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`)}function wU(n,e,t,r){const s=Se(e),o=r[r.length-1],i=new Array(o).fill(0),a=e.length,l=t==="complex64"?wm(n):n;if(a>1)for(let c=0;c<s/o;c++){const d=c*o;for(let p=0;p<o;p++)i[p]=Math.max(i[p],vm(l[d+p],0,t).length)}return i}function vm(n,e,t){let r;return Array.isArray(n)?r=`${parseFloat(n[0].toFixed(Sb))} + ${parseFloat(n[1].toFixed(Sb))}j`:tg(n)?r=`'${n}'`:t==="bool"?r=ZE(n):r=parseFloat(n.toFixed(Sb)).toString(),Zd(r,e)}function ZE(n){return n===0?"false":"true"}function vy(n,e,t,r,s,o=!0){const i=t==="complex64"?2:1,a=e[0],l=e.length;if(l===0){if(t==="complex64"){const b=wm(n);return[vm(b[0],0,t)]}return t==="bool"?[ZE(n[0])]:[n[0].toString()]}if(l===1){if(a>RN){const w=pm*i;let k=Array.from(n.slice(0,w)),I=Array.from(n.slice((a-pm)*i,a*i));return t==="complex64"&&(k=wm(k),I=wm(I)),["["+k.map((N,T)=>vm(N,s[T],t)).join(", ")+", ..., "+I.map((N,T)=>vm(N,s[a-pm+T],t)).join(", ")+"]"]}return["["+(t==="complex64"?wm(n):Array.from(n)).map((w,k)=>vm(w,s[k],t)).join(", ")+"]"]}const c=e.slice(1),d=r.slice(1),p=r[0]*i,m=[];if(a>RN){for(let b=0;b<pm;b++){const w=b*p,k=w+p;m.push(...vy(n.slice(w,k),c,t,d,s,!1))}m.push("...");for(let b=a-pm;b<a;b++){const w=b*p,k=w+p;m.push(...vy(n.slice(w,k),c,t,d,s,b===a-1))}}else for(let b=0;b<a;b++){const w=b*p,k=w+p;m.push(...vy(n.slice(w,k),c,t,d,s,b===a-1))}const y=l===2?",":"";m[0]="["+(a>0?m[0]+y:"");for(let b=1;b<m.length-1;b++)m[b]=" "+m[b]+y;let v=`,
`;for(let b=2;b<l;b++)v+=`
`;return m[m.length-1]=" "+m[m.length-1]+"]"+(o?"":v),m}function wm(n){const e=[];for(let t=0;t<n.length;t+=2)e.push([n[t],n[t+1]]);return e}class Ar{constructor(e,t,r){if(this.dtype=t,this.shape=e.slice(),this.size=Se(e),r!=null){const s=r.length;U(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=r||Mn(t,this.size),this.strides=tt(e)}set(e,...t){t.length===0&&(t=[0]),U(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);const r=this.locToIndex(t);this.values[r]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(const s of e){if(s<0||s>=this.shape[t]){const o=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(o)}t++}let r=e[e.length-1];for(let s=0;s<e.length-1;++s)r+=this.strides[s]*e[s];return this.values[r]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let r=0;r<e.length-1;++r)t+=this.strides[r]*e[r];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let r=0;r<t.length-1;++r)t[r]=Math.floor(e/this.strides[r]),e-=t[r]*this.strides[r];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return yi().makeTensor(this.values,this.shape,this.dtype)}}let yi=null,Hd=null;function bU(n){yi=n}function CU(n){Hd=n}class tr{constructor(e,t,r,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Se(e),this.strides=tt(e),this.dataId=r,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 Hd.buffer(this.shape,this.dtype,e)}bufferSync(){return Hd.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return Ci(this.shape,e,this.dtype==="complex64")}arraySync(){return Ci(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();const e=yi().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(r=>au(r))}catch{throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataToGPU(e){return this.throwIfDisposed(),yi().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();const e=yi().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>au(t))}catch{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 yi().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(this.kerasMask&&this.kerasMask.dispose(),yi().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Hd.print(this,e)}clone(){return this.throwIfDisposed(),Hd.clone(this)}toString(e=!1){const t=this.dataSync();return vU(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Hd.cast(this,e)}variable(e=!0,t,r){return this.throwIfDisposed(),yi().makeVariable(this,e,t,r)}}Object.defineProperty(tr,Symbol.hasInstance,{value:n=>!!n&&n.data!=null&&n.dataSync!=null&&n.throwIfDisposed!=null});function Ce(){return xS("Tensor",()=>tr)}Ce();class By extends tr{constructor(e,t,r,s){super(e.shape,e.dtype,e.dataId,s),this.trainable=t,this.name=r}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(!Xt(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);yi().disposeTensor(this),this.dataId=e.dataId,yi().incRef(this,null)}dispose(){yi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(By,Symbol.hasInstance,{value:n=>n instanceof tr&&n.assign!=null&&n.assign instanceof Function});var AN;(function(n){n.R0="R0",n.R1="R1",n.R2="R2",n.R3="R3",n.R4="R4",n.R5="R5",n.R6="R6"})(AN||(AN={}));var fC;(function(n){n.float32="float32",n.int32="int32",n.bool="int32",n.complex64="complex64"})(fC||(fC={}));var pC;(function(n){n.float32="float32",n.int32="int32",n.bool="bool",n.complex64="complex64"})(pC||(pC={}));var mC;(function(n){n.float32="float32",n.int32="float32",n.bool="float32",n.complex64="complex64"})(mC||(mC={}));var gC;(function(n){n.float32="complex64",n.int32="complex64",n.bool="complex64",n.complex64="complex64"})(gC||(gC={}));const SU={float32:mC,int32:fC,bool:pC,complex64:gC};function Ys(n,e){if(n==="string"||e==="string"){if(n==="string"&&e==="string")return"string";throw new Error(`Can not upcast ${n} with ${e}`)}return SU[n][e]}function ok(n){return Ys(n,"int32")}function eR(n){return n!=null&&typeof n=="object"&&"texture"in n&&n.texture instanceof WebGLTexture}function tR(n){return typeof GPUBuffer<"u"&&n!=null&&typeof n=="object"&&"buffer"in n&&n.buffer instanceof GPUBuffer}function Un(n,e){if(n.dtype===e.dtype)return[n,e];const t=Ys(n.dtype,e.dtype);return[n.cast(t),e.cast(t)]}function nR(n){const e=[];return rR(n,e,new Set),e}function rR(n,e,t){if(n==null)return;if(n instanceof tr){e.push(n);return}if(!kU(n))return;const r=n;for(const s in r){const o=r[s];t.has(o)||(t.add(o),rR(o,e,t))}}function kU(n){return Array.isArray(n)||typeof n=="object"}function kb(n){return n.kernelName!=null}class _N{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class af{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new _N}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const r=e[t];if(await this.initializeBackend(r).success){await this.setBackend(r);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,r=1){return e in this.registryFactory?(go(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:r},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:r}=this.initializeBackend(e);if(!(r?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new pU(this.backendInstance),!0}setupRegisteredKernels(){NN(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){NN(e).forEach(r=>{r.disposeFunc!=null&&r.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const r=t.factory();if(r&&!(r instanceof hS)&&typeof r.then=="function"){const s=++this.pendingBackendInitId,o=r.then(i=>s<this.pendingBackendInitId?!1:(this.registry[e]=i,this.pendingBackendInit=null,!0)).catch(i=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,go(`Initialization of backend ${e} failed`),go(i.stack||i.message)),!1));return this.pendingBackendInit=o,{success:o,asyncInit:!0}}else return this.registry[e]=r,{success:!0,asyncInit:!1}}catch(r){return go(`Initialization of backend ${e} failed`),go(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&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(Object.keys(this.registryFactory).length===0)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 r=e[t],{success:s,asyncInit:o}=this.initializeBackend(r);if(o||s)return{name:r,asyncInit:o}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const r=this.state.tensorInfo.get(t),s=r.backend,o=this.readSync(t),i=s.refCount(t);s.disposeData(t,!0),r.backend=e,e.move(t,o,r.shape,r.dtype,i),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let r=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}let s;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,r){e();try{const s=r();return t(),s}catch(s){throw t(),s}}nextTensorId(){return af.nextTensorId++}nextVariableId(){return af.nextVariableId++}clone(e){const t=ae.runKernel(Cg,{x:e}),r={x:e},s=i=>({x:()=>{const a="float32",l={x:i},c={dtype:a};return ae.runKernel(ug,l,c)}}),o=[];return this.addTapeNode(this.state.activeScope.name,r,[t],s,o,{}),t}runKernel(e,t,r){if(this.backendName==null&&this.backend,!(hC(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:r})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,r){const s=this.backend.numDataIds();let o=0;r.forEach(l=>{o+=l.dtype==="complex64"?3:1});const i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-o-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[];const s=this.isTapeOn(),o=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let l;const c=kb(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(kb(e)){const{kernelName:v,inputs:b,attrs:w}=e;this.backendName==null&&this.backend;const k=hC(v,this.backendName);U(k!=null,()=>`Cannot find registered kernel '${v}' for backend '${this.backendName}'`),a=()=>{const I=this.backend.numDataIds();l=k.kernelFunc({inputs:b,attrs:w,backend:this.backend});const N=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(v,I,N);const T=N.map(A=>A.rank!=null?A:this.makeTensorFromTensorInfo(A));if(s){const A=this.getTensorsForGradient(v,b,T);r=this.saveTensorsForBackwardMode(A)}return T}}else{const{forwardFunc:v}=e,b=w=>{s&&(r=w.map(k=>this.keep(this.clone(k))))};a=()=>{const w=this.backend.numDataIds();l=this.tidy(()=>v(this.backend,b));const k=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,w,k),k}}const{inputs:d,attrs:p}=e,m=kb(e)?null:e.backwardsFunc;let y;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=a():(y=this.profiler.profileKernel(c,d,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(y),t=y.outputs)}),s&&this.addTapeNode(c,d,t,m,r,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:c,bytesAdded:this.state.numBytes-o,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(d).map(v=>d[v]!=null?d[v].shape:null),outputShapes:t.map(v=>v.shape),kernelTimeMs:y.timeMs,extraInfo:y.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(r=>this.keep(this.clone(r)))}getTensorsForGradient(e,t,r){const s=$N(e);if(s!=null){const o=s.inputsToSave||[],i=s.outputsToSave||[];let a;s.saveAllInputs?(U(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(c=>t[c])):a=o.map(c=>t[c]);const l=r.filter((c,d)=>i[d]);return a.concat(l)}return[]}makeTensor(e,t,r,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",s=s||this.backend;let o=e;r==="string"&&tg(e[0])&&(o=e.map(l=>su(l)));const i=s.write(o,t,r),a=new tr(t,r,i,this.nextTensorId());if(this.trackTensor(a,s),r==="string"){const l=this.state.tensorInfo.get(i),c=H4(o);this.state.numBytes+=c-l.bytes,l.bytes=c}return a}makeTensorFromDataId(e,t,r,s){r=r||"float32";const o={dataId:e,shape:t,dtype:r};return this.makeTensorFromTensorInfo(o,s)}makeTensorFromTensorInfo(e,t){const{dataId:r,shape:s,dtype:o}=e,i=new tr(s,o,r,this.nextTensorId());return this.trackTensor(i,t),i}makeVariable(e,t=!0,r,s){r=r||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const o=new By(e,t,r,this.nextTensorId());if(this.state.registeredVariables[o.name]!=null)throw new Error(`Variable with name ${o.name} was already registered`);return this.state.registeredVariables[o.name]=o,this.incRef(o,this.backend),o}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*Py(e.dtype)),this.state.numBytes+=r,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:r})),e instanceof By||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--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){const r=e.size*Py(e.dtype);this.state.numBytes-=r}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),this.state.registeredVariables[e.name]!=null&&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,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,r=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(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-r;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,r,s,o,i){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:r,saved:o},l=$N(e);l!=null&&(s=l.gradFunc),s!=null&&(a.gradient=c=>(c=c.map((d,p)=>{if(d==null){const m=r[p],y=Gr(m.size,m.dtype);return this.makeTensor(y,m.shape,m.dtype)}return d}),s(c.length>1?c:c[0],o,i))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const 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i=K(n,"x","batchNorm"),a=K(e,"mean","batchNorm"),l=K(t,"variance","batchNorm");let c;s!=null&&(c=K(s,"scale","batchNorm"));let d;return r!=null&&(d=K(r,"offset","batchNorm")),U(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),U(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${a.rank}.`),U(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&U(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),d!=null&&U(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),bv(i,a,l,d,c,o)}const tV=he({batchNorm3d_:eV});function nV(n,e,t,r,s,o){const i=K(n,"x","batchNorm"),a=K(e,"mean","batchNorm"),l=K(t,"variance","batchNorm");let c;s!=null&&(c=K(s,"scale","batchNorm"));let d;return r!=null&&(d=K(r,"offset","batchNorm")),U(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),U(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${a.rank}.`),U(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&U(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),d!=null&&U(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),bv(i,a,l,d,c,o)}const rV=he({batchNorm4d_:nV});function sV(n,e,t){const r=K(n,"x","bincount"),s=K(e,"weights","bincount");U(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),U(t>=0,()=>`size must be non-negative, but got ${t}.`),U(s.size===r.size||s.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${s.shape}.`);const o={x:r,weights:s},i={size:t};return ae.runKernel(SS,o,i)}const oV=he({bincount_:sV});function iV(n,e){let t=K(n,"broadcastTo","x");const r=t.shape;if(al(e),e.length<t.rank)throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t.rank}.`);if(e.length>t.rank){const c=t.shape.slice();for(;c.length<e.length;)c.unshift(1);t=ce(t,c)}const s=t.shape,o=Array.from(e);for(let c=e.length-1;c>=0;c--)if(s[c]===e[c])o[c]=1;else if(t.shape[c]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${e}].`);if(o.map((c,d)=>c>1?d:-1).filter(c=>c>=0).length===0)return Lc(t);const a={x:t},l={reps:o};return ae.runKernel(Xg,a,l)}const Nm=he({broadcastTo_:iV});function aV(n){const t={x:K(n,"x","ceil","float32")};return ae.runKernel(cg,t)}const lV=he({ceil_:aV});function Cv(n,e,t){al(n),t=t||Cf(e);const r={shape:n,value:e,dtype:t};return ae.runKernel(MS,{},r)}function uV(n,e,t){const r=K(n,"x","clipByValue");if(U(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`),e===t)return 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s=K(n,"x","dropout");if(U(s.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${s.dtype} tensor instead.`),U(e>=0&&e<1,()=>`rate must be a float in the range [0, 1), but got ${e}.`),e===0)return n instanceof tr?s.clone():s;const o=CH(s,t),i=1-e,a=st(Iv(Pe(tx(o,0,1,"float32",r),i)),i);return ne(s,a)}const kH=he({dropout_:SH});function IH(n,e,t,r,s,o="NHWC",i){let a=n;n.rank===3&&(a=ce(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let l=e;l.rank===3&&(l=ce(e,[1,e.shape[0],e.shape[1],e.shape[2]])),U(a.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${a.shape}.`),U(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),U(t.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${t}.`);const c=o==="NHWC"?a.shape[3]:a.shape[1],d=o==="NHWC"?l.shape[3]:l.shape[1];U(c===t[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter 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$H({x:n,filter:e,strides:t,pad:r,dataFormat:s="NHWC",dilations:o=[1,1],dimRoundingMode:i,bias:a,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:d}){if(l=l||"linear",_k(ae.state.gradientDepth,l)===!1){U(s==="NHWC",()=>`Error in fused conv2d: got dataFormat of ${s} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);let O=Qc(n,e,t,r,s,o,i);return a!=null&&(O=Pe(O,a)),Ak(O,l,c,d)}const p=K(n,"x","conv2d","float32"),m=K(e,"filter","conv2d","float32");let y=p,v=!1;p.rank===3&&(v=!0,y=ce(p,[1,p.shape[0],p.shape[1],p.shape[2]])),U(y.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${y.rank}.`),U(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),Ts("fused conv2d",r,i);const b=s==="NHWC"?y.shape[3]:y.shape[1];U(m.shape[2]===b,()=>`Error in conv2d: depth of input (${b}) must match input depth for filter ${m.shape[2]}.`),U(Hr(t,o),()=>`Error in conv2D: Either strides 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Got strides ${t} and dilations '${o}'`);const w=_r(y.shape,m.shape,t,o,r,i);let k;a!=null&&(k=K(a,"bias","fused conv2d"),[k]=Un(k,p),s==="NHWC"?gt(w.outShape,k.shape):(U(k.shape.length<=1,()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${k.shape.length}.`),U(k.shape.length===0||k.shape[0]===w.outChannels||k.shape[0]===1,()=>`Error in fused conv2d: bias shape (${k.shape}) is not compatible with the number of output channels (${w.outChannels})`)));let I;if(c!=null){const O=c.shape;if(U(O.length<=1||O.length===3,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${O.length}.`),O.length===1)U(O[0]===1||O[0]===w.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${O}) is not compatible with the number of output channels (${w.outChannels}).`);else if(O.length===3)try{gt(O,w.outShape)}catch{const B=`Error in fused conv2d: PReLU activation weights (${O}) is not compatible with the output shape of the conv2d (${w.outShape}).`;throw Error(B)}I=K(c,"prelu weights","fused conv2d")}const N=(O,P)=>{U(s==="NHWC",()=>`Error in gradient of fused conv2D: got dataFormat of ${s} but only NHWC is currently supported.`);const[B,z,L,_]=P,H=Ek(O,L,l);U(Xc(o),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. 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a=e,l=!1;e.rank===3&&(l=!0,a=ce(e,[1,e.shape[0],e.shape[1],e.shape[2]]));const c={dy:a,filter:t},d={strides:r,pad:s,dimRoundingMode:i,dilations:o,inputShape:n},p=ae.runKernel(OS,c,d);return l?ce(p,[p.shape[1],p.shape[2],p.shape[3]]):p}const AH=he({depthwiseConv2dNativeBackpropInput_:RH});function _H({a:n,b:e,transposeA:t=!1,transposeB:r=!1,bias:s,activation:o="linear",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(_k(ae.state.gradientDepth,o)===!1){let _=qt(n,e,t,r);return s!=null&&(_=Pe(_,s)),Ak(_,o,i,a)}let l=K(n,"a","fused matMul"),c=K(e,"b","fused matMul");[l,c]=Un(l,c);const d=t?l.shape[l.rank-2]:l.shape[l.rank-1],p=r?c.shape[c.rank-1]:c.shape[c.rank-2],m=t?l.shape[l.rank-1]:l.shape[l.rank-2],y=r?c.shape[c.rank-2]:c.shape[c.rank-1],v=l.shape.slice(0,-2),b=c.shape.slice(0,-2),w=Se(v),k=Se(b);U(d===p,()=>`Error in fused matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${t} and transposeB=${r} must match.`);const N=gt(l.shape.slice(0,-2),c.shape.slice(0,-2)).concat([m,y]),T=t?ce(l,[w,d,m]):ce(l,[w,m,d]),A=r?ce(c,[k,y,p]):ce(c,[k,p,y]);let O;s!=null&&(O=K(s,"bias","fused matMul"),[O]=Un(O,l),gt(N,O.shape));let P;i!=null&&(P=K(i,"prelu weights","fused matMul"));const B=(_,H)=>{const[Y,re,te,le]=H,de=Ek(ce(_,te.shape),te,o);let j,ie;if(!t&&!r?(j=qt(de,re,!1,!0),ie=qt(Y,de,!0,!1)):!t&&r?(j=qt(de,re,!1,!1),ie=qt(de,Y,!0,!1)):t&&!r?(j=qt(re,de,!1,!0),ie=qt(Y,de,!1,!1)):(j=qt(re,de,!0,!0),ie=qt(de,Y,!0,!0)),s!=null){const se=Rk(le,de);return[j,ie,se]}else return[j,ie]},z={a:T,b:A,bias:O,preluActivationWeights:P},L={transposeA:t,transposeB:r,activation:o,leakyreluAlpha:a};return s==null?cf((H,Y,re)=>{const te=ae.runKernel(Ly,z,L);return re([H,Y,te]),{value:ce(te,N),gradFunc:B}})(T,A):cf((H,Y,re,te)=>{const le=ae.runKernel(Ly,z,L);return te([H,Y,le,re]),{value:ce(le,N),gradFunc:B}})(T,A,O)}const KN=he({fusedMatMul_:_H});function DH(n,e,t,r,s="bilinear",o=0){const i=K(n,"image","cropAndResize"),a=K(e,"boxes","cropAndResize","float32"),l=K(t,"boxInd","cropAndResize","int32"),c=a.shape[0];U(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),U(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${a.shape}.`),U(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${a.shape}.`),U(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),U(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),U(s==="bilinear"||s==="nearest",()=>`method must be bilinear or nearest, but was ${s}`);const d={image:i,boxes:a,boxInd:l},p={method:s,extrapolationValue:o,cropSize:r};return ae.runKernel(RS,d,p)}const OH=he({cropAndResize_:DH});function FH(n){const e=K(n,"image","flipLeftRight","float32");U(e.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank 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6:a=mm("ijklmn,n->ijklm",o,i);break;default:throw new Error("Not a valid tensor rank.")}return a=Ss(a,-1),Ge(a,s)}const BH=he({rgbToGrayscale_:zH});function UH(n,e,t=0,r=.5){const s=K(n,"image","rotateWithOffset","float32");U(s.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${s.rank}.`);const o={image:s},i={radians:e,fillValue:t,center:r};return ae.runKernel(sk,o,i)}const WH=he({rotateWithOffset_:UH});function Nf(n,e,t,r,s,o){r==null&&(r=.5),s==null&&(s=Number.NEGATIVE_INFINITY),o==null&&(o=0);const i=n.shape[0];return t=Math.min(t,i),U(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),U(n.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${n.rank}'`),U(n.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${n.shape[1]}`),U(e.rank===1,()=>"scores must be a 1D tensor"),U(e.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${e.shape[0]}`),U(0<=o&&o<=1,()=>`softNmsSigma must be in [0, 1], but was '${o}'`),{maxOutputSize:t,iouThreshold:r,scoreThreshold:s,softNmsSigma:o}}function VH(n,e,t,r=.5,s=Number.NEGATIVE_INFINITY){const o=K(n,"boxes","nonMaxSuppression","float32"),i=K(e,"scores","nonMaxSuppression","float32"),a=Nf(o,i,t,r,s);t=a.maxOutputSize,r=a.iouThreshold,s=a.scoreThreshold;const l={maxOutputSize:t,iouThreshold:r,scoreThreshold:s};return ae.runKernel(jS,{boxes:o,scores:i},l)}const GH=he({nonMaxSuppression_:VH});function jH(n,e,t){const r=HH(n,e,t),s=r<0?-(r+1):r;n.splice(s,0,e)}function HH(n,e,t){return KH(n,e,t||qH)}function qH(n,e){return n>e?1:n<e?-1:0}function KH(n,e,t){let r=0,s=n.length,o=0,i=!1;for(;r<s;){o=r+(s-r>>>1);const a=t(e,n[o]);a>0?r=o+1:(s=o,i=!a)}return i?r:-r-1}function Dk(n,e,t,r,s){return Pk(n,e,t,r,s,0)}function Ok(n,e,t,r,s,o){return Pk(n,e,t,r,s,0,!1,o,!0)}function Fk(n,e,t,r,s,o){return Pk(n,e,t,r,s,o,!0)}function Pk(n,e,t,r,s,o,i=!1,a=!1,l=!1){const c=[];for(let w=0;w<e.length;w++)e[w]>s&&c.push({score:e[w],boxIndex:w,suppressBeginIndex:0});c.sort(XN);const d=o>0?-.5/o:0,p=[],m=[];for(;p.length<t&&c.length>0;){const w=c.pop(),{score:k,boxIndex:I,suppressBeginIndex:N}=w;if(k<s)break;let T=!1;for(let A=p.length-1;A>=N;--A){const O=XH(n,I,p[A]);if(O>=r){T=!0;break}if(w.score=w.score*YH(r,d,O),w.score<=s)break}w.suppressBeginIndex=p.length,T||(w.score===k?(p.push(I),m.push(w.score)):w.score>s&&jH(c,w,XN))}const y=p.length,v=t-y;a&&v>0&&(p.push(...new Array(v).fill(0)),m.push(...new Array(v).fill(0)));const b={selectedIndices:p};return i&&(b.selectedScores=m),l&&(b.validOutputs=y),b}function XH(n,e,t){const r=n.subarray(e*4,e*4+4),s=n.subarray(t*4,t*4+4),o=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),a=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),c=Math.min(s[0],s[2]),d=Math.min(s[1],s[3]),p=Math.max(s[0],s[2]),m=Math.max(s[1],s[3]),y=(a-o)*(l-i),v=(p-c)*(m-d);if(y<=0||v<=0)return 0;const b=Math.max(o,c),w=Math.max(i,d),k=Math.min(a,p),I=Math.min(l,m),N=Math.max(k-b,0)*Math.max(I-w,0);return N/(y+v-N)}function YH(n,e,t){const r=Math.exp(e*t*t);return t<=n?r:0}function XN(n,e){return n.score-e.score||n.score===e.score&&e.boxIndex-n.boxIndex}async function QH(n,e,t,r=.5,s=Number.NEGATIVE_INFINITY){const o=K(n,"boxes","nonMaxSuppressionAsync"),i=K(e,"scores","nonMaxSuppressionAsync"),a=Nf(o,i,t,r,s);t=a.maxOutputSize,r=a.iouThreshold,s=a.scoreThreshold;const l=await Promise.all([o.data(),i.data()]),c=l[0],d=l[1],{selectedIndices:p}=Dk(c,d,t,r,s);return o!==n&&o.dispose(),i!==e&&i.dispose(),qs(p,"int32")}const JH=QH;function ZH(n,e,t,r=.5,s=Number.NEGATIVE_INFINITY,o=0){const i=K(n,"boxes","nonMaxSuppression"),a=K(e,"scores","nonMaxSuppression"),l=Nf(i,a,t,r,s,o);t=l.maxOutputSize,r=l.iouThreshold,s=l.scoreThreshold,o=l.softNmsSigma;const c={boxes:i,scores:a},d={maxOutputSize:t,iouThreshold:r,scoreThreshold:s,softNmsSigma:o},p=ae.runKernel(qS,c,d);return{selectedIndices:p[0],selectedScores:p[1]}}const e5=he({nonMaxSuppressionWithScore_:ZH});async function t5(n,e,t,r=.5,s=Number.NEGATIVE_INFINITY,o=0){const i=K(n,"boxes","nonMaxSuppressionAsync"),a=K(e,"scores","nonMaxSuppressionAsync"),l=Nf(i,a,t,r,s,o);t=l.maxOutputSize,r=l.iouThreshold,s=l.scoreThreshold,o=l.softNmsSigma;const c=await Promise.all([i.data(),a.data()]),d=c[0],p=c[1],{selectedIndices:m,selectedScores:y}=Fk(d,p,t,r,s,o);return i!==n&&i.dispose(),a!==e&&a.dispose(),{selectedIndices:qs(m,"int32"),selectedScores:qs(y)}}const n5=t5;function r5(n,e,t,r=.5,s=Number.NEGATIVE_INFINITY,o=!1){const i=K(n,"boxes","nonMaxSuppression"),a=K(e,"scores","nonMaxSuppression"),l=Nf(i,a,t,r,s,null),c=l.maxOutputSize,d=l.iouThreshold,p=l.scoreThreshold,m={boxes:i,scores:a},y={maxOutputSize:c,iouThreshold:d,scoreThreshold:p,padToMaxOutputSize:o},v=ae.runKernel(HS,m,y);return{selectedIndices:v[0],validOutputs:v[1]}}const s5=he({nonMaxSuppressionPadded_:r5});async function o5(n,e,t,r=.5,s=Number.NEGATIVE_INFINITY,o=!1){const i=K(n,"boxes","nonMaxSuppressionAsync"),a=K(e,"scores","nonMaxSuppressionAsync"),l=Nf(i,a,t,r,s,null),c=l.maxOutputSize,d=l.iouThreshold,p=l.scoreThreshold,[m,y]=await Promise.all([i.data(),a.data()]),{selectedIndices:v,validOutputs:b}=Ok(m,y,c,d,p,o);return i!==n&&i.dispose(),a!==e&&a.dispose(),{selectedIndices:qs(v,"int32"),validOutputs:Kt(b,"int32")}}const i5=o5;function a5(n,e,t=!1,r=!1){const s=K(n,"images","resizeBilinear");U(s.rank===3||s.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${s.rank}.`),U(e.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${e}.`),U(r===!1||t===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let o=s,i=!1;s.rank===3&&(i=!0,o=ce(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const a={images:o},l={alignCorners:t,halfPixelCenters:r,size:e},c=ae.runKernel(lv,a,l);return i?ce(c,[c.shape[1],c.shape[2],c.shape[3]]):c}const HR=he({resizeBilinear_:a5});function l5(n,e,t=!1,r=!1){const s=K(n,"images","resizeNearestNeighbor");U(s.rank===3||s.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${s.rank}.`),U(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),U(s.dtype==="float32"||s.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),U(r===!1||t===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let o=s,i=!1;s.rank===3&&(i=!0,o=ce(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const a={images:o},l={alignCorners:t,halfPixelCenters:r,size:e},c=ae.runKernel(av,a,l);return i?ce(c,[c.shape[1],c.shape[2],c.shape[3]]):c}const qR=he({resizeNearestNeighbor_:l5});function u5(n,e="binary",t=!1,r=.5){const s=K(n,"image","threshold"),o=.2989,i=.587,a=.114,l=s.shape[0]*s.shape[1];let c=ne(qs([r]),255),d,p,m,y;if(U(s.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${s.rank}.`),U(s.shape[2]===3||s.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${s.shape[2]}.`),U(s.dtype==="int32"||s.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${s.dtype}.`),U(e==="otsu"||e==="binary",()=>`Method must be binary or otsu, but was ${e}`),s.shape[2]===3){[d,p,m]=yo(s,[1,1,1],-1);const w=ne(d,o),k=ne(p,i),I=ne(m,a);y=Pe(Pe(w,k),I)}else y=n;if(e==="otsu"){const w=oV(Ge(OR(y),"int32"),$m([]),256);c=c5(w,l)}const v=t?$f(y,c):bo(y,c);return Ge(ne(v,255),"int32")}function c5(n,e){let t=qs([-1]),r=qs([0]),s=qs([0]),o,i,a,l,c,d;for(let p=0;p<n.size-1;p++){o=mn(n,0,p+1),i=mn(n,p+1),c=st(rt(o),e),d=st(rt(i),e);const m=rt(ne(o,Bm(0,o.size)));a=st(m,rt(o));const y=Cv(i.shape,o.size),v=Pe(Bm(0,i.size),y),b=ne(i,v);l=st(rt(b),rt(i));const w=ut(a,l),k=ut(a,l),I=ne(c,d);s=ne(ne(I,w),k);const N=bo(s,r);r=us(N,s,r),t=us(N,qs([p]),t)}return t}const h5=he({threshold_:u5});function d5(n,e,t="nearest",r="constant",s=0,o){const i=K(n,"image","transform","float32"),a=K(e,"transforms","transform","float32");U(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),U(a.rank===2&&(a.shape[0]===i.shape[0]||a.shape[0]===1)&&a.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),U(o==null||o.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${o}.`);const l={image:i,transforms:a},c={interpolation:t,fillMode:r,fillValue:s,outputShape:o};return ae.runKernel(nk,l,c)}const f5=he({transform_:d5});function p5(n,e,t){const r=K(n,"a","bandPart");U(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);const s=r.shape,[o,i]=r.shape.slice(-2);let a,l;typeof e=="number"?(U(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),U(e<=o,()=>`bandPart(): numLower (${e}) must not be greater than the number of rows (${o}).`),a=K(e<0?o:e,"numLower","bandPart")):(U(e.dtype==="int32",()=>"bandPart(): numLower's dtype must be an int32."),a=us(Vy(e,0),o,zm(e,o))),typeof t=="number"?(U(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`),U(t<=i,()=>`bandPart(): numUpper (${t}) must not be greater than the number of columns (${i}).`),l=K(t<0?i:t,"numUpper","bandPart")):(U(t.dtype==="int32",()=>"bandPart(): numUpper's dtype must be an int32."),l=us(Vy(t,0),i,zm(t,i)));const c=ce(Bm(0,o,1,"int32"),[-1,1]),d=Bm(0,i,1,"int32"),p=ut(c,d),m=rl($f(p,a),lh(p,zn(l))),y=gr([o,i],r.dtype);return ce(sl(eh(ce(r,[-1,o,i])).map(v=>us(m,v,y))),s)}const m5=he({bandPart_:p5});function g5(n){let e;if(Array.isArray(n)){e=!1,U(n!=null&&n.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const s=n[0].shape[0];for(let o=1;o<n.length;++o)U(n[o].shape[0]===s,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${n[o].shape[0]} vs. ${s})`)}else e=!0,n=yo(n,n.shape[0],0).map(s=>nx(s,[0]));U(n.length<=n[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${n.length}) exceeds number of dimensions (${n[0].shape[0]}).`);const t=[],r=n;for(let s=0;s<n.length;++s)t.push(ae.tidy(()=>{let o=r[s];if(s>0)for(let i=0;i<s;++i){const a=ne(rt(ne(t[i],o)),t[i]);o=ut(o,a)}return st(o,kv(o,"euclidean"))}));return e?sl(t,0):t}const x5=he({gramSchmidt_:g5});function y5(n,e=!1){if(U(n.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${n.rank}`),n.rank===2)return YN(n,e);{const t=n.shape.slice(0,n.shape.length-2).reduce((l,c)=>l*c),r=eh(ce(n,[t,n.shape[n.shape.length-2],n.shape[n.shape.length-1]]),0),s=[],o=[];r.forEach(l=>{const[c,d]=YN(l,e);s.push(c),o.push(d)});const i=ce(sl(s,0),n.shape),a=ce(sl(o,0),n.shape);return[i,a]}}function YN(n,e=!1){return ae.tidy(()=>{U(n.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${n.shape.length}D Tensor.`);const t=n.shape[0],r=n.shape[1];let s=$R(t),o=Lc(n);const i=Nb([[1]],[1,1]);let a=Lc(i);const l=t>=r?r:t;for(let c=0;c<l;++c){const d=o,p=a,m=s;[a,o,s]=ae.tidy(()=>{const y=mn(o,[c,c],[t-c,1]),v=kv(y),b=mn(o,[c,c],[1,1]),w=us(bo(b,0),Nb([[-1]]),Nb([[1]])),k=ut(b,ne(w,v)),I=st(y,k);I.shape[0]===1?a=Lc(i):a=Is([i,mn(I,[1,0],[I.shape[0]-1,I.shape[1]])],0);const N=zn(st(qt(w,k),v)),T=mn(o,[c,0],[t-c,r]),A=ne(N,a),O=Lt(a);if(c===0)o=ut(T,qt(A,qt(O,T)));else{const z=ut(T,qt(A,qt(O,T)));o=Is([mn(o,[0,0],[c,r]),z],0)}const P=Lt(A),B=mn(s,[0,c],[t,s.shape[1]-c]);if(c===0)s=ut(B,qt(qt(B,a),P));else{const z=ut(B,qt(qt(B,a),P));s=Is([mn(s,[0,0],[t,c]),z],1)}return[a,o,s]}),kt([d,p,m])}return!e&&t>r&&(s=mn(s,[0,0],[t,r]),o=mn(o,[0,0],[r,r])),[s,o]})}const v5=he({qr_:y5});const nl={flipLeftRight:PH,grayscaleToRGB:MH,resizeNearestNeighbor:qR,resizeBilinear:HR,rgbToGrayscale:BH,rotateWithOffset:WH,cropAndResize:OH,nonMaxSuppression:GH,nonMaxSuppressionAsync:JH,nonMaxSuppressionWithScore:e5,nonMaxSuppressionWithScoreAsync:n5,nonMaxSuppressionPadded:s5,nonMaxSuppressionPaddedAsync:i5,threshold:h5,transform:f5},w5={bandPart:m5,gramSchmidt:x5,qr:v5};const b5=new Map,C5=new Map;class Tf{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}}class Go{constructor(){this.classNameMap={}}static getMap(){return Go.instance==null&&(Go.instance=new Go),Go.instance}static register(e){Go.getMap().classNameMap[e.className]=[e,e.fromConfig]}}function _e(n,e,t){U(n.className!=null,()=>"Class being registered does not have the static className property defined."),U(typeof n.className=="string",()=>"className is required to be a string, but got type "+typeof n.className),U(n.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),typeof e>"u"&&(e="Custom"),typeof t>"u"&&(t=n.className);const r=t,s=e+">"+r;return Go.register(n),b5.set(s,n),C5.set(n,s),n}class bu extends Tf{minimize(e,t=!1,r){const{value:s,grads:o}=this.computeGradients(e,r);if(r!=null){const i=r.map(a=>({name:a.name,tensor:o[a.name]}));this.applyGradients(i)}else this.applyGradients(o);return kt(o),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return $G(e,t)}dispose(){this.iterations_!=null&&kt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Kt(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(bu,Symbol.hasInstance,{value:n=>n.minimize!=null&&n.computeGradients!=null&&n.applyGradients!=null});class KR extends bu{static get className(){return"Adadelta"}constructor(e,t,r=null){super(),this.learningRate=e,this.rho=t,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=ae.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(r=>r.name):Object.keys(e)).forEach((r,s)=>{const o=ae.registeredVariables[r],i=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${r}/accum_grad`,variable:me(()=>Mt(o).variable(i))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${r}/accum_var`,variable:me(()=>Mt(o).variable(i))});const a=Array.isArray(e)?e[s].tensor:e[r];if(a==null)return;const l=this.accumulatedGrads[s].variable,c=this.accumulatedUpdates[s].variable;me(()=>{const d=Pe(ne(l,this.rho),ne(bn(a),1-this.rho)),p=ne(st(jr(Pe(c,this.epsilon)),jr(Pe(l,this.epsilon))),a),m=Pe(ne(c,this.rho),ne(bn(p),1-this.rho));l.assign(d),c.assign(m);const y=Pe(ne(p,-this.learningRate),o);o.assign(y)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(kt(this.accumulatedGrads.map(e=>e.variable)),kt(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,r=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}class XR extends bu{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(r=>r.name):Object.keys(e)).forEach((r,s)=>{const o=ae.registeredVariables[r];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${r}/accumulator`,variable:me(()=>Cv(o.shape,this.initialAccumulatorValue).variable(!1))});const i=Array.isArray(e)?e[s].tensor:e[r];if(i==null)return;const a=this.accumulatedGrads[s].variable;me(()=>{const l=Pe(a,bn(i));a.assign(l);const c=Pe(ne(st(i,jr(Pe(l,ae.backend.epsilon()))),-this.learningRate),o);o.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&kt(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);const t=!1;this.accumulatedGrads=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}class YR extends bu{static get className(){return"Adam"}constructor(e,t,r,s=null){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],me(()=>{this.accBeta1=Kt(t).variable(),this.accBeta2=Kt(r).variable()}),s==null&&(this.epsilon=ae.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);me(()=>{const r=ut(1,this.accBeta1),s=ut(1,this.accBeta2);t.forEach((o,i)=>{const a=ae.registeredVariables[o],l=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${o}/m`,variable:me(()=>Mt(a).variable(l))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${o}/v`,variable:me(()=>Mt(a).variable(l))});const c=Array.isArray(e)?e[i].tensor:e[o];if(c==null)return;const d=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=Pe(ne(d,this.beta1),ne(c,1-this.beta1)),y=Pe(ne(p,this.beta2),ne(bn(c),1-this.beta2)),v=st(m,r),b=st(y,s);d.assign(m),p.assign(y);const w=Pe(ne(st(v,Pe(jr(b),this.epsilon)),-this.learningRate),a);a.assign(w)}),this.accBeta1.assign(ne(this.accBeta1,this.beta1)),this.accBeta2.assign(ne(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&kt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&kt(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),me(()=>{this.accBeta1.assign(Jc(this.beta1,this.iterations_+1)),this.accBeta2.assign(Jc(this.beta2,this.iterations_+1))});const t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}class QR extends bu{static get className(){return"Adamax"}constructor(e,t,r,s=null,o=0){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=s,this.decay=o,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],me(()=>{this.iteration=Kt(0).variable(),this.accBeta1=Kt(t).variable()}),s==null&&(this.epsilon=ae.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);me(()=>{const r=ut(1,this.accBeta1),s=st(-this.learningRate,Pe(ne(this.iteration,this.decay),1));t.forEach((o,i)=>{const a=ae.registeredVariables[o],l=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${o}/m`,variable:Mt(a).variable(l)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${o}/v`,variable:Mt(a).variable(l)});const c=Array.isArray(e)?e[i].tensor:e[o];if(c==null)return;const d=this.accumulatedFirstMoment[i].variable,p=this.accumulatedWeightedInfNorm[i].variable,m=Pe(ne(d,this.beta1),ne(c,1-this.beta1)),y=ne(p,this.beta2),v=os(c),b=vu(y,v);d.assign(m),p.assign(b);const w=Pe(ne(st(s,r),st(m,Pe(b,this.epsilon))),a);a.assign(w)}),this.iteration.assign(Pe(this.iteration,1)),this.accBeta1.assign(ne(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&kt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&kt(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}class Lk extends bu{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(r=>r.name):Object.keys(e)).forEach((r,s)=>{const o=Array.isArray(e)?e[s].tensor:e[r];if(o==null)return;const i=ae.registeredVariables[r];me(()=>{const a=Pe(ne(this.c,o),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=ha(Kt(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}class JR extends Lk{static get className(){return"Momentum"}constructor(e,t,r=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=r,this.accumulations=[],this.m=Kt(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(r=>r.name):Object.keys(e)).forEach((r,s)=>{const o=ae.registeredVariables[r];this.accumulations[s]==null&&(this.accumulations[s]={originalName:`${r}/momentum`,variable:me(()=>Mt(o).variable(!1))});const i=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[r];a!=null&&me(()=>{let l;const c=Pe(ne(this.m,i),a);this.useNesterov?l=Pe(ne(this.c,Pe(a,ne(c,this.m))),o):l=Pe(ne(this.c,c),o),i.assign(c),o.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&kt(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}class ZR extends bu{static get className(){return"RMSProp"}constructor(e,t=.9,r=0,s=null,o=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=r,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=o,s==null&&(this.epsilon=ae.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(r=>r.name):Object.keys(e)).forEach((r,s)=>{const o=ae.registeredVariables[r],i=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${r}/rms`,variable:me(()=>Mt(o).variable(i))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${r}/momentum`,variable:me(()=>Mt(o).variable(i))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${r}/mg`,variable:me(()=>Mt(o).variable(i))});const a=Array.isArray(e)?e[s].tensor:e[r];if(a==null)return;const l=this.accumulatedMeanSquares[s].variable,c=this.accumulatedMoments[s].variable;me(()=>{const d=Pe(ne(l,this.decay),ne(bn(a),1-this.decay));if(this.centered){const p=this.accumulatedMeanGrads[s].variable,m=Pe(ne(p,this.decay),ne(a,1-this.decay)),y=st(ne(a,this.learningRate),jr(ut(d,Pe(bn(m),this.epsilon)))),v=Pe(ne(c,this.momentum),y);l.assign(d),p.assign(m),c.assign(v);const b=ut(o,v);o.assign(b)}else{const p=Pe(ne(l,this.decay),ne(bn(a),1-this.decay)),m=Pe(ne(c,this.momentum),st(ne(a,this.learningRate),jr(Pe(p,this.epsilon))));l.assign(p),c.assign(m);const y=ut(o,m);o.assign(y)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&kt(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&kt(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&kt(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(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,r=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}const S5=[KR,XR,YR,QR,JR,ZR,Lk];function k5(){for(const n of S5)_e(n)}function QN(n,e,t,r){i(n),t=t??0,r=r??1,a(t,r);let s=0;const o=l=>(l.then(c=>{const d=t+ ++s/n.length*(r-t);return e(d),c}),l);function i(l){U(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function a(l,c){U(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),U(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),U(c>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${c}`)}return Promise.all(n.map(o))}async function I5(n,e){e==null&&(e={});const t=e.fetchFunc==null?ye().platform.fetch:e.fetchFunc,r=n.map(p=>t(p,e.requestInit,{isBinary:!0})),a=(e.onProgress==null?await Promise.all(r):await QN(r,e.onProgress,0,.5)).map(p=>p.arrayBuffer());return e.onProgress==null?await Promise.all(a):await QN(a,e.onProgress,.5,1)}function $5(n,e){var t;const r=e.fetchFunc==null?ye().platform.fetch:e.fetchFunc;let s=0,o;return(t=e.onProgress)===null||t===void 0||t.call(e,0),new ReadableStream({pull:async i=>{for(var a;s<n.length;){o||(o=(await r(n[s],e.requestInit,{isBinary:!0})).body.getReader());const{done:l,value:c}=await o.read();if(l){s++,o=void 0,(a=e.onProgress)===null||a===void 0||a.call(e,s/n.length);continue}i.enqueue(c);return}i.close()}})}const N5="application/octet-stream",T5="application/json";class Mk{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?