loss)if("string"!=typeof e)throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map((e=>_p(e)))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t){if("string"!=typeof n[s])throw new Error("Serialization of non-string loss is not supported.");e[s]=_p(n[s])}}return e}getMetricIdentifiers(){if("string"==typeof this.metrics||"function"==typeof this.metrics)return[_p(rm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map((e=>_p(rm(e))));{const e={};for(const t in this.metrics)e[t]=_p(rm(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(null!=e.weighted_metrics)throw new Error("Loading weight_metrics is not supported yet.");if(null!=e.loss_weights)throw new Error("Loading loss_weights is not supported yet.");if(null!=e.sample_weight_mode)throw new Error("Loading sample_weight_mode is not supported yet.");const t=Bf(pm(e.optimizer_config));let n,s;if("string"==typeof e.loss)n=Op(e.loss);else if(Array.isArray(e.loss))n=e.loss.map((e=>Op(e)));else if(null!=e.loss){n={};for(const t in e.loss)n[t]=Op(e.loss[t])}if(Array.isArray(e.metrics))s=e.metrics.map((e=>Op(e)));else if(null!=e.metrics){s={};for(const t in e.metrics)s[t]=Op(e.metrics[t])}this.compile({loss:n,metrics:s,optimizer:t})}async save(e,t){if("string"==typeof e){const t=ka(e);if(0===t.length)throw new Cp(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Cp(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)throw new Cp("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await da(this.getNamedWeights(t)),s={modelTopology:this.toJSON(null,!1),format:"layers-model",generatedBy:"TensorFlow.js tfjs-layers v3.3.0",convertedBy:null};if(null!=t&&t.includeOptimizer&&null!=this.optimizer){s.trainingConfig=this.getTrainingConfig();const e="optimizer",{data:t,specs:a}=await da(await this.optimizer.getWeights(),e);n.specs.push(...a),n.data=ya([n.data,t])}if(null!=this.userDefinedMetadata){const e=!0;im(this.userDefinedMetadata,this.name,e),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){im(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}zm.className="Model",Nr(zm);class Pm extends zm{}Pm.className="Functional",Nr(Pm);class Bm extends zm{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=null!=e.name?e.name:pf("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 Cp(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof Bm||e instanceof zm;let n;if(t){if(n=e,1!==n.outputs.length)throw new Cp("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 Cp("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 Cp("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const t=function(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 Cp("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;null!=e.shape&&null==t&&(t=[null].concat(e.shape));let n=e.dtype;return null==n&&(n="float32"),new Tf({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}({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 Cp(`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 Cp("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=Sf(this.outputs[0])}this.inboundNodes=[],new If({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Ep(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(gf(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 zm({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 $p("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 $p("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 $p("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 $p("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 a,r={};if(t instanceof Array){if(null==t[0].className||"Merge"===t[0].className)throw new Cp("Legacy serialization format not supported yet.");a=t}else E(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.")),a=t.layers,delete t.layers,r=t;const i=new e(r);if(!(i instanceof Bm))throw new Sp(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(const e of a){const t=Bf(e,void 0,s);s&&t.setFastWeightInitDuringBuild(!0),i.add(t)}return i}set stopTraining(e){if(null==this.model)throw new Cp("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(null==this.model)throw new Cp("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}Bm.className="Sequential",Nr(Bm);class Wm extends kr{getConfig(){return{}}}class Vm extends Wm{apply(e,t=1){return function(e,t=1){if(1!==t)throw new Sp(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return xi(e)}(e,t)}}Vm.className="elu",Nr(Vm);class Um extends Wm{apply(e){return xo(e)}}Um.className="selu",Nr(Um);class Gm extends Wm{apply(e){return yo(e)}}Gm.className="relu",Nr(Gm);class Hm extends Wm{apply(e){return Cr((()=>ji(6,yo(e))))}}Hm.className="relu6",Nr(Hm);class jm extends Wm{apply(e){return e}}jm.className="linear",Nr(jm);class Km extends Wm{apply(e){return ko(e)}}Km.className="sigmoid",Nr(Km);class qm extends Wm{apply(e){return function(e){return Cr((()=>{const t=Or(.5,Mi(.2,e));return ri(t,0,1)}))}(e)}}qm.className="hardSigmoid",Nr(qm);class Xm extends Wm{apply(e){return To(e)}}Xm.className="softplus",Nr(Xm);class Ym extends Wm{apply(e){return function(e){return Cr((()=>bi(e,_r(e).add(1))))}(e)}}Ym.className="softsign",Nr(Ym);class Jm extends Wm{apply(e){return Do(e)}}Jm.className="tanh",Nr(Jm);class Zm extends Wm{apply(e,t=-1){return So(e,t)}}Zm.className="softmax",Nr(Zm);class Qm extends Wm{apply(e,t=-1){return Pi(e,t)}}Qm.className="logSoftmax",Nr(Qm);class eg extends Wm{apply(e,t=1){return Cr((()=>ko(e.mul(t)).mul(e)))}}function tg(e){return e.getClassName()}function ng(e,t={}){return Pp(e,vr.getMap().classNameMap,t,"activation")}function sg(e){if(null==e){return ng({className:"linear",config:{}})}if("string"==typeof e){const t={};return t.className=e,t.config={},ng(t)}return e instanceof Wm?e:ng(e)}eg.className="swish",Nr(eg);class ag extends kr{}class rg extends ag{constructor(e){super(),function(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}`)}(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 Cr((()=>{let t=io([1]);return this.hasL1&&(t=Or(t,zi(Mi(this.l1,_r(e))))),this.hasL2&&(t=Or(t,zi(Mi(this.l2,Od(e))))),t.asScalar()}))}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}rg.className="L1L2",Nr(rg);const ig={l1l2:"L1L2"};function og(e){return Lp(e)}function lg(e,t={}){return Pp(e,vr.getMap().classNameMap,t,"regularizer")}function ug(e){return null==e?null:"string"==typeof e?lg({className:e in ig?ig[e]:e,config:{}}):e instanceof ag?e:lg(e)}class cg extends Cf{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,null!=e&&(this.maxValue=e.maxValue)}call(e,t){e=mf(e);let n=yo(e);return null!=this.maxValue&&(n=ri(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}cg.className="ReLU",Nr(cg);class hg extends Cf{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=mf(e);return Fi(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}hg.className="LeakyReLU",Nr(hg);class pg extends Cf{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",null==e&&(e={}),this.supportsMasking=!0,this.alphaInitializer=lf(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ug(e.alphaRegularizer),this.alphaConstraint=sd(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 Cp(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`);this.sharedAxes=[e.sharedAxes]}}build(e){const t=(e=gf(e)).slice(1);if(null!=this.sharedAxes)for(const e of this.sharedAxes)t[e-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(null!=this.sharedAxes)for(let t=1;t<e.length;++t)n[t]=e[t];this.inputSpec=[new kf({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=mf(e),co(e,this.alpha.read())}getConfig(){const e={alphaInitializer:of(this.alphaInitializer),alphaRegularizer:og(this.alphaRegularizer),alphaConstraint:td(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}pg.className="PReLU",Nr(pg);class dg extends Cf{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA=1,null==e&&(e={}),null!=e.alpha&&e.alpha!==this.DEFAULT_ALPHA)throw new Sp(`Non-default alpha value (${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=mf(e);return xi(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}dg.className="ELU",Nr(dg);class fg extends Cf{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=mf(e);return n.mul(Id(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}fg.className="ThresholdedReLU",Nr(fg);class mg extends Cf{constructor(e){super(null==e?{}:e),this.DEFAULT_AXIS=1,null==e&&(e={}),this.softmax=(new Zm).apply,this.axis=null==e.axis?this.DEFAULT_AXIS:e.axis}call(e,t){const n=mf(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 gg(e,t,n){if("number"==typeof e)return Ep(e,t);if(e.length!==t)throw new Cp(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){const r=e[a];if((s=r)!==parseInt(s.toString(),10))throw new Cp(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e;var s}function yg(e,t,n,s,a=1){if(null==e)return e;let r;return r="same"===n?e:e-(t+(t-1)*(a-1))+1,Math.floor((r+s-1)/s)}function bg(e,t,n,s){if(null==e)return null;if("valid"===s)e=e*t+vd([n-t,0]);else{if("same"!==s)throw new Cp(`Unsupport padding mode: ${s}.`);e*=t}return e}function xg(e,t){return Cr((()=>(cd(t),"channelsFirst"===t?Wo(e,[0,2,3,1]):e)))}function wg(e,t){return Cr((()=>(cd(t),"channelsFirst"===t?Wo(e,[0,2,3,4,1]):e)))}function kg(e,t,n,s=[1,1],a="valid",r,i,o=null){return Cr((()=>{if(null==r&&(r="channelsLast"),cd(r),3!==e.rank&&4!==e.rank)throw new Cp(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(3!==t.rank&&4!==t.rank)throw new Cp(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=xg(e,r);if("causal"===a)throw new Sp("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=nl({x:l,filter:t,strides:s,pad:"same"===a?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),"channelsFirst"===r&&(l=Wo(l,[0,3,1,2])),l}))}mg.className="Softmax",Nr(mg);class vg extends Cf{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",vg.verifyArgs(t),this.rank=e,Hp(this.rank,"rank"),1!==this.rank&&2!==this.rank&&3!==this.rank)throw new Sp(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=gg(t.kernelSize,e,"kernelSize"),this.strides=gg(null==t.strides?1:t.strides,e,"strides"),this.padding=null==t.padding?"valid":t.padding,hd(this.padding),this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,cd(this.dataFormat),this.activation=sg(t.activation),this.useBias=null==t.useBias||t.useBias,this.biasInitializer=lf(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=sd(t.biasConstraint),this.biasRegularizer=ug(t.biasRegularizer),this.activityRegularizer=ug(t.activityRegularizer),this.dilationRate=gg(null==t.dilationRate?1:t.dilationRate,e,"dilationRate"),1===this.rank&&Array.isArray(this.dilationRate)&&1!==this.dilationRate.length)throw new Cp(`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 Cp(`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 Cp(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}static verifyArgs(e){if(Ap("kernelSize"in e,"required key 'kernelSize' not in config"),"number"!=typeof e.kernelSize&&!Gp(e.kernelSize,"number",1,3))throw new Cp(`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:tg(this.activation),useBias:this.useBias,biasInitializer:of(this.biasInitializer),biasRegularizer:og(this.biasRegularizer),activityRegularizer:og(this.activityRegularizer),biasConstraint:td(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Ng extends vg{constructor(e,t){super(e,t),this.kernel=null,Ng.verifyArgs(t),this.filters=t.filters,Hp(this.filters,"filters"),this.kernelInitializer=lf(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=sd(t.kernelConstraint),this.kernelRegularizer=ug(t.kernelRegularizer)}build(e){e=gf(e);const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Cp(`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 Cr((()=>{let t;e=mf(e);const n=null==this.bias?null:this.bias.read(),s=Kp(this.activation.getClassName());if(null!=s&&2===this.rank)t=kg(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(1===this.rank)t=function(e,t,n,s=1,a="valid",r,i=1){return Cr((()=>{if(null==r&&(r="channelsLast"),cd(r),3!==e.shape.length)throw new Cp(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(3!==t.shape.length)throw new Cp(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(null!=n&&1!==n.shape.length)throw new Cp(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if("channelsFirst"===r&&(e=Wo(e,[0,2,1])),"causal"===a)throw new Sp("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=pi(e,t,s,"same"===a?"same":"valid","NWC",i);return null!=n&&(o=Ld(o,n)),o}))}(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(2===this.rank)t=kg(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else{if(3!==this.rank)throw new Sp("convolutions greater than 3D are not implemented yet.");t=function(e,t,n,s=[1,1,1],a="valid",r,i){return Cr((()=>{if(null==r&&(r="channelsLast"),cd(r),4!==e.rank&&5!==e.rank)throw new Cp(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(4!==t.rank&&5!==t.rank)throw new Cp(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=wg(e,r);if("causal"===a)throw new Sp("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=mi(o,t,s,"same"===a?"same":"valid","NDHWC",i),null!=n&&(o=Ld(o,n)),"channelsFirst"===r&&(o=Wo(o,[0,4,1,2,3])),o}))}(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate)}null!=this.activation&&(t=this.activation.apply(t))}return t}))}computeOutputShape(e){e=gf(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=yg(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:of(this.kernelInitializer),kernelRegularizer:og(this.kernelRegularizer),kernelConstraint:td(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 Cp(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Ig extends Ng{constructor(e){super(2,e),Ig.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!Gp(e.kernelSize,"number",1,2))throw new Cp(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Ig.className="Conv2D",Nr(Ig);class $g extends Ng{constructor(e){super(3,e),$g.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 Cp(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}$g.className="Conv3D",Nr($g);class Cg extends Ig{constructor(e){if(super(e),this.inputSpec=[new kf({ndim:4})],"same"!==this.padding&&"valid"!==this.padding)throw new Cp(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(4!==(e=gf(e)).length)throw new Cp("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Cp("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new kf({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return Cr((()=>{let t=mf(e);if(4!==t.shape.length)throw new Cp(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);const n=t.shape,s=n[0];let a,r;"channelsFirst"===this.dataFormat?(a=2,r=3):(a=1,r=2);const i=n[a],o=n[r],l=this.kernelSize[0],u=this.kernelSize[1],c=this.strides[0],h=this.strides[1],p=[s,bg(i,c,l,this.padding),bg(o,h,u,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=Wo(t,[0,2,3,1]));let d=fi(t,this.kernel.read(),p,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(d=Wo(d,[0,3,1,2])),null!=this.bias&&(d=Ld(d,this.bias.read(),this.dataFormat)),null!=this.activation&&(d=this.activation.apply(d)),d}))}computeOutputShape(e){const t=(e=gf(e)).slice();let n,s,a;"channelsFirst"===this.dataFormat?(n=1,s=2,a=3):(n=3,s=1,a=2);const r=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=bg(t[s],o,r,this.padding),t[a]=bg(t[a],l,i,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Cg.className="Conv2DTranspose",Nr(Cg);class Sg extends Ng{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 Cp("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=t.kernelInitializer||null!=t.kernelRegularizer||null!=t.kernelConstraint)throw new Cp("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 Cp(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,this.depthwiseInitializer=lf(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ug(t.depthwiseRegularizer),this.depthwiseConstraint=sd(t.depthwiseConstraint),this.pointwiseInitializer=lf(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ug(t.pointwiseRegularizer),this.pointwiseConstraint=sd(t.pointwiseConstraint)}build(e){if((e=gf(e)).length<this.rank+2)throw new Cp(`Inputs to SeparableConv${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 Cp(`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]),a=[];for(let e=0;e<this.rank;++e)a.push(1);a.push(n*this.depthMultiplier,this.filters);const r=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,r,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,r,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,r,this.biasConstraint):this.bias=null,this.inputSpec=[new kf({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return Cr((()=>{let t;if(e=mf(e),1===this.rank)throw new Sp("1D separable convolution is not implemented yet.");return 2===this.rank&&("channelsFirst"===this.dataFormat&&(e=Wo(e,[0,2,3,1])),t=wo(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(t=Ld(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),"channelsFirst"===this.dataFormat&&(t=Wo(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=of(this.depthwiseInitializer),e.pointwiseInitializer=of(this.pointwiseInitializer),e.depthwiseRegularizer=og(this.depthwiseRegularizer),e.pointwiseRegularizer=og(this.pointwiseRegularizer),e.depthwiseConstraint=td(this.depthwiseConstraint),e.pointwiseConstraint=td(this.pointwiseConstraint),e}}Sg.className="SeparableConv";class Tg extends Sg{constructor(e){super(2,e)}}Tg.className="SeparableConv2D",Nr(Tg);class Eg extends Ng{constructor(e){super(1,e),Eg.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&&!Gp(e.kernelSize,"number",1,1))throw new Cp(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Eg.className="Conv1D",Nr(Eg);class Ag extends Cf{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 Cr((()=>{if(e=mf(e),"channelsLast"===this.dataFormat){const t=Td(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Td(t,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}{const t=Td(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Td(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}}Ag.className="Cropping2D",Nr(Ag);class Rg extends Cf{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,cd(this.dataFormat),this.interpolation=null==e.interpolation?"nearest":e.interpolation,t=this.interpolation,Up(rd,"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 Cr((()=>{let t=mf(e);const n=t.shape;if("channelsFirst"===this.dataFormat){t=Wo(t,[0,2,3,1]);const e=this.size[0]*n[2],s=this.size[1]*n[3],a="nearest"===this.interpolation?t.resizeNearestNeighbor([e,s]):t.resizeBilinear([e,s]);return Wo(a,[0,3,1,2])}{const e=this.size[0]*n[1],s=this.size[1]*n[2];return"nearest"===this.interpolation?t.resizeNearestNeighbor([e,s]):t.resizeBilinear([e,s])}}))}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Rg.className="UpSampling2D",Nr(Rg);class Fg extends vg{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=lf(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=sd(e.depthwiseConstraint),this.depthwiseRegularizer=ug(e.depthwiseRegularizer)}build(e){if((e=gf(e)).length<4)throw new Cp(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t="channelsFirst"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new Cp(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 Cr((()=>{let t=function(e,t,n=[1,1],s="valid",a,r){return Cr((()=>{null==a&&(a="channelsLast"),cd(a);let i=xg(e,a);if(4!==e.rank)throw new Cp(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(4!==t.rank)throw new Cp(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=gi(i,t,n,"same"===s?"same":"valid","NHWC",r),"channelsFirst"===a&&(i=Wo(i,[0,3,1,2])),i}))}(e=mf(e),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(t=Ld(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),t}))}computeOutputShape(e){e=gf(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,a=yg(t,this.kernelSize[0],this.padding,this.strides[0]),r=yg(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[e[0],s,a,r]:[e[0],a,r,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=of(this.depthwiseInitializer),e.depthwiseRegularizer=og(this.depthwiseRegularizer),e.depthwiseConstraint=td(this.depthwiseRegularizer),e}}function Dg(e,t,n,s){if(Array.isArray(e)){if(null!=t||null!=n)throw new Cp("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 a(e){return null==e||Array.isArray(e)?e:[e]}return{inputs:e,initialState:t=a(t),constants:n=a(n)}}function _g(e,t,n,s=!1,a,r,i=!1,o=!1){return Cr((()=>{const l=t.shape.length;if(l<3)throw new Cp(`Input should be at least 3D, but is ${l}D.`);const u=[1,0].concat(Nd(2,l));if(t=Wo(t,u),null!=r)throw new Sp("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),null!=a&&((a=a.asType("bool").asType("float32")).rank===l-1&&(a=Ii(a,-1)),a=Wo(a,u)),s&&(t=bo(t,0),null!=a&&(a=bo(a,0)));const c=[];let h,p=n;const d=t.shape[0],f=Lo(t);let m,g;null!=a&&(m=Lo(a));for(let t=0;t<d;++t){const n=f[t],s=Cr((()=>e(n,p)));if(null==a)h=s[0],p=s[1];else{const e=Cr((()=>{const e=m[t],n=lo(e).sub(e);return{output:s[0].mul(e).add(p[0].mul(n)),newStates:p.map(((t,a)=>s[1][a].mul(e).add(t.mul(n))))}}));h=e.output,p=e.newStates}o&&c.push(h)}return o&&(g=Fo(c,1)),[h,g,p]}))}Fg.className="DepthwiseConv2D",Nr(Fg);class Og extends Cf{constructor(e){let t;if(super(e),null==e.cell)throw new Cp("cell property is missing for the constructor of RNN.");if(t=Array.isArray(e.cell)?new Ug({cells:e.cell}):e.cell,null==t.stateSize)throw new Cp("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 kf({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){return null==this.states_?Nd(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map((e=>null)):this.states_}setStates(e){this.states_=e}computeOutputShape(e){df(e)&&(e=e[0]),e=e;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 Cr((()=>{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 Sp("Constants support is not implemented in RNN yet.");df(e)&&(e=e[0]),e=e;const t=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new kf({shape:[t,null,...n]});const s=[e[0]].concat(e.slice(2));let a;if(this.cell.build(s),a=Array.isArray(this.cell.stateSize)?this.cell.stateSize:[this.cell.stateSize],null!=this.stateSpec){if(!_(this.stateSpec.map((e=>e.shape[e.shape.length-1])),a))throw new Cp(`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=a.map((e=>new kf({shape:[null,e]})));this.stateful&&this.resetStates()}resetStates(e,t=!1){Cr((()=>{if(!this.stateful)throw new Ip("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(null==n)throw new Cp("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=>io([n,e]))):this.states_=[io([n,this.cell.stateSize])];else if(null==e)Sr(this.states_),null!=this.keptStates&&(Sr(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>io([n,e]))):this.states_[0]=io([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Cp(`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()):Sr(this.states_);for(let t=0;t<this.states_.length;++t){const s=e[t],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[t]:this.cell.stateSize,r=[n,a];if(!_(s.shape,r))throw new Cp(`State ${t} is incompatible with layer ${this.name}: expected shape=${r}, received shape=${s.shape}`);this.states_[t]=s}}this.states_=this.states_.map((e=>Tr(e.clone())))}))}apply(e,t){let n=null==t?null:t.initialState,s=null==t?null:t.constants;null==t&&(t={});const a=Dg(e,n,s,this.numConstants);e=a.inputs,n=a.initialState,s=a.constants;let r=[],i=[];if(null!=n){t.initialState=n,r=r.concat(n),this.stateSpec=[];for(const e of n)this.stateSpec.push(new kf({shape:e.shape}));i=i.concat(this.stateSpec)}if(null!=s&&(t.constants=s,r=r.concat(s),this.numConstants=s.length),r[0]instanceof vf){const n=[e].concat(r),s=this.inputSpec.concat(i),a=this.inputSpec;this.inputSpec=s;const o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return Cr((()=>{const n=null==t?null:t.mask,s=null==t?null:t.training;let a=null==t?null:t.initialState;e=mf(e),null==a&&(a=this.stateful?this.states_:this.getInitialState(e));const r=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==r)throw new Cp(`RNN Layer has ${r} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const i={training:s},o=_g(((e,t)=>{const n=this.cell.call([e].concat(t),i);return[n[0],n.slice(1)]}),e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,s);const h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h}))}getInitialState(e){return Cr((()=>{let t=io(e.shape);return t=zi(t,[1,2]),t=$d(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map((e=>e>1?Rd(t,[1,e]):t)):this.cell.stateSize>1?[Rd(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()===Og.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=Bf(t.cell,n);return new e(Object.assign(t,{cell:s}))}}Og.className="RNN",Nr(Og);class Mg extends Cf{}class Lg extends Mg{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,Hp(this.units,"units"),this.activation=sg(null==e.activation?this.DEFAULT_ACTIVATION:e.activation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=lf(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=lf(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=lf(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ug(e.kernelRegularizer),this.recurrentRegularizer=ug(e.recurrentRegularizer),this.biasRegularizer=ug(e.biasRegularizer),this.kernelConstraint=sd(e.kernelConstraint),this.recurrentConstraint=sd(e.recurrentConstraint),this.biasConstraint=sd(e.biasConstraint),this.dropout=kd([1,vd([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=kd([1,vd([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=gf(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 Cr((()=>{if(2!==(e=e).length)throw new Cp(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=null!=t.training&&t.training;let a;0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=Gg({ones:()=>lo(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=Gg({ones:()=>lo(n),rate:this.recurrentDropout,training:s}));const r=this.dropoutMask,i=this.recurrentDropoutMask;a=Dd(null!=r?Mi(e,r):e,this.kernel.read()),null!=this.bias&&(a=Ld(a,this.bias.read())),null!=i&&(n=Mi(n,i));let o=Or(a,Dd(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:tg(this.activation),useBias:this.useBias,kernelInitializer:of(this.kernelInitializer),recurrentInitializer:of(this.recurrentInitializer),biasInitializer:of(this.biasInitializer),kernelRegularizer:og(this.kernelRegularizer),recurrentRegularizer:og(this.recurrentRegularizer),biasRegularizer:og(this.biasRegularizer),activityRegularizer:og(this.activityRegularizer),kernelConstraint:td(this.kernelConstraint),recurrentConstraint:td(this.recurrentConstraint),biasConstraint:td(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}Lg.className="SimpleRNNCell",Nr(Lg);class zg extends Og{constructor(e){e.cell=new Lg(e),super(e)}call(e,t){return Cr((()=>{null!=this.cell.dropoutMask&&(Sr(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Sr(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,s=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:a})}))}static fromConfig(e,t){return new e(t)}}zg.className="SimpleRNN",Nr(zg);class Pg extends Mg{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 Cp("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Hp(this.units,"units"),this.activation=sg(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=sg(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=lf(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=lf(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=lf(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ug(e.kernelRegularizer),this.recurrentRegularizer=ug(e.recurrentRegularizer),this.biasRegularizer=ug(e.biasRegularizer),this.kernelConstraint=sd(e.kernelConstraint),this.recurrentConstraint=sd(e.recurrentConstraint),this.biasConstraint=sd(e.biasConstraint),this.dropout=kd([1,vd([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=kd([1,vd([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){const t=(e=gf(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 Cr((()=>{if(2!==(e=e).length)throw new Cp(`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=Gg({ones:()=>lo(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=Gg({ones:()=>lo(s),rate:this.recurrentDropout,training:n,count:3}));const a=this.dropoutMask,r=this.recurrentDropoutMask;let i,o,l;0<this.dropout&&this.dropout<1&&(e=Mi(e,a[0]));let u=Dd(e,this.kernel.read());this.useBias&&(u=Ld(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=Mi(s,r[0]));const c=this.recurrentKernel.read(),[h,p]=Eo(c,[2*this.units,this.units],c.rank-1),d=Dd(s,h),[f,m,g]=Eo(u,3,u.rank-1),[y,b]=Eo(d,2,d.rank-1);i=this.recurrentActivation.apply(Or(f,y)),o=this.recurrentActivation.apply(Or(m,b));const x=Dd(Mi(o,s),p);l=this.activation.apply(Or(g,x));const w=Or(Mi(i,s),Mi(Or(1,so(i)),l));return[w,w]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:tg(this.activation),recurrentActivation:tg(this.recurrentActivation),useBias:this.useBias,kernelInitializer:of(this.kernelInitializer),recurrentInitializer:of(this.recurrentInitializer),biasInitializer:of(this.biasInitializer),kernelRegularizer:og(this.kernelRegularizer),recurrentRegularizer:og(this.recurrentRegularizer),biasRegularizer:og(this.biasRegularizer),activityRegularizer:og(this.activityRegularizer),kernelConstraint:td(this.kernelConstraint),recurrentConstraint:td(this.recurrentConstraint),biasConstraint:td(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}Pg.className="GRUCell",Nr(Pg);class Bg extends Og{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 Pg(e),super(e)}call(e,t){return Cr((()=>{null!=this.cell.dropoutMask&&(Sr(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Sr(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,s=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}Bg.className="GRU",Nr(Bg);class Wg extends Mg{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,Hp(this.units,"units"),this.activation=sg(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=sg(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=lf(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=lf(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=lf(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ug(e.kernelRegularizer),this.recurrentRegularizer=ug(e.recurrentRegularizer),this.biasRegularizer=ug(e.biasRegularizer),this.kernelConstraint=sd(e.kernelConstraint),this.recurrentConstraint=sd(e.recurrentConstraint),this.biasConstraint=sd(e.biasConstraint),this.dropout=kd([1,vd([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=kd([1,vd([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;const n=(e=gf(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 Vd{apply(t,s){const a=e.apply([n]),r=(new Gd).apply([n]),i=e.apply([2*n]);return Ad(Ad(a,r),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 Cr((()=>{const n=null!=t.training&&t.training;if(3!==(e=e).length)throw new Cp(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=Gg({ones:()=>lo(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=Gg({ones:()=>lo(s),rate:this.recurrentDropout,training:n,count:4}));const r=this.dropoutMask,i=this.recurrentDropoutMask;let o,l,u,c;0<this.dropout&&this.dropout<1&&(e=Mi(e,r[0]));let h=Dd(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=Mi(s,i[0])),h=Or(h,Dd(s,this.recurrentKernel.read())),this.useBias&&(h=Ld(h,this.bias.read()));const[p,d,f,m]=Eo(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(d),u=Or(Mi(l,a),Mi(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);const g=Mi(c,this.activation.apply(u));return[g,g,u]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:tg(this.activation),recurrentActivation:tg(this.recurrentActivation),useBias:this.useBias,kernelInitializer:of(this.kernelInitializer),recurrentInitializer:of(this.recurrentInitializer),biasInitializer:of(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:og(this.kernelRegularizer),recurrentRegularizer:og(this.recurrentRegularizer),biasRegularizer:og(this.biasRegularizer),activityRegularizer:og(this.activityRegularizer),kernelConstraint:td(this.kernelConstraint),recurrentConstraint:td(this.recurrentConstraint),biasConstraint:td(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}Wg.className="LSTMCell",Nr(Wg);class Vg extends Og{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 Wg(e),super(e)}call(e,t){return Cr((()=>{null!=this.cell.dropoutMask&&(Sr(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Sr(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,s=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}Vg.className="LSTM",Nr(Vg);class Ug extends Mg{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 Cr((()=>{let n=(e=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 a=[];let r;for(let i=0;i<this.cells.length;++i){const o=this.cells[i];n=s[i],r=0===i?[e[0]].concat(n):[r[0]].concat(n),r=o.call(r,t),a.push(r.slice(1))}n=[];for(const e of a.slice().reverse())n.push(...e);return[r[0]].concat(n)}))}build(e){let t;df(e)&&(e=e[0]),e=e,this.cells.forEach(((n,s)=>{fd(`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({},e,t)}static fromConfig(e,t,n={}){const s=[];for(const e of t.cells)s.push(Bf(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 xf(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,a=e.splice(s);for(let e=0;e<n.weights.length;++e)t.push([n.weights[e],a[e]])}wf(t)}}function Gg(e){const{ones:t,rate:n,training:s=!1,count:a=1}=e,r=()=>zd(t(),n),i=()=>Pd(r,t,s);return!a||a<=1?Tr(i().clone()):Array(a).fill(void 0).map(i).map((e=>Tr(e.clone())))}Ug.className="StackedRNNCells",Nr(Ug);var Hg,jg;class Kg extends Og{constructor(e){if(e.unroll)throw new Sp("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Sp("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new kf({ndim:5})]}call(e,t){return Cr((()=>{if(null!=this.cell.dropoutMask&&(Sr(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(Sr(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new Cp("ConvRNN2D cell does not support constants");const n=null==t?null:t.mask,s=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:a})}))}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return Cr((()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),a=io([s[0],...s.slice(2)]);return Array.isArray(t)?Array(t.length).fill(a):[a]}))}resetStates(e,t=!1){Cr((()=>{if(!this.stateful)throw new Ip("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),a=[s[0],...s.slice(2)];if(null==n[0])throw new Cp("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((()=>io(a))):this.states_=[io(a)];else if(null==e)Sr(this.states_),null!=this.keptStates&&(Sr(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>io(a))):this.states_[0]=io(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Cp(`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()):Sr(this.states_);for(let t=0;t<this.states_.length;++t){const n=e[t],s=a;if(!_(n.shape,s))throw new Cp(`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=>Tr(e.clone())))}))}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:a,strides:r,dilationRate:i}=this.cell,o="channelsFirst"===t,l=e[o?3:2],u=e[o?4:3],c=yg(l,s[0],a,r[0],i[0]),h=yg(u,s[1],a,r[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}}Kg.className="ConvRNN2D";class qg extends Wg{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:a,dataFormat:r,dilationRate:i}=e;super(Object.assign({},e,{units:t})),this.filters=t,Hp(this.filters,"filters"),this.kernelSize=gg(n,2,"kernelSize"),this.kernelSize.forEach((e=>Hp(e,"kernelSize"))),this.strides=gg(s||1,2,"strides"),this.strides.forEach((e=>Hp(e,"strides"))),this.padding=a||"valid",hd(this.padding),this.dataFormat=r||"channelsLast",cd(this.dataFormat),this.dilationRate=gg(i||1,2,"dilationRate"),this.dilationRate.forEach((e=>Hp(e,"dilationRate")))}build(e){var t;e=gf(e);const n="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[n])throw new Cp(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],a=this.kernelSize.concat([s,4*this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const r=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",r,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 Vd{apply(e,t){return Ed([n.apply([s]),oo([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 Cr((()=>{if(3!==e.length)throw new Cp(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],a=e[1],r=e[2];0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=Gg({ones:()=>lo(s),rate:this.dropout,training:n,count:4}));const i=this.dropoutMask,o=(e,t,n)=>t&&t[n]?Mi(t[n],e):e;let l=o(s,i,0),u=o(s,i,1),c=o(s,i,2),h=o(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=Gg({ones:()=>lo(a),rate:this.recurrentDropout,training:n,count:4}));const p=this.recurrentDropoutMask;let d=o(a,p,0),f=o(a,p,1),m=o(a,p,2),g=o(a,p,3);const[y,b,x,w]=Eo(this.kernel.read(),4,3),[k,v,N,I]=this.useBias?Eo(this.bias.read(),4):[null,null,null,null];l=this.inputConv(l,y,k,this.padding),u=this.inputConv(u,b,v,this.padding),c=this.inputConv(c,x,N,this.padding),h=this.inputConv(h,w,I,this.padding);const[$,C,S,T]=Eo(this.recurrentKernel.read(),4,3);d=this.recurrentConv(d,$),f=this.recurrentConv(f,C),m=this.recurrentConv(m,S),g=this.recurrentConv(g,T);const E=this.recurrentActivation.apply(Or(l,d)),A=this.recurrentActivation.apply(Or(u,f)),R=Or(Mi(A,r),Mi(E,this.activation.apply(Or(c,m)))),F=Mi(this.recurrentActivation.apply(Or(h,g)),this.activation.apply(R));return[F,F,R]}))}getConfig(){const e=super.getConfig(),{units:t}=e,n=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 a=0;for(s=Object.getOwnPropertySymbols(e);a<s.length;a++)t.indexOf(s[a])<0&&Object.prototype.propertyIsEnumerable.call(e,s[a])&&(n[s[a]]=e[s[a]])}return n}(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){const a=hi(e,t,this.strides,s||"valid","channelsFirst"===this.dataFormat?"NCHW":"NHWC",this.dilationRate);return n?Ld(a,n,this.dataFormat):a}recurrentConv(e,t){return hi(e,t,1,"same","channelsFirst"===this.dataFormat?"NCHW":"NHWC")}}qg.className="ConvLSTM2DCell",Nr(qg);class Xg extends Kg{constructor(e){const t=new qg(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}Xg.className="ConvLSTM2D",Nr(Xg);class Yg extends Cf{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 Cr((()=>{this.invokeCallHook(e,t);const n=mf(e);if(0<this.rate&&this.rate<1){const e=null!=t.training&&t.training,s=this.getNoiseShape(n);return Pd((()=>zd(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()}}Yg.className="Dropout",Nr(Yg);class Jg extends Yg{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}Jg.className="SpatialDropout1D",Nr(Jg);class Zg extends Cf{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,Hp(this.units,"units"),this.activation=sg(e.activation),null!=e.useBias&&(this.useBias=e.useBias),this.kernelInitializer=lf(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=lf(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=sd(e.kernelConstraint),this.biasConstraint=sd(e.biasConstraint),this.kernelRegularizer=ug(e.kernelRegularizer),this.biasRegularizer=ug(e.biasRegularizer),this.activityRegularizer=ug(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){const t=(e=gf(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=gf(e)).slice();return t[t.length-1]=this.units,t}call(e,t){return Cr((()=>{this.invokeCallHook(e,t);const n=mf(e),s=Kp(this.activation.getClassName());let a;return null!=s?a=Dd(n,this.kernel.read(),s,this.bias?this.bias.read():null):(a=Dd(n,this.kernel.read()),null!=this.bias&&(a=Ld(a,this.bias.read())),null!=this.activation&&(a=this.activation.apply(a))),a}))}getConfig(){const e={units:this.units,activation:tg(this.activation),useBias:this.useBias,kernelInitializer:of(this.kernelInitializer),biasInitializer:of(this.biasInitializer),kernelRegularizer:og(this.kernelRegularizer),biasRegularizer:og(this.biasRegularizer),activityRegularizer:og(this.activityRegularizer),kernelConstraint:td(this.kernelConstraint),biasConstraint:td(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}Zg.className="Dense",Nr(Zg);class Qg extends Cf{constructor(e){super(e=e||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=gf(e);for(const t of e.slice(1))if(null==t)throw new Cp(`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],xd(e,1)]}call(e,t){return Cr((()=>{this.invokeCallHook(e,t);let n=mf(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=n.transpose(e)}return function(e){if(e.rank<=1)throw new Cp(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);const t=[e.shape[0],xd(e.shape,1)];return e.reshape(t)}(n)}))}getConfig(){const e={};null!=this.dataFormat&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}Qg.className="Flatten",Nr(Qg);class ey extends Cf{constructor(e){super(e),this.supportsMasking=!0,this.activation=sg(e.activation)}call(e,t){return Cr((()=>{this.invokeCallHook(e,t);const n=mf(e);return this.activation.apply(n)}))}getConfig(){const e={activation:tg(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}ey.className="Activation",Nr(ey);class ty extends Cf{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return Cr((()=>{return e=mf(e),t=e,n=this.n,Cr((()=>{if(2!==t.shape.length)throw new Cp(`repeat() expects a rank-2 tensor, but received a rank-${t.shape.length} tensor.`);return Rd($d(t,1),[1,n,1])}));var t,n}))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}ty.className="RepeatVector",Nr(ty);class ny extends Cf{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 a=1,r=null;for(let e=0;e<s.length;++e){const t=s[e];if(this.isUnknown(t)){if(null!==r)throw new Cp("Can only specifiy one unknown dimension.");r=e}else a*=t}const i=xd(e);if(null!==r){if(0===a||i%a!=0)throw new Cp(n);s[r]=i/a}else if(i!==a)throw new Cp(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 Cr((()=>{this.invokeCallHook(e,t);const n=mf(e),s=n.shape,a=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return n.reshape(a)}))}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}ny.className="Reshape",Nr(ny);class sy extends Cf{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=Nd(1,e.dims.length+1);if(!_(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 kf({ndim:this.dims.length+1})]}computeOutputShape(e){const t=(e=gf(e)).slice();return this.dims.forEach(((n,s)=>{t[s+1]=e[n]})),t}call(e,t){return Wo(mf(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}sy.className="Permute",Nr(sy);class ay extends Cf{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=mf(e);return Lr(ao(n,this.maskValue),-1)}call(e,t){return Cr((()=>{this.invokeCallHook(e,t);const n=mf(e),s=Lr(ao(n,this.maskValue),-1,!0);return n.mul(s.asType(n.dtype))}))}}ay.className="Masking",Nr(ay);class ry extends Cf{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(Dp(e.inputLength))}this.inputDim=e.inputDim,Hp(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Hp(this.outputDim,"outputDim"),this.embeddingsInitializer=lf(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ug(e.embeddingsRegularizer),this.activityRegularizer=ug(e.activityRegularizer),this.embeddingsConstraint=sd(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 Cr((()=>this.maskZero?(e=mf(e),ao(e,Bo(e))):null))}computeOutputShape(e){if(e=gf(e),null==this.inputLength)return[...e,this.outputDim];const t=Dp(this.inputLength);if(t.length!==e.length-1)throw new Cp(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const a=t[s],r=e[s+1];if(null!=a&&null!=r&&a!==r)throw new Cp(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);null==a&&(t[n]=r),n++}}return[e[0],...t,this.outputDim]}call(e,t){return Cr((()=>{this.invokeCallHook(e,t);let n=mf(e);return"int32"!==n.dtype&&(n=Id(n,"int32")),_d(this.embeddings.read(),n.as1D()).reshape(gf(this.computeOutputShape(n.shape)))}))}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:of(this.embeddingsInitializer),embeddingsRegularizer:og(this.embeddingsRegularizer),activityRegularizer:og(this.activityRegularizer),embeddingsConstraint:td(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}ry.className="Embedding",Nr(ry);class iy extends Cf{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Sp}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 a=e[e.length-t.length+s],r=t[s];if(null==a||null==r||a<0||r<0)n.push(null);else if(1===a)n.push(r);else if(1===r)n.push(a);else{if(a!==r)throw new Cp("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[gf(e)]),(e=e).length<2)throw new Cp(`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=Wp(t),t.length>1)throw new Cp(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=null==e[0]?null:e[0].slice(1);for(let t=1;t<e.length;++t){const s=null==e[t]?null:e[t].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}const s=e.map((e=>e.length));-1===e.indexOf(null)&&1===Wp(s).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return Cr((()=>{if(e=e,this.reshapeRequired){const t=[],n=e.map((e=>e.rank));if(-1===n.indexOf(null)){const s=vd(n);for(let n of e){const e=n.rank;for(let t=0;t<s-e;++t)n=$d(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,a=e[0],r=e.slice(1).concat([a]);let i=s.reshape([a].concat(xd(e.slice(1))));i=Wo(i,[1,0]),i=i.reshape(r),t.push(i),n=!0}else if(e>1){const a=Nd(1,e).concat([0]);t.push(Wo(s,a)),n=!0}else t.push(s)}let s=this.mergeFunction(t);const a=s.rank;if(n)if(null==a){const e=s.shape,t=e[e.length-1],n=[t].concat(e.slice(0,e.length-1));s=Wo(s.reshape([-1,t]),[1,0]).reshape(n)}else if(a>1){const e=[a-1].concat(Nd(0,a-1));s=Wo(s,e)}return s}}return this.mergeFunction(e)}))}computeOutputShape(e){let t;t=null==(e=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=Wp(n),t=1===n.length?n.concat(t):[null].concat(t),t}computeMask(e,t){return Cr((()=>{if(null==t)return null;if(!Array.isArray(t))throw new Cp("`mask` should be an Array");if(!Array.isArray(e))throw new Cp("`inputs` should be an Array");if(t.length!==e.length)throw new Cp(`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:Ii(e,0))))[0];for(let e=1;e<t.length-1;++e)n=Bi(n,t[e]);return n}))}}class oy extends iy{constructor(e){super(e)}mergeFunction(e){return Cr((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Or(t,e[n]);return t}))}}oy.className="Add",Nr(oy);class ly extends iy{constructor(e){super(e)}mergeFunction(e){return Cr((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Mi(t,e[n]);return t}))}}ly.className="Multiply",Nr(ly);class uy extends iy{constructor(e){super(e)}mergeFunction(e){return Cr((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Or(t,e[n]);return Mi(1/e.length,t)}))}}uy.className="Average",Nr(uy);class cy extends iy{constructor(e){super(e)}mergeFunction(e){return Cr((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ui(t,e[n]);return t}))}}cy.className="Maximum",Nr(cy);class hy extends iy{constructor(e){super(e)}mergeFunction(e){return Cr((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ji(t,e[n]);return t}))}}hy.className="Minimum",Nr(hy);class py extends iy{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 Cp("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;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 a=!1;for(const e of n)if(_(e,s)){a=!0;break}a||n.push(s)}if(n.length>1)throw new Cp("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return Cr((()=>Ed(e,this.axis)))}computeOutputShape(e){if(!Array.isArray(e)||!Array.isArray(e[0]))throw new Cp("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 Cp("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new Cp("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new Cp(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return Cr((()=>{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(lo(e[n]).asType("bool")):t[n].rank<e[n].rank?s.push(Ii(t[n],-1)):s.push(t[n]);const a=ii(s,this.axis);return Mr(a,-1,!1)}))}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}function dy(e,t){for(;e<0;)e+=t;return e}py.className="Concatenate",Nr(py);class fy extends iy{constructor(e){super(e),this.axes=e.axes,this.normalize=null!=e.normalize&&e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){E(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 Sp("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 Cp(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(2!==e.length)throw new Cp(`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)=>dy(t,e[n].shape.length))):[dy(this.axes,n.shape.length),dy(this.axes,s.shape.length)],this.normalize&&(n=Wf(n,t[0]),s=Wf(s,t[1])),function(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Sp("batchDot is not implemented for tensors of 4D or higher rank yet");if(E(e.shape.length>=2,(()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`)),E(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 Sp("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,a=t.shape.length;null==n&&(n=[s-1,a-2]);const r=n;return Cr((()=>{let n,i;if(s>a){n=s-a;const e=[];for(let t=0;t<n;++t)e.push(1);t=t.reshape(t.shape.concat(e))}else if(a>s){n=a-s;const t=[];for(let e=0;e<n;++e)t.push(1);e=e.reshape(e.shape.concat(t))}else n=0;if(2===e.shape.length&&2===t.shape.length)i=r[0]===r[1]?e.mul(t).sum(r[0]):e.transpose([1,0]).mul(t).sum(r[1]);else{const n=r[0]!==e.shape.length-1,s=r[1]===t.shape.length-1;i=e.matMul(t,n,s)}if(n>0){let e;e=s>a?s+a-3:s-1;const t=[];for(let s=e;s<e+n;++s)t.push(s);i=i.squeeze(t)}return 1===i.shape.length&&(i=i.expandDims(1)),i}))}(n,s,t)}interpretAxes(e,t){let n;return n=Array.isArray(this.axes)?this.axes:[dy(this.axes,e.length),dy(this.axes,t.length)],n}computeOutputShape(e){E(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 Sp("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 a=t.concat(n);return 1===a.length&&a.push(1),a}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}fy.className="Dot",Nr(fy);class my extends Cf{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 Cr((()=>{this.invokeCallHook(e,t);const n=mf(e);return Pd((()=>Fd(n.shape,0,this.stddev).add(n)),(()=>n),t.training||!1)}))}}my.className="GaussianNoise",Nr(my);class gy extends Cf{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 Cr((()=>{this.invokeCallHook(e,t);const n=mf(e);return this.rate>0&&this.rate<1?Pd((()=>{const e=Math.sqrt(this.rate/(1-this.rate));return n.mul(Fd(n.shape,1,e))}),(()=>n),t.training||!1):n}))}}gy.className="GaussianDropout",Nr(gy);class yy extends Cf{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||mf(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 Cr((()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e);return Pd((()=>{const t=mf(e),s=-1.7580993408473766;let a=Ri(go(n),this.rate);a=Id(a,"float32");const r=((1-this.rate)*(1+this.rate*s**2))**-.5,i=-r*s*this.rate;return t.mul(a).add(a.add(-1).mul(s)).mul(r).add(i)}),(()=>mf(e)),t.training||!1)}return e}))}}function by(e,t,n,s,a,r=.001){let i;if(2===e.rank)i=ni(e,t,n,s,a,r);else if(3===e.rank)i=si(e,t,n,s,a,r);else{if(4!==e.rank)throw new Sp(`batchNormalization is not implemented for array of rank ${e.rank} yet`);i=ai(e,t,n,s,a,r)}return i}yy.className="AlphaDropout",Nr(yy);class xy extends Cf{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=lf(e.betaInitializer||"zeros"),this.gammaInitializer=lf(e.gammaInitializer||"ones"),this.movingMeanInitializer=lf(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=lf(e.movingVarianceInitializer||"ones"),this.betaConstraint=sd(e.betaConstraint),this.gammaConstraint=sd(e.gammaConstraint),this.betaRegularizer=ug(e.betaRegularizer),this.gammaRegularizer=ug(e.gammaRegularizer)}build(e){e=gf(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(null==n)throw new Cp(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new kf({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 Cr((()=>{const n=null!=t.training&&t.training,s=mf(e),a=s.shape,r=a.length,i=Nd(0,r),o=this.axis>=0?this.axis:this.axis+r;i.splice(o,1);const l=Ep(1,r);l[o]=a[o];const u=i.slice();u.sort();const c=!_(u,Nd(0,r).slice(0,r-1));if(!n)return(()=>{if(c){const e=this.movingMean.read().reshape(l),t=this.movingVariance.read().reshape(l),n=this.center?this.beta.read().reshape(l):null,a=this.scale?this.gamma.read().reshape(l):null;return by(s,e,t,n,a,this.epsilon)}return by(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]=function(e,t,n,s,a=.001){return _(s.slice().sort(),Nd(0,e.rank-1))?function(e,t,n,s,a=.001){return Cr((()=>{const r=no(e,s),i=r.mean,o=r.variance;return[by(e,i,o,n,t,a),i,o]}))}(e,t,n,s,a):function(e,t,n,s,a=.001){return Cr((()=>{const r=no(e,s),i=r.mean,o=r.variance,l=[];for(const t of Nd(0,e.rank))-1!==s.indexOf(t)?l.push(1):l.push(e.shape[t]);const u=i.reshape(l),c=o.reshape(l),h=null==t?null:t.reshape(l),p=null==n?null:n.reshape(l);return[by(e,u,c,p,h,a),i,o]}))}(e,t,n,s,a)}(s,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(e,t,n)=>{Cr((()=>{const s=1-n,a=e.read(),r=a.sub(t).mul(s);e.write(a.sub(r))}))};return(()=>{f(this.movingMean,p,this.momentum),f(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:of(this.betaInitializer),gammaInitializer:of(this.gammaInitializer),movingMeanInitializer:of(this.movingMeanInitializer),movingVarianceInitializer:of(this.movingVarianceInitializer),betaRegularizer:og(this.betaRegularizer),gammaRegularizer:og(this.gammaRegularizer),betaConstraint:td(this.betaConstraint),gammaConstraint:td(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}xy.className="BatchNormalization",Nr(xy);class wy extends Cf{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=lf(e.betaInitializer||"zeros"),this.gammaInitializer=lf(e.gammaInitializer||"ones"),this.betaRegularizer=ug(e.betaRegularizer),this.gammaRegularizer=ug(e.gammaRegularizer),this.supportsMasking=!0}build(e){const t=(e=gf(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!==Wp(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map((t=>e[t]));this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,!0):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,!0):this.beta=null,this.built=!0}call(e,t){const n=mf(e),s=n.shape,a=s.length;return Cr((()=>{let{mean:e,variance:t}=no(n,this.axis,!0);const r=Ep(1,a);for(const e of this.axis)r[e]=s[e];const i=e=>null!=e&&e.shape.length!==a&&this.axis!==[a-1]?e.reshape(r):e;let o=i(this.gamma.read()),l=i(this.beta.read());const u=[],c=[];for(let e=0;e<a;++e)-1!==this.axis.indexOf(e)?(u.push(s[e]),c.push(1)):(u.push(1),c.push(s[e]));return e=e.tile(u),t=t.tile(u),o=o.tile(c),l=l.tile(c),by(n,e,t,l,o,this.epsilon)}))}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:of(this.betaInitializer),gammaInitializer:of(this.gammaInitializer),betaRegularizer:og(this.betaRegularizer),gammaRegularizer:og(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}wy.className="LayerNormalization",Nr(wy);class ky extends Cf{constructor(e){if(null==e&&(e={}),super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,null==e.padding)this.padding=[[1,1],[1,1]];else if("number"==typeof e.padding)this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,2!==e.padding.length)throw new Cp(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 Cp(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],2!==e.padding[1].length)throw new Cp(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new kf({ndim:4})]}computeOutputShape(e){let t,n;return e=gf(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 Cr((()=>{return t=mf(e),n=this.padding,s=this.dataFormat,Cr((()=>{if(4!