(U(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=ye().platform.fetch,U(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&U(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{},this.loadOptions=t}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");const t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;const r=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],s=zU(e,r);if(t.body.append("model.json",new Blob([JSON.stringify(s)],{type:T5}),"model.json"),e.weightData!=null){const i=gu.join(e.weightData);t.body.append("model.weights.bin",new Blob([i],{type:N5}),"model.weights.bin")}const o=await this.fetch(this.path,t);if(o.ok)return{modelArtifactsInfo:ak(e),responses:[o]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${o.status}.`)}async loadModelJSON(){const e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch{let i=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?i+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":i+=" Please make sure the server is serving valid JSON for this request.",new Error(i)}const r=t.modelTopology,s=t.weightsManifest;if(r==null&&s==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return t}async load(){if(this.loadOptions.streamWeights)return this.loadStream();const e=await this.loadModelJSON();return UU(e,t=>this.loadWeights(t))}async loadStream(){const e=await this.loadModelJSON(),t=await this.getWeightUrls(e.weightsManifest),r=LN(e.weightsManifest),s=()=>$5(t,this.loadOptions);return Object.assign(Object.assign({},e),{weightSpecs:r,getWeightStream:s})}async getWeightUrls(e){const t=Array.isArray(this.path)?this.path[1]:this.path,[r,s]=E5(t),o=this.weightPathPrefix||r,i=[],a=[];for(const l of e)for(const c of l.paths)this.weightUrlConverter!=null?a.push(this.weightUrlConverter(c)):i.push(o+c+s);return this.weightUrlConverter&&i.push(...await Promise.all(a)),i}async loadWeights(e){const t=await this.getWeightUrls(e),r=LN(e),s=await I5(t,this.loadOptions);return[r,s]}}Mk.URL_SCHEME_REGEX=/^https?:\/\//;function E5(n){const e=n.lastIndexOf("/"),t=n.lastIndexOf("?"),r=n.substring(0,e),s=t>e?n.substring(t):"";return[r+"/",s]}function JN(n){return n.match(Mk.URL_SCHEME_REGEX)!=null}const eA=(n,e)=>{if(typeof fetch>"u"&&(e==null||e.fetchFunc==null))return null;{let t=!0;if(Array.isArray(n)?t=n.every(r=>JN(r)):t=JN(n),t)return tA(n,e)}return null};pr.registerSaveRouter(eA);pr.registerLoadRouter(eA);function tA(n,e){return new Mk(n,e)}function R5(n,e){return tA(n,e)}let Ic;function A5(n,e=3){if(e>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(n==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let t=!1,r=!1,s=!1,o=!1,i=!1,a=!1;if(n.data instanceof Uint8Array)t=!0;else if(typeof ImageData<"u"&&n instanceof ImageData)r=!0;else if(typeof HTMLVideoElement<"u"&&n instanceof HTMLVideoElement)s=!0;else if(typeof HTMLImageElement<"u"&&n instanceof HTMLImageElement)o=!0;else if(n.getContext!=null)i=!0;else if(typeof ImageBitmap<"u"&&n instanceof ImageBitmap)a=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${n.constructor.name}`);if(hC(uC,ae.backendName)!=null){const v={pixels:n},b={numChannels:e};return ae.runKernel(uC,v,b)}const[c,d]=s?[n.videoWidth,n.videoHeight]:[n.width,n.height];let p;if(i)p=n.getContext("2d").getImageData(0,0,c,d).data;else if(r||t)p=n.data;else if(o||s||a){if(Ic==null)if(typeof document>"u")if(typeof OffscreenCanvas<"u"&&typeof OffscreenCanvasRenderingContext2D<"u")Ic=new OffscreenCanvas(1,1).getContext("2d");else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");else Ic=document.createElement("canvas").getContext("2d",{willReadFrequently:!0});Ic.canvas.width=c,Ic.canvas.height=d,Ic.drawImage(n,0,0,c,d),p=Ic.getImageData(0,0,c,d).data}let m;if(e===4)m=new Int32Array(p);else{const v=c*d;m=new Int32Array(v*e);for(let b=0;b<v;b++)for(let w=0;w<e;++w)m[b*e+w]=p[b*4+w]}return hH(m,[d,c,e],"int32")}const _5=he({fromPixels_:A5});function zk(n,e){const t=n.shape.length,r=e.shape.length;if(t<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t}.`);if(r<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${r}.`);if(e.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.shape[r-1]>t)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${e.shape[r-1]} vs. ${t}`);if(Se(n.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${n.shape}.`);const s=e.shape,o=s[s.length-1];let i=1;for(let p=0;p<s.length-1;++p)i*=s[p];const a=n.shape,l=s.slice();l.pop();let c=1;for(let p=o;p<t;++p)c*=a[p],l.push(a[p]);const d=[...tt(n.shape).map(p=>p/c),1].slice(0,o);return[l,i,c,d]}const kC=-2,D5=-1;function Bk(n,e,t){const r=n.shape.length;U(r===e.length,()=>`Error in slice${r}D: Length of begin ${e} must match the rank of the array (${r}).`),U(r===t.length,()=>`Error in slice${r}D: Length of size ${t} must match the rank of the array (${r}).`);for(let s=0;s<r;++s)U(e[s]+t[s]<=n.shape[s],()=>`Error in slice${r}D: begin[${s}] + size[${s}] (${e[s]+t[s]}) would overflow input.shape[${s}] (${n.shape[s]})`)}function O5(n){const e=[];let t=0;for(;n>0;)n&1&&e.push(t),n/=2,t++;return e}function Uk(n,e,t){const r=[];for(let s=0;s<n.length;s++)r[s]=Math.ceil((e[s]-n[s])/t[s]);return r}function nA(n,e,t,r){const s=[...n];for(let o=s.length;o<r.length;o++)s.push(1);for(let o=0;o<t;o++)o===0?s[e]=1:(s.splice(e,0,1),s.pop());return s}function rA(n,e,t){return t<=n?t:t-(e-1)}function sA(n,e){const t=[];for(let r=0;r<n;r++)t.push(e+r);return t}function F5(n,e,t,r,s,o,i,a,l){const c=n.length;let d=new Array(c),p=new Array(c),m=new Array(c);if(e.length&&t>0){const y=e[0],v=t+1;d=oA(i,y,v,r,n),p=iA(a,y,v,s,n),m=nA(o,y,v,n)}else for(let y=0;y<c;y++)d[y]=lA(i,r,o,n,y,l),p[y]=uA(a,s,o,n,y,l),m[y]=aA(o,y,l);return{begin:d,end:p,strides:m}}function oA(n,e,t,r,s){const o=[...s],i=sA(t,e);for(let a=0;a<o.length;a++)if(i.indexOf(a)>-1)o[a]=0;else{const l=rA(e,t,a);let c=r[l];n&1<<l&&(c=0),o[a]=c}return o}function iA(n,e,t,r,s){const o=[...s],i=sA(t,e);for(let a=0;a<o.length;a++)if(i.indexOf(a)>-1)o[a]=Number.MAX_SAFE_INTEGER;else{const l=rA(e,t,a);let c=r[l];n&1<<l&&(c=Number.MAX_SAFE_INTEGER),o[a]=c}for(let a=0;a<o.length;a++){const l=s[a];o[a]<0&&(o[a]+=l),o[a]=Gc(0,o[a],s[a])}return o}function aA(n,e,t){let r=n[e];return(t&1<<e||r==null)&&(r=1),r}function lA(n,e,t,r,s,o){let i=e[s];const a=t[s]||1;(n&1<<s||o&1<<s||i==null)&&(a>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);const l=r[s];return i<0&&(i+=l),i=Gc(0,i,l-1),i}function uA(n,e,t,r,s,o){let i=e[s];const a=t[s]||1;(n&1<<s||o&1<<s||i==null)&&(a>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);const l=r[s];return i<0&&(i+=l),a>0?i=Gc(0,i,l):i=Gc(-1,i,l-1),i}function Wk(n,e,t){let r=t.length;for(let s=0;s<t.length;s++)if(t[s]>1){r=s;break}for(let s=r+1;s<t.length;s++)if(e[s]>0||t[s]!==n[s])return!1;return!0}function Vk(n,e){let t=n.length>0?n[n.length-1]:1;for(let r=0;r<n.length-1;r++)t+=n[r]*e[r];return t}function $v(n,e,t){let r;const s=n.shape.length;typeof e=="number"?r=[e,...new Array(s-1).fill(0)]:e.length<s?r=e.concat(new Array(s-e.length).fill(0)):r=e.slice(),r.forEach(i=>{U(i!==-1,()=>"slice() does not support negative begin indexing.")});let o;return t==null?o=new Array(s).fill(-1):typeof t=="number"?o=[t,...new Array(s-1).fill(-1)]:t.length<s?o=t.concat(new Array(s-t.length).fill(-1)):o=t,o=o.map((i,a)=>i>=0?i:(U(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${a}.`),n.shape[a]-r[a])),[r,o]}function Gk(n,e,t,r,s,o,i,a,l){let c;if(r==null?(c=new Array(e.length),c.fill(1)):c=r,i!=null&&(i&i-1)!==0)throw new Error("Multiple ellipses in slice is not allowed.");let d=!1;const p={dims:c.length,numAddAxisAfterEllipsis:0,begin:e.slice(),end:t.slice(),strides:c.slice(),beginMask:s,endMask:o,ellipsisMask:i,newAxisMask:a,shrinkAxisMask:l};for(let N=0;N<p.dims;N++)d&&(1<<N&a)!==0&&p.numAddAxisAfterEllipsis++,1<<N&i&&(d=!0);d||(p.ellipsisMask|=1<<p.dims,p.dims++);const m={dims:n.length,beginMask:0,endMask:0,beginValid:!1,endValid:!1};P5(p,m);let y=!0,v=!0,b=!0;const w=[],k=[];for(let N=0;N<n.length;++N){if(m.strides[N]===0)throw Error(`strides[${N}] must be non-zero`);const T=!!(m.shrinkAxisMask&1<<N),A=n[N];if(A===-1){w.push(T?1:-1);continue}const O=[m.beginMask&1<<N,m.endMask&1<<N],P=[m.strides[N]>0?0:-1,m.strides[N]>0?A:A-1];if(T&&m.strides[N]<=0)throw Error("only stride 1 allowed on non-range indexing.");b=b&&m.strides[N]===1;const B=!!(m.beginMask&1<<N&&m.endMask&1<<N);if(m.beginValid&&m.endValid){if(T){const H=m.begin[N]<0?A+m.begin[N]:m.begin[N];if(m.begin[N]=H,m.end[N]=m.begin[N]+1,H<0||H>=A)throw Error(`slice index ${m.begin[N]} of dimension ${N} out of bounds.`)}else m.begin[N]=ZN(m.begin[N],0,m.strides[N],A,O,P),m.end[N]=ZN(m.end[N],1,m.strides[N],A,O,P);const _=m.strides[N]===1&&m.begin[N]===0&&m.end[N]===A;y=y&&_,v=v&&(N===0&&m.strides[N]===1||_)}else y=y&&m.strides[N]===1&&B,v=v&&(N===0&&m.strides[N]===1||B);let z,L=!1;if(m.beginValid&&m.endValid?(z=m.end[N]-m.begin[N],L=!0):T?(z=1,L=!0):B&&A>=0&&(m.strides[N]<0?z=-A:z=A,L=!0),L){let _;z===0||z<0!=m.strides[N]<0?_=0:_=Math.trunc(z/m.strides[N])+(z%m.strides[N]!==0?1:0),w.push(_)}else w.push(-1)}for(let N=0;N<m.finalShapeGatherIndices.length;++N){const T=m.finalShapeGatherIndices[N];T>=0?k.push(w[T]):T===kC&&k.push(1)}return{finalShapeSparse:k.filter((N,T)=>m.finalShapeGatherIndices[T]!==kC),finalShape:k,isIdentity:y,sliceDim0:v,isSimpleSlice:b,begin:m.begin,end:m.end,strides:m.strides}}function P5(n,e){e.beginMask=0,e.endMask=0,e.shrinkAxisMask=0;let t=0;e.beginValid=n.begin!=null,e.endValid=n.end!=null,e.begin=new Array(e.dims),e.end=new Array(e.dims),e.strides=new Array(e.dims),e.finalShapeGatherIndices=[],e.finalShapeGatherIndicesSparse=[],e.inputShapeGatherIndicesSparse=new Array(e.dims);for(let r=0;r<n.dims;r++)if(1<<r&n.ellipsisMask){const s=Math.min(e.dims-(n.dims-r)+1+n.numAddAxisAfterEllipsis,e.dims);for(;t<s;t++)e.begin[t]=0,e.end[t]=0,e.strides[t]=1,e.beginMask|=1<<t,e.endMask|=1<<t,e.finalShapeGatherIndices.push(t),e.finalShapeGatherIndicesSparse.push(-1),e.inputShapeGatherIndicesSparse[t]=r}else if(1<<r&n.newAxisMask)e.finalShapeGatherIndices.push(kC),e.finalShapeGatherIndicesSparse.push(-1);else{if(t===e.begin.length)throw Error(`Index out of range using input dim ${t}; input has only ${e.dims} dims, ${e.begin.length}.`);n.begin!=null&&(e.begin[t]=n.begin[r]),n.end!=null&&(e.end[t]=n.end[r]),e.strides[t]=n.strides[r],n.beginMask&1<<r&&(e.beginMask|=1<<t),n.endMask&1<<r&&(e.endMask|=1<<t),n.shrinkAxisMask&1<<r?(e.finalShapeGatherIndices.push(D5),e.finalShapeGatherIndicesSparse.push(-1),e.shrinkAxisMask|=1<<t):(e.finalShapeGatherIndices.push(t),e.finalShapeGatherIndicesSparse.push(r)),e.inputShapeGatherIndicesSparse[t]=r,t++}}function ZN(n,e,t,r,s,o){if(s[e])return t>0?o[e]:o[e+1&1];{const i=n<0?r+n:n;return i<o[0]?o[0]:i>o[1]?o[1]:i}}const 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Input received: ${e}`);for(let s=0;s<t.length;s++){const o=t[s],i=r[s];if(i==null)continue;const a=o.rank;if(i.ndim!=null&&a!==i.ndim)throw new Z(`Input ${s} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${a}`);if(i.maxNDim!=null&&a>i.maxNDim)throw new Z(`Input ${s} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${a}`);if(i.minNDim!=null&&a<i.minNDim)throw new Z(`Input ${s} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${a}.`);if(i.dtype!=null&&o.dtype!==i.dtype)throw new Z(`Input ${s} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${o.dtype}.`);if(i.axes){const l=o.shape;for(const c in i.axes){const d=Number(c),p=i.axes[c],m=d>=0?l[d]:l[l.length+d];if(p!=null&&[p,null].indexOf(m)===-1)throw new Z(`Input ${s} is incompatible with layer ${this.name}: expected axis ${d} of input shape to have value ${p} but got shape ${l}.`)}}if(i.shape!=null)for(let l=0;l<i.shape.length;++l){const c=i.shape[l],d=o.shape[l];if(c!=null&&d!=null&&c!==d)throw new Z(`Input ${s} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${o.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const r=rn(e),s=kq(e),o=Iq(e);if(s===o)throw new Z("Arguments to apply() must be all SymbolicTensors or all Tensors");return Mc(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const i=[];for(const a of rn(e))i.push(a.shape);this.build(ks(i)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&o&&(this._refCount=1)}if(this.assertInputCompatibility(e),o){let i=this.call(e,t);this.supportsMasking&&this.setMaskMetadata(e,i);const a=rn(i),l=[];for(let c of a)r.indexOf(c)!==-1&&(c=c.clone()),l.push(c);if(i=ks(l),this.activityRegularizer!=null)throw new wt("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}else{const i=Cq(e),a=this.computeOutputShape(i);let l;const c=Sq(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?i[0]:i),a!=null&&a.length>0&&Array.isArray(a[0])?l=a.map((d,p)=>new va(c,d,this,rn(e),t,this.name,p)):l=new va(c,a,this,rn(e),t,this.name),this.addInboundNode(e,l,null,null,i,a,t),this._refCount++,this.activityRegularizer!=null)throw new wt("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return l}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,s)=>{r!=null&&e[s]!=null&&e[s]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new sa(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new sa(`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 jo(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Ky(this.weights)}build(e){this.built=!0}getWeights(e=!1){return RC(e?this.trainableWeights:this.weights)}setWeights(e){me(()=>{const t=this.weights;if(t.length!==e.length)throw new Z(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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dx{constructor(e){super(),this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return me(()=>st(e,Pe(nr(),CI(e,this.axis))))}getConfig(){return{axis:this.axis}}}n_.className="UnitNorm";_e(n_);class r_ extends dx{apply(e){return uh(e)}}r_.className="NonNeg";_e(r_);class s_ extends dx{constructor(e){super(),this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=e.minValue!=null?e.minValue:this.defaultMinValue,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.rate=e.rate!=null?e.rate:this.defaultRate,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return me(()=>{const t=CI(e,this.axis),r=Pe(ne(this.rate,wo(t,this.minValue,this.maxValue)),ne(1-this.rate,t));return ne(e,st(r,Pe(nr(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}}s_.className="MinMaxNorm";_e(s_);const m2={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function or(n){return hI(n)}function g2(n,e={}){return ax(n,Go.getMap().classNameMap,e,"constraint")}function ir(n){if(n==null)return null;if(typeof n=="string"){const t={className:n in m2?m2[n]:n,config:{}};return g2(t)}else return n instanceof dx?n:g2(n)}async function $c(n){if(n==null)return;const e=[],t=[],r=[];for(const s in n){const o=n[s];if(typeof o!="number"){const i=o;e.push(i.data()),t.push(s),r.push(i)}}if(e.length>0){const s=await Promise.all(e);for(let o=0;o<s.length;++o)n[t[o]]=s[o][0];kt(r)}}function o_(n){if(n!=null)for(const e in n){const t=n[e];typeof t!="number"&&t.dispose()}}var x2;(function(n){n[n.SILENT=0]="SILENT",n[n.VERBOSE=1]="VERBOSE"})(x2||(x2={}));const Dq=125;class Um{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}}class Oq{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(const t of this.callbacks)t.setParams(e)}setModel(e){for(const t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(const r of this.callbacks)await r.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(const r of this.callbacks)await r.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(const r of this.callbacks)await r.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(const r of this.callbacks)await r.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainEnd(e)}}class Fq extends Um{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});const r=t.size==null?0:t.size;this.seen+=r;for(const s in t){const o=t[s];if(typeof o=="number")this.totals.hasOwnProperty(s)||(this.totals[s]=0),this.totals[s]=this.totals[s]+o*r;else{let i;s in this.totals?i=this.totals[s]:this.totals[s]=0;const a=me(()=>Pe(this.totals[s],ne(o,r)));this.totals[s]=a,i?.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(const r of this.params.metrics)this.totals[r]!=null&&(typeof this.totals[r]=="number"?t[r]=this.totals[r]/this.seen:me(()=>{const s=ne(st(1,this.seen),this.totals[r]);t[r]=s,this.totals[r].dispose(),ha(t[r])}))}}class Pq extends Um{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(const r in t)this.history[r]==null&&(this.history[r]=[]),this.history[r].push(t[r])}async syncData(){const e=[],t=[],r=[];for(const o in this.history){const i=this.history[o];for(let a=0;a<i.length;++a)if(typeof i[a]!="number"){const l=i[a];e.push(l.data()),t.push(o),r.push(a)}}const s=await Promise.all(e);for(let o=0;o<s.length;++o)this.history[t[o]][r[o]].dispose(),this.history[t[o]][r[o]]=s[o][0]}}class Lq extends Um{constructor(e,t){if(super(),this.currentEpoch=0,this.nowFunc=e.nowFunc,this.nextFrameFunc=e.nextFrameFunc||cA,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=Dq),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. 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A,O;w?(this.makeTestFunction(),A=this.testFunction,O=T.slice().concat(T.map(z=>"val_"+z))):(A=null,k=[],O=T.slice());const P=i_(r.callbacks,r.yieldEvery);return await this.fitLoop(N,I,T,y,r.epochs,r.verbose,P,A,k,r.shuffle,O,r.initialEpoch,null,null)}finally{this.isTraining=!1,gi(s,e),gi(o,t),gi(i,e),gi(a,t),gi(d,l),gi(p,c),m!=null&&kt(m)}}async fitLoop(e,t,r,s,o,i,a,l,c,d,p,m,y,v){s==null&&(s=32),o==null&&(o=1),d==null&&(d=!0),m==null&&(m=0);let b=!1;if(l!=null&&c!=null&&(b=!0),v!=null&&(b=!0,y==null))throw new Z("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");const w=this.checkNumSamples(t,s,y,"steps_per_epoch");let k;w!=null&&(k=Ii(0,w)),i==null&&(i=1);const{callbackList:I,history:N}=a_(a,i,o,m,w,y,s,b,p);I.setModel(this),this.history=N,await I.onTrainBegin(),this.stopTraining_=!1;for(let T=m;T<o;++T){await I.onEpochBegin(T);const A={};if(y!=null)throw new wt("stepsPerEpoch mode is not implemented yet.");{if(d==="batch")throw new 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Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[el(ay(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>el(ay(e)));{const e={};for(const t in this.metrics)e[t]=el(ay(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=n1(e.optimizer_config),r=ma(t);let s;if(typeof e.loss=="string")s=Ac(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(i=>Ac(i));else if(e.loss!=null){s={};for(const i 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o=t.strict==null?!0:t.strict,i=r.weightData!=null&&r.weightSpecs!=null&&o,a=ma(n1(s),e,i),l=r.trainingConfig;if(l!=null&&a.loadTrainingConfig(l),r.userDefinedMetadata!=null&&a.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new Z("LayersModel artifacts contains weight data, but not weight specs. 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this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const r={};r.className=t.getClassName(),r.config=t.getConfig(),e.push(r)}return{name:this.name,layers:e}}}Vm.className="Sequential";_e(Vm);let qr=class extends Tf{getConfig(){return{}}};class w_ extends qr{apply(e,t=1){return cq(e,t)}}w_.className="elu";_e(w_);class b_ extends qr{apply(e){return PR(e)}}b_.className="selu";_e(b_);class C_ extends qr{apply(e){return uh(e)}}C_.className="relu";_e(C_);class S_ extends qr{apply(e){return me(()=>zm(6,uh(e)))}}S_.className="relu6";_e(S_);class k_ extends qr{apply(e){return e}}k_.className="linear";_e(k_);class I_ extends qr{apply(e){return If(e)}}I_.className="sigmoid";_e(I_);class $_ extends qr{apply(e){return dq(e)}}$_.className="hardSigmoid";_e($_);class N_ extends qr{apply(e){return ex(e)}}N_.className="softplus";_e(N_);class T_ extends qr{apply(e){return hq(e)}}T_.className="softsign";_e(T_);class E_ extends qr{apply(e){return wv(e)}}E_.className="tanh";_e(E_);let II=class extends qr{apply(e,t=-1){return Nk(e,t)}};II.className="softmax";_e(II);class R_ extends qr{apply(e,t=-1){return TR(e,t)}}R_.className="logSoftmax";_e(R_);class A_ extends qr{apply(e){return me(()=>me(()=>{const t=Math.sqrt(2),r=ne(.5,Pe(1,SR(st(e,t))));return ne(e,r)}))}}A_.className="gelu";_e(A_);class __ extends qr{apply(e){return me(()=>ne(.5,ne(e,Pe(1,wv(ne(jr(st(2,Math.PI)),Pe(e,ne(.044715,Jc(e,3)))))))))}}__.className="gelu_new";_e(__);class D_ extends qr{apply(e){return me(()=>ne(e,wv(ex(e))))}}D_.className="mish";_e(D_);class O_ extends qr{apply(e,t=1){return me(()=>ne(If(ne(e,t)),e))}}O_.className="swish";_e(O_);function uu(n){return n.getClassName()}function Ob(n,e={}){return ax(n,Go.getMap().classNameMap,e,"activation")}function cu(n){if(n==null){const e={};return e.className="linear",e.config={},Ob(e)}if(typeof n=="string"){const e={};return e.className=n,e.config={},Ob(e)}else return n instanceof qr?n:Ob(n)}function TK(n){if(n!=null&&typeof n!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${n}`)}class F_ extends Tf{}class P_ extends F_{constructor(e){super(),TK(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return me(()=>{let t=gr([1]);return this.hasL1&&(t=Pe(t,rt(ne(this.l1,os(e))))),this.hasL2&&(t=Pe(t,rt(ne(this.l2,ux(e))))),ce(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}P_.className="L1L2";_e(P_);const I2={l1l2:"L1L2"};function un(n){return hI(n)}function $2(n,e={}){return ax(n,Go.getMap().classNameMap,e,"regularizer")}function In(n){if(n==null)return null;if(typeof n=="string"){const t={className:n in I2?I2[n]:n,config:{}};return $2(t)}else return n instanceof F_?n:$2(n)}class L_ extends Et{constructor(e){super(e??{}),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ft(e);let r=uh(e);return 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e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new Z(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Gt(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const r={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)r[s]=e[s];this.inputSpec=[new rr({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=ft(e),Sk(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Tn(this.alphaInitializer),alphaRegularizer:un(this.alphaRegularizer),alphaConstraint:or(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}z_.className="PReLU";_e(z_);let B_=class extends Et{constructor(e){if(super(e??{}),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new wt(`Non-default alpha value (${e.alpha}) is 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Z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${n.rank}.`);if(e.rank!==4&&e.rank!==5)throw new Z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${n.rank}.`);let a=V_(n,o);if(s==="causal")throw new wt("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=SV(a,e,r,s==="same"?"same":"valid","NDHWC",i),t!=null&&(a=Ei(a,t)),o==="channelsFirst"&&(a=Lt(a,[0,4,1,2,3])),a})}class Fv extends Et{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Fv.verifyArgs(t),this.rank=e,xr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new wt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented 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if(this.dilationRate.length!==2)throw new Z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new Z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(oa("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!dI(e.kernelSize,"number",1,3))throw new Z(`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:uu(this.activation),useBias:this.useBias,biasInitializer:Tn(this.biasInitializer),biasRegularizer:un(this.biasRegularizer),activityRegularizer:un(this.activityRegularizer),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Ef extends Fv{constructor(e,t){super(e,t),this.kernel=null,Ef.verifyArgs(t),this.filters=t.filters,xr(this.filters,"filters"),this.kernelInitializer=kn(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ir(t.kernelConstraint),this.kernelRegularizer=In(t.kernelRegularizer)}build(e){e=Gt(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new Z(`The channel dimension of the input should be defined. Found ${e[t]}`);const r=e[t],s=this.kernelSize.concat([r,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]:r}}],this.built=!0}call(e,t){return me(()=>{e=ft(e);let r;const s=this.bias==null?null:this.bias.read(),o=zA(this.activation.getClassName());if(o!=null&&this.rank===2)r=N2(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,o);else{if(this.rank===1)r=EK(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=N2(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=RK(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new wt("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=Gt(e);const t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let o=0;o<r.length;++o){const i=ki(r[o],this.kernelSize[o],this.padding,this.strides[o],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[o]);t.push(i)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){const e={filters:this.filters,kernelInitializer:Tn(this.kernelInitializer),kernelRegularizer:un(this.kernelRegularizer),kernelConstraint:or(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new Z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class fx extends Ef{constructor(e){super(2,e),fx.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!dI(e.kernelSize,"number",1,2))throw new Z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}fx.className="Conv2D";_e(fx);class px extends Ef{constructor(e){super(3,e),px.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new Z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}px.className="Conv3D";_e(px);class G_ extends fx{constructor(e){if(super(e),this.inputSpec=[new rr({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new Z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Gt(e),e.length!==4)throw new Z("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new Z("The channel dimension of the inputs should be defined. Found `None`.");const r=e[t],s=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new rr({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return me(()=>{let r=ft(e);if(r.shape.length!==4)throw new Z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);const s=r.shape,o=s[0];let i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);const l=s[i],c=s[a],d=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],y=this.strides[1],v=ia(l,m,d,this.padding),b=ia(c,y,p,this.padding),w=[o,v,b,this.filters];this.dataFormat!=="channelsLast"&&(r=Lt(r,[0,2,3,1]));let k=vR(r,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Lt(k,[0,3,1,2])),this.bias!=null&&(k=Ei(k,this.bias.read(),this.dataFormat)),this.activation!=null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=Gt(e);const t=e.slice();let r,s,o;this.dataFormat==="channelsFirst"?(r=1,s=2,o=3):(r=3,s=1,o=2);const i=this.kernelSize[0],a=this.kernelSize[1],l=this.strides[0],c=this.strides[1];return t[r]=this.filters,t[s]=ia(t[s],l,i,this.padding),t[o]=ia(t[o],c,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}G_.className="Conv2DTranspose";_e(G_);class j_ extends px{constructor(e){if(super(e),this.inputSpec=[new rr({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new Z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Gt(e),e.length!==5)throw new Z("Input should have rank 5; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new Z("The channel dimension of the inputs should be defined. Found `None`.");const r=e[t],s=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new rr({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return me(()=>{let r=ft(e);if(r.shape.length!==5)throw new Z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);const s=r.shape,o=s[0];let i,a,l;this.dataFormat==="channelsFirst"?(l=2,i=3,a=4):(l=1,i=2,a=3);const c=s[l],d=s[i],p=s[a],m=this.kernelSize[0],y=this.kernelSize[1],v=this.kernelSize[2],b=this.strides[0],w=this.strides[1],k=this.strides[2],I=ia(c,b,m,this.padding),N=ia(d,w,y,this.padding),T=ia(p,k,v,this.padding),A=[o,I,N,T,this.filters];this.dataFormat!=="channelsLast"&&(r=Lt(r,[0,2,3,4,1]));let O=$V(r,this.kernel.read(),A,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(O=Lt(O,[0,4,1,2,3])),this.bias!==null&&(O=Ei(O,this.bias.read(),this.dataFormat)),this.activation!==null&&(O=this.activation.apply(O)),O})}computeOutputShape(e){e=Gt(e);const t=e.slice();let r,s,o,i;this.dataFormat==="channelsFirst"?(r=1,s=2,o=3,i=4):(r=4,s=1,o=2,i=3);const a=this.kernelSize[0],l=this.kernelSize[1],c=this.kernelSize[2],d=this.strides[0],p=this.strides[1],m=this.strides[2];return t[r]=this.filters,t[s]=ia(t[s],d,a,this.padding),t[o]=ia(t[o],p,l,this.padding),t[i]=ia(t[i],m,c,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}j_.className="Conv3DTranspose";_e(j_);class H_ extends Ef{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new Z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new Z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new Z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=kn(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=In(t.depthwiseRegularizer),this.depthwiseConstraint=ir(t.depthwiseConstraint),this.pointwiseInitializer=kn(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=In(t.pointwiseRegularizer),this.pointwiseConstraint=ir(t.pointwiseConstraint)}build(e){if(e=Gt(e),e.length<this.rank+2)throw new Z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new Z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const r=e[t],s=this.kernelSize.concat([r,this.depthMultiplier]),o=[];for(let a=0;a<this.rank;++a)o.push(1);o.push(r*this.depthMultiplier,this.filters);const i=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,i,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",o,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,i,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,i,this.biasConstraint):this.bias=null,this.inputSpec=[new rr({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return me(()=>{e=ft(e);let r;if(this.rank===1)throw new wt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Lt(e,[0,2,3,1])),r=LR(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=Ei(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=Lt(r,[0,3,1,2])),r})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Tn(this.depthwiseInitializer),e.pointwiseInitializer=Tn(this.pointwiseInitializer),e.depthwiseRegularizer=un(this.depthwiseRegularizer),e.pointwiseRegularizer=un(this.pointwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseConstraint),e.pointwiseConstraint=or(this.pointwiseConstraint),e}}H_.className="SeparableConv";class q_ extends H_{constructor(e){super(2,e)}}q_.className="SeparableConv2D";_e(q_);class Pv extends Ef{constructor(e){super(1,e),Pv.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!dI(e.kernelSize,"number",1,1))throw new Z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Pv.className="Conv1D";_e(Pv);class K_ extends Et{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return me(()=>{if(e=ft(e),this.dataFormat==="channelsLast"){const r=iy(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return iy(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const r=iy(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return iy(r,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}}K_.className="Cropping2D";_e(K_);class X_ extends Et{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vn(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,rq(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{const t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return me(()=>{let r=ft(e);const s=r.shape;if(this.dataFormat==="channelsFirst"){r=Lt(r,[0,2,3,1]);const o=this.size[0]*s[2],i=this.size[1]*s[3],a=this.interpolation==="nearest"?nl.resizeNearestNeighbor(r,[o,i]):nl.resizeBilinear(r,[o,i]);return Lt(a,[0,3,1,2])}else{const o=this.size[0]*s[1],i=this.size[1]*s[2];return this.interpolation==="nearest"?nl.resizeNearestNeighbor(r,[o,i]):nl.resizeBilinear(r,[o,i])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}}X_.className="UpSampling2D";_e(X_);function AK(n,e,t=[1,1],r="valid",s,o){return me(()=>{s==null&&(s=$i()),Vn(s);let i=$I(n,s);if(n.rank!==4)throw new Z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${n.rank}-D`);if(e.rank!==4)throw new Z(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return i=fk(i,e,t,r==="same"?"same":"valid","NHWC",o),s==="channelsFirst"&&(i=Lt(i,[0,3,1,2])),i})}class Y_ extends Fv{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=kn(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ir(e.depthwiseConstraint),this.depthwiseRegularizer=In(e.depthwiseRegularizer)}build(e){if(e=Gt(e),e.length<4)throw new Z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new Z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const r=e[t],s=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return me(()=>{e=ft(e);let r=AK(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=Ei(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=Gt(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,o=ki(t,this.kernelSize[0],this.padding,this.strides[0]),i=ki(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,o,i]:[e[0],o,i,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Tn(this.depthwiseInitializer),e.depthwiseRegularizer=un(this.depthwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseRegularizer),e}}Y_.className="DepthwiseConv2D";_e(Y_);function Q_(n,e,t,r){if(Array.isArray(n)){if(e!=null||t!=null)throw new Z("When inputs is an array, neither initialState or constants should be provided");r!=null&&(t=n.slice(n.length-r,n.length),n=n.slice(0,n.length-r)),n.length>1&&(e=n.slice(1,n.length)),n=n[0]}function s(o){return o==null||Array.isArray(o)?o:[o]}return e=s(e),t=s(t),{inputs:n,initialState:e,constants:t}}function J_(n,e,t,r=!1,s,o,i=!1,a=!1){return me(()=>{const l=e.shape.length;if(l<3)throw new Z(`Input should be at least 3D, but is ${l}D.`);const c=[1,0].concat(Ii(2,l));e=Lt(e,c),i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=Ge(Ge(s,"bool"),"float32"),s.rank===l-1&&(s=Ss(s,-1)),s=Lt(s,c)),r&&(e=Zc(e,0),s!=null&&(s=Zc(s,0)));const d=[];let p,m=t;const y=e.shape[0],v=eh(e);let b;s!=null&&(b=eh(s));for(let k=0;k<y;++k){const I=v[k],N=me(()=>n(I,m));if(s==null)p=N[0],m=N[1];else{const T=me(()=>{const A=b[k],O=ut(Ko(A),A),P=Pe(ne(N[0],A),ne(m[0],O)),B=m.map((z,L)=>Pe(ne(N[1][L],A),ne(z,O)));return{output:P,newStates:B}});p=T.output,m=T.newStates}a&&d.push(p)}let w;return a&&(w=sl(d,1)),[p,w,m]})}class Cu extends Et{constructor(e){super(e);let t;if(e.cell==null)throw new Z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new EI({cells:e.cell}):t=e.cell,t.stateSize==null)throw new Z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new rr({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ii(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){EC(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const r=t[0];let s;if(this.returnSequences?s=[e[0],e[1],r]:s=[e[0],r],this.returnState){const o=[];for(const i of t)o.push([e[0],i]);return[s].concat(o)}else return s}computeMask(e,t){return me(()=>{Array.isArray(t)&&(t=t[0]);const r=this.returnSequences?t:null;if(this.returnState){const s=this.states.map(o=>null);return[r].concat(s)}else return r})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new wt("Constants support is not implemented in RNN yet.");EC(e)&&(e=e[0]),e=e;const t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new rr({shape:[t,null,...r]});const s=[e[0]].concat(e.slice(2));this.cell.build(s);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!Xt(this.stateSpec.map(i=>i.shape[i.shape.length-1]),o))throw new Z(`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=o.map(i=>new rr({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){me(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");const r=this.inputSpec[0].shape[0];if(r==null)throw new Z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>gr([r,s])):this.states_=[gr([r,this.cell.stateSize])];else if(e==null)kt(this.states_),this.keptStates!=null&&(kt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>gr([r,s])):this.states_[0]=gr([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):kt(this.states_);for(let s=0;s<this.states_.length;++s){const o=e[s],i=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,a=[r,i];if(!Xt(o.shape,a))throw new Z(`State ${s} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${o.shape}`);this.states_[s]=o}}this.states_=this.states_.map(s=>ha(s.clone()))})}apply(e,t){let r=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const o=Q_(e,r,s,this.numConstants);e=o.inputs,r=o.initialState,s=o.constants;let i=[],a=[];if(r!=null){t.initialState=r,i=i.concat(r),this.stateSpec=[];for(const c of r)this.stateSpec.push(new rr({shape:c.shape}));a=a.concat(this.stateSpec)}if(s!=null&&(t.constants=s,i=i.concat(s),this.numConstants=s.length),i[0]instanceof va){const c=[e].concat(i),d=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=d;const m=super.apply(c,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return me(()=>{const r=t==null?null:t.mask,s=t==null?null:t.training;let o=t==null?null:t.initialState;e=ft(e),o==null&&(this.stateful?o=this.states_:o=this.getInitialState(e));const i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(o.length!==i)throw new Z(`RNN Layer has ${i} state(s) but was passed ${o.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=J_((v,b)=>{const w=this.cell.call([v].concat(b),a);return[w[0],w.slice(1)]},e,o,this.goBackwards,r,null,this.unroll,this.returnSequences),d=c[0],p=c[1],m=c[2];this.stateful&&this.resetStates(m,s);const y=this.returnSequences?p:d;return this.returnState?[y].concat(m):y})}getInitialState(e){return me(()=>{let t=gr(e.shape);return t=rt(t,[1,2]),t=lx(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?NC(t,[1,r]):t):this.cell.stateSize>1?[NC(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const r=this.cell.getConfig();return this.getClassName()===Cu.className&&(t.cell={className:this.cell.getClassName(),config:r}),Object.assign(Object.assign(Object.assign({},r),e),t)}static fromConfig(e,t,r={}){const s=t.cell,o=ma(s,r);return new e(Object.assign(t,{cell:o}))}}Cu.className="RNN";_e(Cu);class Lv extends Et{}class NI extends Lv{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,xr(this.units,"units"),this.activation=cu(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=kn(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=kn(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=kn(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=In(e.kernelRegularizer),this.recurrentRegularizer=In(e.recurrentRegularizer),this.biasRegularizer=In(e.biasRegularizer),this.kernelConstraint=ir(e.kernelConstraint),this.recurrentConstraint=ir(e.recurrentConstraint),this.biasConstraint=ir(e.biasConstraint),this.dropout=hf([1,lu([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=hf([1,lu([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Gt(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 me(()=>{if(e=e,e.length!==2)throw new Z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];const s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hu({ones:()=>Ko(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hu({ones:()=>Ko(r),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let o;const i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?o=pa(ne(e,i),this.kernel.read()):o=pa(e,this.kernel.read()),this.bias!=null&&(o=Ei(o,this.bias.read())),a!=null&&(r=ne(r,a));let l=Pe(o,pa(r,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:uu(this.activation),useBias:this.useBias,kernelInitializer:Tn(this.kernelInitializer),recurrentInitializer:Tn(this.recurrentInitializer),biasInitializer:Tn(this.biasInitializer),kernelRegularizer:un(this.kernelRegularizer),recurrentRegularizer:un(this.recurrentRegularizer),biasRegularizer:un(this.biasRegularizer),activityRegularizer:un(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}}NI.className="SimpleRNNCell";_e(NI);class Z_ extends Cu{constructor(e){e.cell=new NI(e),super(e)}call(e,t){return me(()=>{this.cell.dropoutMask!=null&&(kt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(kt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const r=t==null?null:t.mask,s=t==null?null:t.training,o=t==null?null:t.initialState;return super.call(e,{mask:r,training:s,initialState:o})})}static fromConfig(e,t){return new e(t)}}Z_.className="SimpleRNN";_e(Z_);class TI extends Lv{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 Z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,xr(this.units,"units"),this.activation=cu(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=cu(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=kn(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=kn(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=kn(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=In(e.kernelRegularizer),this.recurrentRegularizer=In(e.recurrentRegularizer),this.biasRegularizer=In(e.biasRegularizer),this.kernelConstraint=ir(e.kernelConstraint),this.recurrentConstraint=ir(e.recurrentConstraint),this.biasConstraint=ir(e.biasConstraint),this.dropout=hf([1,lu([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=hf([1,lu([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Gt(e);const t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return me(()=>{if(e=e,e.length!