==t.rank)throw new Cp(`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 Cp("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 Cp(`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]],uo(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 vy(e,t,n,s,a,r){return Cr((()=>{let i;cd(a),pd(r),hd(s),null==n&&(n=[1,1]),null==s&&(s="valid"),null==a&&(a="channelsLast"),null==r&&(r="max"),e=xg(e,a);const o="same"===s?"same":"valid";return i="max"===r?Wi(e,t,n,o):Qr(e,t,n,o),"channelsFirst"===a&&(i=Wo(i,[0,3,1,2])),i}))}function Ny(e,t,n,s,a,r){return Cr((()=>{let i;cd(a),pd(r),hd(s),null==n&&(n=[1,1,1]),null==s&&(s="valid"),null==a&&(a="channelsLast"),null==r&&(r="max"),e=wg(e,a);const o="same"===s?"same":"valid";return i="max"===r?Vi(e,t,n,o):ei(e,t,n,o),"channelsFirst"===a&&(i=Wo(i,[0,4,1,2,3])),i}))}ky.className="ZeroPadding2D",Nr(ky);class Iy extends Cf{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 Cp(`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(Hp(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 Cp(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);this.strides=e.strides}Hp(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,hd(this.padding),this.inputSpec=[new kf({ndim:3})]}computeOutputShape(e){const t=yg((e=gf(e))[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return Cr((()=>{this.invokeCallHook(e,t),e=$d(mf(e),2);const n=this.poolingFunction(mf(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ro(n,[2])}))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class $y extends Iy{constructor(e){super(e)}poolingFunction(e,t,n,s,a){return cd(a),hd(s),vy(e,t,n,s,a,"max")}}$y.className="MaxPooling1D",Nr($y);class Cy extends Iy{constructor(e){super(e)}poolingFunction(e,t,n,s,a){return cd(a),hd(s),vy(e,t,n,s,a,"avg")}}Cy.className="AveragePooling1D",Nr(Cy);class Sy extends Cf{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 Cp(`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];Hp(this.poolSize,"poolSize"),Hp(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,cd(this.dataFormat),hd(this.padding),this.inputSpec=[new kf({ndim:4})]}computeOutputShape(e){e=gf(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2];return t=yg(t,this.poolSize[0],this.padding,this.strides[0]),n=yg(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 Cr((()=>(this.invokeCallHook(e,t),this.poolingFunction(mf(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 Ty extends Sy{constructor(e){super(e)}poolingFunction(e,t,n,s,a){return cd(a),hd(s),vy(e,t,n,s,a,"max")}}Ty.className="MaxPooling2D",Nr(Ty);class Ey extends Sy{constructor(e){super(e)}poolingFunction(e,t,n,s,a){return cd(a),hd(s),vy(e,t,n,s,a,"avg")}}Ey.className="AveragePooling2D",Nr(Ey);class Ay extends Cf{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 Cp(`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];Hp(this.poolSize,"poolSize"),Hp(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,cd(this.dataFormat),hd(this.padding),this.inputSpec=[new kf({ndim:5})]}computeOutputShape(e){e=gf(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=yg(t,this.poolSize[0],this.padding,this.strides[0]),n=yg(n,this.poolSize[1],this.padding,this.strides[1]),s=yg(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 Cr((()=>(this.invokeCallHook(e,t),this.poolingFunction(mf(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 Ry extends Ay{constructor(e){super(e)}poolingFunction(e,t,n,s,a){return cd(a),hd(s),Ny(e,t,n,s,a,"max")}}Ry.className="MaxPooling3D",Nr(Ry);class Fy extends Ay{constructor(e){super(e)}poolingFunction(e,t,n,s,a){return cd(a),hd(s),Ny(e,t,n,s,a,"avg")}}Fy.className="AveragePooling3D",Nr(Fy);class Dy extends Cf{constructor(e){super(e),this.inputSpec=[new kf({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Sp}}class _y extends Dy{constructor(e){super(e||{})}call(e,t){return Cr((()=>{const t=mf(e);return Gi(t,1)}))}}_y.className="GlobalAveragePooling1D",Nr(_y);class Oy extends Dy{constructor(e){super(e||{})}call(e,t){return Cr((()=>{const t=mf(e);return Oi(t,1)}))}}Oy.className="GlobalMaxPooling1D",Nr(Oy);class My extends Cf{constructor(e){super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,cd(this.dataFormat),this.inputSpec=[new kf({ndim:4})]}computeOutputShape(e){return e=e,"channelsLast"===this.dataFormat?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Sp}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class Ly extends My{call(e,t){return Cr((()=>{const t=mf(e);return"channelsLast"===this.dataFormat?Gi(t,[1,2]):Gi(t,[2,3])}))}}Ly.className="GlobalAveragePooling2D",Nr(Ly);class zy extends My{call(e,t){return Cr((()=>{const t=mf(e);return"channelsLast"===this.dataFormat?Oi(t,[1,2]):Oi(t,[2,3])}))}}zy.className="GlobalMaxPooling2D",Nr(zy);class Py extends Cf{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=Bf(t.layer,n);delete t.layer;const a={layer:s};return Object.assign(a,t),new e(a)}}class By extends Py{constructor(e){super(e),this.supportsMasking=!0}build(e){if((e=gf(e)).length<3)throw new Cp(`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=gf(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 Cr((()=>_g(((e,n)=>[mf(this.layer.call(e,t)),[]]),e=mf(e),[],!1,null,null,!1,!0)[1]))}}By.className="TimeDistributed",Nr(By);class Wy extends Py{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Bf(n),t.goBackwards=!0!==t.goBackwards;const s={};var a;if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Bf(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=void 0===e.mergeMode?"concat":e.mergeMode,a=this.mergeMode,Up(ld,"BidirectionalMergeMode",a),e.weights)throw new Sp("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,a=this.forwardLayer.computeOutputShape(e);return Array.isArray(a)&&Array.isArray(a[0])||(a=[a]),a=a,this.returnState?(s=a.slice(1),t=a[0]):t=a[0],t=t,"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()):Fp(n)}apply(e,t){let n=null==t?null:t.initialState,s=null==t?null:t.constants;null==t&&(t={});const a=Dg(e,n,s,this.numConstants);if(e=a.inputs,n=a.initialState,s=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(null==n||0===n.length)&&null==s)return super.apply(e,t);const r=[],i=[];if(null!=n){const e=n.length;if(e%2>0)throw new Cp("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,r.push(...n);const s=n.map((e=>new kf({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 Sp("Support for constants in Bidirectional layers is not implemented yet.");const o=r[0]instanceof vf;for(const e of r)if(e instanceof vf!==o)throw new Cp("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(r),s=this.inputSpec.concat(i),a=this.inputSpec;this.inputSpec=s;const o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return Cr((()=>{const n=t.initialState;let s,a,r,i;if(null==n)s=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{const r=n.slice(0,n.length/2),i=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:r})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:i}))}return this.returnState&&(Array.isArray(s)&&(r=s.slice(1).concat(a.slice(1))),s=s[0],a=a[0]),this.returnSequences&&(a=bo(a,1)),"concat"===this.mergeMode?i=Ed([s,a]):"sum"===this.mergeMode?i=Or(s,a):"ave"===this.mergeMode?i=Mi(.5,Or(s,a)):"mul"===this.mergeMode?i=Mi(s,a):null==this.mergeMode&&(i=[s,a]),this.returnState?null==this.mergeMode?i.concat(r):[i].concat(r):i}))}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){fd(this.forwardLayer.name,(()=>{this.forwardLayer.build(e)})),fd(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=Bf(t.layer);if(delete t.layer,null!=t.numConstants)throw new Sp("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}Wy.className="Bidirectional",Nr(Wy),function(e){e[e.DT_INVALID=0]="DT_INVALID",e[e.DT_FLOAT=1]="DT_FLOAT",e[e.DT_DOUBLE=2]="DT_DOUBLE",e[e.DT_INT32=3]="DT_INT32",e[e.DT_UINT8=4]="DT_UINT8",e[e.DT_INT16=5]="DT_INT16",e[e.DT_INT8=6]="DT_INT8",e[e.DT_STRING=7]="DT_STRING",e[e.DT_COMPLEX64=8]="DT_COMPLEX64",e[e.DT_INT64=9]="DT_INT64",e[e.DT_BOOL=10]="DT_BOOL",e[e.DT_QINT8=11]="DT_QINT8",e[e.DT_QUINT8=12]="DT_QUINT8",e[e.DT_QINT32=13]="DT_QINT32",e[e.DT_BFLOAT16=14]="DT_BFLOAT16",e[e.DT_FLOAT_REF=101]="DT_FLOAT_REF",e[e.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",e[e.DT_INT32_REF=103]="DT_INT32_REF",e[e.DT_UINT8_REF=104]="DT_UINT8_REF",e[e.DT_INT16_REF=105]="DT_INT16_REF",e[e.DT_INT8_REF=106]="DT_INT8_REF",e[e.DT_STRING_REF=107]="DT_STRING_REF",e[e.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",e[e.DT_INT64_REF=109]="DT_INT64_REF",e[e.DT_BOOL_REF=110]="DT_BOOL_REF",e[e.DT_QINT8_REF=111]="DT_QINT8_REF",e[e.DT_QUINT8_REF=112]="DT_QUINT8_REF",e[e.DT_QINT32_REF=113]="DT_QINT32_REF",e[e.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"}(Hg||(Hg={})),function(e){let t;!function(e){e[e.LEGACY=0]="LEGACY",e[e.V1=1]="V1",e[e.V2=2]="V2"}(t=e.CheckpointFormatVersion||(e.CheckpointFormatVersion={}))}(jg||(jg={}));const Vy={};function Uy(e){return Vy[e]}function Gy(e,t,n,s,a){const r=t.inputParams[e];if(r&&void 0!==r.inputIndexStart){const e=r.inputIndexStart,i=0===r.inputIndexEnd?void 0:void 0===r.inputIndexEnd?e+1:r.inputIndexEnd;if("tensor"===r.type)return Hy(t.inputNames[r.inputIndexStart],n,s,a);if("tensors"===r.type)return t.inputNames.slice(e,i).map((e=>Hy(e,n,s,a)));const o=Hy(t.inputNames.slice(e)[0],n,s,a),l=o.dataSync();return"number"===r.type?l[0]:ee(o.shape,l)}const i=t.attrParams[e];return i&&i.value}function Hy(e,t,n,s){const[a,r]=qy(e);if(null!=s){const e=s.getHashTableHandleByName(a);if(null!=e)return e}const i=n.currentContextIds.find((e=>!!t[Ky(a,e)]));return void 0!==i?t[Ky(a,i)][r]:void 0}function jy(e,t){const[n,s]=qy(e);return[Ky(n,t&&t.currentContextId),s]}function Ky(e,t){return t?`${e}-${t}`:e}function qy(e){const 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t){E(e.length===t.length,(()=>n+` Shapes ${e} and ${t} must match`));for(let s=0;s<e.length;s++){const a=e[s],r=t[s];E(a<0||r<0||a===r,(()=>n+` Shapes ${e} and ${t} must match`))}}}function Fb(e){return"number"!=typeof e&&!e.some((e=>e<0))}function Db(e,t,n){let s=_b(e,n);const a=!Fb(s);if(a&&0===t.length)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(a&&t.forEach((e=>{s=_b(e.shape,s)})),!Fb(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function _b(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 a=e[s],r=t[s];if(a>=0&&r>=0&&a!==r)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=a>=0?a:r}return n}class Ob{constructor(e,t,n,s,a,r,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=a,this.dynamicSize=r,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=Fr(0),Tr(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 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zb(e,t,n){const[s,a]=Gy("fusedOps",e,t,n),r="biasadd"===s,i="prelu"===a,o="fusedbatchnorm"===s,l=Gy("numArgs",e,t,n);if(r){if(i&&2!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&1!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");const u=Gy("strides",e,t,n),c=Xy(e,t,n),h=Gy("dataFormat",e,t,n).toUpperCase(),p=Gy("dilations",e,t,n),[d,f]=Gy("args",e,t,n);return{stride:u,pad:c,dataFormat:h,dilations:p,biasArg:d,preluArg:f,activationFunc:a,leakyreluAlpha:Gy("leakyreluAlpha",e,t,n)}}function Pb(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");const s={start:e,stop:t,num:n};return Zs.runKernel(Dt,{},s)}const Bb=la({multinomial_:function(e,t,n,s=!1){const a=ia(e,"logits","multinomial"),r=a.size,i=a.rank;if(r<2)throw new Error(`Error in 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t.forEach(((e,t)=>{r.setItem(e,i[t])})),r}(Gy("tensor",e,t,n),s,Gy("elementShape",e,t,n),Gy("numElements",e,t,n));return n.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{const s=Gy("elementShape",e,t,n),a=Gy("elementDType",e,t,n);let r;r="TensorListReserve"===e.op?"numElements":"maxNumElements";const i=function(e,t,n){return new Mb([],e,t,n)}(s,a,Gy(r,e,t,n));return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{const s=Gy("tensorListId",e,t,n),a=Gy("indices",e,t,n),r=Gy("elementShape",e,t,n),i=Gy("elementDType",e,t,n);return[n.getTensorList(s.id).gather(a,i,r)]}case"TensorListStack":{const s=Gy("tensorListId",e,t,n),a=Gy("elementShape",e,t,n),r=Gy("elementDType",e,t,n),i=Gy("numElements",e,t,n);return[n.getTensorList(s.id).stack(a,r,i)]}case"TensorListFromTensor":{const s=function(e,t,n){const s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data 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implemented`)}})(e,t,n)));case"dynamic":return(async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{const{boxes:s,scores:a,maxOutputSize:r,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=Vb(e,t,n),u=await Gl.nonMaxSuppressionWithScoreAsync(s,a,r,i,o,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{const{boxes:s,scores:a,maxOutputSize:r,iouThreshold:i,scoreThreshold:o}=Vb(e,t,n),l=Gy("padToMaxOutputSize",e,t,n),u=await Gl.nonMaxSuppressionPaddedAsync(s,a,r,i,o,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{const{boxes:s,scores:a,maxOutputSize:r,iouThreshold:i,scoreThreshold:o}=Vb(e,t,n);return[await Gl.nonMaxSuppressionAsync(s,a,r,i,o)]}case"Where":{const s=ja(Gy("condition",e,t,n),"bool"),a=[await Wb(s)];return s.dispose(),a}case"ListDiff":return async function(e,t){const n=ia(e,"x","setdiff1d"),s=ia(t,"y","setdiff1d");E(n.dtype===s.dtype,(()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`)),E(1===n.rank,(()=>`x should be 1D tensor, but got x (${n.shape}).`)),E(1===s.rank,(()=>`y should be 1D tensor, but got y (${s.shape}).`));const a=await n.data(),r=await s.data(),i=new Set(r);let o=0;for(let e=0;e<a.length;e++)i.has(a[e])||o++;const l=new Es([o],n.dtype),u=new Es([o],"int32");for(let e=0,t=0;e<a.length;e++)i.has(a[e])||(l.values[t]=a[e],u.values[t]=e,t++);return[l.toTensor(),u.toTensor()]}(Gy("x",e,t,n),Gy("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n);case"evaluation":return Cr((()=>((e,t,n)=>{switch(e.op){case"TopKV2":{const s=Gy("x",e,t,n),a=Gy("k",e,t,n),r=Gy("sorted",e,t,n),i=wp(s,a,r);return[i.values,i.indices]}case"Unique":{const s=Gy("x",e,t,n),a=kp(s);return[a.values,a.indices]}case"UniqueV2":{const s=Gy("x",e,t,n),a=Gy("axis",e,t,n),r=kp(s,a);return[r.values,r.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"image":return Cr((()=>((e,t,n)=>{switch(e.op){case"ResizeBilinear":{const s=Gy("images",e,t,n),a=Gy("size",e,t,n),r=Gy("alignCorners",e,t,n),i=Gy("halfPixelCenters",e,t,n);return[Gl.resizeBilinear(s,[a[0],a[1]],r,i)]}case"ResizeNearestNeighbor":{const s=Gy("images",e,t,n),a=Gy("size",e,t,n),r=Gy("alignCorners",e,t,n),i=Gy("halfPixelCenters",e,t,n);return[Gl.resizeNearestNeighbor(s,[a[0],a[1]],r,i)]}case"CropAndResize":{const s=Gy("image",e,t,n),a=Gy("boxes",e,t,n),r=Gy("boxInd",e,t,n),i=Gy("cropSize",e,t,n),o=Gy("method",e,t,n),l=Gy("extrapolationValue",e,t,n);return[Gl.cropAndResize(s,a,r,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"graph":return Cr((()=>((e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const s=Gy("default",e,t,n);return[Hy(e.name,t,n)||s];case"Placeholder":return[Hy(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":return[Yy(Gy("x",e,t,n))];case"IdentityN":return Gy("x",e,t,n).map((e=>Yy(e)));case"Snapshot":return[Yy(Gy("x",e,t,n))];case"Shape":return[_o(Gy("x",e,t,n).shape,"int32")];case"ShapeN":return Gy("x",e,t,n).map((e=>_o(e.shape)));case"Size":return[Fr(Gy("x",e,t,n).size,"int32")];case"Rank":return[Fr(Gy("x",e,t,n).rank,"int32")];case"NoOp":return[Fr(1)];case"Print":const a=Gy("x",e,t,n),r=Gy("data",e,t,n),i=Gy("message",e,t,n),o=Gy("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let e=0;e<r.length;e++)console.log(Array.prototype.slice.call(r[e].dataSync()).slice(0,o));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"logical":return Cr((()=>((e,t,n)=>{switch(e.op){case"Equal":return[Ni(Gy("a",e,t,n),Gy("b",e,t,n))];case"NotEqual":return[ao(Gy("a",e,t,n),Gy("b",e,t,n))];case"Greater":return[Ai(Gy("a",e,t,n),Gy("b",e,t,n))];case"GreaterEqual":return[Ri(Gy("a",e,t,n),Gy("b",e,t,n))];case"Less":return[Zc(Gy("a",e,t,n),Gy("b",e,t,n))];case"LessEqual":return[Al(Gy("a",e,t,n),Gy("b",e,t,n))];case"LogicalAnd":return[Bi(Gy("a",e,t,n),Gy("b",e,t,n))];case"LogicalNot":return[Sh(Gy("a",e,t,n))];case"LogicalOr":return[up(Gy("a",e,t,n),Gy("b",e,t,n))];case"Select":case"SelectV2":return[Po(Gy("condition",e,t,n),Gy("a",e,t,n),Gy("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"matrices":return Cr((()=>((e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[il(Gy("a",e,t,n),Gy("b",e,t,n),Gy("transposeA",e,t,n),Gy("transposeB",e,t,n))];case"Transpose":return[Wo(Gy("x",e,t,n),Gy("perm",e,t,n))];case"_FusedMatMul":const[s,a]=Gy("fusedOps",e,t,n),r="biasadd"===s,i="prelu"===a,o=Gy("numArgs",e,t,n),l=Gy("leakyreluAlpha",e,t,n);if(r){if(i&&2!==o)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&1!==o)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[u,c]=Gy("args",e,t,n);return[ol({a:Gy("a",e,t,n),b:Gy("b",e,t,n),transposeA:Gy("transposeA",e,t,n),transposeB:Gy("transposeB",e,t,n),bias:u,activation:a,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"normalization":return Cr((()=>((e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[ti(Gy("x",e,t,n),Gy("mean",e,t,n),Gy("variance",e,t,n),Gy("offset",e,t,n),Gy("scale",e,t,n),Gy("epsilon",e,t,n))];case"LRN":return[op(Gy("x",e,t,n),Gy("radius",e,t,n),Gy("bias",e,t,n),Gy("alpha",e,t,n),Gy("beta",e,t,n))];case"Softmax":return[So(Gy("x",e,t,n))];case"LogSoftmax":return[Pi(Gy("x",e,t,n))];case"SparseToDense":return[Gb(Gy("sparseIndices",e,t,n),Gy("outputShape",e,t,n),Gy("sparseValues",e,t,n),Gy("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"reduction":return Cr((()=>((e,t,n)=>{switch(e.op){case"Max":{const s=Gy("axis",e,t,n),a=Gy("keepDims",e,t,n);return[Oi(Gy("x",e,t,n),s,a)]}case"Mean":{const s=Gy("axis",e,t,n),a=Gy("keepDims",e,t,n);return[Gi(Gy("x",e,t,n),s,a)]}case"Min":{const s=Gy("axis",e,t,n),a=Gy("keepDims",e,t,n);return[Hi(Gy("x",e,t,n),s,a)]}case"Sum":{const s=Gy("axis",e,t,n),a=Gy("keepDims",e,t,n);return[zi(Gy("x",e,t,n),s,a)]}case"All":{const s=Gy("axis",e,t,n),a=Gy("keepDims",e,t,n);return[Mr(Gy("x",e,t,n),s,a)]}case"Any":{const s=Gy("axis",e,t,n),a=Gy("keepDims",e,t,n);return[Lr(Gy("x",e,t,n),s,a)]}case"ArgMax":{const s=Gy("axis",e,t,n);return[zr(Gy("x",e,t,n),s)]}case"ArgMin":{const s=Gy("axis",e,t,n);return[Hh(Gy("x",e,t,n),s)]}case"Prod":{const s=Gy("axis",e,t,n),a=Gy("keepDims",e,t,n);return[fp(Gy("x",e,t,n),s,a)]}case"Cumsum":{const s=Gy("axis",e,t,n),a=Gy("exclusive",e,t,n),r=Gy("reverse",e,t,n);return[vc(Gy("x",e,t,n),s,a,r)]}case"Bincount":const s=Gy("x",e,t,n),a=Gy("weights",e,t,n),r=Gy("size",e,t,n);return[Hb(s,a,r)];case"DenseBincount":{const s=Gy("x",e,t,n),a=Gy("weights",e,t,n),r=Gy("size",e,t,n),i=Gy("binaryOutput",e,t,n);return[jb(s,a,r,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"slice_join":return Cr((()=>((e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{const s=Gy("n",e,t,n),a=Gy("axis",e,t,n);let r=Gy("tensors",e,t,n);return r=r.slice(0,s),[ii(r,a)]}case"Gather":{const s=Gy("x",e,t,n),a=Gy("indices",e,t,n);return[Ei(s,ja(a,"int32"),0)]}case"GatherV2":{const s=Gy("axis",e,t,n),a=Gy("batchDims",e,t,n),r=Gy("x",e,t,n),i=Gy("indices",e,t,n);return[Ei(r,ja(i,"int32"),s,a)]}case"Reverse":{const s=Gy("dims",e,t,n),a=[];for(let e=0;e<s.length;e++)s[e]&&a.push(e);const r=Gy("x",e,t,n);return[bo(r,a)]}case"ReverseV2":{const s=Gy("axis",e,t,n),a=Gy("x",e,t,n);return[bo(a,s)]}case"Slice":{const s=Gy("begin",e,t,n),a=Gy("size",e,t,n);return[vo(Gy("x",e,t,n),s,a)]}case"StridedSlice":{const s=Gy("begin",e,t,n),a=Gy("end",e,t,n),r=Gy("strides",e,t,n),i=Gy("beginMask",e,t,n),o=Gy("endMask",e,t,n),l=Gy("ellipsisMask",e,t,n),u=Gy("newAxisMask",e,t,n),c=Gy("shrinkAxisMask",e,t,n),h=Gy("x",e,t,n);return[bp(h,s,a,r,i,o,l,u,c)]}case"Pack":return Cr((()=>{const s=Gy("axis",e,t,n),a=Gy("tensors",e,t,n),r=a[0].shape,i=Ro(a[0]).shape,o=a.map((e=>{const 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Cr((()=>((e,t,n)=>{switch(e.op){case"FFT":return[Ho(Gy("x",e,t,n))];case"IFFT":return[Ko(Gy("x",e,t,n))];case"RFFT":return[jo(Gy("x",e,t,n))];case"IRFFT":return[qo(Gy("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"transformation":return Cr((()=>((e,t,n)=>{switch(e.op){case"Cast":return[ja(Gy("x",e,t,n),Gy("dtype",e,t,n))];case"ExpandDims":{const s=Gy("axis",e,t,n);return[Ii(Gy("x",e,t,n),s)]}case"Squeeze":{const s=Gy("axis",e,t,n);return[Ro(Gy("x",e,t,n),s)]}case"Reshape":return[Zr(Gy("x",e,t,n),Gy("shape",e,t,n))];case"MirrorPad":return[hp(Gy("x",e,t,n),Gy("padding",e,t,n),Gy("mode",e,t,n))];case"PadV2":case"Pad":return[uo(Gy("x",e,t,n),Gy("padding",e,t,n),Gy("constantValue",e,t,n))];case"SpaceToBatchND":{const s=Gy("blockShape",e,t,n),a=Gy("paddings",e,t,n);return[rc(Gy("x",e,t,n),s,a)]}case"BatchToSpaceND":{const s=Gy("blockShape",e,t,n),a=Gy("crops",e,t,n);return[Ph(Gy("x",e,t,n),s,a)]}case"DepthToSpace":{const s=Gy("blockSize",e,t,n),a=Gy("dataFormat",e,t,n).toUpperCase();return[Zh(Gy("x",e,t,n),s,a)]}case"BroadcastTo":return[zo(Gy("x",e,t,n),Gy("shape",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 a=Gy("keyDType",e,t,n),r=Gy("valueDType",e,t,n),i=new Ub(a,r);return s.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{const a=Gy("tableHandle",e,t,n,s),r=Gy("keys",e,t,n),i=Gy("values",e,t,n),o=s.getHashTableById(a.id);return[await o.import(r,i)]}case"LookupTableFind":case"LookupTableFindV2":{const a=Gy("tableHandle",e,t,n,s),r=Gy("keys",e,t,n),i=Gy("defaultValue",e,t,n),o=s.getHashTableById(a.id);return[await o.find(r,i)]}case"LookupTableSize":case"LookupTableSizeV2":{const a=Gy("tableHandle",e,t,n,s);return[s.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n,s);case"custom":const a=Uy(e.op);if(a&&a.customExecutor)return a.customExecutor(new Eb(e,t,n));throw TypeError(`Custom op ${e.op} is not registered.`);default:throw TypeError(`Unknown op '${e.op}'. 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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 Jb(e,t,n,s){const a=new Set,r=[];let i=null,o=null;const l=new Set,u=Object.keys(e).map((e=>qy(e)[0]));let c=[];null!=s&&(c=s.map((e=>qy(e.name)[0])));const h=[...t];for(;h.length>0;){const e=h.pop();(tx(e)||nx(e)||sx(e))&&null==i&&(i=e,o=i.children.map((e=>e.name)).filter((e=>a.has(e)))),a.add(e.name),null==n[e.name]&&-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))})):r.push(e.name))}return{inputs:e,outputs:t,usedNodes:a,missingInputs:r,dynamicNode:i,syncInputs:o}}const Zb=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Qb=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],ex=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function tx(e){return Zb.indexOf(e.op)>=0}function nx(e){return Qb.indexOf(e.op)>=0}function sx(e){return ex.indexOf(e.op)>=0}class ax{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach((t=>{this._functionExecutorMap[t]=new ax(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=Jb(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:a,syncInputs:r}=n;if(null!=a)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${r}]`);if(s.length>0){const n=t.map((e=>e.name)),a=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${a}]. Missing the following inputs: [${s}]`)}return function(e,t,n){const{usedNodes:s,inputs:a}=n,r=[],i=Object.keys(a).map((e=>qy(e)[0])).map((t=>e.nodes[t])),o=e.initNodes;i.forEach((e=>{s.has(e.name)&&r.push(e)})),e.weights.forEach((e=>{s.has(e.name)&&r.push(e)})),null!=o&&o.forEach((e=>{s.has(e.name)&&r.push(e)}));const l=new Set,u=[];for(;r.length>0;){const e=r.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)))&&r.push(e)}))}return u}(this.graph,this.weightMap,n)}execute(e,t){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[qy(e)[0]])),a=t.map((e=>qy(e)[0]));let r=a.map((e=>this.graph.nodes[e]));0===r.length&&(r=this._outputs);const i=this.getCompilationKey(s,r);let o=this.compiledMap.get(i);null==o&&(o=this.compile(e,r),this.compiledMap.set(i,o));const l={},u={};return Cr((()=>{const n=new Yb(this.weightMap,l,u,this.functionExecutorMap),s=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,a]=qy(t),r=[];r[a]=e[t],s[n]=r}));const r=this.getFrozenTensorIds(s),i={};for(let e=0;e<o.length;e++){const t=o[e];if(!s[t.name]){const e=Xb(t,s,n,this._resourceManager);if(oe(e))throw new Error(`The execution of the op '${t.op}' returned a promise. Please use model.executeAsync() instead.`);s[t.name]=e,this.checkTensorForDisposal(t.name,t,s,n,r,a,i)}}return null==this.parent&&n.dispose(r),t.map((e=>Hy(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,a,r,i){"control"!==t.category&&-1===r.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[Ky(e,n.currentContextId)]}(e.name,n,s);null!=t&&t.forEach((e=>{if(e&&!a.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)}async _executeAsync(e,t,n=!1,s={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const r=new Yb(this.weightMap,s,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,r,t,n),o=t.map((e=>Hy(e,i,r))),l=o.map((e=>e.id)),u=Object.keys(e).map((t=>e[t].id)),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach((e=>{i[e].forEach((e=>{!e||e.isDisposed||c.has(e.id)||e.dispose()}))})),null==this.parent&&r.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 a=Object.keys(e),r=a.map((e=>this.graph.nodes[qy(e)[0]])),i=n.map((e=>qy(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}=Jb(e,o,this.weightMap,this._initNodes),p=[...r,...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]=qy(t),a=[];a[s]=e[t],d[n]=a}));const f={},m=this.getFrozenTensorIds(d),g={};for(;p.length>0;){const e=this.processStack(r,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=>!tx(e)&&!Hy(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 [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${e}`)}return d}processStack(e,t,n,s,a,r,i,o,l){const u=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&Gy("isConstant",e.node,s,n)&&([c]=jy(e.node.name,n)),null==s[e.node.name]){const h=Xb(e.node,s,n,this._resourceManager);c||([c]=jy(e.node.name,n));const p=n.currentContext;oe(h)?u.push(h.then((u=>(s[c]=u,n.currentContext=p,this.checkTensorForDisposal(c,e.node,s,n,r,i,o),this.processChildNodes(e.node,t,n,s,a,l),u)))):(s[c]=h,this.checkTensorForDisposal(c,e.node,s,n,r,i,o),this.processChildNodes(e.node,t,n,s,a,l))}else this.processChildNodes(e.node,t,n,s,a,l)}return u}processChildNodes(e,t,n,s,a,r){e.children.forEach((e=>{const[i]=jy(e.name,n);!a[i]&&r.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!Hy(e,s,n)))&&(a[i]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!Hy(e,s,n)))&&(a[i]=!0,t.push({contexts:n.currentContext,node:e})))}))}dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((t=>{const n=e[t],[s]=qy(t),a=this.graph.nodes[s];if(a.attrParams.shape&&a.attrParams.shape.value){const e=a.attrParams.shape.value;E(e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t)),(()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`))}a.attrParams.dtype&&a.attrParams.dtype.value&&E(n.dtype===a.attrParams.dtype.value,(()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`))}))}mapInputs(e){const t={};for(const n in e)null!=this._signature&&null!=this._signature.inputs&&null!=this._signature.inputs[n]?t[this._signature.inputs[n].name]=e[n]:t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=qy(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>null!=this._signature&&null!=this._signature.outputs&&null!=this._signature.outputs[e]?this._signature.outputs[e].name:e),{})}checkOutputs(e){e.forEach((e=>{const[t]=qy(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}}class rx{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}class ix{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",null==t&&(this.loadOptions={}),this.resourceManager=new rx}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=nr(e,this.loadOptions);else{const s=(t=e,n=this.loadOptions,wa.getLoadHandlers(t,n));if(0===s.length)s.push(nr(e,this.loadOptions));else if(s.length>1)throw new Error(`Found more than one (${s.length}) load handlers for URL '${[e]}'`);this.handler=s[0]}var t,n}async load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n;n=null!=this.artifacts.userDefinedMetadata&&null!=this.artifacts.userDefinedMetadata.signature?this.artifacts.userDefinedMetadata.signature:this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=function(e,t){const n={};let s,a=0;for(const r of t){const t=r.name,i=r.dtype,o=r.shape,l=D(o);let u;if("quantization"in r){const n=r.quantization;if("uint8"===n.dtype||"uint16"===n.dtype){if(!("min"in n)||!("scale"in n))throw new Error(`Weight ${r.name} with quantization ${n.dtype} doesn't have corresponding metadata min and scale.`)}else{if("float16"!==n.dtype)throw new Error(`Weight ${r.name} has unknown quantization dtype ${n.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);if("float32"!==i)throw new Error(`Weight ${r.name} is quantized with ${n.dtype} which only supports weights of type float32 not ${i}.`)}const o=pa[n.dtype],c=e.slice(a,a+l*o),h="uint8"===n.dtype?new Uint8Array(c):new Uint16Array(c);if("float32"===i)if("uint8"===n.dtype||"uint16"===n.dtype){u=new Float32Array(h.length);for(let e=0;e<h.length;e++){const t=h[e];u[e]=t*n.scale+n.min}}else{if("float16"!==n.dtype)throw new Error(`Unsupported quantization type ${n.dtype} for weight type float32.`);void 0===s&&(s=xa()),u=s(h)}else{if("int32"!==i)throw new Error(`Unsupported dtype in weight '${t}': ${i}`);if("uint8"!==n.dtype&&"uint16"!==n.dtype)throw new Error(`Unsupported quantization type ${n.dtype} for weight type int32.`);u=new Int32Array(h.length);for(let e=0;e<h.length;e++){const t=h[e];u[e]=Math.round(t*n.scale+n.min)}}a+=l*o}else if("string"===i){const t=D(r.shape);u=[];for(let n=0;n<t;n++){const t=new Uint32Array(e.slice(a,a+4))[0];a+=4;const n=new Uint8Array(e.slice(a,a+t));u.push(n),a+=t}}else{const s=pa[i],r=e.slice(a,a+l*s);if("float32"===i)u=new Float32Array(r);else if("int32"===i)u=new Int32Array(r);else if("bool"===i)u=new Uint8Array(r);else{if("complex64"!==i)throw new Error(`Unsupported dtype in weight '${t}': ${i}`);{u=new Float32Array(r);const e=new Float32Array(u.length/2),s=new Float32Array(u.length/2);for(let t=0;t<e.length;t++)e[t]=u[2*t],s[t]=u[2*t+1];const a=ha(e,o,"float32"),i=ha(s,o,"float32");n[t]=ua(a,i),a.dispose(),i.dispose()}}a+=l*s}"complex64"!