==2)throw new Z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const r=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hu({ones:()=>Ko(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hu({ones:()=>Ko(s),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));const o=this.dropoutMask,i=this.recurrentDropoutMask;let a,l,c;0<this.dropout&&this.dropout<1&&(e=ne(e,o[0]));let d=pa(e,this.kernel.read());this.useBias&&(d=Ei(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=ne(s,i[0]));const p=this.recurrentKernel.read(),[m,y]=yo(p,[2*this.units,this.units],p.rank-1),v=pa(s,m),[b,w,k]=yo(d,3,d.rank-1),[I,N]=yo(v,2,v.rank-1);a=this.recurrentActivation.apply(Pe(b,I)),l=this.recurrentActivation.apply(Pe(w,N));const T=pa(ne(l,s),y);c=this.activation.apply(Pe(k,T));const A=Pe(ne(a,s),ne(Pe(1,zn(a)),c));return[A,A]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:uu(this.activation),recurrentActivation:uu(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tn(this.kernelInitializer),recurrentInitializer:Tn(this.recurrentInitializer),biasInitializer:Tn(this.biasInitializer),kernelRegularizer:un(this.kernelRegularizer),recurrentRegularizer:un(this.recurrentRegularizer),biasRegularizer:un(this.biasRegularizer),activityRegularizer:un(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},e),t)}}TI.className="GRUCell";_e(TI);class eD extends Cu{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new TI(e),super(e)}call(e,t){return me(()=>{this.cell.dropoutMask!=null&&(kt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(kt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const r=t==null?null:t.mask,s=t==null?null:t.training,o=t==null?null:t.initialState;return super.call(e,{mask:r,training:s,initialState:o})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}eD.className="GRU";_e(eD);class Mv extends Lv{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,xr(this.units,"units"),this.activation=cu(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=cu(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=kn(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=kn(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=kn(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=In(e.kernelRegularizer),this.recurrentRegularizer=In(e.recurrentRegularizer),this.biasRegularizer=In(e.biasRegularizer),this.kernelConstraint=ir(e.kernelConstraint),this.recurrentConstraint=ir(e.recurrentConstraint),this.biasConstraint=ir(e.biasConstraint),this.dropout=hf([1,lu([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=hf([1,lu([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Gt(e);const r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){const o=this.biasInitializer,i=this.units;s=new(t=class extends Yo{apply(l,c){const d=o.apply([i]),p=new pI().apply([i]),m=o.apply([i*2]);return c2(c2(d,p),m)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return me(()=>{const r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new Z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const o=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hu({ones:()=>Ko(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hu({ones:()=>Ko(s),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,a=this.recurrentDropoutMask;let l,c,d,p;0<this.dropout&&this.dropout<1&&(e=ne(e,i[0]));let m=pa(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=ne(s,a[0])),m=Pe(m,pa(s,this.recurrentKernel.read())),this.useBias&&(m=Ei(m,this.bias.read()));const[y,v,b,w]=yo(m,4,m.rank-1);l=this.recurrentActivation.apply(y),c=this.recurrentActivation.apply(v),d=Pe(ne(c,o),ne(l,this.activation.apply(b))),p=this.recurrentActivation.apply(w);const k=ne(p,this.activation.apply(d));return[k,k,d]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:uu(this.activation),recurrentActivation:uu(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tn(this.kernelInitializer),recurrentInitializer:Tn(this.recurrentInitializer),biasInitializer:Tn(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:un(this.kernelRegularizer),recurrentRegularizer:un(this.recurrentRegularizer),biasRegularizer:un(this.biasRegularizer),activityRegularizer:un(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},e),t)}}Mv.className="LSTMCell";_e(Mv);class tD extends Cu{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Mv(e),super(e)}call(e,t){return me(()=>{this.cell.dropoutMask!=null&&(kt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(kt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const r=t==null?null:t.mask,s=t==null?null:t.training,o=t==null?null:t.initialState;return super.call(e,{mask:r,training:s,initialState:o})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}tD.className="LSTM";_e(tD);class EI extends Lv{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 me(()=>{e=e;let r=e.slice(1);const s=[];for(const a of this.cells.slice().reverse())Array.isArray(a.stateSize)?s.push(r.splice(0,a.stateSize.length)):s.push(r.splice(0,1));s.reverse();const o=[];let i;for(let a=0;a<this.cells.length;++a){const l=this.cells[a];r=s[a],a===0?i=[e[0]].concat(r):i=[i[0]].concat(r),i=l.call(i,t),o.push(i.slice(1))}r=[];for(const a of o.slice().reverse())r.push(...a);return[i[0]].concat(r)})}build(e){EC(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,s)=>{Mc(`RNNCell_${s}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=o=>({className:o.getClassName(),config:o.getConfig()}),s={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),s)}static fromConfig(e,t,r={}){const s=[];for(const o of t.cells)s.push(ma(o,r));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 r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return RC(e)}setWeights(e){const t=[];for(const r of this.cells){const s=r.weights.length,o=e.splice(s);for(let i=0;i<r.weights.length;++i)t.push([r.weights[i],o[i]])}bI(t)}}EI.className="StackedRNNCells";_e(EI);function hu(n){const{ones:e,rate:t,training:r=!1,count:s=1,dropoutFunc:o}=n,i=()=>o!=null?o(e(),t):HA(e(),t),a=()=>cx(i,e,r);return!s||s<=1?ha(a().clone()):Array(s).fill(void 0).map(a).map(c=>ha(c.clone()))}var _K=function(n,e){var t={};for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&e.indexOf(r)<0&&(t[r]=n[r]);if(n!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,r=Object.getOwnPropertySymbols(n);s<r.length;s++)e.indexOf(r[s])<0&&Object.prototype.propertyIsEnumerable.call(n,r[s])&&(t[r[s]]=n[r[s]]);return t};class nD extends Cu{constructor(e){if(e.unroll)throw new wt("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new wt("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new rr({ndim:5})]}call(e,t){return me(()=>{if(this.cell.dropoutMask!=null&&(kt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(kt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new Z("ConvRNN2D cell does not support constants");const r=t==null?null:t.mask,s=t==null?null:t.training,o=t==null?null:t.initialState;return super.call(e,{mask:r,training:s,initialState:o})})}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 me(()=>{const{stateSize:t}=this.cell,r=e.shape,s=this.computeSingleOutputShape(r),o=[s[0],...s.slice(2)],i=gr(o);return Array.isArray(t)?Array(t.length).fill(i):[i]})}resetStates(e,t=!1){me(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");const r=this.inputSpec[0].shape,s=this.computeSingleOutputShape(r),o=[s[0],...s.slice(2)];if(r[0]==null)throw new Z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>gr(o)):this.states_=[gr(o)];else if(e==null)kt(this.states_),this.keptStates!=null&&(kt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>gr(o)):this.states_[0]=gr(o);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Z(`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()):kt(this.states_);for(let a=0;a<this.states_.length;++a){const l=e[a],c=o;if(!Xt(l.shape,c))throw new Z(`State ${a} is incompatible with layer ${this.name}: expected shape=${c}, received shape=${l.shape}`);this.states_[a]=l}}this.states_=this.states_.map(a=>ha(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:r,kernelSize:s,padding:o,strides:i,dilationRate:a}=this.cell,l=t==="channelsFirst",c=e[l?3:2],d=e[l?4:3],p=ki(c,s[0],o,i[0],a[0]),m=ki(d,s[1],o,i[1],a[1]);return[...e.slice(0,2),...l?[r,p,m]:[p,m,r]]}}nD.className="ConvRNN2D";class RI extends Mv{constructor(e){const{filters:t,kernelSize:r,strides:s,padding:o,dataFormat:i,dilationRate:a}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,xr(this.filters,"filters"),this.kernelSize=rf(r,2,"kernelSize"),this.kernelSize.forEach(l=>xr(l,"kernelSize")),this.strides=rf(s||1,2,"strides"),this.strides.forEach(l=>xr(l,"strides")),this.padding=o||"valid",Co(this.padding),this.dataFormat=i||"channelsLast",Vn(this.dataFormat),this.dilationRate=rf(a||1,2,"dilationRate"),this.dilationRate.forEach(l=>xr(l,"dilationRate"))}build(e){var t;e=Gt(e);const r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new Z(`The channel dimension of the input should be defined. Found ${e[r]}`);const s=e[r],o=4,i=this.kernelSize.concat([s,this.filters*o]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,this.filters*o]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){const c=this.biasInitializer,d=this.filters;l=new(t=class extends Yo{apply(m,y){const v=c.apply([d]),b=wu([d]),w=c.apply([d*2]);return fI([v,b,w])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*o],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return me(()=>{if(e.length!==3)throw new Z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const r=t.training||!1,s=e[0],o=e[1],i=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hu({ones:()=>Ko(s),rate:this.dropout,training:r,count:a,dropoutFunc:this.dropoutFunc}));const l=this.dropoutMask,c=(se,V,X)=>!V||!V[X]?se:ne(V[X],se);let d=c(s,l,0),p=c(s,l,1),m=c(s,l,2),y=c(s,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hu({ones:()=>Ko(o),rate:this.recurrentDropout,training:r,count:a,dropoutFunc:this.dropoutFunc}));const v=this.recurrentDropoutMask;let b=c(o,v,0),w=c(o,v,1),k=c(o,v,2),I=c(o,v,3);const N=3,[T,A,O,P]=yo(this.kernel.read(),a,N),[B,z,L,_]=this.useBias?yo(this.bias.read(),a):[null,null,null,null];d=this.inputConv(d,T,B,this.padding),p=this.inputConv(p,A,z,this.padding),m=this.inputConv(m,O,L,this.padding),y=this.inputConv(y,P,_,this.padding);const[H,Y,re,te]=yo(this.recurrentKernel.read(),a,N);b=this.recurrentConv(b,H),w=this.recurrentConv(w,Y),k=this.recurrentConv(k,re),I=this.recurrentConv(I,te);const le=this.recurrentActivation.apply(Pe(d,b)),de=this.recurrentActivation.apply(Pe(p,w)),j=Pe(ne(de,i),ne(le,this.activation.apply(Pe(m,k)))),ie=ne(this.recurrentActivation.apply(Pe(y,I)),this.activation.apply(j));return[ie,ie,j]})}getConfig(){const e=super.getConfig(),{units:t}=e,r=_K(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},r),s)}inputConv(e,t,r,s){const o=Qc(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?Ei(o,r,this.dataFormat):o}recurrentConv(e,t){return Qc(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}RI.className="ConvLSTM2DCell";_e(RI);class rD extends nD{constructor(e){const t=new RI(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}}rD.className="ConvLSTM2D";_e(rD);class AI extends Et{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;const t=e.shape,r=[];for(let s=0;s<this.noiseShape.length;++s)r.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return r}call(e,t){return me(()=>{this.invokeCallHook(e,t);const r=ft(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,o=this.getNoiseShape(r);return cx(()=>HA(r,this.rate,o,this.seed),()=>r,s)}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()}}AI.className="Dropout";_e(AI);class sD extends AI{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}sD.className="SpatialDropout1D";_e(sD);class oD extends Et{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",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,xr(this.units,"units"),this.activation=cu(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=kn(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=kn(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ir(e.kernelConstraint),this.biasConstraint=ir(e.biasConstraint),this.kernelRegularizer=In(e.kernelRegularizer),this.biasRegularizer=In(e.biasRegularizer),this.activityRegularizer=In(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Gt(e);const t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Gt(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return me(()=>{this.invokeCallHook(e,t);const r=ft(e),s=zA(this.activation.getClassName());let o;return s!=null?o=pa(r,this.kernel.read(),s,this.bias?this.bias.read():null):(o=pa(r,this.kernel.read()),this.bias!=null&&(o=Ei(o,this.bias.read())),this.activation!=null&&(o=this.activation.apply(o))),o})}getConfig(){const e={units:this.units,activation:uu(this.activation),useBias:this.useBias,kernelInitializer:Tn(this.kernelInitializer),biasInitializer:Tn(this.biasInitializer),kernelRegularizer:un(this.kernelRegularizer),biasRegularizer:un(this.biasRegularizer),activityRegularizer:un(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}oD.className="Dense";_e(oD);class iD extends Et{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Gt(e);for(const t of e.slice(1))if(t==null)throw new Z(`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],iu(e,1)]}call(e,t){return me(()=>{this.invokeCallHook(e,t);let r=ft(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){const s=[0];for(let o=2;o<r.rank;++o)s.push(o);s.push(1),r=Lt(r,s)}return uq(r)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}iD.className="Flatten";_e(iD);class aD extends Et{constructor(e){super(e),this.supportsMasking=!0,this.activation=cu(e.activation)}call(e,t){return me(()=>{this.invokeCallHook(e,t);const r=ft(e);return this.activation.apply(r)})}getConfig(){const e={activation:uu(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}aD.className="Activation";_e(aD);class lD extends Et{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 me(()=>(e=ft(e),aq(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}lD.className="RepeatVector";_e(lD);class uD extends Et{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){const r="Total size of new array must be unchanged.",s=t.slice();let o=1,i=null;for(let l=0;l<s.length;++l){const c=s[l];if(this.isUnknown(c))if(i===null)i=l;else throw new Z("Can only specifiy one unknown dimension.");else o*=c}const a=iu(e);if(i!==null){if(o===0||a%o!==0)throw new Z(r);s[i]=a/o}else if(a!==o)throw new Z(r);return s}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){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 me(()=>{this.invokeCallHook(e,t);const r=ft(e),s=r.shape,o=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return ce(r,o)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}uD.className="Reshape";_e(uD);class cD extends Et{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=Ii(1,e.dims.length+1);if(!Xt(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 rr({ndim:this.dims.length+1})]}computeOutputShape(e){e=Gt(e);const t=e.slice();return this.dims.forEach((r,s)=>{t[s+1]=e[r]}),t}call(e,t){return Lt(ft(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}cD.className="Permute";_e(cD);class hD extends Et{constructor(e){super(e??{}),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.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 r=ft(e);return wC(Gy(r,this.maskValue),-1)}call(e,t){return me(()=>{this.invokeCallHook(e,t);const r=ft(e),i=wC(Gy(r,this.maskValue),-1,!0);return ne(r,Ge(i,r.dtype))})}}hD.className="Masking";_e(hD);class dD extends Et{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(rn(e.inputLength))}this.inputDim=e.inputDim,xr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,xr(this.outputDim,"outputDim"),this.embeddingsInitializer=kn(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=In(e.embeddingsRegularizer),this.activityRegularizer=In(e.activityRegularizer),this.embeddingsConstraint=ir(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 me(()=>this.maskZero?(e=ft(e),Gy(e,Mt(e))):null)}computeOutputShape(e){if(e=Gt(e),this.inputLength==null)return[...e,this.outputDim];const t=rn(this.inputLength);if(t.length!==e.length-1)throw new Z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let s=0;s<t.length;++s){const o=t[s],i=e[s+1];if(o!=null&&i!=null&&o!==i)throw new Z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);o==null&&(t[r]=i),r++}}return[e[0],...t,this.outputDim]}call(e,t){return me(()=>{this.invokeCallHook(e,t);let r=ft(e);r.dtype!=="int32"&&(r=fa(r,"int32"));const s=jA(this.embeddings.read(),ce(r,[r.size]));return ce(s,Gt(this.computeOutputShape(r.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Tn(this.embeddingsInitializer),embeddingsRegularizer:un(this.embeddingsRegularizer),activityRegularizer:un(this.activityRegularizer),embeddingsConstraint:or(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}dD.className="Embedding";_e(dD);class dh extends Et{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new wt}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;const r=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const o=e[e.length-t.length+s],i=t[s];if(o==null||i==null||o<0||i<0)r.push(null);else if(o===1)r.push(i);else if(i===1)r.push(o);else{if(o!==i)throw new Z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(o)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Gt(e)]),e=e,e.length<2)throw new Z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const o of e)o!=null&&o[0]!==null&&t.push(o[0]);if(t=ou(t),t.length>1)throw new Z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let o=1;o<e.length;++o){const i=e[o]==null?null:e[o].slice(1);r=this.computeElementwiseOpOutputShape(r,i)}const s=e.map(o=>o.length);e.indexOf(null)===-1&&ou(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return me(()=>{if(e=e,this.reshapeRequired){const r=[],s=e.map(o=>o.rank);if(s.indexOf(null)===-1){const o=lu(s);for(let i of e){const a=i.rank;for(let l=0;l<o-a;++l)i=lx(i,1);r.push(i)}return this.mergeFunction(r)}else{let o=!1;for(const l of e){const c=l.rank;if(c==null){const d=l.shape,p=d[0],m=d.slice(1).concat([p]);let y=ce(l,[p].concat(iu(d.slice(1))));y=Lt(y,[1,0]),y=ce(y,m),r.push(y),o=!0}else if(c>1){const d=Ii(1,c).concat([0]);r.push(Lt(l,d)),o=!0}else r.push(l)}let i=this.mergeFunction(r);const a=i.rank;if(o){if(a==null){const l=i.shape,c=l.length,d=l[c-1],p=[d].concat(l.slice(0,l.length-1));i=ce(Lt(ce(i,[-1,d]),[1,0]),p)}else if(a>1){const l=[a-1].concat(Ii(0,a-1));i=Lt(i,l)}}return i}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const o=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,o)}let r=[];for(const s of e)s!=null&&s[0]!==null&&r.push(s[0]);return r=ou(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return me(()=>{if(t==null)return null;if(!Array.isArray(t))throw new Z("`mask` should be an Array");if(!Array.isArray(e))throw new Z("`inputs` should be an Array");if(t.length!==e.length)throw new Z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Ss(s,0));let r=t[0];for(let s=1;s<t.length-1;++s)r=rl(r,t[s]);return r})}}class fD extends dh{constructor(e){super(e)}mergeFunction(e){return me(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=Pe(t,e[r]);return t})}}fD.className="Add";_e(fD);class pD extends dh{constructor(e){super(e)}mergeFunction(e){return me(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=ne(t,e[r]);return t})}}pD.className="Multiply";_e(pD);class mD extends dh{constructor(e){super(e)}mergeFunction(e){return me(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=Pe(t,e[r]);return ne(1/e.length,t)})}}mD.className="Average";_e(mD);class gD extends dh{constructor(e){super(e)}mergeFunction(e){return me(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=vu(t,e[r]);return t})}}gD.className="Maximum";_e(gD);class xD extends dh{constructor(e){super(e)}mergeFunction(e){return me(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=zm(t,e[r]);return t})}}xD.className="Minimum";_e(xD);class yD extends dh{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new Z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const s of e)if(s!=null){t=!1;break}if(t)return;const r=[];for(let s=0;s<e.length;++s){const o=e[s].slice();o.splice(this.axis,1);let i=!1;for(const a of r)if(Xt(a,o)){i=!0;break}i||r.push(o)}if(r.length>1)throw new Z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return me(()=>fI(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new Z("A `Concatenate` layer should be called on a list of inputs.");const t=e,r=t[0].slice(),s=this.axis<0?r.length+this.axis:this.axis;for(const o of t.slice(1)){if(r[s]==null||o[s]==null){r[s]=null;break}r[s]+=o[s]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new Z("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new Z("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new Z(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return me(()=>{let r=!0;if(t.forEach(i=>{if(i!=null){r=!1;return}}),r)return null;const s=[];for(let i=0;i<e.length;++i)t[i]==null?s.push(Ge(Ko(e[i]),"bool")):t[i].rank<e[i].rank?s.push(Ss(t[i],-1)):s.push(t[i]);const o=Is(s,this.axis);return xR(o,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}yD.className="Concatenate";_e(yD);function xm(n,e){for(;n<0;)n+=e;return n}function DK(n,e,t){if(n.shape.length>3||e.shape.length>3)throw new wt("batchDot is not implemented for tensors of 4D or higher rank yet");if(U(n.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${n.shape.length}`),U(n.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),n.dtype==="complex64"||e.dtype==="complex64")throw new wt("batchDot is not implemented for complex64-type Tensors yet.");const r=n.shape.length,s=e.shape.length;t==null&&(t=[r-1,s-2]);const o=t;return me(()=>{let i;if(r>s){i=r-s;const l=[];for(let c=0;c<i;++c)l.push(1);e=ce(e,e.shape.concat(l))}else if(s>r){i=s-r;const l=[];for(let c=0;c<i;++c)l.push(1);n=ce(n,n.shape.concat(l))}else i=0;let a;if(n.shape.length===2&&e.shape.length===2)o[0]===o[1]?a=rt(ne(n,e),o[0]):a=rt(ne(Lt(n,[1,0]),e),o[1]);else{const l=o[0]!==n.shape.length-1,c=o[1]===e.shape.length-1;a=qt(n,e,l,c)}if(i>0){let l;r>s?l=r+s-3:l=r-1;const c=[];for(let d=l;d<l+i;++d)c.push(d);a=nx(a,c)}return a.shape.length===1&&(a=Ss(a,1)),a})}class vD extends dh{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){U(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0],r=e[1];if(t.length>3||r.length>3)throw new wt("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,r);if(t[s[0]]!==r[s[1]])throw new Z(`Dimension incompatibility: ${t[s[0]]} !== ${r[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new Z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((o,i)=>xm(o,e[i].shape.length)):s=[xm(this.axes,t.shape.length),xm(this.axes,r.shape.length)],this.normalize&&(t=Qy(t,s[0]),r=Qy(r,s[1])),DK(t,r,s)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[xm(this.axes,e.length),xm(this.axes,t.length)],r}computeOutputShape(e){U(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new wt("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,r);t.splice(s[0],1),r.splice(s[1],1),r.splice(0,1);const o=t.concat(r);return o.length===1&&o.push(1),o}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}vD.className="Dot";_e(vD);class wD extends Et{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 me(()=>{this.invokeCallHook(e,t);const r=ft(e);return cx(()=>Pe(Av(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}}wD.className="GaussianNoise";_e(wD);class bD extends Et{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 me(()=>{this.invokeCallHook(e,t);const r=ft(e);return this.rate>0&&this.rate<1?cx(()=>{const o=Math.sqrt(this.rate/(1-this.rate));return ne(r,Av(r.shape,1,o))},()=>r,t.training||!1):r})}}bD.className="GaussianDropout";_e(bD);class CD extends Et{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ft(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 me(()=>{if(this.rate<1&&this.rate>0){const r=this._getNoiseShape(e);return cx(()=>{const o=ft(e),a=-1.6732632423543772*1.0507009873554805;let l=lh(tx(r),this.rate);l=fa(l,"float32");const c=((1-this.rate)*(1+this.rate*a**2))**-.5,d=-c*a*this.rate,p=Pe(ne(o,l),ne(Pe(l,-1),a));return Pe(ne(p,c),d)},()=>ft(e),t.training||!1)}return e})}}CD.className="AlphaDropout";_e(CD);function Gm(n,e,t,r,s,o=.001){let i;if(n.rank===2)i=ZW(n,e,t,r,s,o);else if(n.rank===3)i=tV(n,e,t,r,s,o);else if(n.rank===4)i=rV(n,e,t,r,s,o);else throw new wt(`batchNormalization is not implemented for array of rank ${n.rank} yet`);return i}function OK(n,e,t,r,s=.001){return me(()=>{const o=wk(n,r),i=o.mean,a=o.variance;return[Gm(n,i,a,t,e,s),i,a]})}function FK(n,e,t,r,s=.001){return me(()=>{const o=wk(n,r),i=o.mean,a=o.variance,l=[];for(const v of Ii(0,n.rank))r.indexOf(v)!==-1?l.push(1):l.push(n.shape[v]);const c=ce(i,l),d=ce(a,l),p=e==null?null:ce(e,l),m=t==null?null:ce(t,l);return[Gm(n,c,d,m,p,s),i,a]})}function PK(n,e,t,r,s=.001){return Xt(r.slice().sort(),Ii(0,n.rank-1))?OK(n,e,t,r,s):FK(n,e,t,r,s)}class SD extends Et{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=kn(e.betaInitializer||"zeros"),this.gammaInitializer=kn(e.gammaInitializer||"ones"),this.movingMeanInitializer=kn(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=kn(e.movingVarianceInitializer||"ones"),this.betaConstraint=ir(e.betaConstraint),this.gammaConstraint=ir(e.gammaConstraint),this.betaRegularizer=In(e.betaRegularizer),this.gammaRegularizer=In(e.gammaRegularizer)}build(e){e=Gt(e);const t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new Z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new rr({ndim:e.length,axes:{[t]:r}})];const s=[r];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 me(()=>{const r=t.training==null?!1:t.training,s=ft(e),o=s.shape,i=o.length,a=Ii(0,i),l=this.axis>=0?this.axis:this.axis+i;a.splice(l,1);const c=th(1,i);c[l]=o[l];const d=a.slice();d.sort();const p=!Xt(d,Ii(0,i).slice(0,i-1)),m=()=>{if(p){const I=ce(this.movingMean.read(),c),N=ce(this.movingVariance.read(),c),T=this.center?ce(this.beta.read(),c):null,A=this.scale?ce(this.gamma.read(),c):null;return Gm(s,I,N,T,A,this.epsilon)}else return Gm(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return m();const[y,v,b]=PK(s,this.gamma.read(),this.beta.read(),a,this.epsilon),w=(I,N,T)=>{me(()=>{const A=1-T,O=I.read(),P=ne(ut(O,N),A);I.write(ut(O,P))})};return(()=>{w(this.movingMean,v,this.momentum),w(this.movingVariance,b,this.momentum)})(),y})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tn(this.betaInitializer),gammaInitializer:Tn(this.gammaInitializer),movingMeanInitializer:Tn(this.movingMeanInitializer),movingVarianceInitializer:Tn(this.movingVarianceInitializer),betaRegularizer:un(this.betaRegularizer),gammaRegularizer:un(this.gammaRegularizer),betaConstraint:or(this.betaConstraint),gammaConstraint:or(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}SD.className="BatchNormalization";_e(SD);class kD extends Et{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=kn(e.betaInitializer||"zeros"),this.gammaInitializer=kn(e.gammaInitializer||"ones"),this.betaRegularizer=In(e.betaRegularizer),this.gammaRegularizer=In(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Gt(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let o=0;o<this.axis.length;++o)this.axis[o]<0&&(this.axis[o]+=t);for(const o of this.axis)if(o<0||o>=t)throw new Error(`Invalid axis: ${o}`);if(this.axis.length!==ou(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const r=this.axis.map(o=>e[o]),s=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){const r=ft(e),s=r.shape,o=s.length;return me(()=>{let{mean:a,variance:l}=wk(r,this.axis,!0);const c=th(1,o);for(const b of this.axis)c[b]=s[b];const d=b=>b!=null&&b.shape.length!==o?ce(b,c):b;let p=this.scale?d(this.gamma.read()):null,m=this.center?d(this.beta.read()):null;const y=[],v=[];for(let b=0;b<o;++b)this.axis.indexOf(b)!==-1?(y.push(s[b]),v.push(1)):(y.push(1),v.push(s[b]));return a=bi(a,y),l=bi(l,y),p!=null&&(p=bi(p,v)),m!=null&&(m=bi(m,v)),Gm(r,a,l,m,p,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tn(this.betaInitializer),gammaInitializer:Tn(this.gammaInitializer),betaRegularizer:un(this.betaRegularizer),gammaRegularizer:un(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}kD.className="LayerNormalization";_e(kD);function LK(n,e,t){return me(()=>{if(n.rank!==4)throw new Z(`temporalPadding expects input tensor to be 4-D, but received a ${n.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new Z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=$i()),t!=="channelsLast"&&t!=="channelsFirst")throw new Z(`Unknown data format: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return t==="channelsFirst"?r=[[0,0],[0,0],e[0],e[1]]:r=[[0,0],e[0],e[1],[0,0]],bk(n,r)})}class ID extends Et{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?$i():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new Z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new Z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new Z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new rr({ndim:4})]}computeOutputShape(e){e=Gt(e);let t,r;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return me(()=>LK(ft(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}ID.className="ZeroPadding2D";_e(ID);function zv(n,e,t,r,s,o){return me(()=>{Vn(s),UA(o),Co(r),t==null&&(t=[1,1]),r==null&&(r="valid"),s==null&&(s=$i()),o==null&&(o="max"),n=$I(n,s);let i;const a=r==="same"?"same":"valid";return o==="max"?i=vk(n,e,t,a):i=uk(n,e,t,a),s==="channelsFirst"&&(i=Lt(i,[0,3,1,2])),i})}function $D(n,e,t,r,s,o){return me(()=>{Vn(s),UA(o),Co(r),t==null&&(t=[1,1,1]),r==null&&(r="valid"),s==null&&(s=$i()),o==null&&(o="max"),n=V_(n,s);let i;const a=r==="same"?"same":"valid";return o==="max"?i=UG(n,e,t,a):i=VW(n,e,t,a),s==="channelsFirst"&&(i=Lt(i,[0,4,1,2,3])),i})}class ND extends Et{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new Z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(xr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new Z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);xr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Co(this.padding),this.inputSpec=[new rr({ndim:3})]}computeOutputShape(e){e=Gt(e);const t=ki(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return me(()=>{this.invokeCallHook(e,t),e=lx(ft(e),2);const r=this.poolingFunction(ft(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return nx(r,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class TD extends ND{constructor(e){super(e)}poolingFunction(e,t,r,s,o){return Vn(o),Co(s),zv(e,t,r,s,o,"max")}}TD.className="MaxPooling1D";_e(TD);class ED extends ND{constructor(e){super(e)}poolingFunction(e,t,r,s,o){return Vn(o),Co(s),zv(e,t,r,s,o,"avg")}}ED.className="AveragePooling1D";_e(ED);class RD extends Et{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new Z(`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];xr(this.poolSize,"poolSize"),xr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vn(this.dataFormat),Co(this.padding),this.inputSpec=[new rr({ndim:4})]}computeOutputShape(e){e=Gt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ki(t,this.poolSize[0],this.padding,this.strides[0]),r=ki(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return me(()=>(this.invokeCallHook(e,t),this.poolingFunction(ft(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 AD extends RD{constructor(e){super(e)}poolingFunction(e,t,r,s,o){return Vn(o),Co(s),zv(e,t,r,s,o,"max")}}AD.className="MaxPooling2D";_e(AD);class _D extends RD{constructor(e){super(e)}poolingFunction(e,t,r,s,o){return Vn(o),Co(s),zv(e,t,r,s,o,"avg")}}_D.className="AveragePooling2D";_e(_D);class DD extends Et{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new Z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];xr(this.poolSize,"poolSize"),xr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vn(this.dataFormat),Co(this.padding),this.inputSpec=[new rr({ndim:5})]}computeOutputShape(e){e=Gt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ki(t,this.poolSize[0],this.padding,this.strides[0]),r=ki(r,this.poolSize[1],this.padding,this.strides[1]),s=ki(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,s]:[e[0],t,r,s,e[4]]}call(e,t){return me(()=>(this.invokeCallHook(e,t),this.poolingFunction(ft(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class OD extends DD{constructor(e){super(e)}poolingFunction(e,t,r,s,o){return Vn(o),Co(s),$D(e,t,r,s,o,"max")}}OD.className="MaxPooling3D";_e(OD);class FD extends DD{constructor(e){super(e)}poolingFunction(e,t,r,s,o){return Vn(o),Co(s),$D(e,t,r,s,o,"avg")}}FD.className="AveragePooling3D";_e(FD);class PD extends Et{constructor(e){super(e),this.inputSpec=[new rr({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new wt}}class LD extends PD{constructor(e){super(e||{})}call(e,t){return me(()=>{const r=ft(e);return Xn(r,1)})}}LD.className="GlobalAveragePooling1D";_e(LD);class MD extends PD{constructor(e){super(e||{})}call(e,t){return me(()=>{const r=ft(e);return Si(r,1)})}}MD.className="GlobalMaxPooling1D";_e(MD);class zD extends Et{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vn(this.dataFormat),this.inputSpec=[new rr({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new wt}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class BD extends zD{call(e,t){return me(()=>{const r=ft(e);return this.dataFormat==="channelsLast"?Xn(r,[1,2]):Xn(r,[2,3])})}}BD.className="GlobalAveragePooling2D";_e(BD);class UD extends zD{call(e,t){return me(()=>{const r=ft(e);return this.dataFormat==="channelsLast"?Si(r,[1,2]):Si(r,[2,3])})}}UD.className="GlobalMaxPooling2D";_e(UD);class WD extends Et{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){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),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,r={}){const s=t.layer,o=ma(s,r);delete t.layer;const i={layer:o};return Object.assign(i,t),new e(i)}}class VD extends WD{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=Gt(e),e.length<3)throw new Z(`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){e=Gt(e);const t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),s=e[1];return[r[0],s].concat(r.slice(1))}call(e,t){return me(()=>(e=ft(e),J_((i,a)=>[ft(this.layer.call(i,t)),[]],e,[],!1,null,null,!1,!0)[1]))}}VD.className="TimeDistributed";_e(VD);function MK(n){hh(nq,"BidirectionalMergeMode",n)}const zK="concat";class GD extends WD{constructor(e){super(e);const t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=ma(r),t.goBackwards=t.goBackwards!==!0;const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=ma(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?zK:e.mergeMode,MK(this.mergeMode),e.weights)throw new wt("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,s,o;return this.returnState&&(o=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,s=[r]):this.mergeMode==null?s=[r,r.slice()]:s=[r],this.returnState?this.mergeMode==null?s.concat(o).concat(o.slice()):[r].concat(o).concat(o.slice()):ks(s)}apply(e,t){let r=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const o=Q_(e,r,s,this.numConstants);if(e=o.inputs,r=o.initialState,s=o.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&s==null)return super.apply(e,t);const i=[],a=[];if(r!=null){const c=r.length;if(c%2>0)throw new Z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,i.push(...r);const d=r.map(p=>new rr({shape:p.shape}));this.forwardLayer.stateSpec=d.slice(0,c/2),this.backwardLayer.stateSpec=d.slice(c/2),a.push(...d)}if(s!=null)throw new wt("Support for constants in Bidirectional layers is not implemented yet.");const l=i[0]instanceof va;for(const c of i)if(c instanceof va!==l)throw new Z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){const c=[e].concat(i),d=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=d;const m=super.apply(c,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return me(()=>{const r=t.initialState;let s,o;if(r==null)s=this.forwardLayer.call(e,t),o=this.backwardLayer.call(e,t);else{const l=r.slice(0,r.length/2),c=r.slice(r.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),o=this.backwardLayer.call(e,Object.assign(t,{initialState:c}))}let i;this.returnState&&(Array.isArray(s)&&(i=s.slice(1).concat(o.slice(1))),s=s[0],o=o[0]),this.returnSequences&&(o=Zc(o,1));let a;return this.mergeMode==="concat"?a=fI([s,o]):this.mergeMode==="sum"?a=Pe(s,o):this.mergeMode==="ave"?a=ne(.5,Pe(s,o)):this.mergeMode==="mul"?a=ne(s,o):this.mergeMode==null&&(a=[s,o]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Mc(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Mc(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){const o=this.forwardLayer.states.map(i=>null);return Array.isArray(r)?r.concat(o).concat(o):[r].concat(o).concat(o)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const r=ma(t.layer);if(delete t.layer,t.numConstants!=null)throw new wt("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=r,new e(s)}}GD.className="Bidirectional";_e(GD);class jD extends Et{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){const e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return me(()=>(e=ft(e),e.dtype!=="float32"&&(e=fa(e,"float32")),Pe(ne(e,this.scale),this.offset)))}}jD.className="Rescaling";_e(jD);const{resizeBilinear:BK,cropAndResize:UK}=nl;class HD extends Et{constructor(e){super(e),this.height=e.height,this.width=e.width}centerCrop(e,t,r,s,o,i,a,l){return me(()=>{let c,d=!1;const p=t/i,m=r/a,y=(s+t)/i,v=(o+r)/a,b=[p,m,y,v],w=[];e.rank===3?(d=!0,c=sl([e])):c=e;for(let A=0;A<c.shape[0];A++)w.push(b);const k=$m(w,[w.length,4]),I=Bm(0,w.length,1,"int32"),T=UK(c,k,I,[s,o],"nearest");return fa(d?ft(eh(T)):T,l)})}upsize(e,t,r,s){return me(()=>{const o=BK(e,[t,r]);return fa(o,s)})}call(e,t){return me(()=>{const r=ft(e),s=r.dtype,o=r.shape,i=o[o.length-3],a=o[o.length-2];let l=0;i!==this.height&&(l=Math.floor((i-this.height)/2));let c=0;return a!==this.width&&(c=Math.floor((a-this.width)/2),c===0&&(c=1)),l>=0&&c>=0?this.centerCrop(r,l,c,this.height,this.width,i,a,s):this.upsize(e,this.height,this.width,s)})}getConfig(){const e={height:this.height,width:this.width},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){e=Gt(e);const t=e.length-3,r=e.length-2;return e[t]=this.height,e[r]=this.width,e}}HD.className="CenterCrop";_e(HD);function WK(n,e,t,r){let s=ft(n);if(s.dtype!=="int32"&&(s=fa(s,"int32")),e==="int")return s;const o=s.shape;if(s.rank===0&&(s=Ss(s,-1)),e==="oneHot"&&s.shape[s.shape.length-1]!==1&&(s=Ss(s,-1)),s.rank>2)throw new Z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${e} and input shape ${o} which would result in output rank ${s.rank}.`);const i=["multiHot","oneHot"].includes(e),a=s;let l;if(typeof r<"u"&&e==="count"?l=zN(a,r,t,i):l=zN(a,[],t,i),e!=="tfIdf")return l;if(r)return ne(l,r);throw new Z("When outputMode is 'tfIdf', weights must be provided.")}class qD extends Et{constructor(e){super(e),this.numTokens=e.numTokens,e.outputMode?this.outputMode=e.outputMode:this.outputMode="multiHot"}getConfig(){const e={numTokens:this.numTokens,outputMode:this.outputMode},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){return e=Gt(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return me(()=>{e=ft(e),e.dtype!=="int32"&&(e=fa(e,"int32"));let r;if(typeof t.countWeights<"u"){if(this.outputMode!=="count")throw new Z(`countWeights is not used when outputMode !== count.
Received countWeights=${t.countWeights}`);r=ft(t.countWeights)}const s=Si(e),o=Wy(e),i=bo(this.numTokens,s).bufferSync().get(0),a=lh(o,0).bufferSync().get(0);if(!(i&&a))throw new Z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return WK(e,this.outputMode,this.numTokens,r)})}}qD.className="CategoryEncoding";_e(qD);const VK=["bilinear","nearest"],T2=new Set(VK);class KD extends Et{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation)if(T2.has(e.interpolation))this.interpolation=e.interpolation;else throw new Z(`Invalid interpolation parameter: ${e.interpolation} is not implemented`);else this.interpolation="bilinear";this.cropToAspectRatio=!!e.cropToAspectRatio}computeOutputShape(e){e=Gt(e);const t=e[2];return[this.height,this.width,t]}getConfig(){const e={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return me(()=>{const r=[this.height,this.width];if(this.interpolation==="bilinear")return nl.resizeBilinear(e,r,!this.cropToAspectRatio);if(this.interpolation==="nearest")return nl.resizeNearestNeighbor(e,r,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...T2]} are supported`)})}}KD.className="Resizing";_e(KD);class XD{constructor(e){this.seed=e}next(){if(this.seed!==void 0)return this.seed++}}XD.className="RandomSeed";class YD extends Et{constructor(e){super(e),this.randomGenerator=new XD(e.seed)}getConfig(){const e={seed:this.randomGenerator.seed},t=super.getConfig();return Object.assign(e,t),e}}YD.className="BaseRandomLayer";const GK=["bilinear","nearest"],E2=new Set(GK);class QD extends YD{constructor(e){super(e);const{factor:t,interpolation:r="bilinear"}=e;if(this.factor=t,Array.isArray(this.factor)&&this.factor.length===2)this.widthLower=this.factor[0],this.widthUpper=this.factor[1];else if(!Array.isArray(this.factor)&&this.factor>0)this.widthLower=-this.factor,this.widthUpper=this.factor;else throw new Z(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`);if(this.widthLower<-1||this.widthUpper<-1)throw new Z(`factor must have values larger than -1. Got: ${this.factor}`);if(this.widthUpper<this.widthLower)throw new Z(`factor cannot have upper bound less than lower bound.
Got upper bound: ${this.widthUpper}.
Got lower bound: ${this.widthLower}
`);if(r)if(E2.has(r))this.interpolation=r;else throw new Z(`Invalid interpolation parameter: ${r} is not implemented`)}getConfig(){const e={factor:this.factor,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){e=Gt(e);const t=e[2];return[this.imgHeight,-1,t]}call(e,t){return me(()=>{const r=ft(e);this.imgHeight=r.shape[r.shape.length-3];const s=r.shape[r.shape.length-2];this.widthFactor=tx([1],1+this.widthLower,1+this.widthUpper,"float32",this.randomGenerator.next());let o=this.widthFactor.dataSync()[0]*s;o=Math.round(o);const i=[this.imgHeight,o];switch(this.interpolation){case"bilinear":return nl.resizeBilinear(e,i);case"nearest":return nl.resizeNearestNeighbor(e,i);default:throw new Error(`Interpolation is ${this.interpolation}
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QK={kernelName:XS,backendName:"cpu",kernelFunc:nh};function ZD(n,e,t,r){if(r==="int32"){const s=Int32Array.from(n);return[e,"int32",s]}if(r==="bool"){const s=ah([0],t),[o,i]=Gn((a,l)=>a!==l?1:0)(e,[],n,s,"bool");return[i,"bool",o]}throw new Error(`Error in Cast: failed to cast ${t} to ${r}`)}function du(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{dtype:o}=r;if(o==="complex64"){if(s.dtype==="complex64")return wa({inputs:{x:s},backend:t});const d=r1(t,s.shape,s.dtype),p=du({inputs:{x:s},backend:t,attrs:{dtype:"float32"}}),m=Hs({inputs:{real:p,imag:d},backend:t});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(p),m}if(s.dtype==="complex64"){const d=nh({inputs:{input:s},backend:t}),p=du({inputs:{x:d},backend:t,attrs:{dtype:o}});return t.disposeIntermediateTensorInfo(d),p}if(!wE(s.dtype,o)){const d=wa({inputs:{x:s},backend:t});return{dataId:d.dataId,shape:d.shape,dtype:o}}const i=t.data.get(s.dataId).values,[a,l,c]=ZD(i,s.shape,s.dtype,o);return t.makeTensorInfo(a,l,c)}const JK={kernelName:ug,backendName:"cpu",kernelFunc:du};function lr(n,e,t,r){return t==null?({inputs:s,backend:o})=>{const{a:i,b:a}=s,l=o;Ye([i,a],n);const c=l.data.get(i.dataId).values,d=l.data.get(a.dataId).values,p=i.dtype==="string"?il(c):c,m=i.dtype==="string"?il(d):d,y=r||i.dtype,[v,b]=e(i.shape,a.shape,p,m,y);return l.makeTensorInfo(b,y,v)}:({inputs:s,backend:o})=>{const{a:i,b:a}=s,l=o;if(i.dtype==="complex64"||a.dtype==="complex64"){const 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s=0;s<r.size;++s){const i=r.indexToLoc(s).slice(),a=i[0],l=i[2],c=e.locToIndex([a,l]);i[2]=e.values[c];const d=n.locToIndex(i);0<=d&&d<n.values.length&&(r.values[s]=n.values[d])}return r}const pO=Gn((n,e)=>n>e?1:0),f7=lr(M1,pO,null,"bool"),p7={kernelName:M1,backendName:"cpu",kernelFunc:f7};const mO=Gn((n,e)=>n>=e?1:0),m7=lr(bg,mO,null,"bool"),g7={kernelName:bg,backendName:"cpu",kernelFunc:m7};const gO=Gn((n,e)=>n<e?1:0),x7=lr(B1,gO,null,"bool"),y7={kernelName:B1,backendName:"cpu",kernelFunc:x7};const xO=Gn((n,e)=>n<=e?1:0),v7=lr(U1,xO,null,"bool"),w7={kernelName:U1,backendName:"cpu",kernelFunc:v7};function yO(n,e,t){const r=(e-n)/(t-1),s=Gr(t,"float32");s[0]=n;for(let o=1;o<s.length;o++)s[o]=s[o-1]+r;return s}const vO=ba(n=>Math.log(n)),b7=Su($g,vO),C7={kernelName:$g,backendName:"cpu",kernelFunc:b7};function wO(n,e,t,r){const s=Vr(r,Se(t));for(let o=0;o<s.length;++o){const i=o*e;let a=n[i];for(let l=0;l<e;++l){const c=n[i+l];(Number.isNaN(c)||c>a)&&(a=c)}s[o]=a}return s}const 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Ns(n){const{inputs:e,attrs:t,backend:r}=n,{x:s}=e,{perm:o}=t;Ye(s,"transpose");const i=s.shape.length,a=new Array(i);for(let p=0;p<a.length;p++)a[p]=s.shape[o[p]];const l=r.data.get(s.dataId).values,c=FI(l,s.shape,s.dtype,o,a);return{dataId:r.write(c,a,s.dtype),shape:a,dtype:s.dtype}}const D7={kernelName:ef,backendName:"cpu",kernelFunc:Ns};function IO(n,e,t,r){const[s,o]=yr(n,r),i=Ys(e,"int32"),a=Gr(Se(s),i),l=Se(o);for(let c=0;c<a.length;++c){const d=c*l;let p=1;for(let m=0;m<l;++m)p*=t[d+m];a[c]=p}return{outVals:a,outShape:s,outDtype:i}}function O7(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,keepDims:i}=r;Ye(s,"prod");const a=s.shape.length,l=Tt(o,s.shape),c=En(l,a);let d=l,p=s;const m=[];c!=null&&(p=Ns({inputs:{x:s},backend:t,attrs:{perm:c}}),m.push(p),d=Wn(d.length,a));const y=t.data.get(p.dataId).values,{outVals:v,outShape:b,outDtype:w}=IO(p.shape,p.dtype,y,d);let k=b;return i&&(k=Yn(b,l)),m.forEach(I=>t.disposeIntermediateTensorInfo(I)),t.makeTensorInfo(k,w,v)}const F7={kernelName:ov,backendName:"cpu",kernelFunc:O7};function P7(n,e,t){n.forEach((r,s)=>{if(r<0||r>=t){const o=Sf(s,e.length,tt(e)).join(",");throw new Error(`indices[${o}] = ${r} is not in [0, ${t})`)}})}function L7(n,e){for(let t=0;t<n.length;++t){const r=n[t],s=t===n.length-1?e:n[t+1].length;if(r.length===0)throw new Error("Ragged splits may not be empty");if(r[0]<0)throw new Error("Ragged splits must be non-negative");if(r[r.length-1]>s)throw new Error("Ragged splits must not point past values");for(let o=1;o<r.length;++o)if(r[o-1]>r[o])throw new Error("Ragged splits must be sorted in ascending order")}}function M7(n,e,t,r){const s=[];let o=0;const i=e.length-1+t.length,a=new Array(i).fill(null).map(()=>[0]);L7(t,r);let l=1;for(let c=0;c<e.length-1;++c){l*=e[c];const d=e[c+1];for(let p=1;p<l+1;++p)a[c].push(p*d)}for(let c=0;c<n.length;++c){let d=n[c],p=n[c]+1;for(let m=0;m<t.length;++m){const y=t[m],v=m+e.length-1;if(v>=0){const b=a[v],w=b[b.length-1]-y[d];for(let k=d;k<p;++k)a[v].push(y[k+1]+w)}d=y[d],p=y[p]}p!==d&&(s.push([d,p]),o+=p-d)}return{outSplits:a,valueSlices:s,numValues:o}}function z7(n){const e=[];for(let t=0;t<n.length;++t){const r=n[t].length,s=Mn("int32",r);e.push(s),n[t].forEach((o,i)=>s[i]=o)}return e}function D2(n,e){const t=n.slice(0,e);for(;t.length<e;)t.push(1);for(let r=e;r<n.length;r++)t[e-1]*=n[r];return t}function B7(n,e,t,r,s,o){const i=D2(e,2)[1],a=D2(o,2)[1];let l=0;for(const c of t)for(let d=c[0];d<c[1];++d){for(let p=0;p<r;++p)s[l*a+p]=n[d*i+p];++l}}function U7(n,e,t,r,s){const o=e.slice();o[0]=s;const i=Mn(t,Se(o)),a=n.length,l=a===0?0:a/e[0];return B7(n,e,r,l,i,o),[i,o]}function $O(n,e,t,r,s,o,i,a){if(n.length===0)throw new Error("paramsNestedSplits must be non empty");if(e[0].length===0)throw new Error("Split tensors must not be scalars");const l=e[0][0]-1;if(P7(o,i,l),r.length===0)throw new Error("params.rank must be nonzero");const c=r[0],{outSplits:d,valueSlices:p,numValues:m}=M7(o,i,n,c),y=z7(d),v=U7(t,r,s,p,m);return[y,v[0],v[1]]}const O2=2147483647;function NO(n,e,t,r,s,o,i){if(e.length>1)throw new Error("starts must be a scalar or vector");if(s.length>1)throw new Error("limits must be a scalar or vector");if(i.length>1)throw new Error("deltas must be a scalar or vector");const a=e.length===0,l=s.length===0,c=i.length===0,d=[];a||d.push(e[0]),l||d.push(s[0]),c||d.push(i[0]);for(let w=1;w<d.length;++w)if(d[w]!==d[w-1])throw new Error("starts, limits, and deltas must have the same shape");const p=d.length===0?1:d[0],m=Mn("int32",p+1);m[0]=0;for(let w=0;w<p;++w){const k=a?n[0]:n[w],I=l?r[0]:r[w],N=c?o[0]:o[w];if(N===0)throw new Error("Requires delta != 0");let T;if(N>0&&I<k||N<0&&I>k)T=0;else if(T=Math.ceil(Math.abs((I-k)/N)),T>O2)throw new Error(`Requires ((limit - start) / delta) <= ${O2}`);m[w+1]=m[w]+T}const y=m[p],v=Mn(t,y);let b=0;for(let w=0;w<p;++w){const k=m[w+1]-m[w];let I=a?n[0]:n[w];const N=c?o[0]:o[w];for(let T=0;T<k;++T)v[b++]=I,I+=N}return[m,v]}var Wo=vi;class s1{constructor(e,t,r,s,o,i,a,l,c,d){this.shape=e,this.shapeShape=t,this.values=r,this.valuesShape=s,this.valuesDType=o,this.defaultValue=i,this.defaultValueShape=a,this.rowPartitionValues=l,this.rowPartitionValuesShapes=c,this.rowPartitionTypes=dA(d),this.raggedRank=fA(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===Wo.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===Wo.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){const t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case Wo.VALUE_ROWIDS:return s1.getMaxWidthValueRowID(t);case Wo.ROW_SPLITS:return s1.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${Wo[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){const t=e.length;if(t===0||t===1)return 0;let r=0;for(let s=0;s<t-1;++s){const o=e[s+1]-e[s];o>r&&(r=o)}return r}static getMaxWidthValueRowID(e){const t=e.length;if(t===0)return 0;let r=0,s=e[0],o=0;for(let i=1;i<t;++i){const a=e[i];a!==s&&(s=a,o=Math.max(i-r,o),r=i)}return Math.max(t-r,o)}tensorShapeFromTensor(e,t,r=!0){if(t.length===0){if(e[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return P2(e,r)}calculateOutputSize(e){const t=this.valuesShape,r=this.defaultValueShape;pA(r,t);const s=this.tensorShapeFromTensor(this.shape,this.shapeShape),i=hA(this.raggedRank,s,t);i[0]<0&&(i[0]=e);for(let a=1;a<=this.raggedRank;++a)i[a]<0&&(i[a]=this.getMaxWidth(a));return i}calculateFirstParentOutputIndex(e,t,r){const s=Math.min(e,r),o=[];let i=0;for(let a=0;a<s;++a,i+=t)o.push(i);for(let a=s;a<e;++a)o.push(-1);return U(o.length===e,()=>"Final length of result must be equal to firstDimension."),o}calculateOutputIndexRowSplit(e,t,r,s){const o=e.length,i=[];for(let a=0;a<o-1;++a){const l=e[a+1]-e[a];let c=Math.min(s,l),d=t[a];d===-1&&(c=0);for(let p=0;p<c;++p)i.push(d),d+=r;for(let p=0;p<l-c;++p)i.push(-1)}if(o>0&&i.length!==e[o-1])throw new Error("Invalid row split size.");return i}calculateOutputIndexValueRowID(e,t,r,s){const o=e.length,i=[];if(o===0)return[];let a=0,l=e[0];if(l>=t.length)throw new Error(`Got currentValueRowId=${l}, which is not less than ${t.length}`);let c=t[l];i.push(c);for(let d=1;d<o;++d){const p=e[d];if(p===l)c>=0&&(++a,a<s?c+=r:c=-1);else{if(a=0,l=p,p>=t.length)throw new Error(`Got nextValueRowId=${p} which is not less than ${t.length}`);c=t[p]}i.push(c)}if(i.length!==e.length)throw new Error("Invalid row ids.");return i}calculateOutputIndex(e,t,r,s){const o=this.getRowPartitionTensor(e),i=this.getRowPartitionTypeByDimension(e);switch(i){case Wo.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(o,t,r,s);case Wo.ROW_SPLITS:if(o.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${o.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(o,t,r,s);default:throw new Error(`Unsupported partition type: ${Wo[i]}`)}}getFirstDimensionSize(){const e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");const t=this.rowPartitionTypes[0];switch(t){case Wo.FIRST_DIM_SIZE:return e[0];case Wo.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case Wo.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${Wo[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");const t=this.getFirstDimensionSize(),r=this.calculateOutputSize(t),s=new Array(this.raggedRank+1);s[s.length-1]=1;for(let l=s.length-2;l>=0;--l)s[l]=s[l+1]*r[l+1];const o=P2(r,!1),i=Mn(this.valuesDType,Se(o));if(s[0]*r[0]>0){let l=this.calculateFirstParentOutputIndex(t,s[0],r[0]);for(let c=1;c<=this.raggedRank;++c)l=this.calculateOutputIndex(c-1,l,s[c],r[c]);this.setOutput(this.raggedRank,l,i,o)}return[o,i]}setOutput(e,t,r,s){if(r.length===0)return;const o=this.values,i=r;let a=s.slice();a=a.slice(e+1);const l=Se(a),c=t.length;let d=this.defaultValue;if(d.length!==l&&d.length!==1){const v=this.defaultValueShape;me(()=>{const b=ce(d,v);d=Nm(b,a).dataSync()})}let p=0,m=0,y=0;for(let v=0;v<=c;++v){let b=v<c?t[v]:-1;if(b===y){++y;continue}if(m<y){const w=o.subarray(p*l),k=i.subarray(m*l),I=(y-m)*l;F2(k,w,I)}if(v>=c){const w=r.length;b=Math.floor(w/l)}if(b>y)if(this.defaultValue.length===1)i.subarray(y*l,b*l).fill(this.defaultValue[0]),y=b;else for(;b>y;){const w=i.slice(y*l);F2(w,d,l),++y}b<0?(p=v+1,m=y):(p=v,m=y,y=m+1)}}}function F2(n,e,t){for(let r=0;r<t;r++)n[r]=e[r]}function P2(n,e){const t=[];for(let r of n){if(r<0){if(!e)throw new Error(`Dimension ${r} must be >= 0`);if(r<-1)throw new Error(`Dimension ${r} must be >= -1`);r=-1}t.push(r)}return t}function TO(n,e,t,r,s,o,i,a,l,c){return new s1(n,e,t,r,s,o,i,a,l,c).compute()}function EO(n,e,t,r){const s=n===e,o=n<e&&t<0,i=e<n&&t>1;if(s||o||i)return Gr(0,r);const a=Math.abs(Math.ceil((e-n)/t)),l=Gr(a,r);e<n&&t===1&&(t=-1),l[0]=n;for(let c=1;c<l.length;c++)l[c]=l[c-1]+t;return l}const RO=ba(n=>1/Math.sqrt(n)),W7=Su(Lg,RO),V7={kernelName:Lg,backendName:"cpu",kernelFunc:W7};function Fc(n,e,t,r,s,o,i,a,l,c){const d=[r/s,s],p=n.values,m=e.values;if(r===0)return Nt(t,e.dtype);const y=l instanceof Ar?l:Nt(d,e.dtype);typeof l=="string"||typeof l=="number"?y.values.fill(l):typeof l=="boolean"&&y.values.fill(+l);for(let v=0;v<o;v++){const b=[];let w=0;for(let k=0;k<i;k++){const I=p[v*i+k];b.push(I),w+=I*a[k]}if(w<0||w>=r/s)throw new Error(`Invalid indices: ${b} does not index into ${t}`);for(let k=0;k<s;k++)c?y.values[w*s+k]+=m[v*s+k]:y.values[w*s+k]=e.rank===0?m[0]:m[v*s+k]}return y}const G7=ba(n=>1/(1+Math.exp(-n))),AO=sn(Wg,n=>1/(1+Math.exp(-n))),j7={kernelName:Wg,backendName:"cpu",kernelFunc:AO};function _O(n,e,t,r,s){const o=Wk(r,e,t),i=Se(t),a=tt(r);if(o){const p=Vk(e,a);return s==="string"?n.slice(p,p+i):n.subarray(p,p+i)}const l=s==="string"?il(n):n,c=Nt(r,s,l),d=Nt(t,s);for(let p=0;p<d.size;++p){const m=d.indexToLoc(p),y=m.map((v,b)=>v+e[b]);d.set(c.get(...y),...m)}return s==="string"?OA(d.values):d.values}function rh(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{begin:o,size:i}=r;Ye(s,"slice");const[a,l]=$v(s,o,i);Bk(s,a,l);const c=t.data.get(s.dataId).values,d=_O(c,a,l,s.shape,s.dtype);return t.makeTensorInfo(l,s.dtype,d)}const H7={kernelName:hv,backendName:"cpu",kernelFunc:rh};function DO(n,e,t,r,s,o,i){const a=e[0],l=o[0],c=new Array(l),d=new Array(a),p=e[1];if(l===0){if(a!==0)throw new Error(bA(a));const w=Mn(t,0),k=Mn(s,0);return[w,[0,p],k,c,d]}let m=!0,y=0;const v=new Array(l).fill(0);for(let w=0;w<a;++w){const k=n[w*p];if(k<0)throw new Error(CA(w,k));if(k>=l)throw new Error(SA(w,k,l));++v[k],m=m&&k>=y,y=k}let b=!0;for(let w=0;w<l;++w){const k=v[w]===0;c[w]=k,b=b&&!k,v[w]=Math.max(v[w],1),w>0&&(v[w]+=v[w-1])}if(b&&m){const w=n,k=r;for(let I=0;I<a;++I)d[I]=I;return[w,[a,p],k,c,d]}else{const w=v[l-1],k=Mn(t,w*p),I=Mn(s,w),N=new Array(l).fill(0);for(let T=0;T<a;++T){const A=n[T*p],O=N[A],P=(A===0?0:v[A-1])+O;N[A]++;for(let B=0;B<p;++B)k[P*p+B]=n[T*p+B];I[P]=r[T],d[T]=P}for(let T=0;T<l;++T)if(N[T]===0){const O=T===0?0:v[T-1];k[O*p+0]=T;for(let P=1;P<p;++P)k[O*p+P]=0;I[O]=i}return[k,[w,p],I,c,d]}}function OO(n,e,t,r,s){const o=Se(r),i=e[0],a=s.length,l=[];let c=1,d=-1;for(let w=0;w<a;++w){const k=s[w];if(k===-1){if(d!==-1)throw new Error(kA(d,w));d=w,l.push(1)}else{if(k<0)throw new Error(IA(w,k));c*=k,l.push(k)}}if(d!==-1){if(c<=0)throw new Error($A());const w=Math.trunc(o/c);if(c*w!==o)throw new Error(NA(r,l));l[d]=w}if(Se(l)!==o)throw new Error(TA(r,l));const m=r.length,y=[];if(m>0){y[m-1]=1;for(let w=m-2;w>=0;--w)y[w]=y[w+1]*r[w+1]}const v=[];if(a>0){v[a-1]=1;for(let w=a-2;w>=0;--w)v[w]=v[w+1]*l[w+1]}const b=Mn(t,i*a);for(let w=0;w<i;++w){let k=0;for(let I=0;I<m;++I)k+=n[w*m+I]*y[I];for(let I=0;I<a;++I)b[w*a+I]=Math.trunc(k/v[I]),k%=v[I]}return[b,[i,a],l]}function PI(n,e,t,r,s,o=!1,i=0){const a=r.length,l=[e[0],n.length/e[0]],c=l[1],p=a>0?s[a-1]+1:0;if(p<0)throw new Error(IC());const m=e.slice();m[0]=p;const y=m.reduce((N,T)=>N*T,1),v=Mn(t,y);if(a===0)return p>0&&v.fill(i),[v,m];if(p<=0)throw new Error(IC());let b=0,w=1,k=0,I=s[b];for(;;){let N=0;if(w<a){if(N=s[w],I===N){++w;continue}if(I>=N)throw new Error(EA())}if(I<0||I>=p)throw new Error(RA(I,p));I>k&&v.fill(i,k*c,I*c);for(let T=b;T<w;++T){const A=r[T];if(A<0||A>=l[0])throw new Error(AA(T,r[T],l[0]));for(let O=0;O<c;O++)v[I*c+O]+=n[A*c+O]}if(o)for(let T=0;T<c;T++)v[I*c+T]/=w-b;if(b=w,++w,k=I+1,I=N,w>a)break}return k<p&&v.fill(i,k*c,p*c),[v,m]}const q7=ba(n=>Math.sqrt(n)),K7=sn(Gg,n=>Math.sqrt(n)),X7={kernelName:Gg,backendName:"cpu",kernelFunc:K7};const FO=Gn(((n,e)=>{const t=n-e;return t*t})),Y7=lr(jg,FO),Q7={kernelName:jg,backendName:"cpu",kernelFunc:Y7};const PO=ba((n,e)=>{const{pattern:t,replaceGlobal:r,rewrite:s}=e;return n.replace(new RegExp(t,r?"g":""),s)}),J7=Su(ZS,PO),Z7={kernelName:ZS,backendName:"cpu",kernelFunc:J7};function LO(n,e,t,r){const s=Nt(n,e.dtype);for(let o=0;o<s.size;o++){const i=s.indexToLoc(o),a=new Array(i.length);for(let l=0;l<a.length;l++)a[l]=i[l]*t[l]+r[l];s.set(e.get(...a),...i)}return s}class eX{constructor(e,t,r,s,o,i){this.separator=su(e),this.nGramWidths=t,this.leftPad=su(r),this.rightPad=su(s),this.padWidth=o,this.preserveShort=i}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){const 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t.makeTensorInfo(b.shape,b.dtype,b.values)}const f9={kernelName:E1,backendName:"cpu",kernelFunc:d9};function p9(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,filter:o}=e,{strides:i,pad:a,dilations:l}=r;Ye([s,o],"conv3d");const c=xu(s.shape,o.shape,i,l,a),{filterDepth:d,filterHeight:p,filterWidth:m,dilationDepth:y,dilationHeight:v,dilationWidth:b,padInfo:w}=c,k=w.front,I=w.left,N=w.top,T=new Ar(c.outShape,s.dtype),A=t.data.get(s.dataId).values,O=t.data.get(o.dataId).values,P=T.values,B=tt(s.shape),z=tt(o.shape);for(let L=0;L<c.batchSize;++L){const _=L*B[0],H=L*T.strides[0];for(let Y=0;Y<c.outDepth;++Y){const re=H+Y*T.strides[1],te=Y*c.strideDepth-k;for(let le=0;le<d;++le){const de=te+le*y;if(de<0||de>=c.inDepth)continue;const j=le*z[0],ie=_+de*B[1];for(let se=0;se<c.outHeight;++se){const V=re+se*T.strides[2],X=se*c.strideHeight-N;for(let xe=0;xe<p;++xe){const Ie=X+xe*v;if(Ie<0||Ie>=c.inHeight)continue;const ke=j+xe*z[1],Ee=ie+Ie*B[2];for(let Me=0;Me<c.outWidth;++Me){const ze=V+Me*c.outChannels,Be=Me*c.strideWidth-I;for(let Je=0;Je<m;++Je){const lt=Be+Je*b;if(lt<0||lt>=c.inWidth)continue;const pt=ke+Je*z[2],bt=Ee+lt*c.inChannels;let Rt=pt;for(let Ot=0;Ot<c.inChannels;++Ot){const Bt=A[bt+Ot];for(let At=0;At<c.outChannels;++At)P[ze+At]+=Bt*O[Rt+At];Rt+=c.outChannels}}}}}}}}return t.makeTensorInfo(T.shape,T.dtype,T.values)}const m9={kernelName:R1,backendName:"cpu",kernelFunc:p9};function g9(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,dy:o}=e,{strides:i,pad:a,filterShape:l}=r;Ye([s,o],"conv3dBackpropFilterV2");const c=tt(s.shape),d=tt(o.shape),p=xu(s.shape,l,i,1,a),m=p.strideDepth,y=p.strideHeight,v=p.strideWidth,b=p.filterDepth,w=p.filterHeight,k=p.filterWidth,I=new Ar(p.filterShape,"float32"),N=I.values,[T,A,O,P]=I.strides,B=t.data.get(o.dataId).values,[z,L,_,H]=d,Y=t.data.get(s.dataId).values,[re,te,le,de]=c,j=p.padInfo.front,ie=p.padInfo.left,se=p.padInfo.top;for(let V=0;V<b;++V){const X=Math.max(0,Math.ceil((j-V)/m)),xe=Math.min(p.outDepth,(p.inDepth+j-V)/m),Ie=V*T;for(let ke=0;ke<w;++ke){const Ee=Math.max(0,Math.ceil((se-ke)/y)),Me=Math.min(p.outHeight,(p.inHeight+se-ke)/y),ze=ke*A+Ie;for(let Be=0;Be<k;++Be){const Je=Math.max(0,Math.ceil((ie-Be)/v)),lt=Math.min(p.outWidth,(p.inWidth+ie-Be)/v),pt=Be*O+ze;for(let bt=0;bt<p.inChannels;++bt){const Rt=bt*P+pt;for(let Ot=0;Ot<p.outChannels;++Ot){let Bt=0;for(let At=0;At<p.batchSize;++At){const Yt=At*re,ds=At*z;for(let Jt=X;Jt<xe;++Jt){const Rn=(V+Jt*m-j)*te+Yt,fs=Jt*L+ds;for(let Xr=Ee;Xr<Me;++Xr){const Zs=(ke+Xr*y-se)*le+Rn,wr=Xr*_+fs;for(let Qn=Je;Qn<lt;++Qn){const eo=(Be+Qn*v-ie)*de+Zs,to=Qn*H+wr;Bt+=Y[eo+bt]*B[to+Ot]}}}}N[Rt+Ot]=Bt}}}}}return t.makeTensorInfo(I.shape,I.dtype,I.values)}const x9={kernelName:NS,backendName:"cpu",kernelFunc:g9};function y9(n){const{inputs:e,backend:t,attrs:r}=n,{dy:s,filter:o}=e,{pad:i,strides:a,inputShape:l}=r;Ye([s],"conv3dBackpropInputV2");const c=tt(s.shape),d=tt(o.shape),p=xu(l,o.shape,a,1,i),m=new Ar(p.inShape,"float32"),y=m.values,[v,b,w,k]=m.strides,I=t.data.get(s.dataId).values,[N,T,A,O]=c,P=t.data.get(o.dataId).values,[B,z,L,_]=d,{batchSize:H,filterDepth:Y,filterHeight:re,filterWidth:te,inChannels:le,inDepth:de,inHeight:j,inWidth:ie,outChannels:se,outDepth:V,outHeight:X,outWidth:xe,strideDepth:Ie,strideHeight:ke,strideWidth:Ee}=p,Me=Y-1-p.padInfo.front,ze=re-1-p.padInfo.top,Be=te-1-p.padInfo.left;for(let Je=0;Je<H;++Je)for(let lt=0;lt<le;++lt)for(let pt=0;pt<de;++pt){const bt=pt-Me,Rt=Math.max(0,Math.ceil(bt/Ie)),Ot=Math.min(V,(Y+bt)/Ie);for(let Bt=0;Bt<j;++Bt){const At=Bt-ze,Yt=Math.max(0,Math.ceil(At/ke)),ds=Math.min(X,(re+At)/ke);for(let Jt=0;Jt<ie;++Jt){const cn=Jt-Be,Rn=Math.max(0,Math.ceil(cn/Ee)),fs=Math.min(xe,(te+cn)/Ee);let Xr=0;for(let vr=Rt;vr<Ot;++vr){const Zs=vr*Ie-bt;for(let wr=Yt;wr<ds;++wr){const Qn=wr*ke-At;for(let So=Rn;So<fs;++So){const eo=So*Ee-cn,to=N*Je+T*vr+A*wr+O*So,Iu=B*(Y-1-Zs)+z*(re-1-Qn)+L*(te-1-eo)+_*lt;for(let Jo=0;Jo<se;++Jo){const hl=I[to+Jo],$u=P[Iu+Jo];Xr+=hl*$u}}}}y[v*Je+b*pt+w*Bt+k*Jt+lt]=Xr}}}return t.makeTensorInfo(m.shape,m.dtype,m.values)}const v9={kernelName:TS,backendName:"cpu",kernelFunc:y9};const w9=sn(dg,n=>Math.cos(n)),b9={kernelName:dg,backendName:"cpu",kernelFunc:w9};const C9=sn(fg,n=>Math.cosh(n)),S9={kernelName:fg,backendName:"cpu",kernelFunc:C9};function k9(n){const{inputs:e,backend:t,attrs:r}=n,{image:s,boxes:o,boxInd:i}=e,{cropSize:a,method:l,extrapolationValue:c}=r,[d,p,m,y]=s.shape,v=o.shape[0],[b,w]=a,k=Nt([v,b,w,y],"float32"),I=t.data.get(o.dataId).values,N=t.data.get(i.dataId).values,T=t.data.get(s.dataId).values,A=tt(s.shape),O=tt(k.shape);for(let P=0;P<v;P++){const B=P*4,z=I[B],L=I[B+1],_=I[B+2],H=I[B+3],Y=N[P];if(Y>=d)continue;const re=b>1?(_-z)*(p-1)/(b-1):0,te=w>1?(H-L)*(m-1)/(w-1):0;for(let le=0;le<b;le++){const de=b>1?z*(p-1)+le*re:.5*(z+_)*(p-1);if(de<0||de>p-1){for(let j=0;j<w;j++)for(let ie=0;ie<y;ie++){const se=ie+j*O[2]+le*O[1]+P*O[0];k.values[se]=c}continue}if(l==="bilinear"){const j=Math.floor(de),ie=Math.ceil(de),se=de-j;for(let V=0;V<w;V++){const X=w>1?L*(m-1)+V*te:.5*(L+H)*(m-1);if(X<0||X>m-1){for(let Ee=0;Ee<y;Ee++){const Me=Ee+V*O[2]+le*O[1]+P*O[0];k.values[Me]=c}continue}const xe=Math.floor(X),Ie=Math.ceil(X),ke=X-xe;for(let Ee=0;Ee<y;Ee++){let Me=Ee+xe*A[2]+j*A[1]+Y*A[0];const ze=T[Me];Me=Ee+Ie*A[2]+j*A[1]+Y*A[0];const Be=T[Me];Me=Ee+xe*A[2]+ie*A[1]+Y*A[0];const Je=T[Me];Me=Ee+Ie*A[2]+ie*A[1]+Y*A[0];const lt=T[Me],pt=ze+(Be-ze)*ke,bt=Je+(lt-Je)*ke;Me=Ee+V*O[2]+le*O[1]+P*O[0],k.values[Me]=pt+(bt-pt)*se}}}else for(let j=0;j<w;++j){const ie=w>1?L*(m-1)+j*te:.5*(L+H)*(m-1);if(ie<0||ie>m-1){for(let X=0;X<y;X++){const xe=X+j*O[2]+le*O[1]+P*O[0];k.values[xe]=c}continue}const se=Math.round(ie),V=Math.round(de);for(let X=0;X<y;X++){const xe=X+se*A[2]+V*A[1]+Y*A[0],Ie=X+j*O[2]+le*O[1]+P*O[0];k.values[Ie]=T[xe]}}}}return t.makeTensorInfo(k.shape,k.dtype,k.values)}const I9={kernelName:RS,backendName:"cpu",kernelFunc:k9};function $9(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,exclusive:i,reverse:a}=r;Ye(s,"cumprod");const l=En([o],s.shape.length);let c=s;l!=null&&(c=Ns({inputs:{x:s},backend:t,attrs:{perm:l}}));const d=Wn(1,s.shape.length)[0];if(d!==c.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${d}`);const p=Ys(c.dtype,"int32"),m=mS(Se(c.shape),p),y=t.data.get(c.dataId).values,v=c.shape[c.shape.length-1],b=a?(k,I)=>k+v-I-1:(k,I)=>k+I;for(let k=0;k<y.length;k+=v)for(let I=0;I<v;I++){const N=b(k,I);if(I===0)m[N]=i?1:y[N];else{const T=b(k,I-1);m[N]=i?y[T]*m[T]:y[N]*m[T]}}const w=t.makeTensorInfo(c.shape,p,m);if(l!=null){const k=yu(l),I=Ns({inputs:{x:w},backend:t,attrs:{perm:k}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(c),I}return w}const N9={kernelName:ES,backendName:"cpu",kernelFunc:$9};function T9(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,exclusive:i,reverse:a}=r;Ye(s,"cumsum");const l=En([o],s.shape.length);let c=s;l!=null&&(c=Ns({inputs:{x:s},backend:t,attrs:{perm:l}}));const d=Wn(1,s.shape.length)[0];if(d!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${d}`);const p=Ys(c.dtype,"int32"),m=Gr(Se(c.shape),p),y=t.data.get(c.dataId).values,v=c.shape[c.shape.length-1],b=a?(k,I)=>k+v-I-1:(k,I)=>k+I;for(let k=0;k<y.length;k+=v)for(let I=0;I<v;I++){const N=b(k,I);if(I===0)m[N]=i?0:y[N];else{const T=b(k,I-1);m[N]=i?y[T]+m[T]:y[N]+m[T]}}const w=t.makeTensorInfo(c.shape,p,m);if(l!=null){const k=yu(l),I=Ns({inputs:{x:w},backend:t,attrs:{perm:k}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(c),I}return w}const E9={kernelName:A1,backendName:"cpu",kernelFunc:T9};function R9(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,weights:o}=e,{size:i,binaryOutput:a}=r;if(s.shape.length===1){const l=t.data.get(s.dataId).values,c=t.data.get(o.dataId).values,d=DI(l,c,o.dtype,o.shape,i);return t.makeTensorInfo([i],o.dtype,d)}else if(s.shape.length===2){const l=t.bufferSync(s),c=t.bufferSync(o),d=tO(l,c,i,a);return t.makeTensorInfo(d.shape,o.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}const A9={kernelName:AS,backendName:"cpu",kernelFunc:R9};function _9(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{blockSize:o,dataFormat:i}=r;U(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);const a=s.shape[0],l=s.shape[1],c=s.shape[2],d=s.shape[3],p=l*o,m=c*o,y=d/(o*o),v=t.data.get(s.dataId).values,b=new Float32Array(a*p*m*y);let w=0;for(let k=0;k<a;++k)for(let I=0;I<p;++I){const N=Math.floor(I/o),T=I%o;for(let A=0;A<m;++A){const O=Math.floor(A/o),P=A%o,B=(T*o+P)*y;for(let z=0;z<y;++z){const _=z+B+d*(O+c*(N+l*k));b[w++]=v[_]}}}return t.makeTensorInfo([a,p,m,y],s.dtype,b)}const D9={kernelName:_S,backendName:"cpu",kernelFunc:_9};function tF(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,filter:o}=e,{strides:i,pad:a,dilations:l,dimRoundingMode:c}=r;Ye([s,o],"depthwiseConv2DNative");const d=tt(s.shape),p=tt(o.shape);let m=l;m==null&&(m=[1,1]),U(Hr(i,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);const y=_r(s.shape,o.shape,i,m,a,c,!0),{filterHeight:v,filterWidth:b,dilationHeight:w,dilationWidth:k,padInfo:I}=y,N=I.left,T=I.top,A=y.outChannels/y.inChannels,O=new Ar(y.outShape,s.dtype),P=t.data.get(s.dataId).values,B=t.data.get(o.dataId).values,z=O.values;for(let L=0;L<y.batchSize;++L){const _=L*d[0],H=L*O.strides[0];for(let Y=0;Y<y.outHeight;++Y){const re=H+Y*O.strides[1],te=Y*y.strideHeight-T;for(let le=0;le<v;++le){const de=te+le*w;if(de<0||de>=y.inHeight)continue;const j=le*p[0],ie=_+de*d[1];for(let se=0;se<y.outWidth;++se){const V=re+se*O.strides[2],X=se*y.strideWidth-N;for(let xe=0;xe<b;++xe){const Ie=X+xe*k;if(Ie<0||Ie>=y.inWidth)continue;const ke=j+xe*p[1],Ee=ie+Ie*y.inChannels;let Me=V,ze=ke;for(let Be=0;Be<y.inChannels;++Be){const Je=P[Ee+Be];for(let lt=0;lt<A;++lt)z[Me+lt]+=Je*B[ze+lt];Me+=A,ze+=A}}}}}}return t.makeTensorInfo(O.shape,O.dtype,O.values)}const O9={kernelName:_1,backendName:"cpu",kernelFunc:tF};function F9(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,dy:o}=e,{strides:i,dilations:a,pad:l,dimRoundingMode:c,filterShape:d}=r;Ye([s,o],"depthwiseConv2dNativeBackpropFilter");const p=_r(s.shape,d,i,a,l,c,!0),{strideHeight:m,strideWidth:y,filterHeight:v,filterWidth:b}=p,w=new Ar(p.filterShape,"float32"),k=p.padInfo.left,I=p.padInfo.top,N=p.outChannels/p.inChannels,T=t.data.get(s.dataId).values,A=new Ar(s.shape,s.dtype,T),O=t.data.get(o.dataId).values,P=new Ar(o.shape,o.dtype,O);for(let B=0;B<v;++B){const z=Math.max(0,Math.ceil((I-B)/m)),L=Math.min(p.outHeight,(p.inHeight+I-B)/m);for(let _=0;_<b;++_){const H=Math.max(0,Math.ceil((k-_)/y)),Y=Math.min(p.outWidth,(p.inWidth+k-_)/y);for(let re=0;re<p.outChannels;++re){const te=Math.trunc(re/N),le=re%N;let de=0;for(let j=0;j<p.batchSize;++j)for(let ie=z;ie<L;++ie){const se=B+ie*m-I;for(let V=H;V<Y;++V){const X=_+V*y-k;de+=A.get(j,se,X,te)*P.get(j,ie,V,re)}}w.set(de,B,_,te,le)}}}return t.makeTensorInfo(w.shape,w.dtype,w.values)}const P9={kernelName:DS,backendName:"cpu",kernelFunc:F9};function L9(n){const{inputs:e,backend:t,attrs:r}=n,{dy:s,filter:o}=e,{strides:i,dilations:a,pad:l,dimRoundingMode:c,inputShape:d}=r;Ye([s,o],"depthwiseConv2DNativeBackpropInput");const p=tt(s.shape),m=tt(o.shape),y=_r(d,o.shape,i,a,l,c,!0),v=new Ar(y.inShape,"float32"),b=v.values,[w,k,I]=v.strides,N=t.data.get(s.dataId).values,[T,A,O]=p,P=t.data.get(o.dataId).values,[B,z,L]=m,{batchSize:_,filterHeight:H,filterWidth:Y,inChannels:re,inHeight:te,inWidth:le,outChannels:de,outHeight:j,outWidth:ie,strideHeight:se,strideWidth:V}=y,X=H-1-y.padInfo.top,xe=Y-1-y.padInfo.left,Ie=de/re;for(let ke=0;ke<_;++ke)for(let Ee=0;Ee<re;++Ee)for(let Me=0;Me<te;++Me){const ze=Me-X,Be=Math.max(0,Math.ceil(ze/se)),Je=Math.min(j,(H+ze)/se);for(let lt=0;lt<le;++lt){const pt=lt-xe,bt=Math.max(0,Math.ceil(pt/V)),Rt=Math.min(ie,(Y+pt)/V);let Ot=0;for(let Bt=Be;Bt<Je;++Bt){const At=Bt*se-ze;for(let Yt=bt;Yt<Rt;++Yt){const ds=Yt*V-pt,Jt=T*ke+A*Bt+O*Yt,cn=B*(H-1-At)+z*(Y-1-ds)+L*Ee;for(let Rn=0;Rn<Ie;++Rn){const fs=Ee*Ie+Rn,Xr=N[Jt+fs],vr=P[cn+Rn];Ot+=Xr*vr}}}b[w*ke+k*Me+I*lt+Ee]=Ot}}return t.makeTensorInfo(v.shape,v.dtype,v.values)}const M9={kernelName:OS,backendName:"cpu",kernelFunc:L9};function z9(n){const{inputs:e,backend:t}=n,{x:r}=e,s=Se(r.shape),o=t.data.get(r.dataId).values,i=Nt([s,s],r.dtype),a=i.values;for(let c=0;c<o.length;c++)a[c*s+c]=o[c];const l=[...r.shape,...r.shape];return t.makeTensorInfo(l,i.dtype,i.values)}const B9={kernelName:$E,backendName:"cpu",kernelFunc:z9};const U9={kernelName:D1,backendName:"cpu",kernelFunc:({inputs:n,backend:e,attrs:t})=>{const{x:r,filter:s}=n,{strides:o,pad:i,dilations:a}=t,l=e,c=l.data.get(r.dataId).values,d=r.shape.length,p=l.data.get(s.dataId).values,m=s.shape.length,{batchSize:y,inHeight:v,inWidth:b,inChannels:w,outHeight:k,outWidth:I,padInfo:N,strideHeight:T,strideWidth:A,filterHeight:O,filterWidth:P,dilationHeight:B,dilationWidth:z,outShape:L}=Zg(r.shape,s.shape,o,i,"NHWC",a),_=Se(L),H=L.length,Y=Mn(r.dtype,_);for(let te=0;te<y;++te)for(let le=0;le<k;++le){const de=le*T-N.top;for(let j=0;j<I;++j){const ie=j*A-N.left;for(let se=0;se<w;++se){let V=Number.MIN_SAFE_INTEGER;for(let xe=0;xe<O;++xe){const Ie=de+xe*B;if(Ie>=0&&Ie<v)for(let ke=0;ke<P;++ke){const Ee=ie+ke*z;if(Ee>=0&&Ee<b){const Me=ca([te,Ie,Ee,se],d,tt(r.shape)),ze=ca([xe,ke,se],m,tt(s.shape)),Be=c[Me]+p[ze];Be>V&&(V=Be)}}}const X=ca([te,le,j,se],H,tt(L));Y[X]=V}}}return{dataId:l.write(ah(Y,r.dtype),L,r.dtype),shape:L,dtype:r.dtype}}};const W9={kernelName:lC,backendName:"cpu",kernelFunc:({inputs:n,backend:e,attrs:t})=>{const{x:r,filter:s,dy:o}=n,{strides:i,pad:a,dilations:l}=t,c=e,d=Ci(r.shape,c.data.get(r.dataId).values),p=Ci(s.shape,c.data.get(s.dataId).values),{batchSize:m,inHeight:y,inWidth:v,inChannels:b,outHeight:w,outWidth:k,padInfo:I,strideHeight:N,strideWidth:T,filterHeight:A,filterWidth:O,dilationHeight:P,dilationWidth:B,outShape:z}=Zg(r.shape,s.shape,i,a,"NHWC",l);U(o.rank===z.length,()=>`Error in ${lC}, dy must have the same rank as output ${z.length}, but got ${o.rank}`);const L=Ci(z,c.data.get(o.dataId).values),_=CE(s.shape,s.dtype);for(let Y=0;Y<m;++Y)for(let re=0;re<w;++re){const te=re*N-I.top;for(let le=0;le<k;++le){const de=le*T-I.left;for(let j=0;j<b;++j){let ie=Number.MIN_SAFE_INTEGER,se=0,V=0;for(let X=0;X<A;++X){const xe=te+X*P;if(xe>=0&&xe<y)for(let Ie=0;Ie<O;++Ie){const ke=de+Ie*B;if(ke>=0&&ke<v){const 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Qe=ye();Qe.registerFlag("HAS_WEBGL",()=>Qe.getNumber("WEBGL_VERSION")>0);Qe.registerFlag("WEBGL_VERSION",()=>U2(2)?2:U2(1)?1:0);Qe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Qe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Qe.get("WEBGL_VERSION")===2);Qe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Qe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Qe.registerFlag("WEBGL_PACK",()=>Qe.getBool("HAS_WEBGL"));Qe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_PACK_CLIP",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_PACK_REDUCE",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_CONV_IM2COL",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE",()=>Qe.getBool("WEBGL_PACK"));Qe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>KZ(Qe.getNumber("WEBGL_VERSION")));Qe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>XZ(Qe.getNumber("WEBGL_VERSION")));Qe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{const n=Qe.getNumber("WEBGL_VERSION");return n===0?0:YZ(n)});Qe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Qe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!oR());Qe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>QZ(Qe.getNumber("WEBGL_VERSION")));Qe.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Qe.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Qe.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Qe.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>JZ(Qe.getNumber("WEBGL_VERSION")));Qe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>eee(Qe.getNumber("WEBGL_VERSION")));Qe.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Qe.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Qe.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,n=>{if(typeof n!="number")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${n}.`);if(n<0&&n!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${n}.`)});Qe.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>oR()?1:-1,n=>{if(typeof n!="number")throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${n}.`);if(n<0&&n!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${n}.`)});Qe.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Qe.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Qe.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Qe.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);Qe.registerFlag("WEBGL_EXP_CONV",()=>!1);Qe.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>Qe.getBool("IS_TEST"));Qe.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);Qe.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);Qe.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);Qe.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function hs(){let n,e,t,r,s,o,i,a,l,c;return ye().getNumber("WEBGL_VERSION")===2?(n="#version 300 es",e="in",t="out",r="in",s="texture",o="outputColor",i="out vec4 outputColor;",a=ye().getBool("WEBGL2_ISNAN_CUSTOM")?`
bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`:"",l="",c=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(n="",e="attribute",t="varying",r="varying",s="texture2D",o="gl_FragColor",i="",a=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,c=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:n,attribute:e,varyingVs:t,varyingFs:r,texture2D:s,output:o,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:l,defineRound:c}}function fh(n,e,t="index"){const r=tt(e);return r.map((s,o)=>{const i=`int ${n[o]} = ${t} / ${s}`,a=o===r.length-1?`int ${n[o+1]} = ${t} - ${n[o]} * ${s}`:`index -= ${n[o]} * ${s}`;return`${i}; ${a};`}).join("")}function Wv(n,e,t="index"){const r=tt(e);return r.map((s,o)=>{const i=`int ${n[o]} = ${t} / outShapeStrides[${o}]`,a=o===r.length-1?`int ${n[o+1]} = ${t} - ${n[o]} * outShapeStrides[${o}]`:`index -= ${n[o]} * outShapeStrides[${o}]`;return`${i}; ${a};`}).join("")}function tee(n,e){const t=n.length,r=n.map(o=>`${e}[${o}]`),s=new Array(t-1);s[t-2]=r[t-1];for(let o=t-3;o>=0;--o)s[o]=`(${s[o+1]} * ${r[o+1]})`;return s}function nee(n,e,t="index"){const r=n.map((o,i)=>i),s=tee(r,e);return s.map((o,i)=>{const a=`int ${n[i]} = ${t} / ${s[i]}`,l=i===s.length-1?`int ${n[i+1]} = ${t} - ${n[i]} * ${s[i]}`:`index -= ${n[i]} * ${s[i]}`;return`${a}; ${l};`}).join("")}function WI(n){const e=tt(n).map(t=>t.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
}
`}function VI(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}const uF=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`;const{getBroadcastDims:cF}=V5;function ree(n,e,t){const r=[];if(n.forEach(y=>{const v=Se(y.shapeInfo.logicalShape);if(y.shapeInfo.isUniform?r.push(`uniform float ${y.name}${v>1?`[${v}]`:""};`):(r.push(`uniform sampler2D ${y.name};`),r.push(`uniform int offset${y.name};`)),t.enableShapeUniforms){const{uniformShape:b}=GI(t.packedInputs,y.shapeInfo.logicalShape,y.shapeInfo.texShape);switch(b.length){case 1:r.push(`uniform int ${y.name}Shape;`);break;case 2:r.push(`uniform ivec2 ${y.name}Shape;`);break;case 3:r.push(`uniform ivec3 ${y.name}Shape;`);break;case 4:r.push(`uniform ivec4 ${y.name}Shape;`);break}r.push(`uniform ivec2 ${y.name}TexShape;`)}}),t.enableShapeUniforms){switch(e.logicalShape.length){case 1:r.push("uniform int outShape;");break;case 2:r.push("uniform ivec2 outShape;"),r.push("uniform int outShapeStrides;");break;case 3:r.push("uniform ivec3 outShape;"),r.push("uniform ivec2 outShapeStrides;");break;case 4:r.push("uniform ivec4 outShape;"),r.push("uniform ivec3 outShapeStrides;");break}r.push("uniform ivec2 outTexShape;")}t.customUniforms&&t.customUniforms.forEach(y=>{r.push(`uniform ${y.type} ${y.name}${y.arrayIndex?`[${y.arrayIndex}]`:""};`)});const s=r.join(`
`),o=n.map(y=>see(y,e,t.packedInputs,t.enableShapeUniforms)).join(`
`),i=e.texShape,a=hs(),l=aee(a);let c,d,p=cee(a);return e.isPacked?(c=oee(e.logicalShape,i,t.enableShapeUniforms),d=uee(a)):(c=iee(e.logicalShape,i,t.enableShapeUniforms),d=lee(a)),t.packedInputs&&(p+=pee),[p,l,d,s,c,o,t.userCode].join(`
`)}function Af(n,e=!1){const t=n.shapeInfo.logicalShape;switch(t.length){case 0:return $ee(n,e);case 1:return Tee(n,e);case 2:return Ree(n,e);case 3:return _ee(n,e);case 4:return Oee(n,e);case 5:return Fee(n);case 6:return Pee(n);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function hF(n,e){switch(n.shapeInfo.logicalShape.length){case 0:return Iee(n);case 1:return Nee(n,e);case 2:return Eee(n,e);case 3:return Aee(n,e);default:return Dee(n,e)}}function see(n,e,t=!1,r){let s="";t?s+=hF(n,r):s+=Af(n,r);const o=n.shapeInfo.logicalShape,i=e.logicalShape;return o.length<=i.length&&(t?s+=Lee(n,e):s+=Mee(n,e)),s}function oee(n,e,t){switch(n.length){case 0:return dF();case 1:return mee(n,e,t);case 2:return See(n,e,t);case 3:return xee(n,e,t);default:return vee(n,e,t)}}function iee(n,e,t){switch(n.length){case 0:return dF();case 1:return gee(n,e,t);case 2:return kee(n,e,t);case 3:return yee(n,e,t);case 4:return wee(n,e,t);case 5:return bee(n,e);case 6:return Cee(n,e);default:throw new Error(`${n.length}-D output sampling is not yet supported`)}}function aee(n){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${n.texture2D}(textureSampler, uv).r;