==i&&(n[t]=ha(u,o,i))}return n}(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ax(fb.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=fb.Instance.transformGraph(e.modelInitializer);this.initializer=new ax(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if("string"==typeof e){const t=ka(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${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)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ds||Array.isArray(e)))return e;if((e=Array.isArray(e)?e:[e]).length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce(((t,n,s)=>(t[n]=e[s],t)),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){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){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}function ox(e,t,n=new Map,s=new Set){if(null==e)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse){if(hx(e)){const a=Array.isArray(e)?[]:{};s.add(e);for(const r in e){const i=ox(e[r],t,n,s);a[r]=i}return s.delete(e),a}throw new Error(`Can't recurse into non-iterable type: ${e}`)}return n.set(e,a.value),a.value}function lx(e,t=cx){return ux(e,t)}function ux(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse){if(hx(s)){const a=Array.isArray(s)?[]:{};n.add(s);for(const r in s){const s=ux(e.map((e=>e[r])),t,n);a[r]=s}return n.delete(s),a}throw new Error(`Can't recurse into non-iterable type: ${s}`)}return a.value}function cx(e){return null===e?null:hx(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}function hx(e){return null!=e&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||"object"==typeof e&&!(e instanceof Ds))}function px(e){return ox(e,dx)}function dx(e){return e instanceof Ds?{value:e.clone(),recurse:!1}:hx(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class fx{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 mx extends fx{constructor(){super(mx.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}}mx.INITIAL_CAPACITY=32;class gx{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 $x(this,e)}filter(e){return new Nx(this,e)}map(e){return new Ix(this,e)}mapAsync(e){return new Cx(this,e)}serialMapAsync(e){return new Cx(this,e).serial()}flatmap(e){return new Tx(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 vx(this,e,t)}columnMajorBatch(e,t=!0,n=cx){return this.rowMajorBatch(e,t).map((e=>lx(e,n)))}concatenate(e,t){return new Ex(new yx([this,e]),t)}take(e){return e<0||null==e?this:new kx(this,e)}skip(e){return e<0||null==e?this:new wx(this,e)}prefetch(e){return new Fx(this,e)}shuffle(e,t){return new Dx(this,e,t)}serial(){return new xx(this)}}class yx extends gx{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:px(e),done:!1}}}class bx extends gx{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: ${e.message}`,e}}}class xx extends gx{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){return this.upstream.next()}}class wx extends gx{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;Sr(e.value)}return this.upstream.next()}}class kx extends gx{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class vx extends gx{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class Nx extends gx{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Sr(e.value)}}}class Ix extends gx{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=js(e.value),n=this.transform(e.value),s=js(n);for(const e of t)Hs(e,s)||e.dispose();return{value:n,done:!1}}}class $x extends gx{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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 Cx extends gx{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=js(e.value),n=await this.transform(e.value),s=js(n);for(const e of t)Hs(e,s)||e.dispose();return{value:n,done:!1}}}class Sx extends gx{constructor(){super(),this.outputQueue=new mx,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;0===this.outputQueue.length();)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class Tx extends Sx{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=js(e.value),n=this.transform(e.value),s=js(n);this.outputQueue.pushAll(n);for(const e of t)Hs(e,s)||e.dispose();return!0}}class Ex extends gx{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 Ax,Rx;(Rx=Ax||(Ax={}))[Rx.FAIL=0]="FAIL",Rx[Rx.SHORTEST=1]="SHORTEST",Rx[Rx.LONGEST=2]="LONGEST";class Fx extends gx{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new fx(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 Dx extends Fx{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=ho.alea(n||bs().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 _x{constructor(){this.size=null}batch(e,t=!0){const n=this;let s;return E(e>0,(()=>`batchSize needs to be positive, but it is\n      ${e}`)),s=this.size===1/0||null==this.size?this.size:t?Math.ceil(this.size/e):Math.floor(this.size/e),Ox((async()=>(await n.iterator()).columnMajorBatch(e,t,Mx)),s)}concatenate(e){const t=this;let n;return n=this.size===1/0||e.size===1/0?1/0:null!=this.size&&null!=e.size?this.size+e.size:null,Ox((async()=>(await t.iterator()).concatenate(await e.iterator())),n)}filter(e){const t=this;let n;return n=this.size===1/0?1/0:null,Ox((async()=>(await t.iterator()).filter((t=>Cr((()=>e(t)))))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return Ox((async()=>(await t.iterator()).map((t=>Cr((()=>e(t)))))),this.size)}mapAsync(e){const t=this;return Ox((async()=>(await t.iterator()).mapAsync(e)),this.size)}prefetch(e){if(null==e)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return Ox((async()=>(await t.iterator()).prefetch(e)),this.size)}repeat(e){const t=this;let n;return n=null!=this.size&&e>0?this.size*e:0===e?0:null!=this.size&&(void 0===e||e<0)?1/0:null,Ox((async()=>{return n=(a=async()=>({value:await t.iterator(),done:!1}),new bx(a)).take(e),new Ex(n,s);var n,s,a}),n)}skip(e){const t=this;let n;return n=null!=this.size&&e>=0&&this.size>=e?this.size-e:null!=this.size&&(this.size<e||void 0===e||e<0)?0:null,Ox((async()=>(await t.iterator()).skip(e)),n)}shuffle(e,t,n=!0){if(null==e||e<0)throw null==this.size?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,a=ho.alea(t||bs().toString());return Ox((async()=>{let t=a.int32();return n&&(t+=a.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,Ox((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 Ox(e,t=null){return new class extends _x{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function Mx(e){if(null===e)return null;return null==(t=e[0])||null===(n=t)||"object"!=typeof n&&"function"!=typeof n||Array.isArray(t)||"object"==typeof t&&t instanceof Ds||H(t)?{value:function(e){if(0===e.length)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ds?Fo(e):ha(e)}(e),recurse:!1}:{value:null,recurse:!0};var t,n}function Lx(e,t){Array.isArray(e)||(e=[e]),e.forEach((e=>{null!=e&&E("complex64"!==e.dtype,(()=>`${t} does not support complex64 tensors in the CPU backend.`))}))}_x.MAX_BUFFER_SIZE=1e4,Symbol("out"),Symbol("field"),Symbol("quote"),Symbol("quoteafterquote"),Symbol("quoteinquote");const zx=Wu;class Px extends I{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new N(this,Ir())}nextDataId(){return Px.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,ue().get("IS_NODE")&&$u("\n============================\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\n============================"));const s={id:this.nextDataId()};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if("string"===t&&null!=n&&n.length>0&&K(n[0])){const a=n.map((e=>xs(e)));s=this.write(a,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){this.data.get(e).refCount++}decRef(e){this.data.has(e)&&this.data.get(e).refCount--}move(e,t,n,s,a){this.data.set(e,{values:t,dtype:s,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensorInfos:n}=this.data.get(e);return"complex64"===t?Su(this.readSync(n.real.dataId),this.readSync(n.imag.dataId)):this.data.get(e).values}bufferSync(e){const t=this.readSync(e.dataId);let n=t;if("string"===e.dtype)try{n=t.map((e=>ws(e)))}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ha(e.shape,e.dtype,n)}makeOutput(e,t,n){const s=this.write(e,t,n);return Ir().makeTensorFromDataId(s,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;const{complexTensorInfos:n}=this.data.get(e);null!=n&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){const t=bs();return e(),{kernelMs:bs()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){Lx([e],"where");const t=this.readSync(e.dataId);return zx(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}}function Bx(e,t,n){return({inputs:s,attrs:a,backend:r})=>{const{x:i}=s;if(Lx(i,e),"string"===i.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const o=r,l=o.data.get(i.dataId).values,u=D(i.shape),c=n||i.dtype,h=U(c,u);for(let e=0;e<u;++e)h[e]=t(l[e],a);return o.makeTensorInfo(i.shape,c,h)}}function Wx(e,t,n){return({inputs:s,attrs:a,backend:r})=>{const{x:i}=s;if(Lx(i,e),"string"===i.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const o=r,l=o.data.get(i.dataId).values,u=n||i.dtype,c=t(l,u,a);return o.makeTensorInfo(i.shape,u,c)}}Px.nextDataId=0,Ar("cpu",(()=>new Px),1);const Vx=Bx(ot,(e=>e>=0?e:Math.exp(e)-1)),Ux={kernelName:ot,backendName:"cpu",kernelFunc:Vx};function Gx(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 Hx={kernelName:It,backendName:"cpu",kernelFunc:Gx};function jx(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{alpha:r}=s;Lx([a],"leakyRelu");const i=D(a.shape),o=n.data.get(a.dataId).values,l=V("float32",i);for(let e=0;e<o.length;e++)l[e]=o[e]<0?r*o[e]:o[e];return n.makeTensorInfo(a.shape,"float32",l)}const Kx={kernelName:At,backendName:"cpu",kernelFunc:jx};function qx(e){return(t,n,s,a,r)=>{const i=vi(t,n),o=i.length,l=Z(i),u=V(r,D(i)),c=t.length,h=n.length,p=Z(t),d=Z(n),f=wi(t,i),m=wi(n,i);if(f.length+m.length===0)for(let t=0;t<u.length;++t)u[t]=e(s[t%s.length],a[t%a.length]);else for(let t=0;t<u.length;++t){const n=ie(t,o,l),r=n.slice(-c);f.forEach((e=>r[e]=0));const i=re(r,c,p),g=n.slice(-h);m.forEach((e=>g[e]=0));const y=re(g,h,d);u[t]=e(s[i],a[y])}return[u,i]}}const Xx=qx(((e,t)=>e<0?t*e:e));function Yx(e){const{inputs:t,backend:n}=e,{x:s,alpha:a}=t;Lx([s,a],"prelu");const r=n.data.get(s.dataId).values,i=n.data.get(a.dataId).values,[o,l]=Xx(s.shape,a.shape,r,i,s.dtype);return n.makeTensorInfo(l,s.dtype,o)}const Jx={kernelName:pn,backendName:"cpu",kernelFunc:Yx},Zx=Bx(yn,(e=>Math.max(0,e))),Qx={kernelName:yn,backendName:"cpu",kernelFunc:Zx},ew=Bx(Nn,(e=>Math.min(Math.max(0,e),6))),tw={kernelName:Nn,backendName:"cpu",kernelFunc:ew};function nw(e,t,n,s,a){if("linear"===n)return Gx({inputs:{x:t},backend:e});if("relu"===n)return Zx({inputs:{x:t},backend:e});if("elu"===n)return Vx({inputs:{x:t},backend:e});if("relu6"===n)return ew({inputs:{x:t},backend:e});if("prelu"===n)return Yx({inputs:{x:t,alpha:s},backend:e});if("leakyrelu"===n)return jx({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function sw(e){const{inputs:t,backend:n}=e,{real:s,imag:a}=t,r=n.data.get(s.dataId).values,i=n.data.get(a.dataId).values,o=n.makeTensorInfo(s.shape,"complex64");return n.data.get(o.dataId).complexTensorInfos={real:n.makeTensorInfo(s.shape,"float32",r),imag:n.makeTensorInfo(a.shape,"float32",i)},o}const aw={kernelName:ze,backendName:"cpu",kernelFunc:sw};function rw(e,t,n="float32"){if("complex64"===n)return sw({inputs:{real:rw(e,t,"float32"),imag:rw(e,t,"float32")},backend:e});const s=ne(D(t),n);return e.makeTensorInfo(t,n,s)}function iw(e){const{inputs:t,backend:n}=e,{input:s}=t,a=n.data.get(s.dataId).complexTensorInfos.real,r=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,r)}const ow={kernelName:mn,backendName:"cpu",kernelFunc:iw};function lw(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{dtype:r}=s;if("complex64"===r){if("complex64"===a.dtype)return Gx({inputs:{x:a},backend:n});const e=rw(n,a.shape,a.dtype),t=lw({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),s=sw({inputs:{real:t,imag:e},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),s}if("complex64"===a.dtype){const e=iw({inputs:{input:a},backend:n}),t=lw({inputs:{x:e},backend:n,attrs:{dtype:r}});return n.disposeIntermediateTensorInfo(e),t}if(!G(a.dtype,r)){const e=Gx({inputs:{x:a},backend:n});return{dataId:e.dataId,shape:e.shape,dtype:r}}if("int32"===r){const e=n.data.get(a.dataId).values,t=Int32Array.from(e);return n.makeTensorInfo(a.shape,"int32",t)}if("bool"===r){const e=n.data.get(a.dataId).values,t=ys([0],a.dtype),[s,r]=qx(((e,t)=>e!==t?1:0))(a.shape,[],e,t,"bool");return n.makeTensorInfo(r,"bool",s)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${r}`)}const uw={kernelName:Oe,backendName:"cpu",kernelFunc:lw};function cw(e,t,n,s){return null==n?({inputs:n,backend:a})=>{const{a:r,b:i}=n,o=a;Lx([r,i],e);const l=o.data.get(r.dataId).values,u=o.data.get(i.dataId).values,c=s||r.dtype,[h,p]=t(r.shape,i.shape,l,u,c);return o.makeTensorInfo(p,c,h)}:({inputs:e,backend:a})=>{const{a:r,b:i}=e,o=a;if("complex64"===r.dtype||"complex64"===i.dtype){const e=lw({inputs:{x:r},backend:o,attrs:{dtype:"complex64"}}),t=o.data.get(e.dataId),s=t.complexTensorInfos.real,a=t.complexTensorInfos.imag,l=o.data.get(s.dataId).values,u=o.data.get(a.dataId).values,c=lw({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(r.shape,i.shape,l,u,f,m),x=o.makeTensorInfo(b,"float32",g),w=o.makeTensorInfo(b,"float32",y),k=sw({inputs:{real:x,imag:w},backend:o});return o.disposeIntermediateTensorInfo(e),o.disposeIntermediateTensorInfo(c),o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(w),k}{const e=o.data.get(r.dataId).values,n=o.data.get(i.dataId).values,a=s||r.dtype,[l,u]=t(r.shape,i.shape,e,n,a);return o.makeTensorInfo(u,a,l)}}}function hw(e){return(t,n,s,a,r,i)=>{const o=vi(t,n),l=D(o),u=o.length,c=Z(o),h=V("float32",l),p=V("float32",l),d=wi(t,o),f=wi(n,o),m=Su(s,a),g=Su(r,i),y=t.length,b=Z(t),x=n.length,w=Z(n);if(d.length+f.length===0)for(let t=0;t<h.length;t++){const n=t%m.length,s=t%g.length,a=e(m[2*n],m[2*n+1],g[2*s],g[2*s+1]);h[t]=a.real,p[t]=a.imag}else for(let t=0;t<h.length;t++){const n=ie(t,u,c),s=n.slice(-y);d.forEach((e=>s[e]=0));const a=re(s,y,b),r=n.slice(-x);f.forEach((e=>r[e]=0));const i=re(r,x,w),o=e(m[2*a],m[2*a+1],g[2*i],g[2*i+1]);h[t]=o.real,p[t]=o.imag}return[h,p,o]}}const pw=qx(((e,t)=>e+t)),dw=hw(((e,t,n,s)=>({real:e+n,imag:t+s}))),fw=cw(ye,pw,dw),mw={kernelName:ye,backendName:"cpu",kernelFunc:fw};function gw(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{shape:r}=s,i=D(a.shape),o=P(r,i),l=D(o);E(i===l,(()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. 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n.makeTensorInfo(C.shape,C.dtype,C.values)}},Zw={kernelName:Ee,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:a,input:r}=t,i=r;Lx([a,r],"avgPoolGrad");const{filterSize:o,strides:l,pad:u}=s,c=Br(i.shape,o,l,1,u),h=c.strideHeight,p=c.strideWidth,d=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,y=c.effectiveFilterHeight,b=c.effectiveFilterWidth,x=b-1-c.padInfo.left,w=y-1-c.padInfo.top,k=Ha(i.shape,"float32"),v=1/(d*f),N=n.data.get(a.dataId).values,I=Ha(a.shape,"float32",N);for(let e=0;e<c.batchSize;++e)for(let t=0;t<c.inChannels;++t)for(let n=0;n<c.inHeight;++n)for(let s=0;s<c.inWidth;++s){const a=n-w,r=s-x;let i=0;for(let n=0;n<y;n+=m){const s=(a+n)/h;if(!(s<0||s>=c.outHeight||Math.floor(s)!==s))for(let n=0;n<b;n+=g){const a=(r+n)/p;a<0||a>=c.outWidth||Math.floor(a)!==a||(i+=I.get(e,s,a,t))}}k.set(i*v,e,n,s,t)}return 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n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(_(e,t))return`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));\n      }\n    `;const s=Math.ceil(e[1]/2);return`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${n[0]}, ${n[1]}));\n\n      int index = resTexRC.x * ${n[1]} + resTexRC.y;\n      int r = 2 * (index / ${s});\n      int c = imod(index, ${s}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}(e,t);case 3:return function(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),a=s*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${n[0]}, ${n[1]}));\n      int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${s});\n      int c = imod(index, ${s}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  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t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;const[s,a]=e.shapeInfo.texShape;if(1===s&&1===a)return`\n      float ${n}() {\n        return sampleTexture(${t}, halfCR);\n      }\n    `;const[r,i]=e.shapeInfo.texShape;return`\n    float ${n}() {\n      vec2 uv = uvFromFlat(${r}, ${i}, ${wC(t)});\n      return sampleTexture(${t}, uv);\n    }\n  `}(e);case 1:return function(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${n}(int index) {\n        ${kC(e)}\n      }\n    `;const s=e.shapeInfo.texShape,a=s[0],r=s[1];if(1===r&&1===a)return`\n      float ${n}(int index) {\n        return sampleTexture(${t}, halfCR);\n      }\n    `;const i=wC(t);return 1===r?`\n      float ${n}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);\n        return sampleTexture(${t}, uv);\n      }\n    `:1===a?`\n      float ${n}(int index) {\n        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${r}.0, 0.5);\n        return sampleTexture(${t}, uv);\n      }\n    `:`\n    float ${n}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${r}, index + ${i});\n      return sampleTexture(${t}, uv);\n    }\n  `}(e);case 2:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(null!=a&&_(t,a)){const e=a[0];return`\n    float ${s}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${a[1]}.0, ${e}.0);\n      return sampleTexture(${n}, uv);\n    }\n  `}const{newShape:r,keptDims:i}=W(t),o=r;if(o.length<t.length){const t=["row","col"];return`\n      ${fC(NC(e,o))}\n      float ${s}(int row, int col) {\n        return ${s}(${IC(t,i)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));\n        ${kC(e)}\n      }\n    `;const l=a[0],u=a[1],c=wC(n);return 1===u?`\n    float ${s}(int row, int col) {\n      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${n}, uv);\n    }\n  `:1===l?`\n    float ${s}(int row, int col) {\n      float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n  float ${s}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${t[1]} + col + ${c};\n    vec2 uv = uvFromFlat(${l}, ${u}, index);\n    return sampleTexture(${n}, uv);\n  }\n`}(e);case 3:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],r=t[2],{newShape:i,keptDims:o}=W(t),l=i;if(l.length<t.length){const t=["row","col","depth"];return`\n        ${fC(NC(e,l))}\n        float ${s}(int row, int col, int depth) {\n          return ${s}(${IC(t,o)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${r}, 1)));\n        ${kC(e)}\n      }\n    `;const u=e.shapeInfo.texShape,c=u[0],h=u[1],p=e.shapeInfo.flatOffset;if(h===a&&null==p)return`\n        float ${s}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${r}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${h}.0, ${c}.0);\n          return sampleTexture(${n}, uv);\n        }\n      `;if(h===r&&null==p)return`\n    float ${s}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${c}.0);\n      return sampleTexture(${n}, uv);\n    }\n  `;return`\n      float ${s}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${r} + depth + ${wC(n)};\n        vec2 uv = uvFromFlat(${c}, ${h}, index);\n        return sampleTexture(${n}, uv);\n      }\n  `}(e);case 4:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],r=t[2]*a,i=t[1]*r,{newShape:o,keptDims:l}=W(t);if(o.length<t.length){const t=["row","col","depth","depth2"];return`\n      ${fC(NC(e,o))}\n      float ${s}(int row, int col, int depth, int depth2) {\n        return ${s}(${IC(t,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${i}, ${r}, ${a}, 1)));\n        ${kC(e)}\n      }\n    `;const u=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],p=c[1];if(p===i&&null==u)return`\n      float ${s}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${r}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${p}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(p===a&&null==u)return`\n      float ${s}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${t[1]*t[2]}, ${t[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${p}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;return`\n    float ${s}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${i} + col * ${r} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${h}, ${p}, index + ${wC(n)});\n      return sampleTexture(${n}, uv);\n    }\n  `}(e);case 5:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],r=t[3]*a,i=t[2]*r,o=t[1]*i,{newShape:l,keptDims:u}=W(t);if(l.length<t.length){const t=["row","col","depth","depth2","depth3"];return`\n      ${fC(NC(e,l))}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${IC(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}, ${r}, ${a})) +\n          depth3;\n        ${kC(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}, ${r}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${d}.0, ${p}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(d===a&&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    `;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 * ${r} +\n          depth2 * ${a} + depth3 + ${wC(n)};\n      vec2 uv = uvFromFlat(${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:a,keptDims:r}=W(t);if(a.length<t.length){const t=["row","col","depth","depth2","depth3","depth4"];return`\n      ${fC(NC(e,a))}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${IC(t,r)});\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        ${kC(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    `;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 + ${wC(n)};\n      vec2 uv = uvFromFlat(${d}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function mC(e){switch(e.shapeInfo.logicalShape.length){case 0:return function(e){const t=e.name;return`\n    vec4 ${"get"+t.charAt(0).toUpperCase()+t.slice(1)}() {\n      return ${X$().texture2D}(${t}, halfCR);\n    }\n  `}(e);case 1:return function(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=e.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],r=X$();return`\n    vec4 ${n}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${a[0]}, ${a[1]}, index);\n      return ${r.texture2D}(${t}, uv);\n    }\n  `}(e);case 2:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,r=a[0],i=a[1],o=X$();if(null!=a&&_(t,a))return`\n      vec4 ${s}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${r}.0);\n\n        return ${o.texture2D}(${n}, uv);\n      }\n    `;const l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];return`\n    vec4 ${s}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${Math.ceil(t[1]/2)}, ${l[0]}, ${l[1]}, row, col);\n      return ${o.texture2D}(${n}, uv);\n    }\n  `}(e);case 3:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(1===t[0]){const n=[1,2],a=["b","row","col"];return`\n        ${mC(NC(e,t.slice(1)))}\n        vec4 ${s}(int b, int row, int col) {\n          return ${s}(${IC(a,n)});\n        }\n      `}const i=r[0],o=r[1],l=Math.ceil(t[2]/2);return`\n    vec4 ${s}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${i}, ${o}, ${l*Math.ceil(t[1]/2)}, ${l}, b, row, col);\n      return ${X$().texture2D}(${n}, uv);\n    }\n  `}(e);default:return function(e){const t=e.shapeInfo.logicalShape,n=t.length,s=e.name,a="get"+s.charAt(0).toUpperCase()+s.slice(1),r=e.shapeInfo.texShape,i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],o=i[0],l=i[1],u=Math.ceil(t[n-1]/2);let c=u*Math.ceil(t[n-2]/2),h="int b, int row, int col",p=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let e=2;e<n-1;e++)h=`int b${e}, `+h,c*=t[n-e-1],p=`b${e} * ${c} + `+p;return`\n    vec4 ${a}(${h}) {\n      int index = ${p};\n      int texR = index / ${l};\n      int texC = index - texR * ${l};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});\n      return ${X$().texture2D}(${s}, uv);\n    }\n  `}(e)}}const gC="\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",yC="\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",bC="\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",xC="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function wC(e){return`offset${e}`}function kC(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 vC(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 NC(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function IC(e,t){return t.map((t=>e[t])).join(", ")}function $C(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,a=t[n],r=a.shape;if(!_(s,r))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${s} and ${r} must match`);if(e.isUniform&&a.isUniform)return;const i=e.texShape,o=a.isUniform?null:a.texData.texShape;if(!_(i,o))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${o} must match`)}))}const{addImpl:CC,bincountImpl:SC,bincountReduceImpl:TC,ceilImpl:EC,concatImpl:AC,expImpl:RC,expm1Impl:FC,floorImpl:DC,gatherV2Impl:_C,greaterImpl:OC,lessImpl:MC,linSpaceImpl:LC,logImpl:zC,maxImpl:PC,maximumImpl:BC,minimumImpl:WC,multiplyImpl:VC,negImpl:UC,prodImpl:GC,rangeImpl:HC,rsqrtImpl:jC,simpleAbsImpl:KC,sliceImpl:qC,stridedSliceImpl:XC,subImpl:YC,tileImpl:JC,topKImpl:ZC,transposeImpl:QC,uniqueImpl:eS}=v;function tS(e,t){return["x","y","z","w","u","v"].slice(0,t).map((t=>`${e}.${t}`))}function nS(e,t){return 1===t?[e]:tS(e,t)}class sS{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;const t=e.length;if(0===t)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{const n=nS("rc",t),s=vC(t),a=function(e,t,n){if(1===e)return`rc > ${t[0]}`;let s="";for(let a=e-2;a<e;a++)s+=`${n[a]} >= ${t[a]}`,a<e-1&&(s+="||");return s}(t,e,n),r=function(e,t,n,s){if(1===e)return"";const a=s.slice(-2);return`\n    int r = ${a[0]};\n    int c = ${a[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${t};\n    bool rEdge = rp1 >= ${n};\n  `}(t,e[e.length-1],e[e.length-2],n),i=function(e,t){const n=e.length,s=function(e,t){const n=[];for(let s=0;s<=1;s++)for(let a=0;a<=1;a++){let r=`${0===s?"r":"rp1"}, ${0===a?"c":"cp1"}`;for(let n=2;n<e;n++)r=`${t[t.length-1-n]},`+r;n.push(r)}return n}(n,t);return 1===n?`getA(rc),\n            rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),\n            0, 0`:`getA(${s[0]}),\n          cEdge ? 0. : getA(${s[1]}),\n          rEdge ? 0. : getA(${s[2]}),\n          rEdge || cEdge ? 0. : getA(${s[3]})`}(e,n);this.userCode=`\n        void main() {\n          ${s} rc = getOutputCoords();\n\n          if(${a}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${i}));\n          }\n        }\n      `}}}class aS{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;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;this.userCode=`\n      ${s=t,`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${Y$(["r","c","d"],s)}\n      return ivec3(r, c, d);\n    }\n  `}\n      ${J$(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${e[1]};\n        int cols = ${e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}}class rS{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=oS(t,n),a=lS(e,s,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);const r=iS(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=r,this.log();const e=this.freeTextures[a].shift();return this.usedTextures[a].push(e),e}let i;return s===k$.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===k$.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===k$.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===k$.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===k$.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=r,this.log(),i}releaseTexture(e,t,n,s){if(null==this.freeTextures)return;const a=oS(n,s),r=lS(t,a,s);r in this.freeTextures||(this.freeTextures[r]=[]);const i=iS(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=ue().get("WEBGL_DELETE_TEXTURE_THRESHOLD");-1!==o&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[r].