}
`}function lee(n){return`
void setOutput(float val) {
${n.output} = vec4(val, 0, 0, 0);
}
`}function uee(n){return`
void setOutput(vec4 val) {
${n.output} = val;
}
`}function cee(n){return`${n.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${n.varyingFs} vec2 resultUV;
${n.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${n.defineSpecialNaN}
${n.defineSpecialInf}
${n.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${hee}
${dee}
${fee}
`}const hee=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,dee=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,fee=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,pee=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function dF(){return`
int getOutputCoords() {
return 0;
}
`}function mee(n,e,t){const r=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return r[0]===1?t?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${r[1]}.0);
}
`:r[1]===1?t?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${r[0]}.0);
}
`:t?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);
}
`}function gee(n,e,t){return e[0]===1?t?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${e[1]}.0);
}
`:e[1]===1?t?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${e[0]}.0);
}
`:t?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
return resTexRC.x * ${e[1]} + resTexRC.y;
}
`}function xee(n,e,t){if(t)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;const r=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],s=Math.ceil(n[2]/2),o=s*Math.ceil(n[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
int b = index / ${o};
index -= b * ${o};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec3(b, r, c);
}
`}function yee(n,e,t){if(t)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Wv(["r","c","d"],n)}
return ivec3(r, c, d);
}
`;const r=fh(["r","c","d"],n);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${r}
return ivec3(r, c, d);
}
`}function vee(n,e,t){if(t)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;const r=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],s=Math.ceil(n[n.length-1]/2),o=s*Math.ceil(n[n.length-2]/2);let i=o,a="",l="b, r, c";for(let c=2;c<n.length-1;c++)i*=n[n.length-c-1],a=`
int b${c} = index / ${i};
index -= b${c} * ${i};
`+a,l=`b${c}, `+l;return`
ivec${n.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
${a}
int b = index / ${o};
index -= b * ${o};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec${n.length}(${l});
}
`}function wee(n,e,t){if(t)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Wv(["r","c","d","d2"],n)}
return ivec4(r, c, d, d2);
}
`;const r=fh(["r","c","d","d2"],n);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${r}
return ivec4(r, c, d, d2);
}
`}function bee(n,e){const t=fh(["r","c","d","d2","d3"],n);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function Cee(n,e){const t=fh(["r","c","d","d2","d3","d4"],n);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function See(n,e,t){const r=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(Xt(n,e))return t?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${r[0]}, ${r[1]}));
}
`;const s=Math.ceil(n[1]/2);return t?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec2(r, c);
}
`}function kee(n,e,t){return Xt(n,e)?t?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
}
`:n[1]===1?t?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:n[0]===1?t?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(0, index);
}
`:t?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int r = index / ${n[1]};
int c = index - r * ${n[1]};
return ivec2(r, c);
}
`}function ph(n){return`offset${n}`}function Iee(n){const e=n.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),r=hs();return`
vec4 ${t}() {
return ${r.texture2D}(${e}, halfCR);
}
`}function $ee(n,e){const t=n.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`float ${r}() {return ${t};}`;const[s,o]=n.shapeInfo.texShape;if(s===1&&o===1)return`
float ${r}() {
return sampleTexture(${t}, halfCR);
}
`;const i=ph(t);if(e)return`
float ${r}() {
vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], ${i});
return sampleTexture(${t}, uv);
}
`;const[a,l]=n.shapeInfo.texShape;return`
float ${r}() {
vec2 uv = uvFromFlat(${a}, ${l}, ${i});
return sampleTexture(${t}, uv);
}
`}function Nee(n,e){const t=n.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),s=n.shapeInfo.texShape,o=hs();if(e)return`
vec4 ${r}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${o.texture2D}(${t}, uv);
}
`;const i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];return`
vec4 ${r}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${o.texture2D}(${t}, uv);
}
`}function Tee(n,e){const t=n.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`
float ${r}(int index) {
${_f(n)}
}
`;const s=n.shapeInfo.texShape,o=s[0],i=s[1];if(i===1&&o===1)return`
float ${r}(int index) {
return sampleTexture(${t}, halfCR);
}
`;const a=ph(t);return i===1?e?`
float ${r}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / float(${t}TexShape[0]));
return sampleTexture(${t}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${o}.0);
return sampleTexture(${t}, uv);
}
`:o===1?e?`
float ${r}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / float(${t}TexShape[1]), 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${i}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:e?`
float ${r}(int index) {
vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], index + ${a});
return sampleTexture(${t}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = uvFromFlat(${o}, ${i}, index + ${a});
return sampleTexture(${t}, uv);
}
`}function Eee(n,e){const t=n.shapeInfo.logicalShape,r=n.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),o=n.shapeInfo.texShape,i=o[0],a=o[1],l=hs();if(o!=null&&Xt(t,o))return e?`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return ${l.texture2D}(${r}, uv);
}
`:`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${i}.0);
return ${l.texture2D}(${r}, uv);
}
`;if(e)return`
vec4 ${s}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${r}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${r}, uv);
}
`;const c=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],d=Math.ceil(t[1]/2);return`
vec4 ${s}(int row, int col) {
vec2 uv = packedUVfrom2D(${d}, ${c[0]}, ${c[1]}, row, col);
return ${l.texture2D}(${r}, uv);
}
`}function Ree(n,e){const t=n.shapeInfo.logicalShape,r=n.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),o=n.shapeInfo.texShape;if(o!=null&&Xt(t,o)){if(e)return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`;const m=o[0],y=o[1];return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${y}.0, ${m}.0);
return sampleTexture(${r}, uv);
}
`}const{newShape:i,keptDims:a}=pu(t),l=i;if(l.length<t.length){const m=Df(n,l),y=["row","col"];return`
${Af(m,e)}
float ${s}(int row, int col) {
return ${s}(${Of(y,a)});
}
`}if(n.shapeInfo.isUniform)return`
float ${s}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${_f(n)}
}
`;const c=o[0],d=o[1],p=ph(r);return d===1?e?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${r}TexShape[0]));
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
return sampleTexture(${r}, uv);
}
`:c===1?e?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${r}TexShape[1]), 0.5);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${d}.0, 0.5);
return sampleTexture(${r}, uv);
}
`:e?`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r}Shape[1] + col + ${p};
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${p};
vec2 uv = uvFromFlat(${c}, ${d}, index);
return sampleTexture(${r}, uv);
}
`}function Aee(n,e){const t=n.shapeInfo.logicalShape,r=n.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),o=n.shapeInfo.texShape,i=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(t[0]===1){const m=t.slice(1),y=[1,2],v=Df(n,m),b=["b","row","col"];return`
${hF(v,e)}
vec4 ${s}(int b, int row, int col) {
return ${s}(${Of(b,y)});
}
`}const a=hs();if(e)return`
vec4 ${s}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${r}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${a.texture2D}(${r}, uv);
}
`;const l=i[0],c=i[1],d=Math.ceil(t[2]/2),p=d*Math.ceil(t[1]/2);return`
vec4 ${s}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${c}, ${p}, ${d}, b, row, col);
return ${a.texture2D}(${r}, uv);
}
`}function _ee(n,e){const t=n.shapeInfo.logicalShape,r=n.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),o=t[1]*t[2],i=t[2],{newShape:a,keptDims:l}=pu(t),c=a;if(c.length<t.length){const b=Df(n,c),w=["row","col","depth"];return`
${Af(b,e)}
float ${s}(int row, int col, int depth) {
return ${s}(${Of(w,l)});
}
`}if(n.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${o}, ${i}, 1)));
${_f(n)}
}
`;const d=n.shapeInfo.texShape,p=d[0],m=d[1],y=n.shapeInfo.flatOffset;if(m===o&&y==null)return e?`
float ${s}(int row, int col, int depth) {
int stride1 = ${r}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;if(m===i&&y==null)return e?`
float ${s}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${r}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;const v=ph(r);return e?`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${r}Shape[1] * ${r}Shape[2];
int stride1 = ${r}Shape[2];
int index = row * stride0 + col * stride1 + depth + ${v};
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth + ${v};
vec2 uv = uvFromFlat(${p}, ${m}, index);
return sampleTexture(${r}, uv);
}
`}function Dee(n,e){const t=n.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),s=hs();if(e)return`
vec4 ${r}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${t}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${t}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${t}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${s.texture2D}(${t}, uv);
}
`;const o=n.shapeInfo.logicalShape,i=o.length,a=n.shapeInfo.texShape,l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=l[0],d=l[1],p=Math.ceil(o[i-1]/2);let m=p*Math.ceil(o[i-2]/2),y="int b, int row, int col",v=`b * ${m} + (row / 2) * ${p} + (col / 2)`;for(let b=2;b<i-1;b++)y=`int b${b}, `+y,m*=o[i-b-1],v=`b${b} * ${m} + `+v;return`
vec4 ${r}(${y}) {
int index = ${v};
int texR = index / ${d};
int texC = index - texR * ${d};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${c});
return ${s.texture2D}(${t}, uv);
}
`}function Oee(n,e){const t=n.shapeInfo.logicalShape,r=n.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),o=t[3],i=t[2]*o,a=t[1]*i,{newShape:l,keptDims:c}=pu(t);if(l.length<t.length){const I=Df(n,l),N=["row","col","depth","depth2"];return`
${Af(I,e)}
float ${s}(int row, int col, int depth, int depth2) {
return ${s}(${Of(N,c)});
}
`}if(n.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${a}, ${i}, ${o}, 1)));
${_f(n)}
}
`;const d=n.shapeInfo.flatOffset,p=n.shapeInfo.texShape,m=p[0],y=p[1],v=`int stride2 = ${r}Shape[3];`,b=`int stride1 = ${r}Shape[2] * stride2;`,w=`int stride0 = ${r}Shape[1] * stride1;`;if(y===a&&d==null)return e?`
float ${s}(int row, int col, int depth, int depth2) {
${v}
${b}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${i}, ${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${y}.0, ${m}.0);
return sampleTexture(${r}, uv);
}
`;if(y===o&&d==null)return e?`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${r}Shape[1] * ${r}Shape[2], ${r}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${y}.0, ${m}.0);
return sampleTexture(${r}, uv);
}
`;const k=ph(r);return e?`
float ${s}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${v}
${b}
${w}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${k});
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${i} +
depth * ${o} + depth2;
vec2 uv = uvFromFlat(${m}, ${y}, index + ${k});
return sampleTexture(${r}, uv);
}
`}function Fee(n){const e=n.shapeInfo.logicalShape,t=n.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),s=e[4],o=e[3]*s,i=e[2]*o,a=e[1]*i,{newShape:l,keptDims:c}=pu(e);if(l.length<e.length){const b=Df(n,l),w=["row","col","depth","depth2","depth3"];return`
${Af(b)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${Of(w,c)});
}
`}if(n.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${a}, ${i}, ${o}, ${s})) +
depth3;
${_f(n)}
}
`;const d=n.shapeInfo.flatOffset,p=n.shapeInfo.texShape,m=p[0],y=p[1];if(y===a&&d==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${o}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${y}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;if(y===s&&d==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]},
${e[2]*e[3]}, ${e[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${y}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;const v=ph(t);return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${i} + depth * ${o} +
depth2 * ${s} + depth3 + ${v};
vec2 uv = uvFromFlat(${m}, ${y}, index);
return sampleTexture(${t}, uv);
}
`}function Pee(n){const e=n.shapeInfo.logicalShape,t=n.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:s,keptDims:o}=pu(e);if(s.length<e.length){const w=Df(n,s),k=["row","col","depth","depth2","depth3","depth4"];return`
${Af(w)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${Of(k,o)});
}
`}const i=e[5],a=e[4]*i,l=e[3]*a,c=e[2]*l,d=e[1]*c;if(n.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${d}, ${c}, ${l}, ${a})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${_f(n)}
}
`;const p=n.shapeInfo.flatOffset,m=n.shapeInfo.texShape,y=m[0],v=m[1];if(v===d&&p==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${a}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${v}.0, ${y}.0);
return sampleTexture(${t}, uv);
}
`;if(v===i&&p==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]*e[4]},
${e[2]*e[3]*e[4]},
${e[3]*e[4]},
${e[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${v}.0, ${y}.0);
return sampleTexture(${t}, uv);
}
`;const b=ph(t);return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${d} + col * ${c} + depth * ${l} +
depth2 * ${a} + depth3 * ${i} + depth4 + ${b};
vec2 uv = uvFromFlat(${y}, ${v}, index);
return sampleTexture(${t}, uv);
}
`}function _f(n){const e=n.name,t=Se(n.shapeInfo.logicalShape);return t<2?`return ${e};`:`
for (int i = 0; i < ${t}; i++) {
if (i == index) {
return ${e}[i];
}
}
`}function Lee(n,e){const t=n.name,r=t.charAt(0).toUpperCase()+t.slice(1),s="get"+r+"AtOutCoords",o=n.shapeInfo.logicalShape.length,i=e.logicalShape.length,a=cF(n.shapeInfo.logicalShape,e.logicalShape),l=on(i),c=i-o;let d;const p=["x","y","z","w","u","v"];o===0?d="":i<2&&a.length>=1?d="coords = 0;":d=a.map(I=>`coords.${p[I+c]} = 0;`).join(`
`);let m="";i<2&&o>0?m="coords":m=n.shapeInfo.logicalShape.map((I,N)=>`coords.${p[N+c]}`).join(", ");let y="return outputValue;";const b=Se(n.shapeInfo.logicalShape)===1,k=Se(e.logicalShape)===1;if(o===1&&!b&&!k)y=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(b&&!k)i===1?y=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:y=`
return vec4(outputValue.x);
`;else if(a.length){const I=o-2,N=o-1;a.indexOf(I)>-1&&a.indexOf(N)>-1?y="return vec4(outputValue.x);":a.indexOf(I)>-1?y="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":a.indexOf(N)>-1&&(y="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${s}() {
${l} coords = getOutputCoords();
${d}
vec4 outputValue = get${r}(${m});
${y}
}
`}function Mee(n,e){const t=n.name,r=t.charAt(0).toUpperCase()+t.slice(1),s="get"+r+"AtOutCoords",o=e.texShape,i=n.shapeInfo.texShape,a=n.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!n.shapeInfo.isUniform&&a===l&&n.shapeInfo.flatOffset==null&&Xt(i,o))return`
float ${s}() {
return sampleTexture(${t}, resultUV);
}
`;const c=on(l),d=cF(n.shapeInfo.logicalShape,e.logicalShape),p=l-a;let m;const y=["x","y","z","w","u","v"];a===0?m="":l<2&&d.length>=1?m="coords = 0;":m=d.map(b=>`coords.${y[b+p]} = 0;`).join(`
`);let v="";return l<2&&a>0?v="coords":v=n.shapeInfo.logicalShape.map((b,w)=>`coords.${y[w+p]}`).join(", "),`
float ${s}() {
${c} coords = getOutputCoords();
${m}
return get${r}(${v});
}
`}function on(n){if(n<=1)return"int";if(n===2)return"ivec2";if(n===3)return"ivec3";if(n===4)return"ivec4";if(n===5)return"ivec5";if(n===6)return"ivec6";throw Error(`GPU for rank ${n} is not yet supported`)}function GI(n,e,t){const{newShape:r,keptDims:s}=pu(e),o=e.length,i=n&&o===3&&e[0]===1,a=i?e.slice(1):r,l=!n&&o>1&&!Xt(e,t)&&r.length<o||i;return{useSqueezeShape:l,uniformShape:l?a:e,keptDims:s}}function Df(n,e){const t=JSON.parse(JSON.stringify(n));return t.shapeInfo.logicalShape=e,t}function Of(n,e){return e.map(t=>n[t]).join(", ")}function zee(n,e,t,r){const s=t.map((d,p)=>{const m={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(m.flatOffset=d.texData.slice.flatOffset),{name:e.variableNames[p],shapeInfo:m}}),o=s.map(d=>d.shapeInfo),i={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},a=ree(s,i,e),l=_Z(n.gl,a),c=n.createProgram(l);return ye().get("ENGINE_COMPILE_ONLY")?{program:e,fragmentShader:l,source:a,webGLProgram:c,inShapeInfos:o,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(n.buildVao(c),Object.assign({program:e,fragmentShader:l,source:a,webGLProgram:c,inShapeInfos:o,outShapeInfo:i},fF(n,e,c)))}function fF(n,e,t){const r=[],s=[];let o,i,a,l=null,c=null;c=n.getUniformLocation(t,"NAN",!1),ye().getNumber("WEBGL_VERSION")===1&&(l=n.getUniformLocation(t,"INFINITY",!1));const d=!1;for(const p of e.variableNames){const m={name:p,uniform:n.getUniformLocation(t,p,d),offset:n.getUniformLocation(t,`offset${p}`,d)};e.enableShapeUniforms&&(m.shape=n.getUniformLocation(t,`${p}Shape`,d),m.texShape=n.getUniformLocation(t,`${p}TexShape`,d)),r.push(m)}if(e.enableShapeUniforms&&(o=n.getUniformLocation(t,"outShape",d),a=n.getUniformLocation(t,"outShapeStrides",d),i=n.getUniformLocation(t,"outTexShape",d)),e.customUniforms)for(const p of e.customUniforms)s.push(n.getUniformLocation(t,p.name,d));return{variablesLocations:r,customUniformLocations:s,infLoc:l,nanLoc:c,outShapeLocation:o,outShapeStridesLocation:a,outTexShapeLocation:i}}function W2(n,e){if(n.length!==e.length)throw Error(`Binary was compiled with ${n.length} inputs, but was executed with ${e.length} inputs`);n.forEach((t,r)=>{const s=t.logicalShape,o=e[r],i=o.shape;if(!Xt(s,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${s} and ${i} must match`);if(t.isUniform&&o.isUniform)return;const a=t.texShape,l=o.isUniform?null:o.texData.texShape;if(!Xt(a,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${l} must match`)})}function Bee(n,e,t,r,s){e.program.enableShapeUniforms||(W2(e.inShapeInfos,t),W2([e.outShapeInfo],[r]));const o=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?n.setOutputPackedMatrixTexture(o.texture,i[0],i[1]):n.setOutputMatrixTexture(o.texture,i[0],i[1]),n.setProgram(e.webGLProgram),n.bindVertexArray(e.webGLProgram.vao),ye().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&n.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&n.gl.uniform1f(e.nanLoc,NaN);for(let l=0;l<t.length;++l){const c=t[l],{uniform:d,offset:p,shape:m,texShape:y}=e.variablesLocations[l];if(m){const{uniformShape:v}=GI(e.program.packedInputs,c.shape,c.texData.texShape);switch(v.length){case 1:n.gl.uniform1iv(m,new Int32Array(v));break;case 2:n.gl.uniform2iv(m,new Int32Array(v));break;case 3:n.gl.uniform3iv(m,new Int32Array(v));break;case 4:n.gl.uniform4iv(m,new Int32Array(v));break}}if(y&&n.gl.uniform2i(y,c.texData.texShape[0],c.texData.texShape[1]),d!=null){if(c.isUniform){if(Se(c.shape)<2)n.gl.uniform1f(d,c.uniformValues[0]);else{let v=c.uniformValues;v instanceof Float32Array||(v=new Float32Array(v)),n.gl.uniform1fv(d,v)}continue}c.texData.slice!=null&&p!=null&&n.gl.uniform1i(p,c.texData.slice.flatOffset),n.setInputMatrixTexture(c.texData.texture.texture,d,l)}}const a=e.outShapeLocation;if(a)switch(r.shape.length){case 1:n.gl.uniform1iv(a,new Int32Array(r.shape));break;case 2:n.gl.uniform2iv(a,new Int32Array(r.shape));break;case 3:n.gl.uniform3iv(a,new Int32Array(r.shape));break;case 4:n.gl.uniform4iv(a,new Int32Array(r.shape));break}if(e.outShapeStridesLocation){const l=tt(r.shape);switch(r.shape.length){case 2:n.gl.uniform1iv(e.outShapeStridesLocation,new Int32Array(l));break;case 3:n.gl.uniform2iv(e.outShapeStridesLocation,new Int32Array(l));break;case 4:n.gl.uniform3iv(e.outShapeStridesLocation,new Int32Array(l));break}}if(e.outTexShapeLocation&&n.gl.uniform2i(e.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),e.program.customUniforms&&s)for(let l=0;l<e.program.customUniforms.length;++l){const c=e.program.customUniforms[l],d=e.customUniformLocations[l],p=s[l];if(c.type==="float")n.gl.uniform1fv(d,p);else if(c.type==="vec2")n.gl.uniform2fv(d,p);else if(c.type==="vec3")n.gl.uniform3fv(d,p);else if(c.type==="vec4")n.gl.uniform4fv(d,p);else if(c.type==="int")n.gl.uniform1iv(d,p);else if(c.type==="ivec2")n.gl.uniform2iv(d,p);else if(c.type==="ivec3")n.gl.uniform3iv(d,p);else if(c.type==="ivec4")n.gl.uniform4iv(d,p);else throw Error(`uniform type ${c.type} is not supported yet.`)}n.executeProgram()}function Uee(n,e,t){let r="";e.concat(t).forEach(i=>{const a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(n.enableShapeUniforms&&!i.isUniform){const l=i.texData.texShape,{useSqueezeShape:c,uniformShape:d,keptDims:p}=GI(n.packedInputs,i.shape,l);let m="",y="",v="";if(d.length===1&&n.packedInputs){const A=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];m=`${A[0]>1}_${A[1]>1}`}else if(d.length===2&&!n.packedInputs)y=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!n.packedInputs){const A=tt(d);v=`${A[0]===l[1]}_${A[A.length-1]===l[1]}`}const b=i.shape.length,w=d.length===2&&Xt(i.shape,l),k=Se(i.shape)===1,I=uf(i.shape,t.shape),N=!n.packedInputs&&b===t.shape.length&&Xt(l,t.texData.texShape),T=n.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;r+=`${b}_${N}_${c?p:""}_${d.length}_${k}_${I}_${w}_${m}_${y}_${v}_${T}_${a}`}else{const l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${a}`}});const s=n.userCode;let o=n.constructor.name;return o+="_"+r+"_"+s+`${ye().getNumber("WEBGL_VERSION")}`,o}function Kr(n){return ye().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&n<=4}class Wee{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=jm.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=hs();this.outputShape=e,this.enableShapeUniforms=Kr(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Wv(["r","c","d"],e):fh(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}}class Vee{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=jm.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=hs();this.outputShape=e,this.enableShapeUniforms=Kr(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Wv(["r","c","d"],e):fh(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}}class Gee{constructor(e){this.variableNames=["A"],this.outTexUsage=xo.DOWNLOAD;const t=hs();this.outputShape=e,this.userCode=`
${uF}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}}class jee{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=xo.DOWNLOAD;const t=hs();this.outputShape=e,this.userCode=`
${uF}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}}const Hee={R:0,G:1,B:2,A:3};class V2{constructor(e,t=!1,r="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];const s=hs();this.outputShape=e,this.enableShapeUniforms=Kr(this.outputShape.length);let o="result";t&&(o="floor(result * 255. + 0.5)");let i="";for(let a=0;a<r.length;a++){const l=r[a];i+=`
if(offset == ${a}) {
result = values[${Hee[l]}];
}`}this.userCode=`
${this.enableShapeUniforms?VI():WI(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
float result = 0.;
int offset = imod(flatIndex, ${r.length});
flatIndex = idiv(flatIndex, ${r.length}, 1.);
int r = flatIndex / texShape[1];
if (r < texShape[0]) {
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${s.texture2D}(A, uv);
${i}
}
${s.output} = vec4(${o}, 0., 0., 0.);
}
`}}class qee{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];const r=hs();this.outputShape=e,this.enableShapeUniforms=Kr(this.outputShape.length);let s="",o="result";t&&(o="floor(result * 255. + 0.5)");for(let i=0;i<=1;i++)for(let a=0;a<=1;a++){const l=i*2+a;s+=`
localCoords = coords;
if(localCoords[2] + ${a} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${a};
if (localCoords[1] + ${i} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${i};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${r.texture2D}(A, uv);
if (offset == 0) {
result[${l}] = values[0];
} else if (offset == 1) {
result[${l}] = values[1];
} else if (offset == 2) {
result[${l}] = values[2];
} else {
result[${l}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?VI():WI(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${s}
${r.output} = ${o};
}
`}}function Kee(n){const e=hs(),t=`${e.version}
precision highp float;
${e.attribute} vec3 clipSpacePos;
${e.attribute} vec2 uv;
${e.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return AZ(n,t)}function Xee(n){const e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return PZ(n,e)}function Yee(n){const e=new Uint16Array([0,1,2,2,1,3]);return LZ(n,e)}function yx(n,e,t,r,s,o){zZ(e,t);const i=MZ(n),a=n.TEXTURE_2D;return Ve(n,()=>n.bindTexture(a,i)),Ve(n,()=>n.texParameteri(a,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE)),Ve(n,()=>n.texParameteri(a,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE)),Ve(n,()=>n.texParameteri(a,n.TEXTURE_MIN_FILTER,n.NEAREST)),Ve(n,()=>n.texParameteri(a,n.TEXTURE_MAG_FILTER,n.NEAREST)),ye().getNumber("WEBGL_VERSION")===1?Ve(n,()=>n.texImage2D(a,0,r,e,t,0,s,o,null)):Ve(n,()=>n.texStorage2D(a,1,r,e,t)),Ve(n,()=>n.bindTexture(n.TEXTURE_2D,null)),{texture:i,texShape:[t,e]}}function pF(n){return n.internalFormatFloat}function Qee(n,e,t,r){const[s,o]=gx(e,t);return yx(n,s,o,pF(r),r.textureFormatFloat,n.FLOAT)}function mF(n){return n.internalFormatHalfFloat}function Jee(n,e,t,r){const[s,o]=gx(e,t);return yx(n,s,o,mF(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function gF(n){return n.downloadTextureFormat}function Zee(n,e,t,r){const[s,o]=gx(e,t);return yx(n,s,o,gF(r),n.RGBA,n.UNSIGNED_BYTE)}function xF(n){return n.internalFormatPackedFloat}function ete(n,e,t,r){const[s,o]=Rf(e,t);return yx(n,s,o,xF(r),n.RGBA,n.FLOAT)}function yF(n){return n.internalFormatPackedHalfFloat}function tte(n,e,t,r){const[s,o]=Rf(e,t);return yx(n,s,o,yF(r),n.RGBA,r.textureTypeHalfFloat)}function nte(n,e,t){return Ve(n,()=>n.bindBuffer(n.ARRAY_BUFFER,t)),z2(n,e,"clipSpacePos",t,3,20,0)&&z2(n,e,"uv",t,2,20,12)}function rte(n,e,t,r,s,o){Ve(n,()=>n.bindTexture(n.TEXTURE_2D,e));let i,a,l;s instanceof Uint8Array?(i=new Uint8Array(t*r*4),a=n.UNSIGNED_BYTE,l=n.RGBA):(i=new Float32Array(t*r*4),a=n.FLOAT,l=o.internalFormatPackedFloat),i.set(s),ye().getNumber("WEBGL_VERSION")===2?Ve(n,()=>n.texSubImage2D(n.TEXTURE_2D,0,0,0,t,r,n.RGBA,a,i)):Ve(n,()=>n.texImage2D(n.TEXTURE_2D,0,l,t,r,0,n.RGBA,a,i)),Ve(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function ste(n,e,t){Ve(n,()=>n.bindTexture(n.TEXTURE_2D,e)),t.data instanceof Uint8Array?ye().getNumber("WEBGL_VERSION")===2?Ve(n,()=>n.texSubImage2D(n.TEXTURE_2D,0,0,0,t.width,t.height,n.RGBA,n.UNSIGNED_BYTE,t.data)):Ve(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,t.width,t.height,0,n.RGBA,n.UNSIGNED_BYTE,t.data)):ye().getNumber("WEBGL_VERSION")===2?Ve(n,()=>n.texSubImage2D(n.TEXTURE_2D,0,0,0,n.RGBA,n.UNSIGNED_BYTE,t)):Ve(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,n.RGBA,n.UNSIGNED_BYTE,t)),Ve(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function ote(n,e,t,r){const s=n.createBuffer();Ve(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,s));const a=4*4*e*t;return Ve(n,()=>n.bufferData(n.PIXEL_PACK_BUFFER,a,n.STREAM_READ)),Ve(n,()=>n.readPixels(0,0,t,e,n.RGBA,n.FLOAT,0)),Ve(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,null)),s}function ite(n,e,t){const r=n,s=new Float32Array(t);return r.bindBuffer(r.PIXEL_PACK_BUFFER,e),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,s),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),s}function ate(n,e,t,r){const[s,o]=gx(e,t),i=4,a=new Uint8Array(kZ(e*t,i));return Ve(n,()=>n.readPixels(0,0,s,o,r.downloadTextureFormat,n.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function lte(n,e,t,r,s,o,i,a){const l=n,c=new Float32Array(IZ(o,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function ute(n,e,t){const r=new Float32Array(e*t*4);return Ve(n,()=>n.readPixels(0,0,t,e,n.RGBA,n.FLOAT,r)),r}class zb{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];const t=ye().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,bZ(t,e)):this.gl=Ni(t),e=this.gl,ye().getNumber("WEBGL_VERSION")===2){const o=e;this.createVertexArray=()=>Ve(o,()=>o.createVertexArray()),this.bindVertexArray=i=>Ve(o,()=>o.bindVertexArray(i)),this.deleteVertexArray=i=>Ve(o,()=>o.deleteVertexArray(i)),this.getVertexArray=()=>Ve(o,()=>o.getParameter(o.VERTEX_ARRAY_BINDING))}else if(e!=null){const o=e.getExtension("OES_vertex_array_object");if(o==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>Ve(e,()=>o.createVertexArrayOES()),this.bindVertexArray=i=>Ve(e,()=>o.bindVertexArrayOES(i)),this.deleteVertexArray=i=>Ve(e,()=>o.deleteVertexArrayOES(i)),this.getVertexArray=()=>Ve(e,()=>e.getParameter(o.VERTEX_ARRAY_BINDING_OES))}let r="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),ye().getNumber("WEBGL_VERSION")===1){const o="OES_texture_float",i="OES_texture_half_float";if(this.textureFloatExtension=cy(this.gl,o),Ho(this.gl,i))this.textureHalfFloatExtension=cy(this.gl,i);else if(ye().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(r),Ho(this.gl,s))this.colorBufferHalfFloatExtension=cy(this.gl,s);else if(ye().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(r="EXT_color_buffer_float",Ho(this.gl,r))this.colorBufferFloatExtension=this.gl.getExtension(r);else if(Ho(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Xee(this.gl),this.indexBuffer=Yee(this.gl),this.framebuffer=BZ(this.gl),this.textureConfig=UI(this.gl,this.textureHalfFloatExtension)}get debug(){return ye().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;Ve(e,()=>e.finish()),Ve(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ve(e,()=>e.deleteFramebuffer(this.framebuffer)),Ve(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ve(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ve(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Qee(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Jee(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Zee(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),ste(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,r,s){this.throwIfDisposed(),rte(this.gl,e,t,r,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),tte(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),ete(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(B2(this.gl,this.framebuffer),this.outputTexture=null),Ve(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,r){return this.downloadMatrixDriver(e,()=>ate(this.gl,t,r,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,r,s,o,i){return lte(this.gl,e,t,r,s,o,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return ite(this.gl,e,t)}createBufferFromTexture(e,t,r){this.bindTextureToFrameBuffer(e);const s=ote(this.gl,t,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,r;if(ye().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,o=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),r=()=>{const i=s.clientWaitSync(o,0,0);return i===s.ALREADY_SIGNALED||i===s.CONDITION_SATISFIED},t=o}else ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),r=()=>this.isQueryAvailable(t,ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):r=()=>!0;return{query:t,isFencePassed:r}}downloadMatrixFromPackedTexture(e,t,r){return this.downloadMatrixDriver(e,()=>ute(this.gl,t,r))}createProgram(e){this.throwIfDisposed();const t=this.gl;this.vertexShader==null&&(this.vertexShader=Kee(t));const r=OZ(t);Ve(t,()=>t.attachShader(r,this.vertexShader)),Ve(t,()=>t.attachShader(r,e)),FZ(t,r);const s=Object.assign(r,{vao:this.createVertexArray()});return this.debug&&Fb(t,s),s}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);const t=this.gl;Ve(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),nte(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(Ve(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Fb(this.gl,this.program),Ve(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,r=!0){return this.throwIfDisposed(),r?WZ(this.gl,e,t):VZ(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ve(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,r){this.throwIfDisposed(),this.throwIfNoProgram(),GZ(this.gl,e,t,r)}setOutputMatrixTexture(e,t,r){this.setOutputMatrixTextureDriver(e,r,t)}setOutputPackedMatrixTexture(e,t,r){this.throwIfDisposed();const[s,o]=Rf(t,r);this.setOutputMatrixTextureDriver(e,s,o)}setOutputMatrixWriteRegion(e,t,r,s){this.setOutputMatrixWriteRegionDriver(r,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,r,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Fb(this.gl,this.program),hy(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;if(this.debug){const t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}Ve(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ve(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=cy(this.gl,ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const r=this.gl,s=this.getQueryTimerExtensionWebGL2(),o=r.createQuery();return r.beginQuery(s.TIME_ELAPSED_EXT,o),o}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,r=this.getQueryTimerExtensionWebGL2();t.endQuery(r.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await kN(()=>this.disposed||this.isQueryAvailable(e,ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const r=this.gl;return r.getQueryParameter(e,r.QUERY_RESULT)/1e6}else{const r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const r=this.gl,s=this.getQueryTimerExtensionWebGL2(),o=r.getQueryParameter(e,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),o&&!this.disjoint}else{const r=this.getQueryTimerExtensionWebGL1(),s=r.getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=cte(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:r}=this.itemsToPoll[t];r()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let r;"setTimeoutCustom"in ye().platform&&(r=ye().platform.setTimeoutCustom.bind(ye().platform)),kN(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,r)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Pb(this.gl,e,this.framebuffer),this.debug&&hy(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Pb(this.gl,this.outputTexture,this.framebuffer),this.debug&&hy(this.gl)):B2(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const r=t();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(e,t,r){this.throwIfDisposed();const s=this.gl;Pb(s,e,this.framebuffer),this.debug&&hy(s),this.outputTexture=e,Ve(s,()=>s.viewport(0,0,t,r)),Ve(s,()=>s.scissor(0,0,t,r))}setOutputMatrixWriteRegionDriver(e,t,r,s){this.throwIfDisposed(),Ve(this.gl,()=>this.gl.scissor(e,t,r,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function cte(n){let e=0;for(;e<n.length&&n[e]();++e);return e-1}const{addImpl:hte,bincountImpl:vF,bincountReduceImpl:dte,bitwiseAndImpl:fte,castImpl:pte,ceilImpl:mte,concatImpl:gte,equalImpl:xte,expImpl:yte,expm1Impl:vte,floorImpl:wte,gatherNdImpl:bte,gatherV2Impl:Cte,greaterImpl:Ste,greaterEqualImpl:kte,lessImpl:Ite,lessEqualImpl:$te,linSpaceImpl:Nte,logImpl:Tte,maxImpl:Ete,maximumImpl:Rte,minimumImpl:Ate,multiplyImpl:_te,negImpl:Dte,notEqualImpl:Ote,prodImpl:Fte,raggedGatherImpl:Pte,raggedRangeImpl:Lte,raggedTensorToTensorImpl:Mte,rangeImpl:zte,rsqrtImpl:Bte,scatterImpl:Ute,sigmoidImpl:Wte,simpleAbsImpl:wF,sliceImpl:Vte,sparseFillEmptyRowsImpl:Gte,sparseReshapeImpl:jte,sparseSegmentReductionImpl:bF,sqrtImpl:Hte,staticRegexReplaceImpl:qte,stridedSliceImpl:Kte,stringNGramsImpl:Xte,stringSplitImpl:Yte,stringToHashBucketFastImpl:Qte,subImpl:Jte,tileImpl:Zte,topKImpl:ene,transposeImpl:jI,uniqueImpl:tne}=sX;function CF(n,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${n}.${t}`)}function ls(n,e){return e===1?[n]:CF(n,e)}function nne(n,e){if(n===1)return"rc";let t="";for(let r=0;r<n;r++)t+=e[r],r<n-1&&(t+=",");return t}class rne{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Kr(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{const t=ls("rc",this.rank),r=on(this.rank),s=this.getOutOfBoundsCondition(t),o=this.getSetup(t),i=this.getOutput(t);this.userCode=`
void main() {
${r} rc = getOutputCoords();
if(${s}) {
setOutput(vec4(0));
} else {
${o}
setOutput(vec4(${i}));
}
}
`}}getSourceCoordsArr(e){const t=[];for(let r=0;r<=1;r++)for(let s=0;s<=1;s++){let o=`${r===0?"r":"rp1"}, ${s===0?"c":"cp1"}`;for(let i=2;i<this.rank;i++)o=`${e[e.length-1-i]},`+o;t.push(o)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let r=this.rank-2;r<this.rank;r++)t+=`${e[r]} >= ${this.enableShapeUniforms?`outShape[${r}]`:this.outputShape[r]}`,r<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";const t=e.slice(-2),r=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
int r = ${t[0]};
int c = ${t[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${r};
bool rEdge = rp1 >= ${s};
`}getOutput(e){const t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
cEdge ? 0. : getA(${t[1]}),