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;const l=this.usedTextures[r],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)}));for(const e in this.usedTextures)this.usedTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e)}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}}function iS(e,t,n,s,a){const r=function(e,t){switch(e){case k$.PACKED_2X2_FLOAT32:return uC(t);case k$.PACKED_2X2_FLOAT16:return cC(t);case k$.UNPACKED_FLOAT32:return iC(t);case k$.UNPACKED_FLOAT16:return oC(t);case k$.PACKED_4X1_UNSIGNED_BYTE:return lC(t);default:throw new Error(`Unknown physical texture type ${e}`)}}(t,s);let i;if(a){const[t,n]=I$(e[0],e[1]);i=t*n}else{const[t,n]=v$(e[0],e[1]);i=t*n}return i*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;throw new Error(`Unknown internal format ${t}`)}(n,r)}function oS(e,t){if(e===w$.UPLOAD)return k$.PACKED_2X2_FLOAT32;if(e===w$.RENDER||null==e)return function(e){return ue().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?k$.PACKED_2X2_FLOAT32:k$.UNPACKED_FLOAT32:e?k$.PACKED_2X2_FLOAT16:k$.UNPACKED_FLOAT16}(t);if(e===w$.DOWNLOAD||e===w$.PIXELS)return k$.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function lS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class uS{constructor(e,t){this.variableNames=["A"],this.outputShape=e,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 cS="return abs(x);",hS="return x;";class pS{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}}class dS{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,n=nS("rc",t),s=vC(t),a=function(e,t){if(1===e)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}(t,n),r=n.slice(-2),i=t<=1?"rc":`vec2(${r.join(",")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${a});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}}const fS=Wu,mS={};class gS extends I{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!ue().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(null==e){const e=b$(ue().getNumber("WEBGL_VERSION"));this.binaryCache=((t=ue().getNumber("WEBGL_VERSION"))in mS||(mS[t]={}),mS[t]),this.gpgpu=new hC(e),this.canvas=e.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;var t;this.textureManager=new rS(this.gpgpu),this.numMBBeforeWarning=null==ue().global.screen?1024:ue().global.screen.height*ue().global.screen.width*window.devicePixelRatio*600/1024/1024,this.texData=new N(this,Ir())}nextDataId(){return gS.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((ue().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ue().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:w$.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){this.texData.get(e).refCount++}decRef(e){this.texData.has(e)&&this.texData.get(e).refCount--}move(e,t,n,s,a){if(ue().getBool("DEBUG")&&this.checkNumericalProblems(t),"complex64"===s)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:w$.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:a,slice:r,shape:i,isPacked:o}=t;if(null!=r){let t;t=o?new pS(i,hS):new uS(i,hS);const n=this.runWebGLProgram(t,[{dataId:e,shape:i,dtype:s}],s),a=this.readSync(n.dataId);return this.disposeIntermediateTensorInfo(n),a}if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===s)return n;const l=null!=this.activeTimers;let u,c;return l&&(u=bs()),c="complex64"===s?Su(this.readSync(a.real.dataId),this.readSync(a.imag.dataId)):this.getValuesFromTexture(e),l&&(this.downloadWaitMs+=bs()-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:a,dtype:r,complexTensorInfos:i,isPacked:o}=t;if(null!=a){let t;t=o?new pS(s,hS):new uS(s,hS);const n=this.runWebGLProgram(t,[{dataId:e,shape:s,dtype:r}],r),a=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),a}if(null!=n)return this.convertAndCacheOnCPU(e);if(!ue().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===ue().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"!==r&&ue().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);const t=this.texData.get(l.dataId);c=this.gpgpu.createBufferFromTexture(t.texture,...N$(s))}if(this.pendingRead.set(e,[]),"complex64"!==r&&await this.gpgpu.createAndWaitForFence(),"complex64"===r){const e=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]);u=Su(e[0],e[1])}else if(null==c)u=this.getValuesFromTexture(e);else{const e=D(s);u=this.gpgpu.downloadFloat32MatrixFromBuffer(c,e)}null!=l&&this.disposeIntermediateTensorInfo(l);const h=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach((e=>e(h))),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ir().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){const t=this.readSync(e.dataId);let n=t;if("string"===e.dtype)try{n=t.map((e=>ws(e)))}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ha(e.shape,e.dtype,n)}checkNumericalProblems(e){if(null!=e)for(let t=0;t<e.length;t++){const n=e[t];if(!S$(n)){if(ue().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 ${n} cannot be represented on this device.`)}}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),a=D(t);if(ue().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const n=this.decode(e),s=this.texData.get(n.dataId),r=this.gpgpu.downloadMatrixFromPackedTexture(s.texture,...N$(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(n),r}const r=ue().getBool("WEBGL_PACK")&&!0===s,i=r?P$(t):t,o=r?new nC(i):new tC(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return ue().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){const t=this.activeTimers,n=[];let s=!1;null==this.programTimersStack?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const a=F(this.activeTimers.map((e=>e.query))).filter((e=>null!=e)),r=F(this.activeTimers.map((e=>e.name))).filter((e=>null!=e));this.activeTimers=t,s&&(this.programTimersStack=null);const i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ue().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const e=await Promise.all(a);i.kernelMs=function(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}(e),i.getExtraProfileInfo=()=>e.map(((e,t)=>({name:r[t],ms:e}))).map((e=>`${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 ue().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:bs(),endMs:null}}endTimer(e){return ue().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=bs(),e)}async getQueryTime(e){if(ue().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:a,isPacked:r,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,a,r)));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}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return ue().getBool("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=Ir().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=128){const n=this.getCPUBackend();return ue().getBool("IS_TEST")||this.warnedAboutCPUBackend||null!=n||(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),null!=n&&e.every((e=>null==this.texData.get(e.dataId).texture&&D(e.shape)<t))}getGPGPUContext(){return this.gpgpu}where(e){$u("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return fS(e.shape,t)}packedUnaryOp(e,t,n){const s=new pS(e.shape,t),a=this.compileAndRun(s,[e],n);return Ir().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&"complex64"!==e.dtype){const t=KC(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,t)}if(ue().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,cS,e.dtype);const t=new uS(e.shape,cS),n=this.compileAndRun(t,[e]);return Ir().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if("string"===t&&null!=n&&n.length>0&&K(n[0])){const a=n.map((e=>xs(e)));s=this.write(a,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){const{dataId:s}=this.makeTensorInfo(e,t,n);return Ir().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new dS(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new sS(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const n=[L$(e.shape),...z$(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},a=[L$(t),...z$(t)],r=new aS(a,n),i=this.runWebGLProgram(r,[s],e.dtype,null,!0);return{dataId:i.dataId,shape:t,dtype:i.dtype}}decode(e){const t=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=t,r=P$(s);let i;return i=n?new eC(r):new Q$(r),{dtype:a,shape:s,dataId:this.runWebGLProgram(i,[{shape:r,dtype:a,dataId:e}],a,null,!0).dataId}}runWebGLProgram(e,t,n,s,a=!1){const r=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(r.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===x$.DENSE){const t=N$(e.outputShape);i.texShape=t.map((e=>2*e))}if(null!=e.outTexUsage&&(i.usage=e.outTexUsage),0===D(r.shape))return i.values=V(r.dtype,0),r;const o=[],l=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)<=ue().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)}else if(!!n.isPacked!=!!e.packedInputs)t=n.isPacked?this.unpackTensor(t):this.packTensor(t),o.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!W$(n.shape,t.shape)){const e=t,s=t.shape;t.shape=n.shape,t=this.packedReshape(t,s),o.push(t),n=this.texData.get(t.dataId),e.shape=s}return this.uploadToGPU(t.dataId),{shape:t.shape,texData:n,isUniform:!1}}));this.uploadToGPU(r.dataId);const u={shape:r.shape,texData:i,isUniform:!1},c=function(e,t,n){let s="";t.concat(n).forEach((e=>{const t=null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0,n=e.isUniform?"uniform":e.texData.texShape;s+=`${e.shape}_${n}_${t}`}));const a=e.userCode;let r=e.constructor.name;return r+="_"+s+"_"+a,r}(e,l,u),h=this.getAndSaveBinary(c,(()=>function(e,t,n,s){const a=t.userCode,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}})),i=r.map((e=>e.shapeInfo)),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},l=dC(r,o,a,t.packedInputs),u=e.createProgram(l);let c=null;const h=e.getUniformLocation(u,"NAN",!1);1===ue().getNumber("WEBGL_VERSION")&&(c=e.getUniformLocation(u,"INFINITY",!1));const p={};for(let n=0;n<t.variableNames.length;n++){const s=t.variableNames[n],a=!1;p[s]=e.getUniformLocation(u,s,a),p[`offset${s}`]=e.getUniformLocation(u,`offset${s}`,a)}return{program:t,source:l,webGLProgram:u,uniformLocations:p,inShapeInfos:i,outShapeInfo:o,infLoc:c,nanLoc:h}}(this.gpgpu,e,l,u))),p=null!=this.activeTimers;let d;p&&(d=this.startTimer()),function(e,t,n,s,a){$C(t.inShapeInfos,n),$C([t.outShapeInfo],[s]);const r=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(r,i[0],i[1]):e.setOutputMatrixTexture(r,i[0],i[1]),e.setProgram(t.webGLProgram),1===ue().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 a=t.program.variableNames[s],r=t.uniformLocations[a],i=t.uniformLocations[`offset${a}`];if(null!=r)if(n.isUniform)if(D(n.shape)<2)e.gl.uniform1f(r,n.uniformValues[0]);else{let t=n.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(r,t)}else null!=n.texData.slice&&null!=i&&e.gl.uniform1i(i,n.texData.slice.flatOffset),e.setInputMatrixTexture(n.texData.texture,r,s)})),null!=a&&a(e,t.webGLProgram),e.executeProgram()}(this.gpgpu,h,l,u,s),o.forEach((e=>this.disposeIntermediateTensorInfo(e))),p&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));const f=ue().get("WEBGL_FLUSH_THRESHOLD");if(f>0){const e=bs();e-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!ue().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&!1===a){const e=this.unpackTensor(r);return this.disposeIntermediateTensorInfo(r),e}return r}compileAndRun(e,t,n,s,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ue().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=Cr((()=>{if(!ue().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=ue().getBool("DEBUG");ue().set("DEBUG",!1);const t=this.abs(Fr(1e-8)).dataSync()[0];if(ue().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:a,texture:r,usage:i,isPacked:o}=t;if(null!=r)return;const l=null!=this.activeTimers;let u;l&&(u=bs());let c=t.texShape;if(null==c&&(c=function(e,t=!1){let n=ue().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(t&&(n*=2,1===(e=e.map(((t,n)=>n>=e.length-2?T(e[n]):e[n]))).length&&(e=[2,e[0]])),2!==e.length){const t=W(e);e=t.newShape}let s=D(e);if(e.length<=1&&s<=n)return[1,s];if(2===e.length&&e[0]<=n&&e[1]<=n)return e;if(3===e.length&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(3===e.length&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){const t=L$(e);let n=2,a=2;return e.length&&([n,a]=z$(e)),s=t*(n/2)*(a/2),M(s).map((e=>2*e))}return M(s)}(n,o),t.texShape=c),null!=a){const e=P$(n);let r,i=c[1],h=c[0];const p=a instanceof Uint8Array;o?([i,h]=I$(c[0],c[1]),r=new aC(e,[h,i],p)):r=new sC(e,[h,i],p);const d=this.makeTensorInfo([h,i],s);this.texData.get(d.dataId).usage=p?w$.PIXELS:w$.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(d.dataId),i,h,a);const f=!0,m=this.runWebGLProgram(r,[d],s,null,f),g=this.texData.get(m.dataId);t.texture=g.texture,t.texShape=g.texShape,t.isPacked=g.isPacked,t.usage=g.usage,this.disposeIntermediateTensorInfo(d),this.texData.delete(m.dataId),t.values=null,l&&(this.uploadWaitMs+=bs()-u)}else{const e=this.acquireTexture(c,i,s,o);t.texture=e}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),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]*j(t)}}gS.nextDataId=0,ta()&&Ar("webgl",(()=>new gS),2);class yS{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=vi(t,n),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 bS{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=vi(t,n);const a=this.outputShape.length;let r="";if(s)if(0===a||1===D(this.outputShape))r="\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        ";else if(r=`\n          ${vC(a)} coords = getOutputCoords();\n        `,1===a)r+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{const e=nS("coords",a);r+=`\n            bool nextRowOutOfBounds =\n              (${e[a-2]} + 1) >= ${this.outputShape[a-2]};\n            bool nextColOutOfBounds =\n              (${e[a-1]} + 1) >= ${this.outputShape[a-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${r}\n\n        setOutput(result);\n      }\n    `}}function xS(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const wS={kernelName:It,backendName:"webgl",kernelFunc:xS};function kS(e){const{inputs:t,backend:n}=e,{real:s,imag:a}=t,r=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(r.dataId),o=xS({inputs:{x:s},backend:n}),l=xS({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},r}const vS={kernelName:ze,backendName:"webgl",kernelFunc:kS},NS="return (a < 0.) ? b * a : a;",IS="\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",$S={kernelName:At,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{alpha:r}=s,i=n.makeTensorInfo([],"float32",gs(r,"float32")),o=ue().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bS(IS,a.shape,i.shape):new yS(NS,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}},CS="return (a < 0.) ? b * a : a;",SS="\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",TS={kernelName:pn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s,alpha:a}=t,r=ue().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bS(SS,s.shape,a.shape):new yS(CS,s.shape,a.shape);return n.runWebGLProgram(r,[s,a],s.dtype)}};function ES({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:r})=>{const{x:i}=a,o=r,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=ue().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&null!=t?new pS(i.shape,t):new uS(i.shape,e),o.runWebGLProgram(u,[i],l)}}function AS({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:a,dtype:r}){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,a]=[[t.complexTensorInfos.real,n.complexTensorInfos.real],[t.complexTensorInfos.imag,n.complexTensorInfos.imag]].map((t=>{const[n,s]=t,a={dataId:n.dataId,dtype:n.dtype,shape:l.shape},r={dataId:s.dataId,dtype:s.dtype,shape:u.shape},i=new yS(e,l.shape,u.shape);return c.runWebGLProgram(i,[a,r],Vs(n.dtype,s.dtype))})),r=kS({inputs:{real:s,imag:a},backend:c});return c.disposeIntermediateTensorInfo(s),c.disposeIntermediateTensorInfo(a),r}const h=r||Vs(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&null!=a){const e=c.texData.get(l.dataId),t=c.texData.get(u.dataId),[n,s]=a(l.shape,u.shape,e.values,t.values,h),r=c.makeTensorInfo(s,h);return c.texData.get(r.dataId).values=n,r}let p;return p=ue().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&null!=t?new bS(t,l.shape,u.shape,n):new yS(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function RS(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?SS:CS;if("leakyrelu"===e)return t?IS:NS;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}class FS{constructor(e,t,n,s=!1,a=!1,r=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;const u=s?e[1]:e[2],c=Math.ceil(u/2),h=s?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",d=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["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=r?"result += getBiasAtOutCoords();":"";r&&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\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    `}}class DS{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=vi(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}}const _S="return a * b;";function OS(e){const{inputs:t,backend:n}=e,{a:s,b:a}=t,r=Vs(s.dtype,a.dtype);if("complex64"===s.dtype){const e=n.texData.get(s.dataId),t=n.texData.get(a.dataId),r=new DS("return areal * breal - aimag * bimag;",s.shape,a.shape),i=new DS("return areal * bimag + aimag * breal;",s.shape,a.shape),o=[{dataId:e.complexTensorInfos.real.dataId,dtype:e.complexTensorInfos.real.dtype,shape:s.shape},{dataId:e.complexTensorInfos.imag.dataId,dtype:e.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:t.complexTensorInfos.real.dataId,dtype:t.complexTensorInfos.real.dtype,shape:a.shape},{dataId:t.complexTensorInfos.imag.dataId,dtype:t.complexTensorInfos.imag.dtype,shape:a.shape}],l=n.runWebGLProgram(r,o,"float32"),u=n.runWebGLProgram(i,o,"float32"),c=kS({inputs:{real:l,imag:u},backend:n});return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(u),c}if(n.shouldExecuteOnCPU([s,a])){const e=n.texData.get(s.dataId),t=n.texData.get(a.dataId),[i,o]=VC(s.shape,a.shape,e.values,t.values,r),l=n.makeTensorInfo(o,r);return n.texData.get(l.dataId).values=i,l}let i;return i=ue().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bS(_S,s.shape,a.shape):new yS(_S,s.shape,a.shape),n.runWebGLProgram(i,[s,a],r)}const MS={kernelName:en,backendName:"webgl",kernelFunc:OS};function LS(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{shape:r}=s,i=n,o=D(a.shape),l=P(r,o),u=D(l);E(o===u,(()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`));const c=i.texData.get(a.dataId);return!c.isPacked||W$(a.shape,l)||null!==c.texture&&W$(c.shape,l)?(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype}):function(e,t,n){const s=[L$(e.shape),...z$(e.shape)],a={dtype:e.dtype,shape:s,dataId:e.dataId},r=[L$(t),...z$(t)],i=new aS(r,s),o=n.runWebGLProgram(i,[a],e.dtype,null,!0);return{dataId:o.dataId,shape:t,dtype:o.dtype}}(a,l,i)}const zS={kernelName:bn,backendName:"webgl",kernelFunc:LS};class PS{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:s,inSize:a,outSize:r}=e;this.outputShape=[s,r];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 * ${O(e)?e.toPrecision(2):e}, ones);`}let u="";a%n>0&&(u=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${u}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${l}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${1===o}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${l}\n        } else if (${2===o}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${l}\n        } else if (${3===o}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${l}\n        }\n        setOutput(sumValue);\n      }\n    `}}class BS{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:s,inSize:a,outSize:r}=e;this.outputShape=[s,r];let i="0.0",o="";"prod"===t?i="1.0":"min"===t?(i="1.0 / 1e-20",o="min"):"max"===t&&(i="-1.0 / 1e-20",o="max");let l=`${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      }\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="";a%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${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 WS(e,t,n,s){const a=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=iu(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}(e.shape);let r=e;for(let i=0;i<a.length;i++){const{inSize:o,windowSize:l,outSize:u}=a[i];let c,h;c="mean"===n?0===i?new PS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new PS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):new BS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),h=r,r=s.runWebGLProgram(c,[r],t),h.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(h)}return r}class VS{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=vC(this.rank),a=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      ${s} resRC = getOutputCoords();\n      setOutput(getA(${a}));\n    }\n    `}}class US{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 ${this.rank} is not yet supported.`);const s=vC(this.rank),a=tS("rc",this.rank),r=new Array(this.rank);for(let e=0;e<t.length;e++)r[t[e]]=a[e];const i=`vec2(${r.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${r.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] = ${l};\n      }\n      --${a[this.rank-1]};\n      if(++${a[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${l};\n        if(${o}) {\n          result[3] = ${l};\n        }\n      }\n      setOutput(result);\n    }\n    `}}function GS(e,t,n){const s=ue().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new US(e.shape,t):new VS(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function HS(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{axis:r,keepDims:i}=s;return function(e,t,n,s){const a=t,r=e.shape.length,i=B(a,e.shape);let o=i;const l=Zi(o,r),u=null!=l;let c=e;u&&(c=GS(e,l,s),o=eo(o.length,r)),Ji("sum",o,r);const[h,p]=Xi(c.shape,o);let d=h;n&&(d=Yi(h,i));const f=D(p),m=LS({inputs:{x:c},attrs:{shape:[D(e.shape)/f,f]},backend:s}),g=WS(m,Us(e.dtype),"sum",s),y=LS({inputs:{x:g},attrs:{shape:d},backend:s});return s.disposeIntermediateTensorInfo(m),s.disposeIntermediateTensorInfo(g),u&&s.disposeIntermediateTensorInfo(c),y}(a,r,i,n)}const jS={kernelName:Ln,backendName:"webgl",kernelFunc:HS};function KS(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{perm:r}=s,i=n,o=a.shape.length,l=new Array(o);for(let e=0;e<l.length;e++)l[e]=a.shape[r[e]];let u;if(i.shouldExecuteOnCPU([a])){const e=i.texData.get(a.dataId).values,t=QC(e,a.shape,a.dtype,r,l);u=i.makeTensorInfo(l,a.dtype),i.texData.get(u.dataId).values=t}else u=GS(a,r,i);return u}const qS={kernelName:Jn,backendName:"webgl",kernelFunc:KS};function XS({a:e,b:t,transposeA:n,transposeB:s,backend:a,bias:r=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){const u=e.shape.length,c=t.shape.length,h=n?e.shape[u-2]:e.shape[u-1],p=s?t.shape[c-1]:t.shape[c-2],d=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=D(m),b=D(g);E(u>=2&&c>=2&&(y===b||1===y||1===b),(()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`));const x=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([d,f]);E(h===p,(()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`));const w=n?[y,h,d]:[y,d,h],k=s?[b,f,p]:[b,p,f],v=LS({inputs:{x:e},backend:a,attrs:{shape:w}}),N=LS({inputs:{x:t},backend:a,attrs:{shape:k}}),I=[v,N],$=Math.max(y,b),C=n?v.shape[1]:v.shape[2],S=null!=r,T=null!=i,A="leakyrelu"===l,R=null!=l?RS(l,!0):null;let F;if((1===d||1===f)&&C>1e3&&!1===(S||T||A||null!=R)){let e=v,t=N;n&&(e=KS({inputs:{x:v},backend:a,attrs:{perm:[0,2,1]}}),I.push(e)),s&&(t=KS({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),I.push(t));const r=1===f;let i=e;1!==f&&(i=LS({inputs:{x:e},backend:a,attrs:{shape:[$,C,1]}}),I.push(i));const o=1===f?2:1;let l=t;r&&(l=LS({inputs:{x:t},backend:a,attrs:{shape:[$,1,C]}}),I.push(l));const u=OS({inputs:{a:i,b:l},backend:a});F=HS({inputs:{x:u},backend:a,attrs:{axis:o,keepDims:!0}}),I.push(u)}else{const l=Vs(e.dtype,t.dtype),u=new FS(w,k,[$,d,f],n,s,S,R,T,A),c=[v,N];if(null!=r&&c.push(r),T&&c.push(i),A){const e=a.makeTensorInfo([],"float32",gs(o,"float32"));c.push(e),I.push(e)}F=a.runWebGLProgram(u,c,l)}const _=LS({inputs:{x:F},backend:a,attrs:{shape:x}});I.push(F);for(const e of I)a.disposeIntermediateTensorInfo(e);return _}const YS={kernelName:rs,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{a,b:r,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=s;return XS({a,b:r,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}},JS="return abs(x);",ZS={kernelName:fe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&"complex64"!==s.dtype){const e=n.texData.get(s.dataId),t=KC(e.values);return n.makeTensorInfo(s.shape,s.dtype,t)}let a;return a=ue().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new pS(s.shape,JS):new uS(s.shape,JS),n.runWebGLProgram(a,[s],s.dtype)}},QS=ES({opSnippet:"if (isnan(x)) return x;\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n"}),eT={kernelName:me,backendName:"webgl",kernelFunc:QS},tT=ES({opSnippet:"if (isnan(x)) return x;\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));"}),nT={kernelName:ge,backendName:"webgl",kernelFunc:tT},sT="return a + b;",aT=AS({opSnippet:sT,packedOpSnippet:sT,supportsComplex:!0,cpuKernelImpl:CC}),rT={kernelName:ye,backendName:"webgl",kernelFunc:aT};class iT{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${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 = ${s};\n        setOutput(result);\n      }\n    `}}class oT{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${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 lT={kernelName:be,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:s}=t,a=n;if(1===a.length)return xS({inputs:{x:a[0]},backend:s});if(a.length>ue().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const t=Math.floor(a.length/2),n=e({inputs:a.slice(0,t),backend:s}),r=e({inputs:a.slice(t),backend:s});return e({inputs:[n,r],backend:s})}const r=a.map((e=>e.dtype)).reduce(((e,t)=>Vs(e,t))),i=a.map((e=>e.shape)),o=ue().getBool("WEBGL_PACK")?new oT(a[0].shape,i):new iT(a[0].shape,i);return s.runWebGLProgram(o,a,r)}},uT={kernelName:xe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{axis:r,keepDims:i}=s,o=a.shape.length,l=B(r,a.shape);let u=l;const c=Zi(u,o);let h=a;null!=c&&(h=KS({inputs:{x:a},backend:n,attrs:{perm:c}}),u=eo(u.length,o)),Ji("all",u,o);const[p,d]=Xi(h.shape,u),f=LS({inputs:{x:h},backend:n,attrs:{shape:[-1,D(d)]}}),m=WS(f,f.dtype,"all",n);let g;return g=LS(i?{inputs:{x:m},backend:n,attrs:{shape:Yi(p,l)}}:{inputs:{x:m},backend:n,attrs:{shape:p}}),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),null!=c&&n.disposeIntermediateTensorInfo(h),g}},cT={kernelName:we,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{axis:r,keepDims:i}=s,o=a.shape.length,l=B(r,a.shape);let u=l;const c=Zi(u,o);let h=a;null!=c&&(h=KS({inputs:{x:a},backend:n,attrs:{perm:c}}),u=eo(u.length,o)),Ji("any",u,o);const[p,d]=Xi(h.shape,u),f=LS({inputs:{x:h},backend:n,attrs:{shape:[-1,D(d)]}}),m=WS(f,f.dtype,"any",n);let g;return g=LS(i?{inputs:{x:m},backend:n,attrs:{shape:Yi(p,l)}}:{inputs:{x:m},backend:n,attrs:{shape:p}}),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),null!=c&&n.disposeIntermediateTensorInfo(h),g}};class hT{constructor(e,t,n){this.variableNames=["A"];const{windowSize:s,batchSize:a,outSize:r}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,r];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 ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}}class pT{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,E(e.length>2,(()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`));const a=e[e.length-1],r=Math.ceil(a/t);this.outputShape=e.slice(0,-1),r>1&&this.outputShape.push(r),s||this.variableNames.push("bestIndicesA");const i=this.outputShape,o=i.length,l=vC(o),u=nS("coords",o);let c,h;if(1===r){h=o+1;const e=vC(h);c=`\n        ${e} sourceLocR = ${e}(${u.join()}, 0);\n        ++${u[o-1]};\n        ${e} sourceLocG = ${e}(${u.join()}, 0);\n        ++${u[o-2]};\n        ${e} sourceLocA = ${e}(${u.join()}, 0);\n        --${u[o-1]};\n        ${e} sourceLocB = ${e}(${u.join()}, 0);\n        --${u[o-2]};`}else h=o,c=`\n        ${l} sourceLocR = coords;\n        ++${u[o-1]};\n        ${l} sourceLocG = coords;\n        ++${u[o-2]};\n        ${l} sourceLocA = coords;\n        --${u[o-1]};\n        ${l} sourceLocB = coords;\n        --${u[o-2]};`;const p=["x","y","z","w","u","v"].slice(0,h),d="."+p[h-1],f=p.map((e=>"int "+e)),m=nS("sourceLocR",h-1).concat("inIdx.r"),g=nS("sourceLocG",h-1).concat("inIdx.g"),y=nS("sourceLocB",h-1).concat("inIdx.b"),b=nS("sourceLocA",h-1).concat("inIdx.a"),x="max"===n?"greaterThan":"lessThan",w=s?"":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${y.join()}),\n                             getBestIndicesAChannel(${b.join()})));`,k=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${y.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,v=s?"":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${p.join()}),\n                                          vec2(${p.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${p.join()}),\n                               vec2(${p.slice(-2).join()}));\n      }\n      ${v}\n      void main() {\n        ${l} coords = getOutputCoords();\n        bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};\n        bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n          sourceLocB${d}, sourceLocA${d}) * ${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          ${w}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}}function dT(e,t,n,s=null){let a=t.shape[0],r=t.shape[1];null!=s&&(a=s.shape[0],r=s.shape[1]);const i=iu(r),o={windowSize:i,inSize:r,batchSize:a,outSize:Math.ceil(r/i)},l=new hT(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=dT(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function fT(e,t,n,s=null){const a=null!=s?s.shape:t.shape,r=iu(a[a.length-1]),i=new pT(a,r,n,null==s),o=null==s?[t]:[t,s],l=e.runWebGLProgram(i,o,"int32");if(l.shape.length===t.shape.length){const s=fT(e,t,n,l);return e.disposeIntermediateTensorInfo(l),s}return l}function mT(e,t,n,s){const a=[n];if(Ji("arg"+s.charAt(0).toUpperCase()+s.slice(1),a,t.shape.length),!ue().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){const n=[],[r,i]=Xi(t.shape,a),o=D(i),l=LS({inputs:{x:t},backend:e,attrs:{shape:[-1,o]}});n.push(l);const u=dT(e,l,s);n.push(u);const c=LS({inputs:{x:u},backend:e,attrs:{shape:r}});return n.forEach((t=>e.disposeIntermediateTensorInfo(t))),c}return fT(e,t,s)}const gT={kernelName:ke,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{axis:r}=s;let i=B(r,a.shape);const o=Zi(i,a.shape.length);let l=a;const u=[];null!=o&&(l=KS({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=eo(i.length,l.shape.length)),Ji("argMax",[i[0]],l.shape.length);const c=mT(n,l,i[0],"max");return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}},yT={kernelName:ve,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{axis:r}=s;let i=B(r,a.shape);const o=Zi(i,a.shape.length);let l=a;const u=[];null!=o&&(l=KS({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=eo(i.length,l.shape.length)),Ji("argMin",[i[0]],l.shape.length);const c=mT(n,l,i[0],"min");return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}},bT=ES({opSnippet:"if (isnan(x)) return x;\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n"}),xT={kernelName:Ne,backendName:"webgl",kernelFunc:bT},wT=ES({opSnippet:"if (isnan(x)) return x;return log(x + sqrt(x * x + 1.0));"}),kT={kernelName:Ie,backendName:"webgl",kernelFunc:wT},vT=ES({opSnippet:"if (isnan(x)) return x;\n  return atan(x);\n"}),NT={kernelName:$e,backendName:"webgl",kernelFunc:vT},IT=AS({opSnippet:"\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n\n  return atan(a, b);\n",packedOpSnippet:"\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n"}),$T={kernelName:Se,backendName:"webgl",kernelFunc:IT},CT=ES({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;"}),ST={kernelName:Ce,backendName:"webgl",kernelFunc:CT};class TT{constructor(e,t,n,s=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const r=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?a?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(r/4),w=r%4,k=`\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            ${k}\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            ${k}\n          } else if (${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            ${k}\n          } else if (${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            ${k}\n          }\n        }\n        setOutput(${b});\n      }\n    `}}class ET{constructor(e,t,n,s=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const r=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?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${f} +\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 k=4*Math.floor(r/4),v=r%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 < ${k}; 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 + ${k};\n            if (${1===v}) {\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===v}) {\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===v}) {\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 AT={kernelName:Te,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t;K$(a,"avgPool");const{filterSize:r,strides:i,pad:o,dimRoundingMode:l}=s;E(Yr(i,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));const u=Br(a.shape,r,i,1,o,l);if(1===u.filterWidth&&1===u.filterHeight&&_(u.inShape,u.outShape))return xS({inputs:{x:a},backend:n});const c=new TT(u,"avg",!1);return n.runWebGLProgram(c,[a],"float32")}},RT={kernelName:Ae,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{filterSize:r,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=s,c=Wr(a.shape,r,i,[1,1,1],o,l,u),h=new ET(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}};class FT{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,a=e.strideWidth,r=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${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 += ${r}) {\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) / ${a}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class DT{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,a=e.strideDepth,r=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${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) / ${a}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${h};\n              wR += ${l}) {\n            float dyR = float(dyRCorner + wR) / ${r}.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 >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const _T={kernelName:Re,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:a,input:r}=t,i=r,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=s,h=Wr(i.shape,o,l,[1,1,1],u,c),p=new DT(h);return n.runWebGLProgram(p,[a],i.dtype)}},OT={kernelName:Ee,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:a,input:r}=t,i=r;K$([a,r],"avgPoolGrad");const{filterSize:o,strides:l,pad:u}=s,c=Br(i.shape,o,l,1,u),h=new FT(c);return n.runWebGLProgram(h,[a],i.dtype)}},MT={kernelName:Fe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{a,b:r}=t,{transposeA:i,transposeB:o}=s;return XS({a,b:r,transposeA:i,transposeB:o,backend:n})}};class LT{constructor(e,t,n,s,a,r){this.outputShape=[],this.variableNames=["x","mean","variance"],vi(e,t),vi(e,n);let i="0.0";null!=s&&(vi(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";null!=a&&(vi(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${i};\n        float scale = ${o};\n        float inv = scale * inversesqrt(variance + float(${r}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `}}class zT{constructor(e,t,n,s,a,r){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],vi(e,t),vi(e,n);let i="vec4(0.0)";null!=s&&(vi(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";null!=a&&(vi(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 offset = ${i};\n        vec4 scale = ${o};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${r}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `}}const PT={kernelName:xt,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,mean:a,variance:r,offset:i,scale:o}=e;E(a.shape.length===r.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),E(null==i||a.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),E(null==o||a.shape.length===o.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));let{varianceEpsilon:l}=n;null==l&&(l=.001);const u=[s,a,r];let c=null;null!=i&&(c=i.shape,u.push(i));let h=null;null!=o&&(h=o.shape,u.push(o));const p=ue().getBool("WEBGL_PACK_NORMALIZATION")?new zT(s.shape,a.shape,r.shape,c,h,l):new LT(s.shape,a.shape,r.shape,c,h,l);return t.runWebGLProgram(p,u,u[0].dtype)}};class BT{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=vC(this.rank),n=`uniform int start[${this.rank}];`,s=function(e){if(1===e)return"sourceLoc";if(e<=6)return WT.slice(0,e).map((e=>"sourceLoc."+e)).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}(this.rank);let a;a=`\n        ${t} sourceLoc;\n        ${t} coords = getOutputCoords();\n        ${e.map(((e,t)=>`sourceLoc.${WT[t]} = start[${t}] + coords.${WT[t]};`)).join("\n")}\n      `,this.userCode=`\n      ${n}\n      void main() {\n        ${a}\n        setOutput(getSource(${s}));\n      }\n    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{null==this.startLoc&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),null==this.startLoc)||t.gl.uniform1iv(this.startLoc,e)}}}const WT=["x","y","z","w","u","v"];class VT{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=vC(this.rank),n=nS("coords",this.rank),s=nS("sourceLoc",this.rank),a=1===this.rank?"sourceLoc":`vec2(${s.slice(-2).join()})`,r=`getChannel(getSource(${s.join()}), ${a})`,i=`\n      result.x = ${r};\n      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n        ++${s[this.rank-1]};\n        result.y = ${r};\n        --${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 = ${r};\n        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n          ++${s[this.rank-1]};\n          result.w = ${r};\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      uniform int start[${this.rank}];\n      void main() {\n        ${t} coords = getOutputCoords();\n        ${t} sourceLoc;\n        ${l}\n        vec4 result = vec4(0.);\n        ${i}\n        ${o}\n        setOutput(result);\n      }\n    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{null==this.startLoc&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),null==this.startLoc)||t.gl.uniform1iv(this.startLoc,e)}}}function UT(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{begin:r,size:i}=s,[o,l]=xr(a,r,i);if(rr(a,o,l),0===D(l))return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||"string"===a.dtype){const e=n.texData.get(a.dataId),t=qC(e.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,t)}const{isPacked:u}=n.texData.get(a.dataId),c=yr(a.shape,o,l);if(u||!c){const e=ue().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VT(l):new BT(l),t=e.getCustomSetupFunc(o);return n.runWebGLProgram(e,[a],a.dtype,t)}return n.uploadToGPU(a.dataId),function(e,t,n,s){const a=s.texData.get(e.dataId),r=s.makeTensorInfo(n,e.dtype),i=s.texData.get(r.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=br(t,Z(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};const l=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,l+1),r}(a,o,l,n)}const GT={kernelName:An,backendName:"webgl",kernelFunc:UT},HT={kernelName:De,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{blockShape:r,crops:i}=s;E(a.shape.length<=4,(()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"));const o=r.reduce(((e,t)=>e*t)),l=lu(a.shape,r,o),u=uu(l.length,r.length),c=cu(a.shape,r,o),h=hu(i,r.length),p=pu(c,i,r.length),d=[],f=LS({inputs:{x:a},backend:n,attrs:{shape:l}}),m=KS({inputs:{x:f},backend:n,attrs:{perm:u}}),g=LS({inputs:{x:m},backend:n,attrs:{shape:c}}),y=UT({inputs:{x:g},backend:n,attrs:{begin:h,size:p}});return d.push(f),d.push(m),d.push(g),d.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}},jT={kernelName:_e,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,weights:r}=t,{size:i}=s,o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=SC(o,l,r.dtype,r.shape,i);return n.makeTensorInfo([i],r.dtype,u)}},KT=AS({opSnippet:"return float(a != b);",dtype:"bool"}),qT={kernelName:nn,backendName:"webgl",kernelFunc:KT};function XT(e){const{inputs:t,backend:n}=e,{input:s}=t;return xS({inputs:{x:n.texData.get(s.dataId).complexTensorInfos.real},backend:n})}const YT={kernelName:mn,backendName:"webgl",kernelFunc:XT},JT={kernelName:Oe,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:s,attrs:a}=t,{x:r}=n,{dtype:i}=a;if("complex64"===i){if("complex64"===r.dtype)return xS({inputs:{x:r},backend:s});const t=io(r.shape),n=e({inputs:{x:r},backend:s,attrs:{dtype:"float32"}}),a=kS({inputs:{real:n,imag:t},backend:s});return t.dispose(),s.disposeIntermediateTensorInfo(n),a}if("complex64"===r.dtype){const t=XT({inputs:{input:r},backend:s}),n=e({inputs:{x:t},backend:s,attrs:{dtype:i}});return s.disposeIntermediateTensorInfo(t),n}if(!G(r.dtype,i)){const e=xS({inputs:{x:r},backend:s});return{dataId:e.dataId,shape:e.shape,dtype:i}}if("int32"===i)return function(e,t){const n=new uS(e.shape,"return float(int(x));"),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}(r,s);if("bool"===i){const e=s.makeTensorInfo([],"bool",V("bool",1)),t=KT({inputs:{a:r,b:e},backend:s});return s.disposeIntermediateTensorInfo(e),t}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${i}`)}},ZT="return ceil(x);",QT=ES({opSnippet:ZT,packedOpSnippet:ZT,cpuKernelImpl:EC}),eE={kernelName:Me,backendName:"webgl",kernelFunc:QT};class tE{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode="\n      uniform float minVal;\n      uniform float maxVal;\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    "}getCustomSetupFunc(e,t){return(n,s)=>{null==this.minLoc&&(this.minLoc=n.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(s,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}class nE{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode="\n      uniform float minVal;\n      uniform float maxVal;\n\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    "}getCustomSetupFunc(e,t){return(n,s)=>{null==this.minLoc&&(this.minLoc=n.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(s,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}const sE={kernelName:Le,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{clipValueMin:r,clipValueMax:i}=s;let o;o=ue().getBool("WEBGL_PACK_CLIP")?new nE(a.shape):new tE(a.shape);const l=o.getCustomSetupFunc(r,i);return n.runWebGLProgram(o,[a],a.dtype,l)}};class aE{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 rE(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}const iE={kernelName:Pe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s}=t,a=n.texData.get(s.dataId),r=new aE(s.shape),i=[rE(s,a.complexTensorInfos.real),rE(s,a.complexTensorInfos.imag)];return n.runWebGLProgram(r,i,i[0].dtype)}};class oE{constructor(e){this.outputShape=[],this.outputShape=au(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-${s}));`)}const s=t.length,a=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${a}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join("\n        ")}\n      }\n    `}}class lE{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=au(e,t);const n=this.outputShape,s=n.length,a=vC(s),r=nS("coords",s),i=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map(((e,t)=>`T${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}(${uE(i,l,t)}),\n            vec2(${uE(u,l,t)}));\n        }`}const p=o.length,d=o[o.length-1];h+=`\n        return getChannel(\n          getT${p}(${uE(i,l,d)}),\n          vec2(${uE(u,l,d)}));`,this.userCode=`\n      float getValue(${i.map((e=>"int "+e))}) {\n        ${h}\n      }\n\n      void main() {\n        ${a} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${r}), 0., 0., 0.);\n\n        ${r[s-1]} = ${r[s-1]} + 1;\n        if (${r[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${r});\n        }\n\n        ${r[s-2]} = ${r[s-2]} + 1;\n        if (${r[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${r});\n        }\n\n        ${r[s-1]} = ${r[s-1]} - 1;\n        if (${r[s-2]} < ${n[s-2]} &&\n            ${r[s-1]} < ${n[s-1]}) {\n          result.b = getValue(${r});\n        }\n        setOutput(result);\n      }\n    `}}function uE(e,t,n){const s=e.indexOf(t);return e.map(((e,t)=>t===s?`${e} - ${n}`:e)).join()}function cE(e){const{inputs:t,backend:n}=e,{input:s}=t;return xS({inputs:{x:n.texData.get(s.dataId).complexTensorInfos.imag},backend:n})}const hE={kernelName:Ct,backendName:"webgl",kernelFunc:cE};function pE(e,t,n){const s=e[0].dtype;if("complex64"===s){const s=e.map((e=>XT({inputs:{input:e},backend:n}))),a=e.map((e=>cE({inputs:{input:e},backend:n}))),r=pE(s,t,n),i=pE(a,t,n),o=kS({inputs:{real:r,imag:i},backend:n});return s.forEach((e=>n.disposeIntermediateTensorInfo(e))),a.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(i),o}if("string"===s){const{tensors2D:a,outShape:r}=dE(e,t,n),i=a.map((e=>({vals:n.readSync(e.dataId),shape:e.shape}))),o=1===a[0].shape[0],l=AC(i,r,s,o),u=au(e.map((e=>e.shape)),t),c=n.makeTensorInfo(u,s,l);return a.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}if(e.length>ue().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const s=Math.floor(e.length/2),a=pE(e.slice(0,s),t,n),r=pE(e.slice(s),t,n),i=pE([a,r],t,n);return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(r),i}if(ue().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){const a=new lE(e.map((e=>e.shape)),t);return n.runWebGLProgram(a,e,s)}const{tensors2D:a,outShape:r}=dE(e,t,n),i=new oE(a.map((e=>e.shape))),o=n.runWebGLProgram(i,a,s);a.forEach((e=>n.disposeIntermediateTensorInfo(e)));const l=LS({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(o),l}function dE(e,t,n){const s=au(e.map((e=>e.shape)),t);return{tensors2D:e.map((e=>LS({inputs:{x:e},attrs:{shape:[-1,D(e.shape.slice(t))]},backend:n}))),outShape:s}}function fE(e){const{inputs:t,backend:n,attrs:s}=e,{axis:a}=s,r=B(a,t[0].shape)[0],i=au(t.map((e=>e.shape)),r);if(0===D(i))return n.makeTensorInfo(i,t[0].dtype,[]);const o=t.filter((e=>D(e.shape)>0));return 1===o.length?xS({inputs:{x:o[0]},backend:n}):(su(o.map((e=>e.shape)),r),pE(o,r,n))}const mE={kernelName:Be,backendName:"webgl",kernelFunc:fE};class gE{constructor(e,t=!1,n=null,s=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const r=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        }`:a?`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`\n          float activation(float x) {\n            ${n}\n          }\n        `,w="result = activation(result);");const k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${x}\n\n      const ivec2 strides = ivec2(${o}, ${l});\n      const ivec2 pads = ivec2(${r}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${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        ${k}\n        ${w}\n        setOutput(result);\n      }\n    `}}class yE{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,a=e.strideDepth,r=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,p=e.filterWidth,d=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${a}, ${r}, ${i});\n      const ivec3 pads = ivec3(${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, ${d} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class bE{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:s,inChannels:a,strideWidth:r,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:p,top:d}=o,f=a*s,m=X$(),g="channelsLast"===h,y=g?0:1,b=g?1:2;let x="";for(let n=0;n<=1;n++)for(let s=0;s<=1;s++)x+=`\n          blockIndex = rc.y + ${s};\n          pos = rc.x + ${n};\n\n          if(blockIndex < ${e[1]} && pos < ${e[0]}) {\n            offsetY = int(blockIndex / (${l})) * ${i} - ${d};\n            d0 = offsetY + ${c} * (pos / ${f});\n\n            if(d0 < ${t[y]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${l}.) * ${r}. - ${p}.);\n              d1 = offsetX + ${u} * (int(mod(float(pos), ${f}.) / ${a}.));\n\n              if(d1 < ${t[b]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${a}.));\n\n                if (${g}) {\n                  innerDims = vec2(d1, ch);\n                  result[${2*n+s}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${2*n+s}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${x}\n\n        ${m.output} = result;\n      }\n    `}}function xE({x:e,filter:t,convInfo:n,backend:s,bias:a=null,preluActivationWeights:r=null,leakyreluAlpha:i=0,activation:o=null}){const l=e.shape,u=s.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],p=n.outChannels,d="channelsLast"===n.dataFormat;let f;const m=[],g=(1===h||1===p)&&c>1e3,y=l[2]%2!=0&&!!u.isPacked;if(!g&&ue().getBool("WEBGL_LAZILY_UNPACK")&&ue().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&y){const c=d?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),h={dataId:e.dataId,shape:[1,c,n.inChannels],dtype:e.dtype},p=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,E(W$(u.shape,h.shape),(()=>`packed reshape ${u.shape} to ${h.shape} isn't free`));const g=LS({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});m.push(g);const y=XS({a:h,b:g,backend:s,transposeA:!1,transposeB:!1,bias:a,activation:o,preluActivationWeights:r,leakyreluAlpha:i}),b=s.texData.get(y.dataId);E(b.isPacked,(()=>"batchMatMul result is expected to be packed")),u.shape=p,b.shape=n.outShape,f=xS({inputs:{x:y},backend:s}),f.shape=n.outShape,m.push(y)}else{const u=LS({inputs:{x:e},backend:s,attrs:{shape:[1,d?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],n.inChannels]}}),c=LS({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),h=XS({a:u,b:c,transposeA:!1,transposeB:!1,backend:s,bias:a,activation:o,preluActivationWeights:r,leakyreluAlpha:i});f=LS({inputs:{x:h},backend:s,attrs:{shape:n.outShape}}),m.push(u),m.push(c),m.push(h)}for(const e of m)s.disposeIntermediateTensorInfo(e);return f}function wE({x:e,filter:t,convInfo:n,backend:s,bias:a=null,preluActivationWeights:r=null,leakyreluAlpha:i=0,activation:o=null}){const{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:p,dataFormat:d}=n,f="channelsLast"===d,m=l*u*c,g=p*h,y=[m,g],b=[],x=LS({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),w=LS({inputs:{x:t},backend:s,attrs:{shape:[1,m,D(t.shape)/m]}});b.push(x),b.push(w);const k=new bE(y,x.shape,n),v=s.runWebGLProgram(k,[x],"float32"),N=LS({inputs:{x:v},backend:s,attrs:{shape:[1,y[0],y[1]]}});b.push(v),b.push(N);const I=null!=a,$=null!=r,C="leakyrelu"===o,S=o?RS(o,!0):null,T=new FS(N.shape,w.shape,[1,g,n.outChannels],!0,!1,I,S,$,C),E=[N,w];if(a&&E.push(a),$&&E.push(r),C){const e=s.makeTensorInfo([],"float32",gs(i,"float32"));E.push(e),b.push(e)}const A=s.runWebGLProgram(T,E,"float32"),R=LS({inputs:{x:A},backend:s,attrs:{shape:f?[1,p,h,n.outChannels]:[1,n.outChannels,p,h]}});b.push(A);for(const e of b)s.disposeIntermediateTensorInfo(e);return R}const kE={kernelName:We,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,filter:r}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=s,h=Jr(l),p=Vr(a.shape,r.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(ue().getBool("WEBGL_CONV_IM2COL")&&1===a.shape[0])d=wE({x:a,filter:r,convInfo:p,backend:n});else{const e=new gE(p);d=n.runWebGLProgram(e,[a,r],"float32")}else d=xE({x:a,filter:r,convInfo:p,backend:n});const f=LS({inputs:{x:d},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(d),f}};class vE{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,a=e.padInfo.left,r="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} - ${a};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${r}) {\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 NE{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,a=e.strideWidth,r="channelsLast"===e.dataFormat,i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=r?1:2,u=r?2:3,c=r?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) / ${a}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${r}) {\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 IE{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,a=e.padInfo.front,r=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} - ${a};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${r};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${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 $E{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,a=e.strideDepth,r=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${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) / ${a}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${r}.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 CE={kernelName:Ve,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,dy:r}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,h=Jr(l),p=Vr(a.shape,c,i,1,o,u,!1,h),d=new vE(p);return n.runWebGLProgram(d,[a,r],"float32")}},SE={kernelName:Ue,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:a,filter:r}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=s,h=Jr(u),p=Vr(i,r.shape,o,1,l,c,!1,h),d=new NE(p);return n.runWebGLProgram(d,[a,r],"float32")}},TE={kernelName:Ge,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,filter:r}=t,{strides:i,pad:o,dilations:l}=s,u=Ur(a.shape,r.shape,i,l,o),c=new yE(u);return n.runWebGLProgram(c,[a,r],"float32")}},EE={kernelName:He,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,dy:r}=t,{strides:i,pad:o,filterShape:l}=s,u=Ur(a.shape,l,i,1,o),c=new IE(u);return n.runWebGLProgram(c,[a,r],"float32")}},AE={kernelName:je,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:a,filter:r}=t,{pad:i,strides:o,inputShape:l}=s,u=Ur(l,r.shape,o,1,i),c=new $E(u);return n.runWebGLProgram(c,[a,r],"float32")}},RE=ES({opSnippet:"if (isnan(x)) return x;\n  return cos(x);\n"}),FE={kernelName:Ke,backendName:"webgl",kernelFunc:RE},DE=ES({opSnippet:"\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n"}),_E={kernelName:qe,backendName:"webgl",kernelFunc:DE};class OE{constructor(e,t,n,s,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[r,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];const p="bilinear"===s?1:0,[d,f]=[i-1+".0",o-1+".0"],[m,g,y]=c>1?[""+(i-1)/(c-1),"(y2-y1) * height_ratio",`y1*${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 >= ${r}) {\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 > ${d} ) {\n          setOutput(float(${a}));\n          return;\n        }\n        float in_x = ${w};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${a}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${p} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}}const ME={kernelName:Ye,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{image:a,boxes:r,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=s,c=new OE(a.shape,r.shape,o,l,u);return n.runWebGLProgram(c,[a,r,i],"float32")}};class LE{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;const s=e.length,a=t?"0.0":`getX(${zE(s,"coords")})`,r=e[e.length-1];let i="",o="";t?(i=n?"end != "+(r-1):"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${r}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`\n      uniform float index;\n      void main() {\n        ${vC(s)} coords = getOutputCoords();\n        int end = ${PE(s,"coords")};\n        float val = ${a};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${PE(s,"coords")} = idx;\n          val += getX(${zE(s,"coords")});\n        }\n        setOutput(val);\n      }\n    `}getCustomSetupFunc(e){return(t,n)=>{null==this.index&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}}function zE(e,t){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 Error(`Cumulative sum for rank ${e} is not yet supported`)}function PE(e,t){if(1===e)return`${t}`;if(2===e)return`${t}.y`;if(3===e)return`${t}.z`;if(4===e)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}const BE={kernelName:Xe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{axis:r,exclusive:i,reverse:o}=s,l=a.shape.length,u=Zi([r],l);let c=a;null!=u&&(c=KS({inputs:{x:a},backend:n,attrs:{perm:u}}));const h=eo(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${r}`);const p=c.shape[h];let d=xS({inputs:{x:c},backend:n});for(let e=0;e<=Math.ceil(Math.log2(p))-1;e++){const t=new LE(c.shape,!1,o),s=t.getCustomSetupFunc(e),a=d;d=n.runWebGLProgram(t,[d],d.dtype,s),n.disposeIntermediateTensorInfo(a)}if(i){const e=new LE(c.shape,i,o),t=d;d=n.runWebGLProgram(e,[d],d.dtype),n.disposeIntermediateTensorInfo(t)}if(null!=u){const e=KS({inputs:{x:d},backend:n,attrs:{perm:Qi(u)}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),e}return d}},WE={kernelName:Je,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,weights:r}=t,{size:i,binaryOutput:o}=s;if(1===a.shape.length){const e=n.readSync(a.dataId),t=n.readSync(r.dataId),s=SC(e,t,r.dtype,r.shape,i);return n.makeTensorInfo([i],r.dtype,s)}if(2===a.shape.length){const e=n.bufferSync(a),t=n.bufferSync(r),s=TC(e,t,i,o);return n.makeTensorInfo(s.shape,r.dtype,s.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}};class VE{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 UE={kernelName:Ze,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{blockSize:r,dataFormat:i}=s;E(r>1,(()=>`blockSize should be > 1 for depthToSpace, but was: ${r}`));const o=a.shape[0],l="NHWC"===i?a.shape[1]:a.shape[2],u="NHWC"===i?a.shape[2]:a.shape[3],c="NHWC"===i?a.shape[3]:a.shape[1],h=l*r,p=u*r,d=c/(r*r),f=new VE("NHWC"===i?