rEdge ? 0. : getA(${t[2]}),
rEdge || cEdge ? 0. : getA(${t[3]})`}}class SF{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Kr(this.outputShape.length);let r="";for(let s=0;s<4;s++){let o="thisRC = rc;";s%2===1&&(o+="thisRC.z += 1;"),s>1&&(o+="thisRC.y += 1;"),r+=`
${o}
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${s}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${s>0?"}":""}
`}this.userCode=`
${sne(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?VI():WI(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${r}
setOutput(result);
}
`}}function sne(n,e){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${e?nee(["r","c","d"],"inputShape"):fh(["r","c","d"],n)}
return ivec3(r, c, d);
}
`}class one{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,r){const s=j2(t,r),o=H2(e,s,r);o in this.freeTextures||(this.freeTextures[o]=[]),o in this.usedTextures||(this.usedTextures[o]=[]);const i=G2(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[o].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();const l=this.freeTextures[o].pop();return this.usedTextures[o].push(l),l}let a;return s===Rr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Rr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Rr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Rr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Rr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[o].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(e,t,r,s){if(this.freeTextures==null)return;const o=j2(r,s),i=H2(t,o,s);i in this.freeTextures||(this.freeTextures[i]=[]);const a=G2(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,s),l=ye().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(e),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;const c=this.usedTextures[i],d=c&&c.indexOf(e);if(d==null||d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");c[d]=c[c.length-1],c.pop(),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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(this.freeTextures!=null){for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}}function ine(n,e){const t=n;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===n.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function G2(n,e,t,r,s){const o=ane(e,r);let i;if(s){const[l,c]=Rf(n[0],n[1]);i=l*c}else{const[l,c]=gx(n[0],n[1]);i=l*c}const a=ine(t,o);return i*a}function ane(n,e){switch(n){case Rr.PACKED_2X2_FLOAT32:return xF(e);case Rr.PACKED_2X2_FLOAT16:return yF(e);case Rr.UNPACKED_FLOAT32:return pF(e);case Rr.UNPACKED_FLOAT16:return mF(e);case Rr.PACKED_4X1_UNSIGNED_BYTE:return gF(e);default:throw new Error(`Unknown physical texture type ${n}`)}}function lne(n){return ye().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?n?Rr.PACKED_2X2_FLOAT32:Rr.UNPACKED_FLOAT32:n?Rr.PACKED_2X2_FLOAT16:Rr.UNPACKED_FLOAT16}function j2(n,e){if(n===xo.UPLOAD)return Rr.PACKED_2X2_FLOAT32;if(n===xo.RENDER||n==null)return lne(e);if(n===xo.DOWNLOAD||n===xo.PIXELS)return Rr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${n}`)}function H2(n,e,t){return`${n[0]}_${n[1]}_${e}_${t}`}class la{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Kr(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}}const Qo="if (isnan(x)) return x;",une="return x;",q2="return abs(x);",cne="return (x >= 0.0) ? x : (exp(x) - 1.0);",hne=Qo+`
return (x < 0.0) ? 0.0 : x;
`,dne=Qo+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Yl="return x;",fne="return 1.0 / (1.0 + exp(-1.0 * x));";const pne="return x;",mne=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,gne=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,xne=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,yne="return 1.0 / (1.0 + exp(-1.0 * x));";class tu{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Kr(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}}class vne{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Kr(this.outputShape.length);const t=e.length,r=ls("rc",t),s=on(t),o=nne(t,r),i=r.slice(-2),a=t<=1?"rc":`vec2(${i.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${o});
setOutput(getChannel(packedInput, ${a}));
}
`}}const wne=jR,bne=1e-7,Cne=1e-4,py={};function Sne(n){return n in py||(py[n]={}),py[n]}const kne=ye().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Ine=600;function $ne(){return ye().global.screen==null?1024:ye().global.screen.height*ye().global.screen.width*window.devicePixelRatio*Ine/1024/1024}class Vv extends hS{nextDataId(){return Vv.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!ye().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof zb)t=e;else{const r=Ni(ye().getNumber("WEBGL_VERSION"),e);t=new zb(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{const r=Ni(ye().getNumber("WEBGL_VERSION"));t=new zb(r),this.binaryCache=Sne(ye().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new one(this.gpgpu),this.numMBBeforeWarning=$ne(),this.texData=new yE(this,na())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,r,s,o,i){const a=this.makeTensorInfo(t,r),l=this.texData.get(a.dataId);l.isPacked=!1,l.texture={texture:e,texShape:[s,o]},l.texShape=[s,o];const c=dy(t),d=new V2(c,!1,i),p=this.runWebGLProgram(d,[a],r,[[s,o]]);return p.shape=t,l.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(e,t,r){if((ye().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ye().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)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:r,values:e,usage:xo.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,r,s,o){if(ye().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:s,values:t,usage:xo.UPLOAD,refCount:o})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){const t=this.texData.get(e),{values:r,dtype:s,complexTensorInfos:o,slice:i,shape:a,isPacked:l}=t;if(i!=null){let m;l?m=new tu(a,Yl):m=new la(a,Yl);const y=this.runWebGLProgram(m,[{dataId:e,shape:a,dtype:s}],s),v=this.readSync(y.dataId);return this.disposeIntermediateTensorInfo(y),v}if(r!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return r;const c=this.activeTimers!=null;let d;c&&(d=Cs());let p;if(s==="complex64"){const m=this.readSync(o.real.dataId),y=this.readSync(o.imag.dataId);p=ol(m,y)}else p=this.getValuesFromTexture(e);return c&&(this.downloadWaitMs+=Cs()-d),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){const v=this.pendingRead.get(e);return new Promise(b=>v.push(b))}const t=this.texData.get(e),{values:r,shape:s,slice:o,dtype:i,complexTensorInfos:a,isPacked:l}=t;if(o!=null){let v;l?v=new tu(s,Yl):v=new la(s,Yl);const b=this.runWebGLProgram(v,[{dataId:e,shape:s,dtype:i}],i),w=this.read(b.dataId);return this.disposeIntermediateTensorInfo(b),w}if(r!=null)return this.convertAndCacheOnCPU(e);if(ye().getBool("DEBUG")&&!ye().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ye().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let c=null,d;if(i!=="complex64"&&ye().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);const v=this.texData.get(d.dataId);c=this.gpgpu.createBufferFromTexture(v.texture.texture,...uy(s))}this.pendingRead.set(e,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){const v=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),b=v[0],w=v[1];p=ol(b,w)}else if(c==null)p=this.getValuesFromTexture(e);else{const v=Se(s);p=this.gpgpu.downloadFloat32MatrixFromBuffer(c,v)}if(d!=null&&this.disposeIntermediateTensorInfo(d),c!=null){const v=this.gpgpu.gl;Ve(v,()=>v.deleteBuffer(c))}const m=this.convertAndCacheOnCPU(e,p),y=this.pendingRead.get(e);return this.pendingRead.delete(e),y.forEach(v=>v(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&na().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){const r=this.texData.get(e),{values:s,shape:o,slice:i,dtype:a,isPacked:l,texture:c}=r;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let y;l?y=new tu(o,Yl):y=new la(o,Yl);const v=this.runWebGLProgram(y,[{dataId:e,shape:o,dtype:a}],a),b=this.readToGPU(v,t);return this.disposeIntermediateTensorInfo(v),b}if(c==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");const d=this.decode(e,t.customTexShape),p=na().makeTensorFromTensorInfo(d),m=this.texData.get(d.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(e){const t=this.readSync(e.dataId);if(e.dtype==="string")try{const r=t.map(s=>au(s));return Nt(e.shape,e.dtype,r)}catch{throw new Error("Failed to decode encoded string bytes into utf-8")}return Nt(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){const r=e[t];if(!EZ(r))throw ye().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:r,isPacked:s}=this.texData.get(e),o=Se(t);if(ye().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const m=this.decode(e),y=this.texData.get(m.dataId),v=this.gpgpu.downloadMatrixFromPackedTexture(y.texture.texture,...uy(t)).subarray(0,o);return this.disposeIntermediateTensorInfo(m),v}const i=ye().getBool("WEBGL_PACK")&&s===!0,a=i?dy(t):t,l=i?new jee(a):new Gee(a),c=this.runWebGLProgram(l,[{shape:a,dtype:r,dataId:e}],"float32"),d=this.texData.get(c.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture.texture,d.texShape[0],d.texShape[1]).subarray(0,o);return this.disposeIntermediateTensorInfo(c),p}timerAvailable(){return ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){const t=this.activeTimers,r=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=r,s=!0):this.activeTimers.push(r),this.activeTimers=r,e();const o=jc(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=jc(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const l=await Promise.all(o);a.kernelMs=V4(l),a.getExtraProfileInfo=()=>l.map((c,d)=>({name:i[d],ms:c})).map(c=>`${c.name}: ${c.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:Cs(),endMs:null}}endTimer(e){return ye().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Cs(),e)}async getQueryTime(e){if(ye().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:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){const{texture:t,dtype:r,texShape:s,usage:o,isPacked:i,slice:a}=this.texData.get(e),l=a&&a.origDataId||e,c=this.dataRefCount.get(l);c>1?this.dataRefCount.set(l,c-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,r),this.textureManager.releaseTexture(t,s,o,i)));const d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=kne){return ye().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&Se(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){go("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return wne(e.shape,t)}packedUnaryOp(e,t,r){const s=new tu(e.shape,t),o=this.compileAndRun(s,[e],r);return na().makeTensorFromTensorInfo(o)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){const s=wF(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(ye().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,q2,e.dtype);const t=new la(e.shape,q2),r=this.compileAndRun(t,[e]);return na().makeTensorFromTensorInfo(r)}makeTensorInfo(e,t,r){let s;if(t==="string"&&r!=null&&r.length>0&&tg(r[0])){const o=r.map(i=>su(i));s=this.write(o,e,t)}else s=this.write(r,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,r){return na().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,r),this)}unpackTensor(e){const t=new vne(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new rne(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const r=[mf(e.shape),...gf(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},o=[mf(t),...gf(t)],i=new SF(o,r),a=!0,l=[r],c=this.runWebGLProgram(i,[s],e.dtype,l,a);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e,t){const r=this.texData.get(e),{isPacked:s,shape:o,dtype:i}=r;if(t!=null){const m=Se(o),y=t[0]*t[1]*4;U(m<=y,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}const a=dy(o);let l;s?l=new Vee(a):l=new Wee(a);const c=!0,d=[t??uy(a)],p=this.runWebGLProgram(l,[{shape:a,dtype:i,dataId:e}],i,d,c,t);return{dtype:i,shape:o,dataId:p.dataId}}runWebGLProgram(e,t,r,s,o=!1,i){const a=this.makeTensorInfo(e.outputShape,r),l=this.texData.get(a.dataId);if(e.packedOutput&&(l.isPacked=!0),e.outPackingScheme===jm.DENSE){const k=i??uy(e.outputShape);l.texShape=k.map(I=>I*2)}if(e.outTexUsage!=null&&(l.usage=e.outTexUsage),Se(a.shape)===0)return l.values=Vr(a.dtype,0),a;const c=[],d=t.map(k=>{if(k.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let I=this.texData.get(k.dataId);if(I.texture==null){if(!e.packedInputs&&Se(k.shape)<=ye().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:k.shape,texData:null,isUniform:!0,uniformValues:I.values};e.packedInputs&&(I.isPacked=!0,I.shape=k.shape)}if(this.uploadToGPU(k.dataId),!!I.isPacked!=!!e.packedInputs)k=I.isPacked?this.unpackTensor(k):this.packTensor(k),c.push(k),I=this.texData.get(k.dataId);else if(I.isPacked&&!l1(I.shape,k.shape)){const N=k,T=k.shape;k.shape=I.shape,k=this.packedReshape(k,T),c.push(k),I=this.texData.get(k.dataId),N.shape=T}return{shape:k.shape,texData:I,isUniform:!1}});this.uploadToGPU(a.dataId);const p={shape:a.shape,texData:l,isUniform:!1},m=Uee(e,d,p),y=this.getAndSaveBinary(m,()=>zee(this.gpgpu,e,d,p)),v=this.activeTimers!=null;let b;v&&(b=this.startTimer()),ye().get("ENGINE_COMPILE_ONLY")||Bee(this.gpgpu,y,d,p,s),c.forEach(k=>this.disposeIntermediateTensorInfo(k)),v&&(b=this.endTimer(b),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(b)}));const w=ye().getNumber("WEBGL_FLUSH_THRESHOLD");if(w>0){const k=Cs();k-this.lastGlFlushTime>w&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=k)}if(!ye().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&o===!1){const k=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),k}return a}compileAndRun(e,t,r,s,o=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,s,o)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ye().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement<"u"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=me(()=>{if(!ye().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=ye().getBool("DEBUG");ye().set("DEBUG",!1);const t=this.abs(Kt(1e-8)).dataSync()[0];if(ye().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?bne:Cne}uploadToGPU(e){const t=this.texData.get(e),{shape:r,dtype:s,values:o,texture:i,usage:a,isPacked:l}=t;if(i!=null)return;const c=this.activeTimers!=null;let d;c&&(d=Cs());let p=t.texShape;if(p==null&&(p=qZ(r,l),t.texShape=p),o!=null){const m=dy(r);let y,v=p[1],b=p[0];const w=o instanceof Uint8Array||o instanceof Uint8ClampedArray;(l||!w)&&([v,b]=Rf(p[0],p[1])),l?y=new qee(m,w):y=new V2(m,w);const k=w?[b,v]:p,I=this.makeTensorInfo(k,s),N=this.texData.get(I.dataId);w?N.usage=xo.PIXELS:N.usage=xo.UPLOAD,N.texShape=k,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(I.dataId),v,b,o);const T=[[b,v]],O=this.runWebGLProgram(y,[I],s,T,!0),P=this.texData.get(O.dataId);t.texShape=P.texShape,t.isPacked=P.isPacked,t.usage=P.usage,ye().get("ENGINE_COMPILE_ONLY")?this.disposeData(O.dataId):(t.texture=P.texture,t.values=null,this.texData.delete(O.dataId)),this.disposeIntermediateTensorInfo(I),c&&(this.uploadWaitMs+=Cs()-d)}else{const m=this.acquireTexture(p,a,s,l);t.texture=m}}convertAndCacheOnCPU(e,t){const r=this.texData.get(e),{dtype:s}=r;return t!=null&&(r.values=Nne(t,s)),r.values}acquireTexture(e,t,r,s){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const o=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${o} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*Py(t)}checkCompileCompletion(){for(const[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){const e=[];if(this.gpgpu.parallelCompilationExtension){for(const[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(const[,t]of Object.entries(this.binaryCache)){const r=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(o){throw o}});e.push(r)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await cA(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(lF(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(const e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);const{variablesLocations:t,customUniformLocations:r,infLoc:s,nanLoc:o,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:l}=fF(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=r,e.infLoc=s,e.nanLoc=o,e.outShapeLocation=i,e.outShapeStridesLocation=a,e.outTexShapeLocation=l}}createTensorFromGPUData(e,t,r){e.channels=e.channels||"RGBA";const{texture:s,height:o,width:i,channels:a}=e,l=na().backend;if(!l.gpgpu.gl.isTexture(s))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");const c=l.writeTexture(s,t,r,o,i,a);return na().makeTensorFromDataId(c,t,r,l)}}Vv.nextDataId=0;function Nne(n,e){if(e==="float32"||e==="complex64")return n;if(e==="int32"||e==="bool"){const t=e==="int32"?new Int32Array(n.length):new Uint8Array(n.length);for(let r=0;r<t.length;++r)t[r]=Math.round(n[r]);return t}else throw new Error(`Unknown dtype ${e}`)}iR()&&uR("webgl",()=>new Vv,2);const HI=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`;class sh{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=gt(t,r),this.enableShapeUniforms=Kr(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}}const mh=`
result.r = isNaN.r ? NAN : result.r;
result.g = isNaN.g ? NAN : result.g;
result.b = isNaN.b ? NAN : result.b;
result.a = isNaN.a ? NAN : result.a;
`;class Ff{constructor(e,t,r,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=gt(t,r);const o=this.outputShape.length;this.enableShapeUniforms=Kr(o);let i="";if(s)if(o===0||Se(this.outputShape)===1)i=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(i=`
${on(o)} coords = getOutputCoords();
`,o===1)this.enableShapeUniforms?i+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:i+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{const l=ls("coords",o);this.enableShapeUniforms?i+=`
bool nextRowOutOfBounds =
(${l[o-2]} + 1) >= outShape[${o} - 2];
bool nextColOutOfBounds =
(${l[o-1]} + 1) >= outShape[${o} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:i+=`
bool nextRowOutOfBounds =
(${l[o-2]} + 1) >= ${this.outputShape[o-2]};
bool nextColOutOfBounds =
(${l[o-1]} + 1) >= ${this.outputShape[o-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${i}
setOutput(result);
}
`}}function Js(n){const{inputs:e,backend:t}=n,{x:r}=e;return t.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const Tne={kernelName:Cg,backendName:"webgl",kernelFunc:Js};function ku(n){const{inputs:e,backend:t}=n,{real:r,imag:s}=e,o=t.makeTensorInfo(r.shape,"complex64"),i=t.texData.get(o.dataId),a=Js({inputs:{x:r},backend:t}),l=Js({inputs:{x:s},backend:t});return i.complexTensorInfos={real:a,imag:l},o}const Ene={kernelName:IS,backendName:"webgl",kernelFunc:ku};const kF="return (a < 0.) ? b * a : a;",IF=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Rne(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{alpha:o}=r,i=t.makeTensorInfo([],"float32",mu(o,"float32")),a=ye().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ff(IF,s.shape,i.shape):new sh(kF,s.shape,i.shape),l=t.runWebGLProgram(a,[s,i],"float32");return t.disposeIntermediateTensorInfo(i),l}const Ane={kernelName:z1,backendName:"webgl",kernelFunc:Rne};const $F="return (a < 0.) ? b * a : a;",NF=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function _ne(n){const{inputs:e,backend:t}=n,{x:r,alpha:s}=e,o=ye().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ff(NF,r.shape,s.shape):new sh($F,r.shape,s.shape);return t.runWebGLProgram(o,[r,s],"float32")}const Dne={kernelName:sv,backendName:"webgl",kernelFunc:_ne};const Pf="if (isnan(x)) return x;";function zt({opSnippet:n,packedOpSnippet:e,cpuKernelImpl:t,dtype:r}){return({inputs:s,backend:o})=>{const{x:i}=s,a=o,l=r||i.dtype;if(a.shouldExecuteOnCPU([i])&&t!=null){const p=a.texData.get(i.dataId),m=t(p.values,l);return a.makeTensorInfo(i.shape,l,m)}const c=ye().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null;let d;return c?d=new tu(i.shape,e):d=new la(i.shape,n),a.runWebGLProgram(d,[i],l)}}function Or({opSnippet:n,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:o}){return({inputs:i,backend:a})=>{const{a:l,b:c}=i,d=a;if(r&&l.dtype==="complex64"){const v=d.texData.get(l.dataId),b=d.texData.get(c.dataId),[w,k]=[[v.complexTensorInfos.real,b.complexTensorInfos.real],[v.complexTensorInfos.imag,b.complexTensorInfos.imag]].map(N=>{const[T,A]=N,O={dataId:T.dataId,dtype:T.dtype,shape:l.shape},P={dataId:A.dataId,dtype:A.dtype,shape:c.shape},B=new sh(n,l.shape,c.shape);return d.runWebGLProgram(B,[O,P],Ys(T.dtype,A.dtype))}),I=ku({inputs:{real:w,imag:k},backend:d});return d.disposeIntermediateTensorInfo(w),d.disposeIntermediateTensorInfo(k),I}const p=o||Ys(l.dtype,c.dtype);if((l.dtype==="string"||c.dtype==="string"||d.shouldExecuteOnCPU([l,c]))&&s!=null){const v=d.texData.get(l.dataId).values,b=d.texData.get(c.dataId).values,w=l.dtype==="string"?il(v):v,k=l.dtype==="string"?il(b):b,[I,N]=s(l.shape,c.shape,w,k,p),T=d.makeTensorInfo(N,p),A=d.texData.get(T.dataId);return A.values=I,T}const m=ye().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null;let y;return m?y=new Ff(e,l.shape,c.shape,t):y=new sh(n,l.shape,c.shape),d.runWebGLProgram(y,[l,c],p)}}function Hm(n,e=!1){if(n==="linear")return e?pne:une;if(n==="relu")return e?gne:hne;if(n==="elu")return e?mne:cne;if(n==="relu6")return e?xne:dne;if(n==="prelu")return e?NF:$F;if(n==="leakyrelu")return e?IF:kF;if(n==="sigmoid")return e?yne:fne;throw new Error(`Activation ${n} has not been implemented for the WebGL backend.`)}class TF{constructor(e,t,r,s=!1,o=!1,i=!1,a=null,l=!1,c=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=Kr(this.outputShape.length);const d=s?e[1]:e[2],p=Math.ceil(d/2),m=s?"i * 2, rc.y":"rc.y, i * 2",y=o?"rc.z, i * 2":"i * 2, rc.z",v=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],b=o?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let w="",k="";a&&(l?w=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${a}
}`:c?w=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${a}
}`:w=`vec4 activation(vec4 x) {
${a}
}`,k="result = activation(result);");const I=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),c&&this.variableNames.push("leakyreluAlpha");let N="rc.x",T="rc.x";e[0]<t[0]?N=`imod(rc.x, ${e[0]})`:t[0]<e[0]&&(T=`imod(rc.x, ${t[0]})`),this.userCode=`
${w}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${p}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
int batchA = ${N};
int batchB = ${T};
for (int i = 0; i < ${p}; i++) {
vec4 a = getMatrixA(batchA, ${m});
vec4 b = getMatrixB(batchB, ${y});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${v[0]} * ${b[0]});
result += (${v[1]} * ${b[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${I}
${k}
setOutput(result);
}
`}}const K2={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class X2{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=gt(t,r),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}}const Y2="return a * b;";function qI(n){const{inputs:e,backend:t}=n,{a:r,b:s}=e,o=Ys(r.dtype,s.dtype);if(r.dtype==="complex64"){const a=t.texData.get(r.dataId),l=t.texData.get(s.dataId),c=new X2(K2.REAL,r.shape,s.shape),d=new X2(K2.IMAG,r.shape,s.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:r.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:s.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:s.shape}],m=t.runWebGLProgram(c,p,"float32"),y=t.runWebGLProgram(d,p,"float32"),v=ku({inputs:{real:m,imag:y},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(y),v}if(t.shouldExecuteOnCPU([r,s])){const a=t.texData.get(r.dataId),l=t.texData.get(s.dataId),[c,d]=_te(r.shape,s.shape,a.values,l.values,o),p=t.makeTensorInfo(d,o),m=t.texData.get(p.dataId);return m.values=c,p}let i;return ye().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ff(Y2,r.shape,s.shape):i=new sh(Y2,r.shape,s.shape),t.runWebGLProgram(i,[r,s],o)}const One={kernelName:Ag,backendName:"webgl",kernelFunc:qI};function Fne(n,e,t){const r=[mf(n.shape),...gf(n.shape)],s={dtype:n.dtype,shape:r,dataId:n.dataId},o=[mf(e),...gf(e)],i=new SF(o,r),a=!0,l=[r],c=t.runWebGLProgram(i,[s],n.dtype,l,a);return{dataId:c.dataId,shape:e,dtype:c.dtype}}function Ue(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{shape:o}=r,i=t,a=Se(s.shape),l=vE(o,a),c=Se(l);U(a===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);const d=i.texData.get(s.dataId);return d.isPacked&&!l1(s.shape,l)&&!(d.texture!==null&&l1(d.shape,l))?Fne(s,l,i):(i.incRef(s.dataId),{dataId:s.dataId,shape:l,dtype:s.dtype})}const Pne={kernelName:iv,backendName:"webgl",kernelFunc:Ue};class Q2{constructor(e,t){this.variableNames=["x"];const{windowSize:r,batchSize:s,inSize:o,outSize:i}=e;this.outputShape=[s,i];const a=Math.floor(r/4)*4,l=r%4;let c="sumValue += dot(values, ones);";if(t!=null){const p=1/t;c=`sumValue += dot(values * ${of(p)?p.toPrecision(2):p}, ones);`}let d="";o%r>0&&(d=`
if (inIdx < 0 || inIdx >= ${o}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
float sumValue = 0.0;
for (int i = 0; i < ${a}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${c}
}
int inIdx = inOffset + ${a};
if (${l===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${c}
} else if (${l===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${c}
} else if (${l===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${c}
}
setOutput(sumValue);
}
`}}class Lne{constructor(e,t){this.variableNames=["x"];const{windowSize:r,batchSize:s,inSize:o,outSize:i}=e;this.outputShape=[s,i];let a="0.0",l="";t==="prod"?a="1.0":t==="min"?(a="1.0 / 1e-20",l="min"):t==="max"&&(a="-1.0 / 1e-20",l="max");let c=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?c="sumValue":t==="prod"?c="prodValue":t==="all"?c="allValue":t==="any"&&(c="anyValue");const d=Math.floor(r/4)*4,p=r%4;let m=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${l}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${l}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,y="vec4";t==="all"?(a="1.0",m=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,y="bvec4"):t==="any"&&(a="0.0",m=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,y="bvec4");let v="";o%r>0&&(v=`
if (inIdx < 0 || inIdx >= ${o}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${a};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${v}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
vec4 minMaxValue = vec4(${a});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${d}; i += 4) {
int inIdx = inOffset + i;
${y} values = ${y}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${m}
}
int inIdx = inOffset + ${d};
if (${p===1}) {
${y} values = ${y}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${m}
} else if (${p===2}) {
${y} values = ${y}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${m}
} else if (${p===3}) {
${y} values = ${y}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${m}
}
setOutput(${c});
}
`}}function Mne(n){const e=[];for(;e.length===0||e[e.length-1].outSize!==1;){const t=e.length?e[e.length-1].outSize:n[1],r=Nv(t);e.push({inSize:t,windowSize:r,outSize:Math.ceil(t/r)})}return e}function gh(n,e,t,r){const s=Mne(n.shape);let o=n;for(let i=0;i<s.length;i++){const{inSize:a,windowSize:l,outSize:c}=s[i];let d,p;t==="mean"?d=i===0?new Q2({windowSize:l,inSize:a,batchSize:n.shape[0],outSize:c},a):new Q2({windowSize:l,inSize:a,batchSize:n.shape[0],outSize:c}):d=new Lne({windowSize:l,inSize:a,batchSize:n.shape[0],outSize:c},t),p=o,o=r.runWebGLProgram(d,[o],e),p.dataId!==n.dataId&&r.disposeIntermediateTensorInfo(p)}return o}class zne{constructor(e,t){this.variableNames=["A"];const r=new Array(e.length);for(let i=0;i<r.length;i++)r[i]=e[t[i]];this.outputShape=r,this.rank=r.length;const s=on(this.rank),o=Bne(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${o}));
}
`}}function Bne(n){const e=n.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);const t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(e);for(let s=0;s<n.length;s++)r[n[s]]=t[s];return r.join()}class Une{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const r=new Array(e.length);for(let d=0;d<r.length;d++)r[d]=e[t[d]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const s=on(this.rank),o=CF("rc",this.rank),i=new Array(this.rank);for(let d=0;d<t.length;d++)i[t[d]]=o[d];const a=`vec2(${i.slice(-2).join()})`,l=`++${o[this.rank-1]} < ${r[this.rank-1]}`,c=`getChannel(getA(${i.join()}), ${a})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${c};
if(${l}) {
result[1] = ${c};
}
--${o[this.rank-1]};
if(++${o[this.rank-2]} < ${r[this.rank-2]}) {
result[2] = ${c};
if(${l}) {
result[3] = ${c};
}
}
setOutput(result);
}
`}}function Gv(n,e,t){const r=ye().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Une(n.shape,e):new zne(n.shape,e);return t.runWebGLProgram(r,[n],n.dtype)}function Wne(n,e,t,r){const s=e,o=n.shape.length,i=Tt(s,n.shape);let a=i;const l=En(a,o),c=l!=null;let d=n;c&&(d=Gv(n,l,r),a=Wn(a.length,o)),Dr("sum",a,o);const[p,m]=yr(d.shape,a);let y=p;t&&(y=Yn(p,i));const v=Se(m),w=Se(n.shape)/v,k=Ue({inputs:{x:d},attrs:{shape:[w,v]},backend:r}),I=ok(n.dtype),N=gh(k,I,"sum",r),T=Ue({inputs:{x:N},attrs:{shape:y},backend:r});return r.disposeIntermediateTensorInfo(k),r.disposeIntermediateTensorInfo(N),c&&r.disposeIntermediateTensorInfo(d),T}function jv(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,keepDims:i}=r;return Wne(s,o,i,t)}const Vne={kernelName:dv,backendName:"webgl",kernelFunc:jv};function cs(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{perm:o}=r,i=t,a=s.shape.length,l=new Array(a);for(let d=0;d<l.length;d++)l[d]=s.shape[o[d]];let c;if(i.shouldExecuteOnCPU([s])){const p=i.texData.get(s.dataId).values,m=jI(p,s.shape,s.dtype,o,l);c=i.makeTensorInfo(l,s.dtype);const y=i.texData.get(c.dataId);y.values=m}else c=Gv(s,o,i);return c}const Gne={kernelName:ef,backendName:"webgl",kernelFunc:cs};const EF=1e3;function u1({a:n,b:e,transposeA:t,transposeB:r,backend:s,bias:o=null,preluActivationWeights:i=null,leakyreluAlpha:a=0,activation:l=null}){const c=n.shape.length,d=e.shape.length,p=t?n.shape[c-2]:n.shape[c-1],m=r?e.shape[d-1]:e.shape[d-2],y=t?n.shape[c-1]:n.shape[c-2],v=r?e.shape[d-2]:e.shape[d-1],b=n.shape.slice(0,-2),w=e.shape.slice(0,-2),k=Se(b),I=Se(w),T=gt(n.shape.slice(0,-2),e.shape.slice(0,-2)).concat([y,v]);U(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${n.shape} and ${e.shape} and transposeA=${t} and transposeB=${r} must match.`);const A=t?[k,p,y]:[k,y,p],O=r?[I,v,m]:[I,m,v],P=Ue({inputs:{x:n},backend:s,attrs:{shape:A}}),B=Ue({inputs:{x:e},backend:s,attrs:{shape:O}}),z=[P,B],L=Math.max(k,I),_=t?P.shape[1]:P.shape[2],H=o!=null,Y=i!=null,re=l==="leakyrelu",te=l!=null?Hm(l,!0):null,le=H||Y||re||te!=null;let de;if((y===1||v===1)&&_>EF&&le===!1){let ie=P,se=B;t&&(ie=cs({inputs:{x:P},backend:s,attrs:{perm:[0,2,1]}}),z.push(ie)),r&&(se=cs({inputs:{x:B},backend:s,attrs:{perm:[0,2,1]}}),z.push(se));const V=v!==1,X=v===1;let xe=ie;V&&(xe=Ue({inputs:{x:ie},backend:s,attrs:{shape:[L,_,1]}}),z.push(xe));const Ie=v===1?2:1;let ke=se;X&&(ke=Ue({inputs:{x:se},backend:s,attrs:{shape:[L,1,_]}}),z.push(ke));const Ee=qI({inputs:{a:xe,b:ke},backend:s});de=jv({inputs:{x:Ee},backend:s,attrs:{axis:Ie,keepDims:!0}}),z.push(Ee)}else{const ie=Ys(n.dtype,e.dtype),se=new TF(A,O,[L,y,v],t,r,H,te,Y,re),V=[P,B];if(o!=null&&V.push(o),Y&&V.push(i),re){const X=s.makeTensorInfo([],"float32",mu(a,"float32"));V.push(X),z.push(X)}de=s.runWebGLProgram(se,V,ie)}const j=Ue({inputs:{x:de},backend:s,attrs:{shape:T}});z.push(de);for(const ie of z)s.disposeIntermediateTensorInfo(ie);return j}function jne(n){const{inputs:e,backend:t,attrs:r}=n,{a:s,b:o,bias:i,preluActivationWeights:a}=e,{transposeA:l,transposeB:c,activation:d,leakyreluAlpha:p}=r;return u1({a:s,b:o,transposeA:l,transposeB:c,backend:t,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:d})}const Hne={kernelName:Ly,backendName:"webgl",kernelFunc:jne};const J2="return abs(x);";function qne(n){const{inputs:e,backend:t}=n,{x:r}=e;if(t.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){const o=t.texData.get(r.dataId),i=wF(o.values);return t.makeTensorInfo(r.shape,r.dtype,i)}let s;return ye().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new tu(r.shape,J2):s=new la(r.shape,J2),t.runWebGLProgram(s,[r],r.dtype)}const Kne={kernelName:v1,backendName:"webgl",kernelFunc:qne};const Xne=Qo+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,Yne=zt({opSnippet:Xne}),Qne={kernelName:ng,backendName:"webgl",kernelFunc:Yne};const Jne=Qo+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,Zne=zt({opSnippet:Jne}),ere={kernelName:rg,backendName:"webgl",kernelFunc:Zne};const Z2="return a + b;",tre=Or({opSnippet:Z2,packedOpSnippet:Z2,supportsComplex:!0,cpuKernelImpl:hte}),nre={kernelName:kf,backendName:"webgl",kernelFunc:tre};class rre{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((o,i)=>`T${i}`);const r=[];this.variableNames.forEach(o=>{r.push(`float v${o} = get${o}AtOutCoords();`)});const s=this.variableNames.map(o=>`v${o}`).join(" + ");this.userCode=`
void main() {
${r.join(`
`)}
float result = ${s};
setOutput(result);
}
`}}class sre{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((o,i)=>`T${i}`);const r=[];this.variableNames.forEach(o=>{r.push(`vec4 v${o} = get${o}AtOutCoords();`)});const s=this.variableNames.map(o=>`v${o}`).join(" + ");this.userCode=`
void main() {
${r.join(`
`)}
vec4 result = ${s};
setOutput(result);
}
`}}function Ny(n){const{inputs:e,backend:t}=n,r=e;if(r.length===1)return Js({inputs:{x:r[0]},backend:t});if(r.length>ye().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const l=Math.floor(r.length/2),c=Ny({inputs:r.slice(0,l),backend:t}),d=Ny({inputs:r.slice(l),backend:t});return Ny({inputs:[c,d],backend:t})}const s=r.map(l=>l.dtype).reduce((l,c)=>Ys(l,c)),o=r.map(l=>l.shape),a=ye().getBool("WEBGL_PACK")?new sre(r[0].shape,o):new rre(r[0].shape,o);return t.runWebGLProgram(a,r,s)}const ore={kernelName:yS,backendName:"webgl",kernelFunc:Ny};function ire(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,keepDims:i}=r,a=s.shape.length,l=Tt(o,s.shape);let c=l;const d=En(c,a);let p=s;d!=null&&(p=cs({inputs:{x:s},backend:t,attrs:{perm:d}}),c=Wn(c.length,a)),Dr("all",c,a);const[m,y]=yr(p.shape,c),v=Se(y),b=Ue({inputs:{x:p},backend:t,attrs:{shape:[-1,v]}}),w=gh(b,b.dtype,"all",t);let k;if(i){const I=Yn(m,l);k=Ue({inputs:{x:w},backend:t,attrs:{shape:I}})}else k=Ue({inputs:{x:w},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(b),t.disposeIntermediateTensorInfo(w),d!=null&&t.disposeIntermediateTensorInfo(p),k}const are={kernelName:vS,backendName:"webgl",kernelFunc:ire};function lre(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,keepDims:i}=r,a=s.shape.length,l=Tt(o,s.shape);let c=l;const d=En(c,a);let p=s;d!=null&&(p=cs({inputs:{x:s},backend:t,attrs:{perm:d}}),c=Wn(c.length,a)),Dr("any",c,a);const[m,y]=yr(p.shape,c),v=Se(y),b=Ue({inputs:{x:p},backend:t,attrs:{shape:[-1,v]}}),w=gh(b,b.dtype,"any",t);let k;if(i){const I=Yn(m,l);k=Ue({inputs:{x:w},backend:t,attrs:{shape:I}})}else k=Ue({inputs:{x:w},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(b),t.disposeIntermediateTensorInfo(w),d!=null&&t.disposeIntermediateTensorInfo(p),k}const ure={kernelName:wS,backendName:"webgl",kernelFunc:lre};class cre{constructor(e,t,r){this.variableNames=["A"];const{windowSize:s,batchSize:o,outSize:i}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[o,i];const a=t==="max"?">":"<",l=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${s};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${s}; i++) {
int inIdx = ${l};
float candidate = getA(batch, inIdx);
if (candidate ${a} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}}class hre{constructor(e,t,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,U(e.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);const o=e[e.length-1],i=Math.ceil(o/t);this.outputShape=e.slice(0,-1),i>1&&this.outputShape.push(i),s||this.variableNames.push("bestIndicesA");const a=this.outputShape,l=a.length,c=on(l),d=ls("coords",l);let p,m;if(i===1){m=l+1;const B=on(m);p=`
${B} sourceLocR = ${B}(${d.join()}, 0);
++${d[l-1]};
${B} sourceLocG = ${B}(${d.join()}, 0);
++${d[l-2]};
${B} sourceLocA = ${B}(${d.join()}, 0);
--${d[l-1]};
${B} sourceLocB = ${B}(${d.join()}, 0);
--${d[l-2]};`}else m=l,p=`
${c} sourceLocR = coords;
++${d[l-1]};
${c} sourceLocG = coords;
++${d[l-2]};
${c} sourceLocA = coords;
--${d[l-1]};
${c} sourceLocB = coords;
--${d[l-2]};`;const y=["x","y","z","w","u","v"].slice(0,m),v="."+y[m-1],b=y.map(B=>"int "+B),w=ls("sourceLocR",m-1).concat("inIdx.r"),k=ls("sourceLocG",m-1).concat("inIdx.g"),I=ls("sourceLocB",m-1).concat("inIdx.b"),N=ls("sourceLocA",m-1).concat("inIdx.a"),T=r==="max"?"greaterThan":"lessThan",A=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${w.join()}),
getBestIndicesAChannel(${k.join()}),
getBestIndicesAChannel(${I.join()}),
getBestIndicesAChannel(${N.join()})));`,O=`vec4(
getAChannel(${w.join()}),
hasNextCol ? getAChannel(${k.join()}) : 0.,
hasNextRow ? getAChannel(${I.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${N.join()}) : 0.)`,P=s?"":`
float getBestIndicesAChannel(${b.join()}) {
return getChannel(getBestIndicesA(${y.join()}),
vec2(${y.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${b.join()}) {
return getChannel(getA(${y.join()}),
vec2(${y.slice(-2).join()}));
}
${P}
void main() {
${c} coords = getOutputCoords();
bool hasNextCol = ${d[l-1]} < ${a[l-1]-1};
bool hasNextRow = ${d[l-2]} < ${a[l-2]-1};
${p}
ivec4 srcIdx = ivec4(sourceLocR${v}, sourceLocG${v},
sourceLocB${v}, sourceLocA${v}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${O};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${A}
vec4 candidate = ${O};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${T}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}}function RF(n,e,t,r=null){let s=e.shape[0],o=e.shape[1];r!=null&&(s=r.shape[0],o=r.shape[1]);const i=Nv(o),a={windowSize:i,inSize:o,batchSize:s,outSize:Math.ceil(o/i)},l=new cre(a,t,r==null),c=[e];r!=null&&c.push(r);const d=n.runWebGLProgram(l,c,"int32");if(d.shape[1]===1)return d;const p=RF(n,e,t,d);return n.disposeIntermediateTensorInfo(d),p}function AF(n,e,t,r=null){const s=r!=null?r.shape:e.shape,o=s[s.length-1],i=Nv(o),a=new hre(s,i,t,r==null),l=r==null?[e]:[e,r],c=n.runWebGLProgram(a,l,"int32");if(c.shape.length===e.shape.length){const d=AF(n,e,t,c);return n.disposeIntermediateTensorInfo(c),d}return c}function _F(n,e,t,r){const s=[t];if(Dr("arg"+r.charAt(0).toUpperCase()+r.slice(1),s,e.shape.length),!ye().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){const o=[],i=n.texData.get(e.dataId),a=i!==null&&i.isPacked;let l=e;a&&(l=n.unpackTensor(e),o.push(l));const[c,d]=yr(l.shape,s),p=Se(d),m=Ue({inputs:{x:l},backend:n,attrs:{shape:[-1,p]}});o.push(m);const y=RF(n,m,r);o.push(y);const v=Ue({inputs:{x:y},backend:n,attrs:{shape:c}});return o.forEach(b=>n.disposeIntermediateTensorInfo(b)),v}return AF(n,e,r)}function dre(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o}=r;let i=Tt(o,s.shape);const a=En(i,s.shape.length);let l=s;const c=[];a!=null&&(l=cs({inputs:{x:s},backend:t,attrs:{perm:a}}),c.push(l),i=Wn(i.length,l.shape.length)),Dr("argMax",[i[0]],l.shape.length);const d=_F(t,l,i[0],"max");return c.forEach(p=>t.disposeIntermediateTensorInfo(p)),d}const fre={kernelName:w1,backendName:"webgl",kernelFunc:dre};function pre(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o}=r;let i=Tt(o,s.shape);const a=En(i,s.shape.length);let l=s;const c=[];a!=null&&(l=cs({inputs:{x:s},backend:t,attrs:{perm:a}}),c.push(l),i=Wn(i.length,l.shape.length)),Dr("argMin",[i[0]],l.shape.length);const d=_F(t,l,i[0],"min");return c.forEach(p=>t.disposeIntermediateTensorInfo(p)),d}const mre={kernelName:b1,backendName:"webgl",kernelFunc:pre};const gre=Qo+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,xre=zt({opSnippet:gre}),yre={kernelName:sg,backendName:"webgl",kernelFunc:xre};const vre=Qo+"return log(x + sqrt(x * x + 1.0));",wre=zt({opSnippet:vre}),bre={kernelName:og,backendName:"webgl",kernelFunc:wre};const Cre=Qo+`
return atan(x);
`,Sre=zt({opSnippet:Cre}),kre={kernelName:ig,backendName:"webgl",kernelFunc:Sre};const Ire=HI+`
return atan(a, b);
`,$re=`
vec4 result = atan(a, b);
bvec4 isNaNA = isnan(a);
bvec4 isNaNB = isnan(b);
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
`+mh+`
return result;
`,Nre=Or({opSnippet:Ire,packedOpSnippet:$re}),Tre={kernelName:lg,backendName:"webgl",kernelFunc:Nre};const Ere=Qo+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Rre=zt({opSnippet:Ere}),Are={kernelName:ag,backendName:"webgl",kernelFunc:Rre};class qm{constructor(e,t,r,s=!1,o=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");const i=e.filterWidth,a=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,y=e.padInfo.top,v=e.padInfo.left;this.outputShape=e.outShape;const b=t==="avg",w=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,k=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let I="0.0";if(b||(I="-1.0 / 1e-20"),r){this.userCode=`
const ivec2 strides = ivec2(${a}, ${l});
const ivec2 pads = ivec2(${y}, ${v});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${p};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${d}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value >= currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?o?w:k:`wR * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const N="max";let T=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(T="avgValue / max(count, 1.0)");const A=Math.floor(i/4)*4,O=i%4,P=`
if (${b}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${N}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${a}, ${l});
const ivec2 pads = ivec2(${y}, ${v});
const float initializationValue = ${I};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${I});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${p};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${A}; wC += 4) {
int xC = xCCorner + wC * ${d};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
getValue(batch, xR, xC + 2 * ${d}, d),
getValue(batch, xR, xC + 3 * ${d}, d)
);
${P}
}
int xC = xCCorner + ${A};
if (${O===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${P}
} else if (${O===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
initializationValue,
initializationValue
);
${P}
} else if (${O===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
getValue(batch, xR, xC + 2 * ${d}, d),
initializationValue
);
${P}
}
}
setOutput(${T});
}
`}}class KI{constructor(e,t,r,s=!1,o=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");const i=e.filterWidth,a=e.strideDepth,l=e.strideHeight,c=e.strideWidth,d=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,y=e.effectiveFilterDepth,v=e.effectiveFilterHeight,b=e.effectiveFilterWidth,w=e.padInfo.front,k=e.padInfo.top,I=e.padInfo.left;this.outputShape=e.outShape;const N=t==="avg";let T="0.0";if(N||(T="-1.0 / 1e-20"),r){this.userCode=`
const ivec3 strides =
ivec3(${a}, ${l}, ${c});
const ivec3 pads = ivec3(${w}, ${k}, ${I});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${y};
wD += ${d}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${v};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b};
wC += ${m}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value >= currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?o?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${v} * ${b} +
wR * ${b} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const A="max";let O=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(O="avgValue / max(count, 1.0)");const P=Math.floor(i/4)*4,B=i%4,z=`
if (${N}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${A}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${a}, ${l}, ${c});
const ivec3 pads = ivec3(${w}, ${k}, ${I});
const float initializationValue = ${T};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${T});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${y};
wD += ${d}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${v};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${P}; wC += 4) {
int xC = xCCorner + wC * ${m};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
getValue(batch, xD, xR, xC + 3 * ${m}, ch)
);
${z}
}
int xC = xCCorner + ${P};
if (${B===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${z}
} else if (${B===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
initializationValue,
initializationValue
);
${z}
} else if (${B===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
initializationValue
);
${z}
}
}
}
setOutput(${O});
}
`}}function _re(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e;xx(s,"avgPool");const{filterSize:o,strides:i,pad:a,dimRoundingMode:l}=r,c=1;U(Hr(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);const d=Xo(s.shape,o,i,c,a,l);if(d.filterWidth===1&&d.filterHeight===1&&Xt(d.inShape,d.outShape))return Js({inputs:{x:s},backend:t});const p=new qm(d,"avg",!1);return t.runWebGLProgram(p,[s],"float32")}const Dre={kernelName:C1,backendName:"webgl",kernelFunc:_re};function Ore(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{filterSize:o,strides:i,pad:a,dimRoundingMode:l,dataFormat:c}=r,d=[1,1,1],p=ll(s.shape,o,i,d,a,l,c),m=new KI(p,"avg",!1);return t.runWebGLProgram(m,[s],"float32")}const Fre={kernelName:S1,backendName:"webgl",kernelFunc:Ore};class Pre{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,r=e.filterWidth,s=e.strideHeight,o=e.strideWidth,i=e.dilationHeight,a=e.dilationWidth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,m=1/(t*r);this.userCode=`
const ivec2 pads = ivec2(${d}, ${p});
const float avgMultiplier = float(${m});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${l};
wR += ${i}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC+= ${a}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}}class Lre{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,r=e.filterHeight,s=e.filterWidth,o=e.strideDepth,i=e.strideHeight,a=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,y=e.effectiveFilterWidth,v=p-1-e.padInfo.front,b=m-1-e.padInfo.top,w=y-1-e.padInfo.left,k=1/(t*r*s);this.userCode=`
const ivec3 pads = ivec3(${v}, ${b}, ${w});
const float avgMultiplier = float(${k});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${p};
wD += ${l}) {
float dyD = float(dyDCorner + wD) / ${o}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${m};
wR += ${c}) {
float dyR = float(dyRCorner + wR) / ${i}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${y};
wC += ${d}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}}function Mre(n){const{inputs:e,backend:t,attrs:r}=n,{dy:s,input:o}=e,i=o,{filterSize:a,strides:l,pad:c,dimRoundingMode:d}=r,p=[1,1,1],m=ll(i.shape,a,l,p,c,d),y=new Lre(m);return t.runWebGLProgram(y,[s],i.dtype)}const zre={kernelName:CS,backendName:"webgl",kernelFunc:Mre};function Bre(n){const{inputs:e,backend:t,attrs:r}=n,{dy:s,input:o}=e,i=o;xx([s,o],"avgPoolGrad");const{filterSize:a,strides:l,pad:c}=r,d=Xo(i.shape,a,l,1,c),p=new Pre(d);return t.runWebGLProgram(p,[s],i.dtype)}const Ure={kernelName:bS,backendName:"webgl",kernelFunc:Bre};function Wre(n){const{inputs:e,backend:t,attrs:r}=n,{a:s,b:o}=e,{transposeA:i,transposeB:a}=r;return u1({a:s,b:o,transposeA:i,transposeB:a,backend:t})}const Vre={kernelName:k1,backendName:"webgl",kernelFunc:Wre};class Gre{constructor(e,t,r,s,o,i){this.outputShape=[],this.variableNames=["x","mean","variance"],gt(e,t),gt(e,r);let a="0.0";s!=null&&(gt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let l="1.0";o!=null&&(gt(e,o),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${a};
float scale = ${l};
float inv = scale * inversesqrt(variance + float(${i}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}}class jre{constructor(e,t,r,s,o,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],gt(e,t),gt(e,r);let a="vec4(0.0)";s!=null&&(gt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let l="vec4(1.0)";o!=null&&(gt(e,o),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${a};
vec4 scale = ${l};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${i}));
setOutput((x - mean) * inv + offset);
}
`}}const Hre=({inputs:n,backend:e,attrs:t})=>{const{x:r,mean:s,variance:o,offset:i,scale:a}=n;U(s.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),U(i==null||s.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),U(a==null||s.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);const c=[r,s,o];let d=null;i!=null&&(d=i.shape,c.push(i));let p=null;a!=null&&(p=a.shape,c.push(a));const m=ye().getBool("WEBGL_PACK_NORMALIZATION")?new jre(r.shape,s.shape,o.shape,d,p,l):new Gre(r.shape,s.shape,o.shape,d,p,l);return e.runWebGLProgram(m,c,c[0].dtype)},qre={kernelName:P1,backendName:"webgl",kernelFunc:Hre};class Kre{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=on(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const r=Xre(this.rank);let s;const o=e.map((i,a)=>`sourceLoc.${MC[a]} = start[${a}] + coords.${MC[a]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${o.join(`
`)}
`,this.userCode=`
void main() {
${s}
setOutput(getSource(${r}));
}
`}}const MC=["x","y","z","w","u","v"];function Xre(n){if(n===1)return"sourceLoc";if(n<=6)return MC.slice(0,n).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${n} is not yet supported`)}class Yre{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const t=on(this.rank),r=ls("coords",this.rank),s=ls("sourceLoc",this.rank),o=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,i=`getChannel(getSource(${s.join()}), ${o})`,a=`
result.x = ${i};
if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.y = ${i};
--${s[this.rank-1]};
}
`,l=this.rank===1?"":`
--${r[this.rank-1]};
if (++${r[this.rank-2]} < ${e[this.rank-2]}) {
++${s[this.rank-2]};
result.z = ${i};
if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.w = ${i};
}
}
`,c=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((d,p)=>`start[${p}]`).join()});`:e.map((d,p)=>`${s[p]} = ${r[p]} + start[${p}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${c}
vec4 result = vec4(0.);
${a}
${l}
setOutput(result);
}
`}}function Qre(n,e,t,r){const s=r.texData.get(n.dataId),o=r.makeTensorInfo(t,n.dtype),i=r.texData.get(o.dataId);Object.assign(i,s),i.refCount=1,i.shape=t,i.dtype=n.dtype;let a=Vk(e,tt(n.shape));s.slice&&(a+=s.slice.flatOffset),i.slice={flatOffset:a,origDataId:s.slice&&s.slice.origDataId||n.dataId};const l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),o}function Lf(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{begin:o,size:i}=r,[a,l]=$v(s,o,i);if(Bk(s,a,l),Se(l)===0)return t.makeTensorInfo(l,s.dtype,[]);if(t.shouldExecuteOnCPU([s])||s.dtype==="string"){const p=t.texData.get(s.dataId),m=Vte(p.values,a,l,s.shape,s.dtype);return t.makeTensorInfo(l,s.dtype,m)}const{isPacked:c}=t.texData.get(s.dataId),d=Wk(s.shape,a,l);if(c||!d){const p=ye().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Yre(l):new Kre(l),m=[a];return t.runWebGLProgram(p,[s],s.dtype,m)}return t.uploadToGPU(s.dataId),Qre(s,a,l,t)}const Jre={kernelName:hv,backendName:"webgl",kernelFunc:Lf};const Zre=n=>{const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{blockShape:o,crops:i}=r;U(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const a=o.reduce((I,N)=>I*N),l=sx(s.shape,o,a),c=ox(l.length,o.length),d=ix(s.shape,o,a),p=Kk(i,o.length),m=Xk(d,i,o.length),y=[],v=Ue({inputs:{x:s},backend:t,attrs:{shape:l}}),b=cs({inputs:{x:v},backend:t,attrs:{perm:c}}),w=Ue({inputs:{x:b},backend:t,attrs:{shape:d}}),k=Lf({inputs:{x:w},backend:t,attrs:{begin:p,size:m}});return y.push(v),y.push(b),y.push(w),y.forEach(I=>t.disposeIntermediateTensorInfo(I)),k},ese={kernelName:I1,backendName:"webgl",kernelFunc:Zre};function tse(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,weights:o}=e,{size:i}=r,a=t.readSync(s.dataId),l=t.readSync(o.dataId),c=vF(a,l,o.dtype,o.shape,i);return t.makeTensorInfo([i],o.dtype,c)}const nse={kernelName:SS,backendName:"webgl",kernelFunc:tse};const rse=`
int r = int(a.r) & int(b.r);
int g = int(a.g) & int(b.g);
int rb = int(a.b) & int(b.b);
int ra = int(a.a) & int(b.a);
return vec4(r, g, rb, ra);
`,sse=`
return float(int(a.r) & int(b.r));
`;function ose(n){const{inputs:e,backend:t}=n,{a:r,b:s}=e,o=ye().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=ye().getNumber("WEBGL_VERSION");if(t.shouldExecuteOnCPU([r,s])||i===1){const l=t.texData.get(r.dataId).values,c=t.texData.get(s.dataId).values,[d,p]=fte(r.shape,s.shape,l,c,r.dtype),m=t.makeTensorInfo(p,r.dtype),y=t.texData.get(m.dataId);return y.values=d,m}let a;return o?a=new Ff(rse,r.shape,s.shape,!1):a=new sh(sse,r.shape,s.shape),t.runWebGLProgram(a,[r,s],r.dtype)}const ise={kernelName:kS,backendName:"webgl",kernelFunc:ose};function ase(n){const{inputs:e,backend:t}=n,{s0:r,s1:s}=e,o=t.readSync(r.dataId),i=t.readSync(s.dataId),a=gt(Array.from(o),Array.from(i));return t.makeTensorInfo([a.length],"int32",Int32Array.from(a))}const lse={kernelName:IE,backendName:"webgl",kernelFunc:ase};const use="return float(a != b);",DF=Or({opSnippet:use,cpuKernelImpl:Ote,dtype:"bool"}),cse={kernelName:Z1,backendName:"webgl",kernelFunc:DF};function vx(n){const{inputs:e,backend:t}=n,{input:r}=e,s=t.texData.get(r.dataId);return Js({inputs:{x:s.complexTensorInfos.real},backend:t})}const hse={kernelName:XS,backendName:"webgl",kernelFunc:vx};const dse="return float(int(x));";function fse(n,e){const t=new la(n.shape,dse),r=e.runWebGLProgram(t,[n],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function zC(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{dtype:o}=r;if(o==="complex64"){if(s.dtype==="complex64")return Js({inputs:{x:s},backend:t});const i=gr(s.shape),a=zC({inputs:{x:s},backend:t,attrs:{dtype:"float32"}}),l=ku({inputs:{real:a,imag:i},backend:t});return i.dispose(),t.disposeIntermediateTensorInfo(a),l}if(s.dtype==="complex64"){const i=vx({inputs:{input:s},backend:t}),a=zC({inputs:{x:i},backend:t,attrs:{dtype:o}});return t.disposeIntermediateTensorInfo(i),a}if(!wE(s.dtype,o)){const i=Js({inputs:{x:s},backend:t});return{dataId:i.dataId,shape:i.shape,dtype:o}}if(t.shouldExecuteOnCPU([s])){const i=t.texData.get(s.dataId).values,[a,l,c]=pte(i,s.shape,s.dtype,o);return t.makeTensorInfo(a,l,c)}if(o==="int32")return fse(s,t);if(o==="bool"){const i=t.makeTensorInfo([],"bool",Vr("bool",1)),l=DF({inputs:{a:s,b:i},backend:t});return t.disposeIntermediateTensorInfo(i),l}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${o}`)}const pse={kernelName:ug,backendName:"webgl",kernelFunc:zC};const eT="return ceil(x);",mse=zt({opSnippet:eT,packedOpSnippet:eT,cpuKernelImpl:mte}),gse={kernelName:cg,backendName:"webgl",kernelFunc:mse};class xse{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}}class yse{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}}function vse(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{clipValueMin:o,clipValueMax:i}=r;let a;ye().getBool("WEBGL_PACK_CLIP")?a=new yse(s.shape):a=new xse(s.shape);const l=[[o],[i]];return t.runWebGLProgram(a,[s],s.dtype,l)}const wse={kernelName:hg,backendName:"webgl",kernelFunc:vse};class bse{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}}function tT(n,e){return{dataId:e.dataId,dtype:e.dtype,shape:n.shape}}function Cse(n){const{inputs:e,backend:t}=n,{x:r}=e,s=t.texData.get(r.dataId),o=new bse(r.shape),i=[tT(r,s.complexTensorInfos.real),tT(r,s.complexTensorInfos.imag)];return t.runWebGLProgram(o,i,i[0].dtype)}const Sse={kernelName:$1,backendName:"webgl",kernelFunc:Cse};class kse{constructor(e){this.outputShape=[],this.outputShape=da(e,1),this.variableNames=e.map((i,a)=>`T${a}`);const t=new Array(e.length-1);t[0]=e[0][1];for(let i=1;i<t.length;i++)t[i]=t[i-1]+e[i][1];const r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<t.length;i++){const a=t[i-1];r.push(`else if (yC < ${t[i]}) setOutput(getT${i}(yR, yC-${a}));`)}const s=t.length,o=t[t.length-1];r.push(`else setOutput(getT${s}(yR, yC-${o}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${r.join(`
`)}
}
`}}class Ise{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=da(e,t);const r=this.outputShape,s=r.length,o=on(s),i=ls("coords",s),a=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((b,w)=>`T${w}`);const l=new Array(e.length-1);l[0]=e[0][t];for(let b=1;b<l.length;b++)l[b]=l[b-1]+e[b][t];const c=a[t],d=a.slice(-2),p=a.join();let m=`if (${c} < ${l[0]}) {
return getChannel(
getT0(${p}), vec2(${d.join()}));
}`;for(let b=1;b<l.length;b++){const w=l[b-1];m+=`
if (${c} < ${l[b]} && ${c} >= ${l[b-1]}) {
return getChannel(
getT${b}(${my(a,c,w)}),
vec2(${my(d,c,w)}));
}`}const y=l.length,v=l[l.length-1];m+=`
return getChannel(
getT${y}(${my(a,c,v)}),
vec2(${my(d,c,v)}));`,this.userCode=`
float getValue(${a.map(b=>"int "+b)}) {
${m}
}
void main() {
${o} coords = getOutputCoords();
vec4 result = vec4(getValue(${i}), 0., 0., 0.);
${i[s-1]} = ${i[s-1]} + 1;
if (${i[s-1]} < ${r[s-1]}) {
result.g = getValue(${i});
}
${i[s-2]} = ${i[s-2]} + 1;
if (${i[s-2]} < ${r[s-2]}) {
result.a = getValue(${i});
}
${i[s-1]} = ${i[s-1]} - 1;
if (${i[s-2]} < ${r[s-2]} &&
${i[s-1]} < ${r[s-1]}) {
result.b = getValue(${i});
}
setOutput(result);
}
`}}function my(n,e,t){const r=n.indexOf(e);return n.map((o,i)=>i===r?`${o} - ${t}`:o).join()}function Hv(n){const{inputs:e,backend:t}=n,{input:r}=e,s=t.texData.get(r.dataId);return Js({inputs:{x:s.complexTensorInfos.imag},backend:t})}const $se={kernelName:US,backendName:"webgl",kernelFunc:Hv};function km(n,e,t){const r=n[0].dtype;if(r==="complex64"){const y=n.map(I=>vx({inputs:{input:I},backend:t})),v=n.map(I=>Hv({inputs:{input:I},backend:t})),b=km(y,e,t),w=km(v,e,t),k=ku({inputs:{real:b,imag:w},backend:t});return y.forEach(I=>t.disposeIntermediateTensorInfo(I)),v.forEach(I=>t.disposeIntermediateTensorInfo(I)),t.disposeIntermediateTensorInfo(b),t.disposeIntermediateTensorInfo(w),k}let s=t.shouldExecuteOnCPU(n);if(r==="string"&&(s=!0),s){const y=n.map(T=>{const O=[-1,Se(T.shape.slice(e))];return Ue({inputs:{x:T},backend:t,attrs:{shape:O}})}),v=y.map(T=>({vals:t.readSync(T.dataId),shape:T.shape})),b=da(y.map(T=>T.shape),1),w=y[0].shape[0]===1,k=gte(v,b,r,w),I=da(n.map(T=>T.shape),e),N=t.makeTensorInfo(I,r,k);return y.forEach(T=>t.disposeIntermediateTensorInfo(T)),N}const o=n.filter(y=>Se(y.shape)>0),i=ye().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&o[0].shape.length>1;if(o.length===1){const y=i?new la(n[0].shape,Yl):new tu(n[0].shape,Yl);return t.runWebGLProgram(y,n,r)}const a=ye().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(o.length>a){const y=[];for(let b=0;b<o.length;b+=a){const w=o.slice(b,b+a);y.push(km(w,e,t))}const v=km(y,e,t);for(const b of y)t.disposeIntermediateTensorInfo(b);return v}if(i){const y=new Ise(o.map(v=>v.shape),e);return t.runWebGLProgram(y,o,r)}const{tensors2D:l,outShape:c}=Nse(o,e,t),d=new kse(l.map(y=>y.shape)),p=t.runWebGLProgram(d,l,r);l.forEach(y=>t.disposeIntermediateTensorInfo(y));const m=Ue({inputs:{x:p},attrs:{shape:c},backend:t});return t.disposeIntermediateTensorInfo(p),m}function Nse(n,e,t){const r=da(n.map(o=>o.shape),e);return{tensors2D:n.map(o=>Ue({inputs:{x:o},attrs:{shape:[-1,Se(o.shape.slice(e))]},backend:t})),outShape:r}}function OF(n){const{inputs:e,backend:t,attrs:r}=n,{axis:s}=r,o=Tt(s,e[0].shape)[0],i=e.map(c=>c.shape);jk(i,o);const a=da(e.map(c=>c.shape),o);if(Se(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);const l=e.filter(c=>Se(c.shape)>0);return l.length===1?Js({inputs:{x:l[0]},backend:t}):km(l,o,t)}const Tse={kernelName:N1,backendName:"webgl",kernelFunc:OF};class FF{constructor(e,t=!1,r=null,s=!1,o=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.padInfo.top,a=e.padInfo.left,l=e.strideHeight,c=e.strideWidth,d=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,y=e.filterWidth,v=Math.floor(e.inChannels/4)*4,b=e.inChannels%4,w=e.dataFormat==="channelsLast",k=w?1:2,I=w?2:3,N=w?3:1;let T="",A="";r&&(s?T=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${r}
}`:o?T=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${r}
}`:T=`
float activation(float x) {
${r}
}
`,A="result = activation(result);");const O=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),o&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${T}
const ivec2 strides = ivec2(${l}, ${c});
const ivec2 pads = ivec2(${i}, ${a});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${N}];
ivec2 xRCCorner =
ivec2(coords[${k}], coords[${I}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${d};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${y}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${v}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${w}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${b===1}) {
if (${w}) {
dotProd +=
getX(batch, xR, xC, ${v}) *
getW(wR, wC, ${v}, d2);
} else {
dotProd +=
getX(batch, ${v}, xR, xC) *
getW(wR, wC, ${v}, d2);
}
} else if (${b===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${v}, d2),
getW(wR, wC, ${v} + 1, d2)
);
if (${w}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${v}),
getX(batch, xR, xC, ${v} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${v}, xR, xC),
getX(batch, ${v} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${b===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${v}, d2),
getW(wR, wC, ${v} + 1, d2),
getW(wR, wC, ${v} + 2, d2)
);
if (${w}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${v}),
getX(batch, xR, xC, ${v} + 1),
getX(batch, xR, xC, ${v} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${v}, xR, xC),
getX(batch, ${v} + 1, xR, xC),
getX(batch, ${v} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${O}
${A}
setOutput(result);
}
`}}class Ese{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,r=e.padInfo.top,s=e.padInfo.left,o=e.strideDepth,i=e.strideHeight,a=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,d=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,y=e.filterWidth,v=Math.floor(e.inChannels/4)*4,b=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${o}, ${i}, ${a});
const ivec3 pads = ivec3(${t}, ${r}, ${s});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${p}; wF++) {
int xF = xFCorner + wF * ${l};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${y}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${v}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${b===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${v}) *
getW(wF, wR, wC, ${v}, d2);
} else if (${b===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${v}),
getX(batch, xF, xR, xC, ${v} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${v}, d2),
getW(wF, wR, wC, ${v} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${b===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${v}),
getX(batch, xF, xR, xC, ${v} + 1),
getX(batch, xF, xR, xC, ${v} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${v}, d2),
getW(wF, wR, wC, ${v} + 1, d2),
getW(wF, wR, wC, ${v} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}}class PF{constructor(e,t=!1,r=null,s=!1,o=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Kr(this.outputShape.length);const i=e.padInfo.left,a=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,p=d;let m=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let w=0;w<d;w++)m+=`
vec4 xTexelC${w*2};
int xTexelC${w*2}Ready;
vec4 xTexelC${w*2+1};
int xTexelC${w*2+1}Ready;
vec4 xC${w};`;m+=`
for (int r = 0; r < ${c}; r++) {
for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {
`;for(let w=0;w<d;w++)m+=`
xTexelC${w*2} = vec4(0.0);
xTexelC${w*2}Ready = 0;
xTexelC${w*2+1} = vec4(0.0);
xTexelC${w*2+1}Ready = 0;
xC${w} = vec4(0.0);`;m+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let w=0;w<(p+1)/2;w++){const k=w*2;if(m+=`
xC = xCCorner + ${k*l};
`,a===1){if(k<d&&(i%2===1?(m+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${k}Ready == 0) {
xTexelC${k} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${k}.zw = vec2(0.0);
}
xTexelC${k}Ready = 1;
}
`,l===1&&k>0?m+=`
xC${k} = vec4(xTexelC${k-2}.zw, xTexelC${k}.xy);
`:m+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${k} = vec4(previous.zw, xTexelC${k}.xy);
} else {
xC${k} = vec4(0.0, 0.0, xTexelC${k}.xy);
}
`):m+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${k}Ready == 0) {
xTexelC${k} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${k}.zw = vec2(0.0);
}
xTexelC${k}Ready = 1;
}
xC${k} = xTexelC${k};
`,k+1<d)){const I=i%2===0?dS(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(m+=`
xCOffset = xC + imod(pads[1], 2) + ${I};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${k+1}Ready == 0) {
xTexelC${k+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${k+1}.zw = vec2(0.0);
}
xTexelC${k+1}Ready = 1;
}
`,l>1?m+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
xC${k+1} = vec4(previous.zw, xTexelC${k+1}.xy);
} else {
xC${k+1} = vec4(0.0, 0.0, xTexelC${k+1}.xy);
}
`:m+=`
xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+1}.xy);
`):I===1?m+=`
xC${k+1} = xTexelC${k};
`:m+=`
xCOffset = xC + ${I};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${k+1}Ready == 0) {
xTexelC${k+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${k+1}.zw = vec2(0.0);
}
xTexelC${k+1}Ready = 1;
}
xC${k+1} = xTexelC${k+1};
`}}else k<d&&(i%2===1?(m+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${k}Ready == 0) {
xTexelC${k} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${k}.zw = vec2(0.0);
}
xTexelC${k}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${k+1}Ready == 0) {
xTexelC${k+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${k+1}.zw = vec2(0.0);
}
xTexelC${k+1}Ready = 1;
}
xC${k} = vec4(xTexelC${k}.zw, xTexelC${k+1}.zw);
`,k+1<d&&(m+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${k+1} = vec4(xTexelC${k+1}.xy, final.xy);
`)):(m+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${k}Ready == 0) {
xTexelC${k} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${k}.zw = vec2(0.0);
}
xTexelC${k}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${k+1}Ready == 0) {
xTexelC${k+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${k+1}.zw = vec2(0.);
}
xTexelC${k+1}Ready = 1;
}
xC${k} = vec4(
xTexelC${k}.xy, xTexelC${k+1}.xy);
`,k+1<d&&(m+=`
xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+1}.zw);
`)));k<d&&(m+=`
wTexel = getW(r, ${k}, d1, d2);
dotProd += xC${k}.xxzz * vec4(wTexel.xy, wTexel.xy);
if(d1 + 1 < ${e.inChannels}) {
dotProd += xC${k}.yyww * vec4(wTexel.zw, wTexel.zw);
}
`,k+1<d&&(m+=`
wTexel = getW(r, ${k+1}, d1, d2);
dotProd += xC${k+1}.xxzz * vec4(wTexel.xy, wTexel.xy);
if(d1 + 1 < ${e.inChannels}) {
dotProd += xC${k+1}.yyww * vec4(wTexel.zw, wTexel.zw);
}
`))}m+=`
}
`,m+=`
}
`,m+=`
}
`;let y="",v="";r&&(s?y=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${r}
}`:o?y=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${r}
}`:y=`vec4 activation(vec4 x) {
${r}
}`,v="result = activation(result);");const b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),o&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${y}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${m}
vec4 result = dotProd - vec4(0.000000000000001);
${b}
${v}
setOutput(result);
}
`}}class Rse{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Kr(this.outputShape.length);const{dataFormat:r}=t,s=hs(),o=r==="channelsLast",i=o?1:2,a=o?2:3,l=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`;let c="";for(let d=0;d<=1;d++)for(let p=0;p<=1;p++)c+=`
blockIndex = rc.z + ${p};
pos = rc.y + ${d};
${l}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${i}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${a}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${o}) {
innerDims = vec2(d1, ch);
result[${d*2+p}] = getChannel(
getA(rc.x, d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${d*2+p}] = getChannel(
getA(rc.x, ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${c}
${s.output} = result;
}
`}}function c1(n,e){const t=n.length;return t>=3?e?[...n.slice(0,-3),n[t-3]*n[t-2],n[t-1]]:[...n.slice(0,-3),n[t-3],n[t-2]*n[t-1]]:!e&&t===1&&n[0]>1?[n[0],1]:null}function LF({x:n,filter:e,convInfo:t,backend:r,bias:s=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:a=null}){const l=n.shape,c=r.texData.get(n.dataId),d=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,y=t.dataFormat==="channelsLast",v=!1,b=!1;let w;const k=[];if(o!=null){const T=c1(o.shape,y);T!=null&&(o=Ue({inputs:{x:o},backend:r,attrs:{shape:T}}),k.push(o))}if(s!=null){const T=c1(s.shape,y);T!=null&&(s=Ue({inputs:{x:s},backend:r,attrs:{shape:T}}),k.push(s))}if(!((p===1||m===1)&&d>EF)&&c.isPacked&&y&&c.texture!=null&&l[2]%2!==0&&Xt(c.shape.slice(-3),l.slice(-3))){const T=l[0]*l[1]*(l[2]+1),A={dataId:n.dataId,shape:[1,T,t.inChannels],dtype:n.dtype},O=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,U(l1(c.shape,A.shape),()=>`packed reshape ${c.shape} to ${A.shape} isn't free`);const P=Ue({inputs:{x:e},backend:r,attrs:{shape:[1,t.inChannels,t.outChannels]}});k.push(P);const B=u1({a:A,b:P,backend:r,transposeA:v,transposeB:b,bias:s,activation:a,preluActivationWeights:o,leakyreluAlpha:i}),z=r.texData.get(B.dataId);U(z.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=O,z.shape=t.outShape,w=Js({inputs:{x:B},backend:r}),w.shape=t.outShape,k.push(B)}else{const T=t.outHeight*t.outWidth,A=Ue({inputs:{x:n},backend:r,attrs:{shape:y?[t.batchSize,T,t.inChannels]:[t.batchSize,t.inChannels,T]}}),O=Ue({inputs:{x:e},backend:r,attrs:{shape:[1,t.inChannels,t.outChannels]}}),P=u1({a:y?A:O,b:y?O:A,transposeA:!y,transposeB:b,backend:r,bias:s,activation:a,preluActivationWeights:o,leakyreluAlpha:i});w=Ue({inputs:{x:P},backend:r,attrs:{shape:t.outShape}}),k.push(A),k.push(O),k.push(P)}for(const T of k)r.disposeIntermediateTensorInfo(T);return w}function MF({x:n,filter:e,convInfo:t,backend:r,bias:s=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:a=null}){const{filterWidth:l,filterHeight:c,inChannels:d,outWidth:p,outHeight:m,dataFormat:y}=t,v=y==="channelsLast",b=l*c*d,w=m*p,k=[t.batchSize,b,w],I=!0,N=!1,T=[];if(o!=null){const j=c1(o.shape,v);j!=null&&(o=Ue({inputs:{x:o},backend:r,attrs:{shape:j}}),T.push(o))}if(s!=null){const j=c1(s.shape,v);j!=null&&(s=Ue({inputs:{x:s},backend:r,attrs:{shape:j}}),T.push(s))}const A=Ue({inputs:{x:e},backend:r,attrs:{shape:[1,b,Se(e.shape)/b]}});T.push(A);const O=new Rse(k,t),P=[n.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],B=r.runWebGLProgram(O,[n],"float32",P),z=Ue({inputs:{x:B},backend:r,attrs:{shape:k}});T.push(B),T.push(z);const L=s!=null,_=o!=null,H=a==="leakyrelu",Y=a?Hm(a,!0):null,re=new TF(v?z.shape:A.shape,v?A.shape:z.shape,v?[t.batchSize,w,t.outChannels]:[t.batchSize,t.outChannels,w],I,N,L,Y,_,H),te=v?[z,A]:[A,z];if(s&&te.push(s),_&&te.push(o),H){const j=r.makeTensorInfo([],"float32",mu(i,"float32"));te.push(j),T.push(j)}const le=r.runWebGLProgram(re,te,"float32"),de=Ue({inputs:{x:le},backend:r,attrs:{shape:t.outShape}});T.push(le);for(const j of T)r.disposeIntermediateTensorInfo(j);return de}function Ase(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,filter:o}=e,{strides:i,pad:a,dataFormat:l,dilations:c,dimRoundingMode:d}=r,p=ul(l),m=_r(s.shape,o.shape,i,c,a,d,!1,p);let y;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))y=LF({x:s,filter:o,convInfo:m,backend:t});else if(m.strideWidth<=2&&p==="channelsLast"&&ye().getBool("WEBGL_EXP_CONV")){const b=new PF(m),w=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];y=t.runWebGLProgram(b,[s,o],"float32",w)}else if(ye().getBool("WEBGL_CONV_IM2COL"))y=MF({x:s,filter:o,convInfo:m,backend:t});else{const b=new FF(m);y=t.runWebGLProgram(b,[s,o],"float32")}const v=Ue({inputs:{x:y},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(y),v}const _se={kernelName:T1,backendName:"webgl",kernelFunc:Ase};class Dse{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,r=e.strideWidth,s=e.padInfo.top,o=e.padInfo.left,i=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${o};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
${i?`float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);`}
}
}
}
setOutput(dotProd);
}
`}}class Ose{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,r=e.filterWidth,s=e.strideHeight,o=e.strideWidth,i=e.dataFormat==="channelsLast",a=t-1-e.padInfo.top,l=r-1-e.padInfo.left,c=i?1:2,d=i?2:3,p=i?3:1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${p}];
ivec2 dyCorner = ivec2(coords[${c}], coords[${d}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${i}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}}class Fse{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,r=e.strideHeight,s=e.strideWidth,o=e.padInfo.front,i=e.padInfo.top,a=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${r} - ${i};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${s} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}}class Pse{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,r=e.filterHeight,s=e.filterWidth,o=e.strideDepth,i=e.strideHeight,a=e.strideWidth,l=t-1-e.padInfo.front,c=r-1-e.padInfo.top,d=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${l}, ${c}, ${d});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${o}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${r}; wR++) {
float dyR = float(dyRCorner + wR) / ${i}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${r} - 1 - wR;
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${s} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}}function Lse(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,dy:o}=e,{strides:i,pad:a,dataFormat:l,dimRoundingMode:c,filterShape:d}=r,p=ul(l),m=_r(s.shape,d,i,1,a,c,!1,p),y=new Dse(m);return t.runWebGLProgram(y,[s,o],"float32")}const Mse={kernelName:$S,backendName:"webgl",kernelFunc:Lse};class zse{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=Kr(this.outputShape.length);const t=e.filterHeight,r=e.filterWidth,s=t-1-e.padInfo.top,o=r-1-e.padInfo.left;this.userCode=`
const ivec2 pads = ivec2(${s}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
vec4 result = vec4(0.);
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / strides[0];
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
int wCPerm = ${r} - 1 - wC;
float dyC = float(dyCCorner + wC) / strides[1];
bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)
&& (fract(dyC) == 0.0);
int idyC = int(dyC);
float dyC2 = float(dyCCorner + wC + 1) / strides[1];
bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)
&& (fract(dyC2) == 0.0);
int idyC2 = int(dyC2);
if (idyCVal && idyCVal2) {
for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
vec4 dySample = getDy(batch, idyR, idyC, d2);
vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?
dySample : getDy(batch, idyR, idyC2, d2);
vec2 dyValue = mod(float(idyC), 2.) == 0. ?
dySample.xy : dySample.zw;
result.xy += vec2(dot(dyValue, wValue.xy),
dot(dyValue, wValue.zw));
dyValue = mod(float(idyC2), 2.) == 0. ?
dySample2.xy : dySample2.zw;
result.zw += vec2(dot(dyValue, wValue.xy),
dot(dyValue, wValue.zw));
}
} else if (idyCVal) {
for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
vec4 dySample = getDy(batch, idyR, idyC, d2);
vec2 dyValue = mod(float(idyC), 2.) == 0. ?
dySample.xy : dySample.zw;
result.xy += vec2(dot(dyValue, wValue.xy),
dot(dyValue, wValue.zw));
}
} else if (idyCVal2) {
for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
vec4 dySample = getDy(batch, idyR, idyC2, d2);
vec2 dyValue = mod(float(idyC2), 2.) == 0. ?