[o,h,p,d]:[o,d,h,p],r,i);return n.runWebGLProgram(f,[a],a.dtype)}};class GE{constructor(e,t=!1,n=null,s=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const r=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels;let g="",y="";n&&(g=s?`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:a?`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`\n          float activation(float x) {\n            ${n}\n          }\n        `,y="result = activation(result);");const b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${g}\n\n      const ivec2 strides = ivec2(${u}, ${c});\n      const ivec2 pads = ivec2(${o}, ${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 / ${m};\n        int q = d2 - d1 * ${m};\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 < ${d}; wR++) {\n          int xR = xRCorner + wR * ${h};\n\n          if (xR < 0 || xR >= ${r}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${f}; wC++) {\n            int xC = xCCorner + wC * ${p};\n\n            if (xC < 0 || xC >= ${i}) {\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        ${b}\n        ${y}\n        setOutput(result);\n      }\n    `}}class HE{constructor(e,t=!1,n=null,s=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const r=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,f=e.filterWidth,m=f;let g="int xR; int xC; int xCOffset;";for(let e=0;e<d;e++)for(let t=0;t<f;t++)g+=`\n          vec4 xTexelR${e}C${2*t} = vec4(0.);\n          vec4 wR${e}C${t} = vec4(0.);\n          vec4 xR${e}C${t} = vec4(0.);`;for(let e=0;e<d;e++)for(let t=0;t<m;t++){const n=2*t;if(g+=`\n          xR = xRCorner + ${e*h};\n          xC = xCCorner + ${n*p};\n        `,1===c){if(n<f&&(g+=l%2==1?`\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${r} && xCOffset >= 0 && xCOffset < ${i}) {\n                  xTexelR${e}C${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 >= ${i}) {\n                    xTexelR${e}C${n}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${e}C${n} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${r} && xCOffset >= 0 && xCOffset < ${i}) {\n                  vec4 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 >= ${i}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${e}C${n} = vec4(previous.zw, xTexelR${e}C${n}.xy);\n                } else {\n                  xR${e}C${n} = vec4(0, 0, xTexelR${e}C${n}.xy);\n                }\n              `:`\n                if(xR >= 0 && xR < ${r} && xC >= 0 && xC < ${i}) {\n                  xTexelR${e}C${n} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${e}C${n} = vec4(0.);\n                }\n\n                xR${e}C${n} = xTexelR${e}C${n};\n              `,n+1<f)){const t=l%2==0?T(p):p;p%2==0&&l%2==1||p%2!=0&&l%2!=1?(g+=`\n                  xCOffset = xC + ${l%2} + ${t};\n\n                  if(xR >= 0 && xR < ${r} &&\n                    xCOffset >= 0 && xCOffset < ${i}) {\n                    xTexelR${e}C${n+2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `,p>1&&(g+=`\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${r} &&\n                      xCOffset >= 0 && xCOffset < ${i}) {\n                      xTexelR${e}C${n} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${e}C${n} = vec4(0.);\n                    }\n                  `),g+=`\n                  xR${e}C${n+1} = vec4(\n                    xTexelR${e}C${n}.zw, xTexelR${e}C${n+2}.xy);\n                `):g+=`\n                  xCOffset = xC + ${t};\n\n                  if(xR >= 0 && xR < ${r} &&\n                    xCOffset >= 0 && xCOffset < ${i}) {\n                    xTexelR${e}C${n+2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${e}C${n+1} = xTexelR${e}C${n+2};\n                `}}else n<f&&(g+=`\n              if(xR >= 0 && xR < ${r}) {\n            `,l%2==1?(g+=`\n                xCOffset = xC + 1 - ${c};\n                if(xCOffset >= 0 && xCOffset < ${i}) {\n                  xTexelR${e}C${n} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${e}C${n} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${i}) {\n                  xTexelR${e}C${n+2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${e}C${n+2} = vec4(0.);\n                }\n\n                xR${e}C${n} = vec4(\n                  xTexelR${e}C${n}.zw, xTexelR${e}C${n+2}.zw);\n              `,n+1<f&&(g+=`\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${c};\n                  if(xCOffset >= 0 && xCOffset < ${i}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${e}C${n+1} = vec4(xTexelR${e}C${n+2}.xy, final.xy);\n                `)):(g+=`\n                if(xC >= 0 && xC < ${i}) {\n                  xTexelR${e}C${n} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${e}C${n} = vec4(0.);\n                }\n\n                xCOffset = xC + ${c};\n                if(xCOffset >= 0 && xCOffset < ${i}) {\n                  xTexelR${e}C${n+2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${e}C${n+2} = vec4(0.);\n                }\n\n                xR${e}C${n} = vec4(\n                  xTexelR${e}C${n}.xy, xTexelR${e}C${n+2}.xy);\n              `,n+1<f&&(g+=`\n                  xR${e}C${n+1} = vec4(\n                    xTexelR${e}C${n}.zw, xTexelR${e}C${n+2}.zw);\n                `)),g+="}");n<f&&(g+=`\n            vec4 wTexelR${e}C${n} = getW(${e}, ${n}, d1, q);\n            wR${e}C${n} = vec4(wTexelR${e}C${n}.xz, wTexelR${e}C${n}.xz);\n          `,n+1<f&&(g+=`\n              vec4 wTexelR${e}C${n+1} = getW(${e}, ${n+1}, d1, q);\n              wR${e}C${n+1} =\n                vec4(wTexelR${e}C${n+1}.xz, wTexelR${e}C${n+1}.xz);`))}for(let e=0;e<d;e++)for(let t=0;t<f;t++)g+=`dotProd += xR${e}C${t} * wR${e}C${t};`;let y="",b="";n&&(y=s?`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:a?`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`vec4 activation(vec4 x) {\n          ${n}\n        }`,b="result = activation(result);");const x=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${y}\n\n      const ivec2 strides = ivec2(${u}, ${c});\n      const ivec2 pads = ivec2(${o}, ${l});\n\n      void main() {\n\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;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${g}\n\n        vec4 result = dotProd;\n        ${x}\n        ${b}\n        setOutput(result);\n      }\n    `}}const jE={kernelName:Qe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,filter:r}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=s;let c=l;null==c&&(c=[1,1]),E(Yr(i,c),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`));const h=Vr(a.shape,r.shape,i,c,o,u,!0);let p;return p=ue().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?new HE(h):new GE(h),n.runWebGLProgram(p,[a,r],"float32")}};class KE{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,a=e.padInfo.left,r=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 * ${r} + 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} - ${a};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class qE{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,a=e.strideWidth,r=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${r}, ${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) / ${a}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const XE={kernelName:et,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,dy:r}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=s,h=Vr(a.shape,c,i,o,l,u,!0),p=new KE(h);return n.runWebGLProgram(p,[a,r],"float32")}},YE={kernelName:tt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:a,filter:r}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=s,h=Vr(c,r.shape,i,o,l,u,!0),p=new qE(h);return n.runWebGLProgram(p,[a,r],"float32")}};class JE{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode="\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    "}}const ZE={kernelName:nt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:s}=t,a=[...s.shape,...s.shape],r=D(s.shape),i=LS({inputs:{x:s},backend:n,attrs:{shape:[r]}}),o=new JE(r),l=n.runWebGLProgram(o,[i],i.dtype),u=LS({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}};class QE{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const{inHeight:t,inWidth:n,padInfo:s,strideHeight:a,strideWidth:r,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=s;this.userCode=`\n      const ivec2 strides = ivec2(${a}, ${r});\n      const ivec2 pads = ivec2(${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 eA={kernelName:st,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,filter:r}=t,{strides:i,pad:o,dilations:l}=s,u=Pr(a.shape,r.shape,i,o,"NHWC",l);let c;const h=new QE(u);c=n.runWebGLProgram(h,[a,r],"float32");const p=LS({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),p}},tA=ES({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"}),nA={kernelName:ot,backendName:"webgl",kernelFunc:tA},sA={kernelName:lt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n}=e,{dy:s,y:a}=t,r=ue().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bS("\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",s.shape,a.shape):new yS("return (b >= 1.0) ? a : a * (b + 1.0);",s.shape,a.shape);return n.runWebGLProgram(r,[s,a],s.dtype)}},aA=AS({opSnippet:"return float(a == b);",packedOpSnippet:"\n  return vec4(equal(a, b));\n",dtype:"bool"}),rA={kernelName:ct,backendName:"webgl",kernelFunc:aA},iA=ES({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 = ${xu};\n  float a1 = ${wu};\n  float a2 = ${ku};\n  float a3 = ${vu};\n  float a4 = ${Nu};\n  float a5 = ${Iu};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`}),oA={kernelName:ut,backendName:"webgl",kernelFunc:iA},lA="return exp(x);",uA=ES({opSnippet:lA,packedOpSnippet:lA,cpuKernelImpl:RC}),cA={kernelName:ht,backendName:"webgl",kernelFunc:uA};function hA(e){const{inputs:t,attrs:n,backend:s}=e,{dim:a}=n,{input:r}=t,i=r.shape.length,o=r.shape.slice();let l=a;return a<0&&(E(-(i+1)<=a,(()=>`Axis must be in the interval [${-(i+1)}, ${i}]`)),l=i+a+1),o.splice(l,0,1),LS({inputs:{x:r},backend:s,attrs:{shape:o}})}const pA={kernelName:pt,backendName:"webgl",kernelFunc:hA},dA="return exp(x) - 1.0;",fA=ES({opSnippet:dA,packedOpSnippet:dA,cpuKernelImpl:FC}),mA={kernelName:dt,backendName:"webgl",kernelFunc:fA};class gA{constructor(e,t,n){this.variableNames=["real","imag"];const s=t[1];this.outputShape=t;const a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,r=n?`${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 = ${a};\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) / ${r};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `}}function yA(e,t,n){const s=n.texData.get(e.dataId),a=D(e.shape),r=e.shape[e.shape.length-1],i=LS({inputs:{x:e},backend:n,attrs:{shape:[a/r,r]}}),o=i.shape,l=new gA("real",o,t),u=new gA("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"),d=kS({inputs:{real:h,imag:p},backend:n});n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p);const f=LS({inputs:{x:d},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(d),f}const bA={kernelName:ft,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:s}=t;return yA(s,!1,n)}};class xA{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode="\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    "}getCustomSetupFunc(e){return(t,n)=>{null==this.valueLoc&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}}function wA(e){const{backend:t,attrs:n}=e,{shape:s,value:a}=n;let{dtype:r}=n;if(r=r||X(a),"string"===r){const e=U(r,D(s));return e.fill(a),t.makeTensorInfo(s,r,e)}{const e=new xA(s,a),n=e.getCustomSetupFunc(a);return t.runWebGLProgram(e,[],r,n)}}const kA={kernelName:mt,backendName:"webgl",kernelFunc:wA};class vA{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;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}}const NA={kernelName:gt,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,s=t,a=new vA(n.shape);return s.runWebGLProgram(a,[n],n.dtype)}},IA="return floor(x);",$A=ES({opSnippet:IA,packedOpSnippet:IA,cpuKernelImpl:DC}),CA={kernelName:yt,backendName:"webgl",kernelFunc:$A},SA=AS({opSnippet:"\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n",packedOpSnippet:"\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n",dtype:"int32"}),TA={kernelName:bt,backendName:"webgl",kernelFunc:SA};class EA{constructor(e){this.variableNames=["A"];const t=X$(),[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 = ${t.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `}}class AA{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=X$(),[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        ${t.output} = result;\n      }\n    `}}const RA={kernelName:ss,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:a}=t;const{numChannels:r}=s,i="undefined"!=typeof HTMLVideoElement&&a instanceof HTMLVideoElement,o="undefined"!=typeof HTMLImageElement&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],c=[u,l],h=[u,l,r];(o||i)&&(null==FA&&(FA=document.createElement("canvas").getContext("2d")),FA.canvas.width=l,FA.canvas.height=u,FA.drawImage(a,0,0,l,u),a=FA.canvas);const p=n.makeTensorInfo(c,"int32");n.texData.get(p.dataId).usage=w$.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);const d=ue().getBool("WEBGL_PACK")?new AA(h):new EA(h),f=n.runWebGLProgram(d,[p],"int32");return n.disposeData(p.dataId),f}};let FA;const DA={kernelName:is,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,filter:r,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=s,m=Jr(c),g=Vr(a.shape,r.shape,l,h,u,p,!1,m);let y;const b=[];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(ue().getBool("WEBGL_CONV_IM2COL")&&1===a.shape[0])y=wE({x:a,filter:r,convInfo:g,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:f});else{const e=null!=i,t=null!=o,s="leakyrelu"===d,l=d?RS(d,!1):null,u=new gE(g,e,l,t,s),c=[a,r];if(i&&c.push(i),o&&c.push(o),s){const e=n.makeTensorInfo([],"float32",gs(f,"float32"));c.push(e),b.push(e)}y=n.runWebGLProgram(u,c,"float32")}else y=xE({x:a,filter:r,convInfo:g,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:f});const x=LS({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach((e=>n.disposeIntermediateTensorInfo(e))),x}},_A={kernelName:os,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,filter:r,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:p,leakyreluAlpha:d}=s,f=[];let m=c;null==m&&(m=[1,1]),E(Yr(l,m),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`));const g=Vr(a.shape,r.shape,l,m,u,h,!0),y=ue().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=p?RS(p,y):null,x=[a,r],w=null!=i,k=null!=o,v="leakyrelu"===p;if(w&&x.push(i),k&&x.push(o),v){const e=n.makeTensorInfo([],"float32",gs(d,"float32"));x.push(e),f.push(e)}let N;N=y?new HE(g,w,b,k,v):new GE(g,w,b,k,v);const I=n.runWebGLProgram(N,x,"float32");return f.forEach((e=>n.disposeIntermediateTensorInfo(e))),I}};class OA{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;const s=vC(t.length),a=vC(n.length),r=this.sliceDim>1?"strides[j]":"strides";this.userCode=`\n        ${s} strides = ${s}(${this.strides});\n         void main() {\n          ${a} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${r};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `}}const MA={kernelName:kt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:s,indices:a}=t,r=a.shape,i=r[r.length-1],[o,l,u,c]=du(s,a),h=LS({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),p=LS({inputs:{x:s},backend:n,attrs:{shape:[D(s.shape)/u,u]}}),d=new OA(i,c,[l,u]),f=n.runWebGLProgram(d,[p,h],p.dtype),m=LS({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}};class LA{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;const n=vC(this.rank),s=function(e,t){const n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let t=0;t<e.length;t++)2===t?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[t]}`);return s.join()}(e);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        setOutput(getA(${s}));\n      }\n    `}}const zA={kernelName:wt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,indices:r}=t,{axis:i,batchDims:o}=s,l=zu(a,r,B(i,a.shape)[0],o),u=D(r.shape),c=[],h=LS({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),p=LS({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,u/l.batchSize]}});c.push(h),c.push(p);const d=[l.batchSize,l.outerSize,u/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([a,r])||"string"===a.dtype){const e=n.bufferSync(p),t=n.bufferSync(h),s=_C(t,e,d);return c.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(l.outputShape,s.dtype,s.values)}const f=new LA(h.shape,d),m=n.runWebGLProgram(f,[h,p],h.dtype);c.push(m);const g=LS({inputs:{x:m},backend:n,attrs:{shape:l.outputShape}});return c.forEach((e=>n.disposeIntermediateTensorInfo(e))),g}},PA=AS({opSnippet:"return float(a > b);",packedOpSnippet:"\n  return vec4(greaterThan(a, b));\n",cpuKernelImpl:OC,dtype:"bool"}),BA={kernelName:vt,backendName:"webgl",kernelFunc:PA},WA=AS({opSnippet:"return float(a >= b);",packedOpSnippet:"\n  return vec4(greaterThanEqual(a, b));\n",dtype:"bool"}),VA={kernelName:Nt,backendName:"webgl",kernelFunc:WA},UA={kernelName:$t,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:s}=t;return yA(s,!0,n)}},GA=ES({opSnippet:"return float(!isnan(x) && !isinf(x));",dtype:"bool"}),HA={kernelName:St,backendName:"webgl",kernelFunc:GA},jA=ES({opSnippet:"return float(isinf(x));",dtype:"bool"}),KA={kernelName:Tt,backendName:"webgl",kernelFunc:jA},qA=ES({opSnippet:"return float(isnan(x));",dtype:"bool"}),XA={kernelName:Et,backendName:"webgl",kernelFunc:qA},YA=AS({opSnippet:"return float(a < b);",packedOpSnippet:"\n  return vec4(lessThan(a, b));\n",cpuKernelImpl:MC,dtype:"bool"}),JA={kernelName:Rt,backendName:"webgl",kernelFunc:YA},ZA=AS({opSnippet:"return float(a <= b);",packedOpSnippet:"\n  return vec4(lessThanEqual(a, b));\n",dtype:"bool"}),QA={kernelName:Ft,backendName:"webgl",kernelFunc:ZA},eR={kernelName:Dt,backendName:"webgl",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:s,stop:a,num:r}=n,i=LC(s,a,r);return t.makeTensorInfo([i.length],"float32",i)}},tR=ES({opSnippet:"if (x < 0.0) return NAN;\n  return log(x);",packedOpSnippet:"\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n",cpuKernelImpl:zC}),nR={kernelName:_t,backendName:"webgl",kernelFunc:tR},sR=ES({opSnippet:"return log(1.0 + x);"}),aR={kernelName:Ot,backendName:"webgl",kernelFunc:sR},rR=AS({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"}),iR={kernelName:Mt,backendName:"webgl",kernelFunc:rR},oR=ES({opSnippet:"return float(!(x >= 1.0));"}),lR={kernelName:Lt,backendName:"webgl",kernelFunc:oR},uR=AS({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"}),cR={kernelName:zt,backendName:"webgl",kernelFunc:uR};class hR{constructor(e,t,n,s,a){this.variableNames=["x"],this.outputShape=[];const r=t,i=e[3]-1;let o;this.outputShape=e;const l=`float(${n}) + float(${s}) * sum`;o=.5===a?`inversesqrt(${l})`:1===a?`1.0/(${l})`:`exp(log(${l}) * float(-${a}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${r}; j <= ${r}; 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 * ${o};\n        setOutput(val);\n      }\n    `}}class pR{constructor(e,t,n,s,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const r=t,i=e[3]-1;let o;this.outputShape=e;const l=`float(${n}) + float(${s}) * sum`;o=.5===a?`inversesqrt(${l})`:1===a?`1.0/(${l})`:`exp(log(${l}) * float(-${a}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${r};\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 = - ${r}; j <= ${r}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${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 dR={kernelName:Pt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{depthRadius:r,bias:i,alpha:o,beta:l}=s,u=ue().getBool("WEBGL_PACK_NORMALIZATION")?new pR(a.shape,r,i,o,l):new hR(a.shape,r,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)}};class fR{constructor(e,t,n,s,a){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=a,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${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(${a})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${a});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}}const mR={kernelName:Bt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{x:a,y:r,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=s,h=new fR(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,r,i],a.dtype)}};function gR(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{reductionIndices:r,keepDims:i}=s,o=a.shape.length,l=B(r,a.shape);let u=l;const c=Zi(u,o),h=null!=c,p=n.shouldExecuteOnCPU([a]);let d=a;if(h){if(p){const e=n.texData.get(d.dataId).values,t=new Array(o);for(let e=0;e<t.length;e++)t[e]=a.shape[c[e]];const s=QC(e,a.shape,a.dtype,c,t);d=n.makeTensorInfo(t,a.dtype),n.texData.get(d.dataId).values=s}else d=GS(a,c,n);u=eo(u.length,o)}Ji("max",u,o);const[f,m]=Xi(d.shape,u);let g,y=f;if(i&&(y=Yi(f,l)),p){const e=n.texData.get(d.dataId).values,t=PC(e,D(m),y,a.dtype);g=n.makeTensorInfo(y,a.dtype),n.texData.get(g.dataId).values=t}else g=function(e,t,n,s){const a=D(t),r=LS({inputs:{x:e},attrs:{shape:[D(e.shape)/a,a]},backend:s}),i=WS(r,e.dtype,"max",s),o=LS({inputs:{x:i},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(r),s.disposeIntermediateTensorInfo(i),o}(d,m,y,n);return h&&n.disposeIntermediateTensorInfo(d),g}const yR={kernelName:Wt,backendName:"webgl",kernelFunc:gR},bR=AS({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  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n",cpuKernelImpl:BC}),xR={kernelName:Vt,backendName:"webgl",kernelFunc:bR},wR={kernelName:Ut,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t;K$(a,"maxPool");const{filterSize:r,strides:i,pad:o,dimRoundingMode:l}=s;E(Yr(i,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));const u=Br(a.shape,r,i,1,o,l);if(1===u.filterWidth&&1===u.filterHeight&&_(u.inShape,u.outShape))return xS({inputs:{x:a},backend:n});const c=new TT(u,"max",!1);return n.runWebGLProgram(c,[a],a.dtype)}},kR={kernelName:Ht,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{filterSize:r,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=s,c=Wr(a.shape,r,i,[1,1,1],o,u,l),h=new ET(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}};class vR{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,a=e.effectiveFilterHeight,r=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=r-1-e.padInfo.left,l=a*r-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${a};\n          wR += ${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 < ${r}; 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 * ${r} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class NR{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,a=e.dilationDepth,r=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,d=o*l*u-1;this.userCode=`\n      const ivec3 pads = ivec3(${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 += ${a}) {\n          float dyD = float(dyDCorner + wD) / ${t}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${l};\n              wR += ${r}) {\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 * ${u} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const IR={kernelName:jt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:a,input:r}=t,i=r,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=s,h=Wr(i.shape,o,l,[1,1,1],u,c),p=new ET(h,"max",!0),d=n.runWebGLProgram(p,[i],i.dtype),f=new NR(h),m=n.runWebGLProgram(f,[a,d],i.dtype);return n.disposeIntermediateTensorInfo(d),m}},$R={kernelName:Gt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{dy:a,input:r,output:i}=t,o=r;K$([r,i],"maxPoolGrad");const{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=s,p=Br(o.shape,l,u,1,c,h),d=new TT(p,"max",!0),f=n.runWebGLProgram(d,[o],o.dtype),m=new vR(p),g=n.runWebGLProgram(m,[a,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}},CR={kernelName:Kt,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:a,strides:r,pad:i,includeBatchInIndex:o}=t,l=n;E(4===s.shape.length,(()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`));const u=[1,1];E(Yr(r,u),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${r} and dilations '${u}'`));const c=Br(s.shape,a,r,u,i),[h,p]=function(e,t,n,s){let a=new TT(n,"max",!1);const r=s.runWebGLProgram(a,[e],"float32");return a=new TT(n,"max",!0,!0,t),[r,s.runWebGLProgram(a,[e],"float32")]}(s,o,c,l);return[h,p]}},SR={kernelName:qt,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{keepDims:a,axis:r}=t,i=n,o=s.shape.length,l=B(r,s.shape);let u=l;const c=Zi(u,o),h=null!=c,p=i.shouldExecuteOnCPU([s]),d=[];let f=s;if(h){if(p){const e=i.texData.get(f.dataId).values,t=new Array(o);for(let e=0;e<t.length;e++)t[e]=s.shape[c[e]];const n=QC(e,s.shape,s.dtype,c,t);f=i.makeTensorInfo(t,s.dtype),i.texData.get(f.dataId).values=n}else f=GS(s,c,i);d.push(f),u=eo(u.length,o)}Ji("sum",u,o);const[m,g]=Xi(f.shape,u);let y=m;a&&(y=Yi(m,l));const b=function(e,t,n,s){const a=D(t),r=LS({inputs:{x:e},attrs:{shape:[D(e.shape)/a,a]},backend:s}),i=WS(r,"float32","mean",s),o=LS({inputs:{x:i},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(r),s.disposeIntermediateTensorInfo(i),o}(f,g,y,i);for(const e of d)i.disposeIntermediateTensorInfo(e);return b}},TR={kernelName:Xt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{axis:r,keepDims:i}=s,o=a.shape.length,l=B(r,a.shape);let u=l;const c=Zi(u,o);let h=a;null!=c&&(h=KS({inputs:{x:a},backend:n,attrs:{perm:c}}),u=eo(u.length,a.shape.length)),Ji("min",u,o);const[p,d]=Xi(h.shape,u),f=LS({inputs:{x:h},backend:n,attrs:{shape:[-1,D(d)]}}),m=WS(f,f.dtype,"min",n);let g;return g=LS(i?{inputs:{x:m},backend:n,attrs:{shape:Yi(p,l)}}:{inputs:{x:m},backend:n,attrs:{shape:p}}),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),null!=c&&n.disposeIntermediateTensorInfo(h),g}},ER=AS({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  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n",cpuKernelImpl:WC}),AR={kernelName:Yt,backendName:"webgl",kernelFunc:ER};class RR{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const s=e.length,a=vC(s),r=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      ${a} start = ${a}(${r});\n      ${a} end = ${a}(${i});\n\n      void main() {\n        ${a} 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        ${a} coords = outC - start;\n        setOutput(getX(${o}));\n      }\n    `:`\n        int start = ${r};\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 + ${l};\n          }\n          setOutput(getX(outC - start));\n        }\n      `}}class FR{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const s=e.length,a=vC(s),r=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=nS("rc",s),l=nS("source",s),u=`${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        ${a} 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        ${a} 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        ${a} source = rc;\n        ${a} lt = ${a}(lessThan(source, start));\n        ${a} gte = ${a}(greaterThanEqual(source, end));\n        ${a} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${h}) +\n                gte * ((end - 1) * 2 - source + ${h});\n        source -= start;\n      `;p=`\n        ${a} 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 ${a} start = ${a}(${r});\n      const ${a} end = ${a}(${i});\n\n      void main() {\n        ${a} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${p}\n        setOutput(result);\n      }\n    `}}const DR={kernelName:Jt,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s}=e,{paddings:a,mode:r}=n,i=ue().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new FR(s.shape,a,r):new RR(s.shape,a,r);return t.runWebGLProgram(i,[s],s.dtype)}},_R=AS({opSnippet:"if (b == 0.0) return NAN;\n  return mod(a, b);",packedOpSnippet:"\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n"}),OR={kernelName:Zt,backendName:"webgl",kernelFunc:_R};class MR{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`\n      uniform float seed;\n\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    `}getCustomSetupFunc(e){return(t,n)=>{null==this.seedLoc&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}}const LR=AS({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}),zR={kernelName:it,backendName:"webgl",kernelFunc:LR},PR="return a - b;",BR=AS({opSnippet:PR,packedOpSnippet:PR,supportsComplex:!0,cpuKernelImpl:YC}),WR={kernelName:Un,backendName:"webgl",kernelFunc:BR};function VR(e){const{inputs:t,backend:n,attrs:s}=e,{logits:a}=t,{dim:r}=s,i=B([r],a.shape),o=gR({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=Yi(o.shape,i),u=LS({inputs:{x:o},backend:n,attrs:{shape:l}}),c=BR({inputs:{a,b:u},backend:n}),h=uA({inputs:{x:c},backend:n}),p=HS({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),d=LS({inputs:{x:p},backend:n,attrs:{shape:l}}),f=LR({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 UR={kernelName:Bn,backendName:"webgl",kernelFunc:VR},GR={kernelName:Qt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{logits:a}=t,{numSamples:r,seed:i,normalized:o}=s,l=o?a:VR({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new MR(u,c,r),p=h.getCustomSetupFunc(i),d=n.runWebGLProgram(h,[l],"int32",p);return o||n.disposeIntermediateTensorInfo(l),d}},HR="return -x;",jR={kernelName:tn,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,a]=UC(e.values,s.shape,s.dtype);return n.makeTensorInfo(a,s.dtype,t)}let a;return a=ue().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new pS(s.shape,HR):new uS(s.shape,HR),n.runWebGLProgram(a,[s],s.dtype)}},KR=bl,qR={kernelName:sn,backendName:"webgl",kernelFunc:function(e){$u("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:s}=e,{boxes:a,scores:r}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=s,u=n.readSync(a.dataId),c=n.readSync(r.dataId),{selectedIndices:h}=KR(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}},XR=xl,YR={kernelName:an,backendName:"webgl",kernelFunc:function(e){$u("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:s}=e,{boxes:a,scores:r}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(a.dataId),h=n.readSync(r.dataId),{selectedIndices:p,validOutputs:d}=XR(c,h,i,o,l,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([d]))]}},JR=wl,ZR={kernelName:rn,backendName:"webgl",kernelFunc:function(e){$u("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:s}=e,{boxes:a,scores:r}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(a.dataId),h=n.readSync(r.dataId),p=i,d=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=JR(c,h,p,d,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}};class QR{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${s}), float(${n}),\n                      float(index == coords.y)));\n      }\n    `}}const eF={kernelName:ln,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{indices:a}=t,{depth:r,onValue:i,offValue:o}=s,l=D(a.shape),u=new QR(l,r,i,o),c=LS({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);const p=LS({inputs:{x:h},backend:n,attrs:{shape:[...a.shape,r]}});return n.disposeIntermediateTensorInfo(h),p}};function tF(e){const{inputs:t,backend:n}=e,{x:s}=t;if("complex64"===s.dtype){const e=XT({inputs:{input:s},backend:n}),t=tF({inputs:{x:e},backend:n}),a=cE({inputs:{input:s},backend:n}),r=tF({inputs:{x:a},backend:n}),i=kS({inputs:{real:t,imag:r},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(r),i}return wA({attrs:{shape:s.shape,dtype:s.dtype,value:"string"===s.dtype?"":0},backend:n})}const nF={kernelName:ts,backendName:"webgl",kernelFunc:tF},sF={kernelName:on,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:s}=t,{x:a}=n;if("string"===a.dtype)throw new Error("onesLike is not supported under string dtype");if("complex64"===a.dtype){const t=XT({inputs:{input:a},backend:s}),n=e({inputs:{x:t},backend:s}),r=cE({inputs:{input:a},backend:s}),i=tF({inputs:{x:r},backend:s}),o=kS({inputs:{real:n,imag:i},backend:s});return s.disposeIntermediateTensorInfo(t),s.disposeIntermediateTensorInfo(n),s.disposeIntermediateTensorInfo(r),s.disposeIntermediateTensorInfo(i),o}return wA({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:s})}},aF={kernelName:un,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{axis:a}=s;if(1===t.length)return hA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});const r=t[0].shape,i=t[0].dtype;t.forEach((e=>{A(r,e.shape,"All tensors passed to stack must have matching shapes"),E(i===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));const o=[],l=fE({inputs:t.map((e=>{const t=hA({inputs:{input:e},backend:n,attrs:{dim:a}});return o.push(t),t})),backend:n,attrs:{axis:a}});return o.forEach((e=>n.disposeIntermediateTensorInfo(e))),l}};class rF{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const s=e.length,a=vC(s),r=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      ${a} start = ${a}(${r});\n      ${a} end = ${a}(${i});\n      uniform float value;\n\n      void main() {\n        ${a} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          ${a} coords = outC - start;\n          setOutput(getX(${o}));\n        }\n      }\n    `:`\n        int start = ${r};\n        int end = ${i};\n        uniform float value;\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `}getCustomSetupFunc(e){return(t,n)=>{null==this.valueLoc&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}}class iF{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const s=e.length,a=vC(s),r=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=nS("rc",s),l=nS("source",s),u=`${o[s-1]} < ${this.outputShape[s-1]}`,c=1===s?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${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          ${a} source = rc - start;\n          result[${e}] = getChannel(getX(${l.join()}), ${c});\n        }\n      `;d+=1===s?"} ":"}}",this.userCode=`\n      const ${a} start = ${a}(${r});\n      const ${a} end = ${a}(${i});\n      uniform float value;\n\n      void main() {\n        ${a} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${d}\n        setOutput(result);\n      }\n    `}getCustomSetupFunc(e){return(t,n)=>{null==this.valueLoc&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}}const oF=e=>{const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{paddings:r,constantValue:i}=s,o=ue().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iF(a.shape,r,i):new rF(a.shape,r,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},lF={kernelName:cn,backendName:"webgl",kernelFunc:oF},uF=AS({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  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n"}),cF={kernelName:hn,backendName:"webgl",kernelFunc:uF},hF={kernelName:dn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{axis:r,keepDims:i}=s,o=a.shape.length,l=[],u=B(r,a.shape);let c=u;const h=Zi(c,o);let p,d=a;if(null!=h&&(d=KS({inputs:{x:a},backend:n,attrs:{perm:h}}),c=eo(c.length,o),l.push(d)),Ji("prod",c,o),n.shouldExecuteOnCPU([d])){const e=n.texData.get(d.dataId).values,{outVals:t,outShape:s,outDtype:a}=GC(d.shape,d.dtype,e,c);p=n.makeTensorInfo(s,a,t)}else{const[e,t]=Xi(d.shape,c),s=D(t),r=LS({inputs:{x:d},backend:n,attrs:{shape:[-1,s]}}),i=WS(r,Us(a.dtype),"prod",n);p=LS({inputs:{x:i},backend:n,attrs:{shape:e}}),l.push(r),l.push(i)}if(i){l.push(p);const e=Yi(p.shape,u);p=LS({inputs:{x:p},backend:n,attrs:{shape:e}})}return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),p}},pF=e=>{const{backend:t,attrs:n}=e,{start:s,stop:a,step:r,dtype:i}=n,o=HC(s,a,r,i);return t.makeTensorInfo([o.length],i,o)},dF={kernelName:fn,backendName:"webgl",kernelFunc:pF},fF=ES({opSnippet:"return 1.0 / x;"}),mF={kernelName:gn,backendName:"webgl",kernelFunc:fF},gF=ES({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"}),yF={kernelName:yn,backendName:"webgl",kernelFunc:gF},bF=ES({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"}),xF={kernelName:Nn,backendName:"webgl",kernelFunc:bF};class wF{constructor(e,t,n,s,a){this.variableNames=["A"],this.outputShape=[];const[r,i,o,l]=e;this.outputShape=[r,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=a?"(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 kF{constructor(e,t,n,s,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[r,i,o,l]=e;this.outputShape=[r,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=a?"(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 vF={kernelName:kn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{images:a}=t,{alignCorners:r,halfPixelCenters:i,size:o}=s,[l,u]=o,c=ue().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new kF(a.shape,l,u,r,i):new wF(a.shape,l,u,r,i);return n.runWebGLProgram(c,[a],"float32")}};class NF{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,s,a]=t,[,r,i]=e,o=[n&&r>1?s-1:s,n&&i>1?a-1:a],l=[n&&r>1?r-1:r,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 >= ${r}) {\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), ${a-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}}const IF={kernelName:vn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{images:a,dy:r}=t,{alignCorners:i}=s,o=new NF(r.shape,a.shape,i);return n.runWebGLProgram(o,[r],r.dtype)}};class $F{constructor(e,t,n,s,a){this.variableNames=["A"],this.outputShape=[];const[r,i,o,l]=e;this.outputShape=[r,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=a?"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    `}}const CF={kernelName:xn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{images:a}=t,{alignCorners:r,halfPixelCenters:i,size:o}=s,[l,u]=o,c=new $F(a.shape,l,u,r,i);return n.runWebGLProgram(c,[a],a.dtype)}};class SF{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,s,a]=t,[,r,i]=e,o=[n&&r>1?s-1:s,n&&i>1?a-1:a],l=[n&&r>1?r-1:r,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,p=1/c,d=2*Math.ceil(h)+2,f=2*Math.ceil(p)+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${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 >= ${r}) {\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                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${a}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}}const TF={kernelName:wn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{images:a,dy:r}=t,{alignCorners:i}=s,o=new SF(r.shape,a.shape,i);return n.runWebGLProgram(o,[r],r.dtype)}};class EF{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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(","),a=vC(n);this.userCode=`\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${s}));\n      }\n    `}}class AF{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-${n} tensor is not yet supported`);this.outputShape=e;const s=nS("rc",n),a=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,r=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=vC(n);function o(n){const s=e.map(((s,a)=>function(n,s){return-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - ${s[n]} - 1`:`${s[n]}`}(a,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            ${e[0]} - rc - 1);\n          if(${a}){\n              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),\n                ${e[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `:`\n        void main() {\n          ${i} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${function(e){return o(e)}(s.slice())};\n          if(${a}){\n            result.g = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",o(e)}(s.slice())};\n          }\n          if(${r}) {\n            result.b = ${function(e){return e[n-2]="("+e[n-2]+" + 1)",o(e)}(s.slice())};\n            if(${a}) {\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 RF={kernelName:In,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{dims:r}=s,i=a.shape.length,o=B(r,a.shape);if(0===i)return xS({inputs:{x:a},backend:n});const l=ue().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AF(a.shape,o):new EF(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}};class FF{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];const n=e[1],s=e[2];this.outputShape=e;let a="";a="number"==typeof t?`float outputValue = ${t.toFixed(2)};`:`\n        vec3 fill = vec3(${t.join(",")});\n        float outputValue = fill[coords[3]];`,this.userCode=`\n        uniform vec4 params;\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${a}\n          if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}getCustomSetupFunc(e,t,n,s){return(a,r)=>{null==this.paramsLoc&&(this.paramsLoc=a.getUniformLocationNoThrow(r,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,s)}}}const DF={kernelName:as,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:s}=e,{radians:a,fillValue:r,center:i}=t,o=n,l=new FF(s.shape,r),[u,c]=ou(i,s.shape[1],s.shape[2]),h=l.getCustomSetupFunc(u,c,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[s],s.dtype,h)}},_F=ES({opSnippet:"\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n"}),OF={kernelName:$n,backendName:"webgl",kernelFunc:_F},MF=ES({opSnippet:"return inversesqrt(x);",cpuKernelImpl:jC}),LF={kernelName:Cn,backendName:"webgl",kernelFunc:MF};class zF{constructor(e,t,n,s,a,r,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=r;const o=vC(a.length),l=vC(r.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 = ${o}(${a});\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 PF={kernelName:Sn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{indices:a,updates:r}=t,{shape:i}=s,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=gu(0,a,i),p=[h/u,u];if(0===h)return n.makeTensorInfo(i,a.dtype);const d=LS({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=LS({inputs:{x:r},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new zF(l,o,d.shape.length,f.shape.length,c,p),y=n.runWebGLProgram(g,[f,d,m],f.dtype),b=LS({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),b}};class BF{constructor(e,t,n){let s,a;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error(`Where for rank ${n} is not yet supported`);if(1===n)a="resRC",s="resRC";else{const n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],i=[];for(let s=0;s<t.length;s++)i.push(`${n[s]}`),s<e&&r.push(`${n[s]}`);s=r.join(),a=i.join()}const r=vC(n);this.userCode=`\n      void main() {\n        ${r} resRC = getOutputCoords();\n        float cVal = getC(${s});\n        if (cVal >= 1.0) {\n          setOutput(getA(${a}));\n        } else {\n          setOutput(getB(${a}));\n        }\n      }\n    `}}const WF={kernelName:Tn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{condition:s,t:a,e:r}=t,i=new BF(s.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[s,a,r],Vs(a.dtype,r.dtype))}},VF=ES({opSnippet:`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${yu};\n  float scale = ${bu};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`}),UF={kernelName:En,backendName:"webgl",kernelFunc:VF},GF=ES({opSnippet:"return 1.0 / (1.0 + exp(-1.0 * x));"}),HF={kernelName:_n,backendName:"webgl",kernelFunc:GF},jF=ES({opSnippet:"\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n"}),KF={kernelName:Dn,backendName:"webgl",kernelFunc:jF},qF=ES({opSnippet:"if (isnan(x)) return x;\n  return sin(x);\n"}),XF={kernelName:Rn,backendName:"webgl",kernelFunc:qF},YF=ES({opSnippet:"\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n"}),JF={kernelName:Fn,backendName:"webgl",kernelFunc:YF},ZF=ES({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"}),QF={kernelName:On,backendName:"webgl",kernelFunc:ZF},eD={kernelName:zn,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{blockShape:r,paddings:i}=s;E(a.shape.length<=4,(()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"));const o=r.reduce(((e,t)=>e*t)),l=[[0,0]];l.push(...i);for(let e=1+r.length;e<a.shape.length;++e)l.push([0,0]);const u=[],c=oF({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=lu(c.shape,r,o,!1),p=uu(h.length,r.length,!1),d=cu(c.shape,r,o,!1),f=LS({inputs:{x:c},backend:n,attrs:{shape:h}}),m=KS({inputs:{x:f},backend:n,attrs:{perm:p}}),g=LS({inputs:{x:m},backend:n,attrs:{shape:d}});return u.push(c),u.push(f),u.push(m),u.forEach((e=>n.disposeIntermediateTensorInfo(e))),g}},tD={kernelName:Gn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{sparseIndices:a,sparseValues:r,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=gu(0,a,o),p=new zF(u,l,a.shape.length,r.shape.length,c,[h,1],!1),d=n.runWebGLProgram(p,[r,a,i],r.dtype),f=LS({inputs:{x:d},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(d),f}},nD={kernelName:Pn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{numOrSizeSplits:r,axis:i}=s,o=B(i,a.shape)[0],l=Ou(a,r,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map((e=>{const t=[...h];t[o]=e;const s=UT({inputs:{x:a},backend:n,attrs:{begin:c,size:t}});return c[o]+=e,s}))}},sD=ES({opSnippet:"return sqrt(x);"}),aD={kernelName:Mn,backendName:"webgl",kernelFunc:sD},rD={kernelName:Vn,backendName:"webgl",kernelFunc:ES({opSnippet:"return x * x;"})},iD="return (a - b) * (a - b);",oD=AS({opSnippet:iD,packedOpSnippet:iD}),lD={kernelName:Wn,backendName:"webgl",kernelFunc:oD},uD={kernelName:ns,backendName:"webgl",kernelFunc:function({inputs:e,attrs:t,backend:n}){const{x:s}=e,a=`if (isnan(x)) return x;\n    return x > 0.0 ? 1.0 : float(${t.alpha});\n  `,r=new uS(s.shape,a);return n.runWebGLProgram(r,[s],s.dtype)}};class cD{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const s=n.length,a=vC(n.length),r=vC(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      ${a} begin = ${a}(${e});\n      ${a} strides = ${a}(${t});\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        setOutput(getX(${i}));\n      }\n    `}}const hD={kernelName:Hn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{begin:r,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:p}=s,{nonStrided:d,$begin:f,$strides:m,size:g,newShape:y,outShape:b}=wr(a.shape,r,i,o,l,u,c,h,p),x=LS({inputs:{x:a},backend:n,attrs:{shape:y}});let w;if(d){const e=UT({inputs:{x},backend:n,attrs:{begin:f,size:g}});w=LS({inputs:{x:e},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(e)}else if(b.some((e=>0===e)))w=n.makeTensorInfo(b,a.dtype,[]);else if(n.shouldExecuteOnCPU([x])){const e=n.texData.get(x.dataId).values,t=Ha(x.shape,x.dtype,e),s=XC(b,t,m,f);w=n.makeTensorInfo(b,x.dtype,s.values)}else{const e=new cD(f,m,b);w=n.runWebGLProgram(e,[x],x.dtype)}const k=LS({inputs:{x:w},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),k}},pD=ES({opSnippet:"return tan(x);"}),dD={kernelName:jn,backendName:"webgl",kernelFunc:pD},fD=ES({opSnippet:"\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n"}),mD={kernelName:Kn,backendName:"webgl",kernelFunc:fD};class gD{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=vC(this.rank),a=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        ${s} resRC = getOutputCoords();\n        setOutput(getA(${a}));\n      }\n    `}}function yD(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{reps:r}=s;if("string"===a.dtype){const e=n.readSync(a.dataId).map((e=>ws(e))),t=Ha(a.shape,a.dtype,e),s=JC(t,r);return n.makeTensorInfo(s.shape,s.dtype,s.values)}const i=new gD(a.shape,r);return n.runWebGLProgram(i,[a],a.dtype)}const bD={kernelName:qn,backendName:"webgl",kernelFunc:yD},xD={kernelName:Xn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a}=t,{k:r,sorted:i}=s,o=n.readSync(a.dataId),[l,u]=ZC(o,a.shape,a.dtype,r,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}};class wD{constructor(e,t,n,s,a,r){this.variableNames=["Image","Transforms"],this.outputShape=r;const i="nearest"===n?1:2;let o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1}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 < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${a});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${a});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${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 kD={kernelName:Yn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{image:a,transforms:r}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=s,[c,h,p,d]=a.shape,[f,m]=null!=u?u:[h,p],g=new wD(h,p,i,o,l,[c,f,m,d]);return n.runWebGLProgram(g,[a,r],"float32")}},vD={kernelName:Zn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,attrs:n,backend:s}=e,{axis:a}=n,{x:r}=t;K$(r,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const i=s.readSync(r.dataId),{outputValues:o,outputShape:l,indices:u}=eS(i,a,r.shape,r.dtype);return[s.makeTensorInfo(l,r.dtype,o),s.makeTensorInfo([u.length],"int32",u)]}},ND={kernelName:Qn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{value:a}=t;let{axis:r}=s;r<0&&(r+=a.shape.length);const i=a,o=i.shape.length,l=a.shape[r],u=new Array(o-1);let c=0;for(let e=0;e<o;e++)e!==r&&(u[c++]=i.shape[e]);const h=[],p=new Array(o).fill(0),d=i.shape.slice();d[r]=1;const f=new Array(l);for(let e=0;e<f.length;e++){p[r]=e;const t=UT({inputs:{x:i},backend:n,attrs:{begin:p,size:d}}),s=LS({inputs:{x:t},backend:n,attrs:{shape:u}});f[e]=s,h.push(t)}return h.forEach((e=>n.disposeIntermediateTensorInfo(e))),f}};class ID{constructor(e,t){this.variableNames=["x","segmentIds"];const n=e.windowSize,s=e.batchSize,a=e.inSize,r=e.numSegments,i=r*Math.ceil(a/n);this.outputShape=[s,i];const o=4*Math.floor(n/4),l=n%4,u="\n        sumValue += dot(values, segFilter);\n    ";let c="";a%n>0&&(c=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return initializationValue;\n        }\n      `);let h="";a%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        ${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          ${r})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${r})));\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 $D=[dR,mR,YS,ZS,eT,nT,rT,lT,uT,cT,gT,yT,xT,kT,$T,NT,ST,RT,AT,_T,OT,MT,PT,HT,jT,JT,eE,sE,iE,vS,mE,CE,SE,kE,EE,AE,TE,FE,_E,ME,BE,WE,UE,XE,YE,jE,ZE,eA,nA,sA,rA,oA,cA,pA,mA,bA,kA,NA,CA,TA,RA,DA,_A,MA,zA,BA,VA,wS,UA,hE,HA,KA,XA,$S,JA,QA,eR,aR,nR,iR,lR,cR,yR,kR,wR,IR,$R,CR,xR,SR,TR,AR,DR,OR,GR,MS,jR,qR,YR,ZR,qT,eF,sF,aF,lF,cF,TS,hF,dF,YT,zR,mF,xF,yF,zS,vF,IF,CF,TF,RF,DF,OF,LF,PF,WF,UF,HF,KF,XF,JF,GT,UR,QF,eD,tD,nD,aD,rD,lD,uD,hD,WR,jS,dD,mD,bD,xD,kD,qS,vD,ND,{kernelName:es,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:s}=e,{x:a,segmentIds:r}=t,{numSegments:i}=s,o=a.shape.length,l=[];let u=0;const c=Zi([u],o);let h=a;null!=c&&(h=KS({inputs:{x:a},backend:n,attrs:{perm:c}}),l.push(h),u=eo(1,o)[0]);const p=Lu(h.shape,u,i),d=D([h.shape[u]]),f=LS({inputs:{x:h},backend:n,attrs:{shape:[-1,d]}});l.push(f);const m=Us(a.dtype),g=(e,t,s,a,r)=>{const i=e.shape[0],o=e.shape[1],u=Mu(o,r),c=new ID({windowSize:u,inSize:o,batchSize:i,numSegments:r},t),h=n.compileAndRun(c,[e,s],a);if(l.push(h),h.shape[1]===r)return h;const p=pF({backend:n,attrs:{start:0,stop:r,step:1,dtype:"float32"}}),d=yD({inputs:{x:p},backend:n,attrs:{reps:[o/u]}});return l.push(p),l.push(d),g(h,t,d,a,r)},y=LS({inputs:{x:g(f,"unsortedSegmentSum",r,m,i)},backend:n,attrs:{shape:p}});let b=y;if(null!=c){l.push(y);const e=Qi(c);b=KS({inputs:{x:b},backend:n,attrs:{perm:e}})}return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),b}},nF];for(const e of $D)ds(e);const CD="0.1.5",SD={flipHorizontal:!1,outputStride:16,imageScaleFactor:1,maxNumBoxes:20,iouThreshold:.2,scoreThreshold:.6,modelType:"ssd320fpnlite",modelSize:"small",bboxLineWidth:"2",fontSize:17,basePath:"https://cdn.jsdelivr.net/npm/handtrackjs@latest/models/webmodel/",labelMap:{1:"open",2:"closed",3:"pinch",4:"point",5:"face",6:"pointtip",7:"pinchtip"},renderThresholds:null},TD={large:"base",medium:"fp16",small:"int8"},ED={open:"#374151",closed:"#B91C1C",pinch:"#F59E0B",point:"#10B981",face:"#3B82F6",pointtip:"#6366F1",pinchtip:"#EC4899"},AD={ssd320fpnlite:["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],ssd640fpnlite:["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],centernet512fpn:["Identity_4:0","Identity:0","Identity_2:0"]};async function RD(e){let t=Object.assign({},SD,e);const n=new _D(t);return await n.load(),n}function FD(e){return new Promise((function(t,n){e||t({status:!1,msg:"please provide a valid video element"}),e.width=e.width||640,e.height=e.width*(e.videoHeight/e.videoWidth),e.style.width="100%",navigator.mediaDevices.getUserMedia({audio:!1,video:{facingMode:"user"}}).then((n=>{window.localStream=n,e.srcObject=n,e.onloadedmetadata=()=>{e.height=e.width*(e.videoHeight/e.videoWidth),e.style.height=parseInt(e.style.width)*(e.videoHeight/e.videoWidth).toFixed(2)+"px",e.play(),t({status:!0,msg:"webcam successfully initiated."})}})).catch((function(e){t({status:!1,msg:e})}))}))}async function DD(){if(!window.localStream)return!1;window.localStream.getTracks().forEach((e=>(e.stop(),!0)))}class _D{constructor(e){this.modelPath=e.basePath+e.modelType+"/"+(TD[e.modelSize]||"base")+"/model.json ",this.modelParams=e}async load(){this.fps=0,this.model=await async function(e,t={}){if(null==e)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");null==t&&(t={}),t.fromTFHub&&null==e.load&&(e.endsWith("/")||(e+="/"),e=`${e}model.json?tfjs-format=file`);const n=new ix(e,t);return await n.load(),n}(this.modelPath);const e=io([1,300,300,3],"int32"),t=await this.model.executeAsync(e,AD[this.modelParams.modelType]);t.map((async e=>await e.data())),t.map((async e=>e.dispose())),Sr(e)}async detect(e){let t=Date.now();const[n,s]=function(e){return e instanceof Ds?[e.shape[0],e.shape[1]]:[e.height,e.width]}(e),a=OD(this.modelParams.imageScaleFactor,n,this.modelParams.outputStride),r=OD(this.modelParams.imageScaleFactor,s,this.modelParams.outputStride),i=Cr((()=>{const t=ar(e);return this.modelParams.flipHorizontal?t.reverse(1).resizeBilinear([a,r]).expandDims(0).toInt():t.resizeBilinear([a,r]).expandDims(0).toInt()})),o=this;return this.model.executeAsync(i,AD[this.modelParams.modelType]).then((function(e){i.dispose();let a=[];if("centernet512fpn"===o.modelParams.modelType){const t=e[0].dataSync(),r=e[1].arraySync(),i=e[2].dataSync();Sr(e),a=o.buildDetectObjectsCenternet(s,n,r[0],t,i),console.log(a)}else{const t=e[0].dataSync(),r=e[1].dataSync();Sr(e);const[i,l]=function(e,t,n){const s=[],a=[];for(let r=0;r<t;r++){let t=Number.MIN_VALUE,i=-1;for(let s=0;s<n;s++)e[r*n+s]>t&&(t=e[r*n+s],i=s);s[r]=t,a[r]=i}return[s,a]}(t,e[0].shape[1],e[0].shape[2]),u=Zs.backendName;Er("cpu");const c=Cr((()=>{const t=Oo(r,[e[1].shape[1],e[1].shape[3]]);return Gl.nonMaxSuppression(t,i,o.modelParams.maxNumBoxes,o.modelParams.iouThreshold,o.modelParams.scoreThreshold)})),h=c.dataSync();c.dispose(),Er(u),a=o.buildDetectedObjects(s,n,r,i,h,l)}let r=Date.now();return o.fps=Math.round(1e3/(r-t)),a}))}buildDetectedObjects(e,t,n,s,a,r){const i=a.length,o=[];for(let l=0;l<i;l++){const i=[];for(let e=0;e<4;e++)i[e]=n[4*a[l]+e];const u=i[0]*t,c=i[1]*e,h=i[2]*t,p=i[3]*e;i[0]=c,i[1]=u,i[2]=p-c,i[3]=h-u;const d=Math.round(r[a[l]])+1;o.push({bbox:i,class:d,label:this.modelParams.labelMap[d],score:s[a[l]].toFixed(2)})}return o}buildDetectObjectsCenternet(e,t,n,s,a){const r=[];for(let i=0;i<s.length;i++){const o=n[i],l=o[0]*t,u=o[1]*e,c=o[2]*t,h=o[3]*e;o[0]=u,o[1]=l,o[2]=h-u,o[3]=c-l;const p=Math.round(a[i])+1;r.push({bbox:o,class:p,label:this.modelParams.labelMap[p],score:s[i].toFixed(2)})}return r.slice(0,10)}getFPS(){return this.fps}setModelParameters(e){this.modelParams=Object.assign({},this.modelParams,e)}getModelParameters(){return this.modelParams}roundRect(e,t,n,s,a,r,i,o){if(void 0===o&&(o=!0),void 0===r&&(r=5),"number"==typeof r)r={tl:r,tr:r,br:r,bl:r};else{var l={tl:0,tr:0,br:0,bl:0};for(var u in l)r[u]=r[u]||l[u]}e.beginPath(),e.moveTo(t+r.tl,n),e.lineTo(t+s-r.tr,n),e.quadraticCurveTo(t+s,n,t+s,n+r.tr),e.lineTo(t+s,n+a-r.br),e.quadraticCurveTo(t+s,n+a,t+s-r.br,n+a),e.lineTo(t+r.bl,n+a),e.quadraticCurveTo(t,n+a,t,n+a-r.bl),e.lineTo(t,n+r.tl),e.quadraticCurveTo(t,n,t+r.tl,n),e.closePath(),i&&e.fill(),o&&e.stroke()}renderPredictions(e,t,n,s){n.clearRect(0,0,t.width,t.height),t.width=s.width,t.height=s.height,t.style.height=parseInt(t.style.width)*(s.height/s.width).toFixed(2)+"px",n.save(),this.modelParams.flipHorizontal&&(n.scale(-1,1),n.translate(-s.width,0)),n.drawImage(s,0,0,s.width,s.height),n.restore(),n.font="bold "+this.modelParams.fontSize+"px Arial";const a=this.modelParams.renderThresholds;for(let t=0;t<e.length;t++){const s=e[t];(!a||s.score>a[s.label])&&(n.beginPath(),n.fillStyle="rgba(255, 255, 255, 0.6)",n.fillRect(s.bbox[0]+1,s.bbox[1]+1,s.bbox[2]-1,1.5*this.modelParams.fontSize),n.lineWidth=this.modelParams.bboxLineWidth,this.roundRect(n,s.bbox[0],s.bbox[1],s.bbox[2],s.bbox[3],5,!1,!0),n.strokeStyle=ED[s.label],n.fillStyle=ED[s.label],n.stroke(),n.beginPath(),n.arc(s.bbox[0]+s.bbox[2]/2,s.bbox[1]+s.bbox[3]/2,2,0,2*Math.PI),n.fill(),n.stroke(),n.fillText(s.score+" | "+s.label,s.bbox[0]+5,s.bbox[1]+1.1*this.modelParams.fontSize))}n.fillStyle="rgba(255, 255, 255, 0.6)",this.roundRect(n,10,10,4.6*this.modelParams.fontSize,this.modelParams.fontSize+8,5,!0,!1),n.strokeStyle="#374151",n.fillStyle="#374151",n.font="bold "+this.modelParams.fontSize+"px Arial",n.fillText("FPS: "+this.fps,18,this.modelParams.fontSize+12)}dispose(){this.model&&this.model.dispose()}}function OD(e,t,n){const s=t*e-1;return s-s%n+1}})(),s})()}));