dySample.xy : dySample.zw;
result.zw += vec2(dot(dyValue, wValue.xy),
dot(dyValue, wValue.zw));
}
}
}
}
setOutput(result);
}
`}}function Bse(n){const{inputs:e,backend:t,attrs:r}=n,{dy:s,filter:o}=e,{inputShape:i,strides:a,pad:l,dataFormat:c,dimRoundingMode:d}=r,p=ul(c),m=_r(i,o.shape,a,1,l,d,!1,p);if(ye().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&p==="channelsLast"){const y=[[m.strideHeight,m.strideWidth]],v=new zse(m);return t.runWebGLProgram(v,[s,o],"float32",y)}else{const y=new Ose(m);return t.runWebGLProgram(y,[s,o],"float32")}}const Use={kernelName:E1,backendName:"webgl",kernelFunc:Bse};function Wse(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,filter:o}=e,{strides:i,pad:a,dilations:l}=r,c=xu(s.shape,o.shape,i,l,a),d=new Ese(c);return t.runWebGLProgram(d,[s,o],"float32")}const Vse={kernelName:R1,backendName:"webgl",kernelFunc:Wse};function Gse(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,dy:o}=e,{strides:i,pad:a,filterShape:l}=r,c=xu(s.shape,l,i,1,a),d=new Fse(c);return t.runWebGLProgram(d,[s,o],"float32")}const jse={kernelName:NS,backendName:"webgl",kernelFunc:Gse};function Hse(n){const{inputs:e,backend:t,attrs:r}=n,{dy:s,filter:o}=e,{pad:i,strides:a,inputShape:l}=r,c=xu(l,o.shape,a,1,i),d=new Pse(c);return t.runWebGLProgram(d,[s,o],"float32")}const qse={kernelName:TS,backendName:"webgl",kernelFunc:Hse};const Kse=Pf+`
return cos(x);
`,Xse=`
vec4 result = cos(x);
bvec4 isNaN = isnan(x);
${mh}
return result;
`,Yse=zt({opSnippet:Kse,packedOpSnippet:Xse}),Qse={kernelName:dg,backendName:"webgl",kernelFunc:Yse};const Jse=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,Zse=zt({opSnippet:Jse}),eoe={kernelName:fg,backendName:"webgl",kernelFunc:Zse};class toe{constructor(e,t,r,s,o){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[i,a,l,c]=e,[d]=t,[p,m]=r;this.outputShape=[d,p,m,c];const y=s==="bilinear"?1:0,[v,b]=[`${a-1}.0`,`${l-1}.0`],[w,k,I]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${v} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${v}`],[N,T,A]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${b} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${b}`];this.userCode=`
const float height_ratio = float(${w});
const float width_ratio = float(${N});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${i}) {
return;
}
float height_scale = ${k};
float width_scale = ${T};
float in_y = ${I};
if( in_y < 0.0 || in_y > ${v} ) {
setOutput(float(${o}));
return;
}
float in_x = ${A};
if( in_x < 0.0 || in_x > ${b} ) {
setOutput(float(${o}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${y} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}}const noe=n=>{const{inputs:e,backend:t,attrs:r}=n,{image:s,boxes:o,boxInd:i}=e,{cropSize:a,method:l,extrapolationValue:c}=r,d=new toe(s.shape,o.shape,a,l,c);return t.runWebGLProgram(d,[s,o,i],"float32")},roe={kernelName:RS,backendName:"webgl",kernelFunc:noe};var Km;(function(n){n.Prod="*",n.Sum="+"})(Km||(Km={}));class nT{constructor(e,t,r,s){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];const o=this.outputShape.length,i=this.op===Km.Prod?"1.0":"0.0",a=r?i:`getX(${rT(o,"coords",this.op)})`,l=this.outputShape[this.outputShape.length-1];let c="",d="";r?(c=s?`end != ${l-1}`:"end != 0",d=s?"end + 1":"end - 1"):(c=s?`end + pow2 < ${l}`:"end >= pow2",d=s?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${on(o)} coords = getOutputCoords();
int end = ${sT(o,"coords",this.op)};
float val = ${a};
int pow2 = int(pow(2.0, index));
if (${c}) {
int idx = ${d};
${sT(o,"coords",this.op)} = idx;
val ${this.op}= getX(${rT(o,"coords",this.op)});
}
setOutput(val);
}
`}}function rT(n,e,t){if(n===1)return`${e}`;if(n===2)return`${e}.x, ${e}.y`;if(n===3)return`${e}.x, ${e}.y, ${e}.z`;if(n===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${n} is not yet supported`)}function sT(n,e,t){if(n===1)return`${e}`;if(n===2)return`${e}.y`;if(n===3)return`${e}.z`;if(n===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${n} is not yet supported`)}function zF(n,e,t,r,s,o){const i=e.shape.length,a=En([r],i);let l=e;a!=null&&(l=cs({inputs:{x:e},backend:t,attrs:{perm:a}}));const c=Wn(1,i)[0];if(c!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${r}`);const d=l.shape[c];let p=Js({inputs:{x:l},backend:t});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){const y=new nT(n,l.shape,!1,o),v=[[m]],b=p;p=t.runWebGLProgram(y,[p],p.dtype,v),t.disposeIntermediateTensorInfo(b)}if(s){const m=new nT(n,l.shape,s,o),y=p;p=t.runWebGLProgram(m,[p],p.dtype),t.disposeIntermediateTensorInfo(y)}if(a!=null){const m=yu(a),y=cs({inputs:{x:p},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(l),y}return p}function soe(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,exclusive:i,reverse:a}=r;return zF(Km.Prod,s,t,o,i,a)}const ooe={kernelName:ES,backendName:"webgl",kernelFunc:soe};function ioe(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,exclusive:i,reverse:a}=r;return zF(Km.Sum,s,t,o,i,a)}const aoe={kernelName:A1,backendName:"webgl",kernelFunc:ioe};function loe(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,weights:o}=e,{size:i,binaryOutput:a}=r;if(s.shape.length===1){const l=t.readSync(s.dataId),c=t.readSync(o.dataId),d=vF(l,c,o.dtype,o.shape,i);return t.makeTensorInfo([i],o.dtype,d)}else if(s.shape.length===2){const l=t.bufferSync(s),c=t.bufferSync(o),d=dte(l,c,i,a);return t.makeTensorInfo(d.shape,o.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}const uoe={kernelName:AS,backendName:"webgl",kernelFunc:loe};class coe{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}function hoe(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{blockSize:o,dataFormat:i}=r,a=s.shape[0],l=i==="NHWC"?s.shape[1]:s.shape[2],c=i==="NHWC"?s.shape[2]:s.shape[3],d=i==="NHWC"?s.shape[3]:s.shape[1],p=l*o,m=c*o,y=d/(o*o),v=i==="NHWC"?[a,p,m,y]:[a,y,p,m],b=new coe(v,o,i);return t.runWebGLProgram(b,[s],s.dtype)}const doe={kernelName:_S,backendName:"webgl",kernelFunc:hoe};class BF{constructor(e,t=!1,r=null,s=!1,o=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Kr(this.outputShape.length);const i=e.filterHeight,a=e.filterWidth,l=e.outChannels/e.inChannels;let c="",d="";r&&(s?c=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${r}
}`:o?c=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${r}
}`:c=`
float activation(float x) {
${r}
}
`,d="result = activation(result);");const p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),o&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${c}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${l};
int q = d2 - d1 * ${l};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${i}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${a}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${p}
${d}
setOutput(result);
}
`}}class UF{constructor(e,t=!1,r=null,s=!1,o=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Kr(this.outputShape.length);const i=e.outChannels/e.inChannels,a=e.padInfo.left,l=e.strideWidth,c=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,m=p;let y=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let k=0;k<p;k++)y+=`
vec4 xTexelC${k*2};
int xTexelC${k*2}Ready;
vec4 xTexelC${k*2+1};
int xTexelC${k*2+1}Ready;
vec4 xC${k};`;y+=`
for (int r = 0; r < ${d}; r++) {
`;for(let k=0;k<p;k++)y+=`
xTexelC${k*2} = vec4(0.0);
xTexelC${k*2}Ready = 0;
xTexelC${k*2+1} = vec4(0.0);
xTexelC${k*2+1}Ready = 0;
xC${k} = vec4(0.0);`;y+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let k=0;k<(m+1)/2;k++){const I=k*2;if(y+=`
xC = xCCorner + ${I*c};
`,l===1){if(I<p&&(a%2===1?(y+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${I}Ready == 0) {
xTexelC${I} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${I}.zw = vec2(0.0);
}
xTexelC${I}Ready = 1;
}
`,c===1&&I>0?y+=`
xC${I} = vec4(xTexelC${I-2}.zw, xTexelC${I}.xy);
`:y+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${I} = vec4(previous.zw, xTexelC${I}.xy);
} else {
xC${I} = vec4(0.0, 0.0, xTexelC${I}.xy);
}
`):y+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${I}Ready == 0) {
xTexelC${I} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${I}.zw = vec2(0.0);
}
xTexelC${I}Ready = 1;
}
xC${I} = xTexelC${I};
`,I+1<p)){const N=a%2===0?dS(c):c;c%2===0&&a%2===1||c%2!==0&&a%2!==1?(y+=`
xCOffset = xC + imod(pads[1], 2) + ${N};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${I+1}Ready == 0) {
xTexelC${I+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${I+1}.zw = vec2(0.0);
}
xTexelC${I+1}Ready = 1;
}
`,c>1?y+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
xC${I+1} = vec4(previous.zw, xTexelC${I+1}.xy);
} else {
xC${I+1} = vec4(0.0, 0.0, xTexelC${I+1}.xy);
}
`:y+=`
xC${I+1} = vec4(xTexelC${I}.zw, xTexelC${I+1}.xy);
`):N===1?y+=`
xC${I+1} = xTexelC${I};
`:y+=`
xCOffset = xC + ${N};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${I+1}Ready == 0) {
xTexelC${I+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${I+1}.zw = vec2(0.0);
}
xTexelC${I+1}Ready = 1;
}
xC${I+1} = xTexelC${I+1};
`}}else I<p&&(a%2===1?(y+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${I}Ready == 0) {
xTexelC${I} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${I}.zw = vec2(0.0);
}
xTexelC${I}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${I+1}Ready == 0) {
xTexelC${I+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${I+1}.zw = vec2(0.0);
}
xTexelC${I+1}Ready = 1;
}
xC${I} = vec4(xTexelC${I}.zw, xTexelC${I+1}.zw);
`,I+1<p&&(y+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${I+1} = vec4(xTexelC${I+1}.xy, final.xy);
`)):(y+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${I}Ready == 0) {
xTexelC${I} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${I}.zw = vec2(0.0);
}
xTexelC${I}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${I+1}Ready == 0) {
xTexelC${I+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${I+1}.zw = vec2(0.);
}
xTexelC${I+1}Ready = 1;
}
xC${I} = vec4(
xTexelC${I}.xy, xTexelC${I+1}.xy);
`,I+1<p&&(y+=`
xC${I+1} = vec4(xTexelC${I}.zw, xTexelC${I+1}.zw);
`)));I<p&&(y+=`
wTexel = getW(r, ${I}, d1, q);
dotProd += xC${I} * vec4(wTexel.xz, wTexel.xz);
`,I+1<p&&(y+=`
wTexel = getW(r, ${I+1}, d1, q);
dotProd += xC${I+1} * vec4(wTexel.xz, wTexel.xz);
`))}y+=`
}
`,y+=`
}
`;let v="",b="";r&&(s?v=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${r}
}`:o?v=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${r}
}`:v=`vec4 activation(vec4 x) {
${r}
}`,b="result = activation(result);");const w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),o&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${y}
vec4 result = dotProd - vec4(0.000000000000001);
${w}
${b}
setOutput(result);
}
`}}function foe(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,filter:o}=e,{strides:i,pad:a,dilations:l,dimRoundingMode:c}=r;let d=l;d==null&&(d=[1,1]),U(Hr(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);const p=_r(s.shape,o.shape,i,d,a,c,!0);let m;ye().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new UF(p):m=new BF(p);const y=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return t.runWebGLProgram(m,[s,o],"float32",y)}const poe={kernelName:_1,backendName:"webgl",kernelFunc:foe};class moe{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,r=e.strideWidth,s=e.padInfo.top,o=e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${i} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${o};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}}class goe{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,r=e.filterWidth,s=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.top,a=r-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${i}, ${a});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${l}; dm++) {
int d2 = d1 * ${l} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}}function xoe(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,dy:o}=e,{strides:i,dilations:a,pad:l,dimRoundingMode:c,filterShape:d}=r,p=_r(s.shape,d,i,a,l,c,!0),m=new moe(p);return t.runWebGLProgram(m,[s,o],"float32")}const yoe={kernelName:DS,backendName:"webgl",kernelFunc:xoe};function voe(n){const{inputs:e,backend:t,attrs:r}=n,{dy:s,filter:o}=e,{strides:i,dilations:a,pad:l,dimRoundingMode:c,inputShape:d}=r,p=_r(d,o.shape,i,a,l,c,!0),m=new goe(p);return t.runWebGLProgram(m,[s,o],"float32")}const woe={kernelName:OS,backendName:"webgl",kernelFunc:voe};class boe{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}}function Coe(n){const{inputs:e,backend:t}=n,{x:r}=e,s=[...r.shape,...r.shape],o=Se(r.shape),i=Ue({inputs:{x:r},backend:t,attrs:{shape:[o]}}),a=new boe(o),l=t.runWebGLProgram(a,[i],i.dtype),c=Ue({inputs:{x:l},backend:t,attrs:{shape:s}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(l),c}const Soe={kernelName:$E,backendName:"webgl",kernelFunc:Coe};class koe{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const{inHeight:t,inWidth:r,padInfo:s,strideHeight:o,strideWidth:i,filterHeight:a,filterWidth:l,dilationHeight:c,dilationWidth:d}=e,{top:p,left:m}=s;this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${m});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${a}; h++) {
int hIn = hBeg + h * ${c};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${l}; w++) {
int wIn = wBeg + w * ${d};
if (wIn >= 0 && wIn < ${r}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}}function Ioe(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,filter:o}=e,{strides:i,pad:a,dilations:l}=r,c=Zg(s.shape,o.shape,i,a,"NHWC",l);let d;const p=new koe(c);d=t.runWebGLProgram(p,[s,o],"float32");const m=Ue({inputs:{x:d},backend:t,attrs:{shape:c.outShape}});return t.disposeIntermediateTensorInfo(d),m}const $oe={kernelName:D1,backendName:"webgl",kernelFunc:Ioe};function Noe(n){const{inputs:e,backend:t,attrs:r}=n,{equation:s}=r,o=e,{allDims:i,summedDims:a,idDims:l}=rI(s,o.length);oI(i.length,l,o);const{path:c,steps:d}=iI(a,l),p=d.length;let m=null,y=i.length;const v=[];for(let b=0;b<p;++b){for(const w of d[b]){const{permutationIndices:k,expandDims:I}=sI(y,l[w]);let N;aI(k)?N=o[w]:(N=cs({inputs:{x:o[w]},backend:t,attrs:{perm:k}}),v.push(N));const T=N.shape.slice();for(let A=0;A<I.length;++A)T.splice(I[A],0,1);Xt(N.shape,T)||(N=Ue({inputs:{x:N},backend:t,attrs:{shape:T}}),v.push(N)),m===null?m=N:(m=qI({inputs:{a:N,b:m},backend:t}),v.push(m))}b<p-1&&(c[b]>=0&&(m=jv({inputs:{x:m},backend:t,attrs:{axis:c[b]-(i.length-y),keepDims:!1}}),v.push(m)),y--)}for(const b of v)b!==m&&t.disposeIntermediateTensorInfo(b);return m}const Toe={kernelName:FS,backendName:"webgl",kernelFunc:Noe};const Eoe="return (x >= 0.0) ? x : (exp(x) - 1.0);",Roe=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,Aoe=zt({opSnippet:Eoe,packedOpSnippet:Roe}),_oe={kernelName:mg,backendName:"webgl",kernelFunc:Aoe};const Doe="return (b >= 0.0) ? a : a * (b + 1.0);",Ooe=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Foe=n=>{const{inputs:e,backend:t}=n,{dy:r,y:s}=e,o=ye().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ff(Ooe,r.shape,s.shape):new sh(Doe,r.shape,s.shape);return t.runWebGLProgram(o,[r,s],r.dtype)},Poe={kernelName:PS,backendName:"webgl",kernelFunc:Foe};const Loe=`
return vec4(equal(a, b));
`,Moe="return float(a == b);",zoe=Or({opSnippet:Moe,packedOpSnippet:Loe,dtype:"bool",cpuKernelImpl:xte}),Boe={kernelName:O1,backendName:"webgl",kernelFunc:zoe};const Uoe=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${Yk};
float a1 = ${Qk};
float a2 = ${Jk};
float a3 = ${Zk};
float a4 = ${eI};
float a5 = ${tI};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Woe=zt({opSnippet:Uoe}),Voe={kernelName:gg,backendName:"webgl",kernelFunc:Woe};const Goe=Pf+`
return exp(x);
`,joe=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,WF=zt({opSnippet:Goe,packedOpSnippet:joe,cpuKernelImpl:yte,dtype:"float32"}),Hoe={kernelName:xg,backendName:"webgl",kernelFunc:WF};function BC(n){const{inputs:e,attrs:t,backend:r}=n,{dim:s}=t,{input:o}=e,i=o.shape.length,a=o.shape.slice();let l=s;return s<0&&(U(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),a.splice(l,0,1),Ue({inputs:{x:o},backend:r,attrs:{shape:a}})}const qoe={kernelName:F1,backendName:"webgl",kernelFunc:BC};const oT="return exp(x) - 1.0;",Koe=zt({opSnippet:oT,packedOpSnippet:oT,cpuKernelImpl:vte}),Xoe={kernelName:yg,backendName:"webgl",kernelFunc:Koe};class iT{constructor(e,t,r){this.variableNames=["real","imag"];const s=t[1];this.outputShape=t;const o=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=r?`${s}.0`:"1.0";let a;if(e==="real")a="return real * expR - imag * expI;";else if(e==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${o};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${a}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${s});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${s}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${i};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}}function VF(n,e,t){const r=t.texData.get(n.dataId),s=Se(n.shape),o=n.shape[n.shape.length-1],i=s/o,a=Ue({inputs:{x:n},backend:t,attrs:{shape:[i,o]}}),l=a.shape,c=new iT("real",l,e),d=new iT("imag",l,e),p=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(c,p,"float32"),y=t.runWebGLProgram(d,p,"float32"),v=ku({inputs:{real:m,imag:y},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(y);const b=Ue({inputs:{x:v},backend:t,attrs:{shape:n.shape}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(v),b}function Yoe(n){const{inputs:e,backend:t}=n,{input:r}=e;return VF(r,!1,t)}const Qoe={kernelName:LS,backendName:"webgl",kernelFunc:Yoe};class Joe{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}}function wx(n){const{backend:e,attrs:t}=n,{shape:r,value:s}=t;let{dtype:o}=t;if(o=o||Cf(s),o==="string"){const i=Mn(o,Se(r));return i.fill(s),e.makeTensorInfo(r,o,i)}else{const i=new Joe(r,s),a=[[s]];return e.runWebGLProgram(i,[],o,a)}}const Zoe={kernelName:MS,backendName:"webgl",kernelFunc:wx};class eie{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}}const tie={kernelName:zS,backendName:"webgl",kernelFunc:({inputs:n,backend:e})=>{const{image:t}=n,r=e,s=new eie(t.shape);return r.runWebGLProgram(s,[t],t.dtype)}};const aT="return floor(x);",nie=zt({opSnippet:aT,packedOpSnippet:aT,cpuKernelImpl:wte}),rie={kernelName:vg,backendName:"webgl",kernelFunc:nie};const sie=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,oie=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,iie=Or({opSnippet:sie,packedOpSnippet:oie,dtype:"int32"}),aie={kernelName:wg,backendName:"webgl",kernelFunc:iie};class lie{constructor(e){this.variableNames=["A"];const t=hs(),[r,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${r}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}}class uie{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=hs(),[r,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}.0, ${r}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}}const cie={kernelName:uC,backendName:"webgl",kernelFunc:hie};let Gd,Bb=ye().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function hie(n){const{inputs:e,backend:t,attrs:r}=n;let{pixels:s}=e;const{numChannels:o}=r,i=typeof HTMLVideoElement<"u"&&s instanceof HTMLVideoElement,a=typeof HTMLImageElement<"u"&&s instanceof HTMLImageElement,[l,c]=i?[s.videoWidth,s.videoHeight]:[s.width,s.height],d=[c,l],p=[c,l,o];if(a||i){const b=ye().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Gd==null||b!==Bb)&&(Bb=b,Gd=document.createElement("canvas").getContext("2d",{willReadFrequently:Bb})),Gd.canvas.width=l,Gd.canvas.height=c,Gd.drawImage(s,0,0,l,c),s=Gd.canvas}const m=t.makeTensorInfo(d,"int32");t.texData.get(m.dataId).usage=xo.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),s);const y=ye().getBool("WEBGL_PACK")?new uie(p):new lie(p),v=t.runWebGLProgram(y,[m],"int32");return t.disposeData(m.dataId),v}function die(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,filter:o,bias:i,preluActivationWeights:a}=e,{strides:l,pad:c,dataFormat:d,dilations:p,dimRoundingMode:m,activation:y,leakyreluAlpha:v}=r,b=ul(d),w=_r(s.shape,o.shape,l,p,c,m,!1,b);let k;const I=[],N=i!=null,T=a!=null,A=y==="leakyrelu",O=()=>{const B=[s,o],z=(L,_)=>{if(_==="NCHW"&&L.shape.length===1&&L.shape[0]!==1){const H=Ue({inputs:{x:L},backend:t,attrs:{shape:[L.shape[0],1,1]}});return I.push(H),H}return L};if(N&&B.push(z(i,d)),T&&B.push(z(a,d)),A){const L=t.makeTensorInfo([],"float32",mu(v,"float32"));B.push(L),I.push(L)}return B};if(w.filterHeight===1&&w.filterWidth===1&&w.dilationHeight===1&&w.dilationWidth===1&&w.strideHeight===1&&w.strideWidth===1&&(w.padInfo.type==="SAME"||w.padInfo.type==="VALID"))k=LF({x:s,filter:o,convInfo:w,backend:t,bias:i,activation:y,preluActivationWeights:a,leakyreluAlpha:v});else if(w.strideWidth<=2&&b==="channelsLast"&&ye().getBool("WEBGL_EXP_CONV")){const B=y?Hm(y,!0):null,z=new PF(w,N,B,T,A),L=[[w.padInfo.top,w.padInfo.left],[w.strideHeight,w.strideWidth],[w.dilationHeight,w.dilationWidth],[w.inHeight,w.inWidth]],_=O();k=t.runWebGLProgram(z,_,"float32",L)}else if(ye().getBool("WEBGL_CONV_IM2COL"))k=MF({x:s,filter:o,convInfo:w,backend:t,bias:i,activation:y,preluActivationWeights:a,leakyreluAlpha:v});else{const B=y?Hm(y,!1):null,z=new FF(w,N,B,T,A),L=O();k=t.runWebGLProgram(z,L,"float32")}const P=Ue({inputs:{x:k},backend:t,attrs:{shape:w.outShape}});return I.push(k),I.forEach(B=>t.disposeIntermediateTensorInfo(B)),P}const fie={kernelName:My,backendName:"webgl",kernelFunc:die};function pie(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,filter:o,bias:i,preluActivationWeights:a}=e,{strides:l,pad:c,dilations:d,dimRoundingMode:p,activation:m,leakyreluAlpha:y}=r,v=[];let b=d;b==null&&(b=[1,1]),U(Hr(l,b),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${b}'`);const w=_r(s.shape,o.shape,l,b,c,p,!0),k=ye().getBool("WEBGL_PACK_DEPTHWISECONV")&&w.strideWidth<=2&&w.outChannels/w.inChannels===1,I=m?Hm(m,k):null,N=[s,o],T=i!=null,A=a!=null,O=m==="leakyrelu";if(T&&N.push(i),A&&N.push(a),O){const L=t.makeTensorInfo([],"float32",mu(y,"float32"));N.push(L),v.push(L)}let P;k?P=new UF(w,T,I,A,O):P=new BF(w,T,I,A,O);const B=[[w.padInfo.top,w.padInfo.left],[w.strideHeight,w.strideWidth],[w.dilationHeight,w.dilationWidth],[w.inHeight,w.inWidth]],z=t.runWebGLProgram(P,N,"float32",B);return v.forEach(L=>t.disposeIntermediateTensorInfo(L)),z}const mie={kernelName:jE,backendName:"webgl",kernelFunc:pie};class gie{constructor(e,t,r,s){this.sliceDim=e,this.strides=t,this.paramsShape=s,this.variableNames=["x","indices"],this.outputShape=r;const o=on(r.length);let i=`
int index;`;for(let a=0;a<this.sliceDim;a++)i+=`
index = round(getIndices(coords[0], ${a}));
out_of_bounds = out_of_bounds || index < 0;
out_of_bounds = out_of_bounds || index >= ${this.paramsShape[a]};
flattenIndex += index * ${this.strides[a]};`;this.userCode=`
void main() {
${o} coords = getOutputCoords();
int flattenIndex = 0;
bool out_of_bounds = false;
${i}
setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
}
`}}function xie(n){const{inputs:e,backend:t}=n,{params:r,indices:s}=e,o=s.shape,i=o[o.length-1],a=Se(r.shape),[l,c,d,p]=zk(r,s),m=Ue({inputs:{x:s},backend:t,attrs:{shape:[c,i]}}),y=Ue({inputs:{x:r},backend:t,attrs:{shape:[Se(r.shape)/d,d]}});if(t.shouldExecuteOnCPU([r,s])||r.dtype==="string"){const k=t.readSync(s.dataId),I=t.bufferSync(r),N=bte(k,I,r.dtype,c,i,d,p,r.shape,a);return t.makeTensorInfo(l,r.dtype,N.values)}const v=new gie(i,p,[c,d],r.shape),b=t.runWebGLProgram(v,[y,m],y.dtype),w=Ue({inputs:{x:b},backend:t,attrs:{shape:l}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(y),t.disposeIntermediateTensorInfo(b),w}const yie={kernelName:NE,backendName:"webgl",kernelFunc:xie};class vie{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;const r=on(this.rank),s=wie(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${s}));
}
`}}function wie(n,e){const t=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<n.length;s++)s===2?r.push("index"):r.push(`${t[s]}`);return r.join()}function GF(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,indices:o}=e,{axis:i,batchDims:a}=r,l=Tt(i,s.shape)[0];if(ye().get("DEBUG")){const I=t.readSync(o.dataId),N=s.shape[l];for(let T=0;T<I.length;++T){const A=I[T];U(A<=N-1&&A>=0,()=>`GatherV2: the index value ${A} is not in [0, ${N-1}]`)}}const c=uI(s,o,l,a),d=Se(o.shape),p=[],m=Ue({inputs:{x:s},backend:t,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),y=Ue({inputs:{x:o},backend:t,attrs:{shape:[c.batchSize,d/c.batchSize]}});p.push(m),p.push(y);const v=[c.batchSize,c.outerSize,d/c.batchSize,c.sliceSize];if(t.shouldExecuteOnCPU([s,o])||s.dtype==="string"){const I=t.bufferSync(y),N=t.bufferSync(m),T=Cte(N,I,v);return p.forEach(A=>t.disposeIntermediateTensorInfo(A)),t.makeTensorInfo(c.outputShape,T.dtype,T.values)}const b=new vie(m.shape,v),w=t.runWebGLProgram(b,[m,y],m.dtype);p.push(w);const k=Ue({inputs:{x:w},backend:t,attrs:{shape:c.outputShape}});return p.forEach(I=>t.disposeIntermediateTensorInfo(I)),k}const bie={kernelName:L1,backendName:"webgl",kernelFunc:GF};const Cie="return float(a > b);",Sie=`
return vec4(greaterThan(a, b));
`,kie=Or({opSnippet:Cie,packedOpSnippet:Sie,cpuKernelImpl:Ste,dtype:"bool"}),Iie={kernelName:M1,backendName:"webgl",kernelFunc:kie};const $ie="return float(a >= b);",Nie=`
return vec4(greaterThanEqual(a, b));
`,Tie=Or({opSnippet:$ie,packedOpSnippet:Nie,dtype:"bool",cpuKernelImpl:kte}),Eie={kernelName:bg,backendName:"webgl",kernelFunc:Tie};function Rie(n){const{inputs:e,backend:t}=n,{input:r}=e;return VF(r,!0,t)}const Aie={kernelName:BS,backendName:"webgl",kernelFunc:Rie};const _ie="return float(!isnan(x) && !isinf(x));",Die=zt({opSnippet:_ie,dtype:"bool"}),Oie={kernelName:Sg,backendName:"webgl",kernelFunc:Die};const Fie="return float(isinf(x));",Pie=zt({opSnippet:Fie,dtype:"bool"}),Lie={kernelName:kg,backendName:"webgl",kernelFunc:Pie};const Mie="return float(isnan(x));",zie=zt({opSnippet:Mie,dtype:"bool"}),Bie={kernelName:Ig,backendName:"webgl",kernelFunc:zie};const Uie="return float(a < b);",Wie=`
return vec4(lessThan(a, b));
`,Vie=Or({opSnippet:Uie,packedOpSnippet:Wie,cpuKernelImpl:Ite,dtype:"bool"}),Gie={kernelName:B1,backendName:"webgl",kernelFunc:Vie};const jie="return float(a <= b);",Hie=`
return vec4(lessThanEqual(a, b));
`,qie=Or({opSnippet:jie,packedOpSnippet:Hie,cpuKernelImpl:$te,dtype:"bool"}),Kie={kernelName:U1,backendName:"webgl",kernelFunc:qie};function Xie(n){const{backend:e,attrs:t}=n,{start:r,stop:s,num:o}=t,i=Nte(r,s,o);return e.makeTensorInfo([i.length],"float32",i)}const Yie={kernelName:TE,backendName:"webgl",kernelFunc:Xie};const Qie=Pf+`
return x < 0.0 ? 0./0. : log(x);
`,Jie=`
vec4 result = log(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
return result;
`,Zie=zt({opSnippet:Qie,packedOpSnippet:Jie,cpuKernelImpl:Tte}),eae={kernelName:$g,backendName:"webgl",kernelFunc:Zie};const tae=Pf+`
return log(1.0 + x);
`,nae=zt({opSnippet:tae}),rae={kernelName:Ng,backendName:"webgl",kernelFunc:nae};const sae="return float(a >= 1.0 && b >= 1.0);",oae=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,iae=Or({opSnippet:sae,packedOpSnippet:oae,dtype:"bool"}),aae={kernelName:W1,backendName:"webgl",kernelFunc:iae};const lae="return float(!(x >= 1.0));",uae=zt({opSnippet:lae}),cae={kernelName:V1,backendName:"webgl",kernelFunc:uae};const hae="return float(a >= 1.0 || b >= 1.0);",dae=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,fae=Or({opSnippet:hae,packedOpSnippet:dae,dtype:"bool"}),pae={kernelName:G1,backendName:"webgl",kernelFunc:fae};class mae{constructor(e,t,r,s,o){this.variableNames=["x"],this.outputShape=[];const i=t,a=e[3]-1;this.outputShape=e;let l;const c=`float(${r}) + float(${s}) * sum`;o===.5?l=`inversesqrt(${c})`:o===1?l=`1.0/(${c})`:l=`exp(log(${c}) * float(-${o}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${i}; j <= ${i}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${a}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${l};
setOutput(val);
}
`}}class gae{constructor(e,t,r,s,o){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const i=t,a=e[3]-1;this.outputShape=e;let l;const c=`float(${r}) + float(${s}) * sum`;o===.5?l=`inversesqrt(${c})`:o===1?l=`1.0/(${c})`:l=`exp(log(${c}) * float(-${o}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${i};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${i}; j <= ${i}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${l};
setOutput(result);
}
`}}const xae=n=>{const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{depthRadius:o,bias:i,alpha:a,beta:l}=r,c=ye().getBool("WEBGL_PACK_NORMALIZATION")?new gae(s.shape,o,i,a,l):new mae(s.shape,o,i,a,l);return t.runWebGLProgram(c,[s],s.dtype)},yae={kernelName:j1,backendName:"webgl",kernelFunc:xae};class vae{constructor(e,t,r,s,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=s,this.beta=o,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${s}) * norm + float(${r});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${s})
* float(${o})
* getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${o});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}}const wae=n=>{const{inputs:e,backend:t,attrs:r}=n,{x:s,y:o,dy:i}=e,{depthRadius:a,bias:l,alpha:c,beta:d}=r,p=new vae(s.shape,a,l,c,d);return t.runWebGLProgram(p,[s,o,i],s.dtype)},bae={kernelName:WS,backendName:"webgl",kernelFunc:wae};function Cae(n,e,t,r){const s=Se(e),i=Se(n.shape)/s,a=Ue({inputs:{x:n},attrs:{shape:[i,s]},backend:r}),l=gh(a,n.dtype,"max",r),c=Ue({inputs:{x:l},attrs:{shape:t},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(l),c}function jF(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{reductionIndices:o,keepDims:i}=r,a=s.shape.length,l=Tt(o,s.shape);let c=l;const d=En(c,a),p=d!=null,m=t.shouldExecuteOnCPU([s]);let y=s;if(p){if(m){const N=t.texData.get(y.dataId).values,T=new Array(a);for(let P=0;P<T.length;P++)T[P]=s.shape[d[P]];const A=jI(N,s.shape,s.dtype,d,T);y=t.makeTensorInfo(T,s.dtype);const O=t.texData.get(y.dataId);O.values=A}else y=Gv(s,d,t);c=Wn(c.length,a)}Dr("max",c,a);const[v,b]=yr(y.shape,c);let w=v;i&&(w=Yn(v,l));let k;if(m){const N=t.texData.get(y.dataId).values,T=Ete(N,Se(b),w,s.dtype);k=t.makeTensorInfo(w,s.dtype);const A=t.texData.get(k.dataId);A.values=T}else k=Cae(y,b,w,t);return p&&t.disposeIntermediateTensorInfo(y),k}const Sae={kernelName:H1,backendName:"webgl",kernelFunc:jF};const kae=HI+`
return max(a, b);
`,Iae=`
vec4 result = vec4(max(a, b));
bvec4 isNaNA = isnan(a);
bvec4 isNaNB = isnan(b);
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
`+mh+`
return result;
`,$ae=Or({opSnippet:kae,packedOpSnippet:Iae,cpuKernelImpl:Rte}),Nae={kernelName:Tg,backendName:"webgl",kernelFunc:$ae};function Tae(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e;xx(s,"maxPool");const{filterSize:o,strides:i,pad:a,dimRoundingMode:l}=r,c=1;U(Hr(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);const d=Xo(s.shape,o,i,c,a,l);if(d.filterWidth===1&&d.filterHeight===1&&Xt(d.inShape,d.outShape))return Js({inputs:{x:s},backend:t});const p=new qm(d,"max",!1);return t.runWebGLProgram(p,[s],s.dtype)}const Eae={kernelName:q1,backendName:"webgl",kernelFunc:Tae};function Rae(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{filterSize:o,strides:i,pad:a,dataFormat:l,dimRoundingMode:c}=r,d=[1,1,1],p=ll(s.shape,o,i,d,a,c,l),m=new KI(p,"max",!1);return t.runWebGLProgram(m,[s],s.dtype)}const Aae={kernelName:K1,backendName:"webgl",kernelFunc:Rae};class _ae{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,o=e.effectiveFilterHeight,i=e.effectiveFilterWidth,a=o-1-e.padInfo.top,l=i-1-e.padInfo.left,c=o*i-1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${i}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${c} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${i} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}}class Dae{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,r=e.strideHeight,s=e.strideWidth,o=e.dilationDepth,i=e.dilationHeight,a=e.dilationWidth,l=e.effectiveFilterDepth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=c-1-e.padInfo.top,y=d-1-e.padInfo.left,v=l*c*d-1;this.userCode=`
const ivec3 pads = ivec3(${p}, ${m}, ${y});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${l};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${c};
wR += ${i}) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${d};
wC += ${a}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${v} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${c} * ${d} +
wR * ${d} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}}function Oae(n){const{inputs:e,backend:t,attrs:r}=n,{dy:s,input:o}=e,i=o,{filterSize:a,strides:l,pad:c,dimRoundingMode:d}=r,p=[1,1,1],m=ll(i.shape,a,l,p,c,d),y=new KI(m,"max",!0),v=t.runWebGLProgram(y,[i],i.dtype),b=new Dae(m),w=t.runWebGLProgram(b,[s,v],i.dtype);return t.disposeIntermediateTensorInfo(v),w}const Fae={kernelName:GS,backendName:"webgl",kernelFunc:Oae};function Pae(n){const{inputs:e,backend:t,attrs:r}=n,{dy:s,input:o,output:i}=e,a=o;xx([o,i],"maxPoolGrad");const{filterSize:l,strides:c,pad:d,dimRoundingMode:p}=r,m=Xo(a.shape,l,c,1,d,p),y=!0,v=new qm(m,"max",y),b=t.runWebGLProgram(v,[a],a.dtype),w=new _ae(m),k=t.runWebGLProgram(w,[s,b],a.dtype);return t.disposeIntermediateTensorInfo(b),k}const Lae={kernelName:VS,backendName:"webgl",kernelFunc:Pae};function Mae(n,e,t,r){let s=new qm(t,"max",!1);const o=r.runWebGLProgram(s,[n],"float32");s=new qm(t,"max",!0,!0,e);const i=r.runWebGLProgram(s,[n],"float32");return[o,i]}const zae={kernelName:EE,backendName:"webgl",kernelFunc:({inputs:n,attrs:e,backend:t})=>{const{x:r}=n,{filterSize:s,strides:o,pad:i,includeBatchInIndex:a}=e,l=t;U(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);const c=[1,1];U(Hr(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);const d=Xo(r.shape,s,o,c,i),[p,m]=Mae(r,a,d,l);return[p,m]}};function Bae(n,e,t,r){const s=Se(e),i=Se(n.shape)/s,a=Ue({inputs:{x:n},attrs:{shape:[i,s]},backend:r}),l=gh(a,"float32","mean",r),c=Ue({inputs:{x:l},attrs:{shape:t},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(l),c}const Uae={kernelName:X1,backendName:"webgl",kernelFunc:({inputs:n,attrs:e,backend:t})=>{const{x:r}=n,{keepDims:s,axis:o}=e,i=t,a=r.shape.length,l=Tt(o,r.shape);let c=l;const d=En(c,a),p=d!=null,m=i.shouldExecuteOnCPU([r]),y=[];let v=r;if(p){if(m){const T=i.texData.get(v.dataId).values,A=new Array(a);for(let B=0;B<A.length;B++)A[B]=r.shape[d[B]];const O=jI(T,r.shape,r.dtype,d,A);v=i.makeTensorInfo(A,r.dtype);const P=i.texData.get(v.dataId);P.values=O}else v=Gv(r,d,i);y.push(v),c=Wn(c.length,a)}Dr("sum",c,a);const[b,w]=yr(v.shape,c);let k=b;s&&(k=Yn(b,l));const I=Bae(v,w,k,i);for(const N of y)i.disposeIntermediateTensorInfo(N);return I}};function Wae(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,keepDims:i}=r,a=s.shape.length,l=Tt(o,s.shape);let c=l;const d=En(c,a);let p=s;d!=null&&(p=cs({inputs:{x:s},backend:t,attrs:{perm:d}}),c=Wn(c.length,s.shape.length)),Dr("min",c,a);const[m,y]=yr(p.shape,c),v=Se(y),b=Ue({inputs:{x:p},backend:t,attrs:{shape:[-1,v]}}),w=gh(b,b.dtype,"min",t);let k;if(i){const I=Yn(m,l);k=Ue({inputs:{x:w},backend:t,attrs:{shape:I}})}else k=Ue({inputs:{x:w},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(b),t.disposeIntermediateTensorInfo(w),d!=null&&t.disposeIntermediateTensorInfo(p),k}const Vae={kernelName:Y1,backendName:"webgl",kernelFunc:Wae};const Gae=HI+`
return min(a, b);
`,jae=`
vec4 result = vec4(min(a, b));
bvec4 isNaNA = isnan(a);
bvec4 isNaNB = isnan(b);
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
`+mh+`
return result;
`,Hae=Or({opSnippet:Gae,packedOpSnippet:jae,cpuKernelImpl:Ate}),qae={kernelName:Eg,backendName:"webgl",kernelFunc:Hae};class Kae{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((d,p)=>d[0]+e[p]+d[1]);const s=e.length,o=on(s),i=t.map(d=>d[0]).join(","),a=t.map((d,p)=>d[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),c=r==="reflect"?0:1;if(s===1){this.userCode=`
int start = ${i};
int end = ${a};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${c};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${c};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${o} start = ${o}(${i});
${o} end = ${o}(${a});
void main() {
${o} outC = getOutputCoords();
for (int i = 0; i < ${s}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${c};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${c};
}
}
${o} coords = outC - start;
setOutput(getX(${l}));
}
`}}class Xae{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((v,b)=>v[0]+e[b]+v[1]);const s=e.length,o=on(s),i=t.map(v=>v[0]).join(","),a=t.map((v,b)=>v[0]+e[b]).join(","),l=ls("rc",s),c=ls("source",s),d=`${l[s-1]} < ${this.outputShape[s-1]}`,p=s===1?"source":`vec2(${c.slice(-2).join()})`,m=r==="reflect"?0:1;let y="";if(s===1){const v=`
${o} source = rc;
if (source < start) {
source = start * 2 - source - ${m};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${m};
}
source -= start;
`;y=`
${o} rc = outputLoc;
${v}
result[0] = getChannel(getX(${c.join()}), ${p});
${l[s-1]} += 1;
if(${d}) {
${v}
result[1] = getChannel(getX(${c.join()}), ${p});
}
`}else{const v=`
${o} source = rc;
${o} lt = ${o}(lessThan(source, start));
${o} gte = ${o}(greaterThanEqual(source, end));
${o} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${m}) +
gte * ((end - 1) * 2 - source + ${m});
source -= start;
`;y=`
${o} rc = outputLoc;
${v}
result[0] = getChannel(getX(${c.join()}), ${p});
${l[s-1]} += 1;
if(${d}) {
${v}
result[1] = getChannel(getX(${c.join()}), ${p});
}
rc = outputLoc;
${l[s-2]} += 1;
if(${l[s-2]} < ${this.outputShape[s-2]}) {
${v}
result[2] = getChannel(getX(${c.join()}), ${p});
${l[s-1]} += 1;
if(${d}) {
${v}
result[3] = getChannel(getX(${c.join()}), ${p});
}
}
`}this.userCode=`
const ${o} start = ${o}(${i});
const ${o} end = ${o}(${a});
void main() {
${o} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${y}
setOutput(result);
}
`}}const Yae=({inputs:n,backend:e,attrs:t})=>{const{x:r}=n,{paddings:s,mode:o}=t,i=ye().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xae(r.shape,s,o):new Kae(r.shape,s,o);return e.runWebGLProgram(i,[r],r.dtype)},Qae={kernelName:Q1,backendName:"webgl",kernelFunc:Yae};const Jae=`if (b == 0.0) return NAN;
return mod(a, b);`,Zae=`
vec4 result = mod(a, b);
bvec4 isNaN = equal(b, vec4(0.0));
`+mh+`
return result;
`,ele=Or({opSnippet:Jae,packedOpSnippet:Zae}),tle={kernelName:Rg,backendName:"webgl",kernelFunc:ele};class nle{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}}const rle=`
if (a == b) {
return 1.0;
};
return a / b;`,sle=`
// vec4 one = vec4(equal(a, b));
// return one + (vec4(1.0) - one) * a / b;
vec4 result = a / b;
if(a.x == b.x) {
result.x = 1.;
}
if(a.y == b.y) {
result.y = 1.;
}
if(a.z == b.z) {
result.z = 1.;
}
if(a.w == b.w) {
result.w = 1.;
}
return result;
`,HF=Or({opSnippet:rle,packedOpSnippet:sle,checkOutOfBounds:!0}),ole={kernelName:pg,backendName:"webgl",kernelFunc:HF};const lT="return a - b;",qF=Or({opSnippet:lT,packedOpSnippet:lT,supportsComplex:!0,cpuKernelImpl:Jte}),ile={kernelName:Hg,backendName:"webgl",kernelFunc:qF};function KF(n){const{inputs:e,backend:t,attrs:r}=n,{logits:s}=e,{dim:o}=r,i=Tt([o],s.shape),a=jF({inputs:{x:s},backend:t,attrs:{reductionIndices:i,keepDims:!1}}),l=Yn(a.shape,i),c=Ue({inputs:{x:a},backend:t,attrs:{shape:l}}),d=qF({inputs:{a:s,b:c},backend:t}),p=WF({inputs:{x:d},backend:t}),m=jv({inputs:{x:p},backend:t,attrs:{axis:i,keepDims:!1}}),y=Ue({inputs:{x:m},backend:t,attrs:{shape:l}}),v=HF({inputs:{a:p,b:y},backend:t});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(y),v}const ale={kernelName:mv,backendName:"webgl",kernelFunc:KF};function lle(n){const{inputs:e,backend:t,attrs:r}=n,{logits:s}=e,{numSamples:o,seed:i,normalized:a}=r,l=a?s:KF({inputs:{logits:s},backend:t,attrs:{dim:s.shape.length-1}}),c=l.shape[0],d=l.shape[1],p=new nle(c,d,o),m=[[i]],y=t.runWebGLProgram(p,[l],"int32",m);return a||t.disposeIntermediateTensorInfo(l),y}const ule={kernelName:RE,backendName:"webgl",kernelFunc:lle};const cle=Qo+`
return -x;
`,hle=`
vec4 result = -x;
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;function dle(n){const{inputs:e,backend:t}=n,{x:r}=e;if(t.shouldExecuteOnCPU([r])){const o=t.texData.get(r.dataId),[i,a]=Dte(o.values,r.shape,r.dtype);return t.makeTensorInfo(a,r.dtype,i)}let s;return ye().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new tu(r.shape,hle):s=new la(r.shape,cle),t.runWebGLProgram(s,[r],r.dtype)}const fle={kernelName:J1,backendName:"webgl",kernelFunc:dle};const ple=Dk;function mle(n){go("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:e,backend:t,attrs:r}=n,{boxes:s,scores:o}=e,{maxOutputSize:i,iouThreshold:a,scoreThreshold:l}=r,c=t.readSync(s.dataId),d=t.readSync(o.dataId),{selectedIndices:p}=ple(c,d,i,a,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}const gle={kernelName:jS,backendName:"webgl",kernelFunc:mle};const xle=Ok;function yle(n){go("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:e,backend:t,attrs:r}=n,{boxes:s,scores:o}=e,{maxOutputSize:i,iouThreshold:a,scoreThreshold:l,padToMaxOutputSize:c}=r,d=t.readSync(s.dataId),p=t.readSync(o.dataId),{selectedIndices:m,validOutputs:y}=xle(d,p,i,a,l,c);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([y]))]}const vle={kernelName:HS,backendName:"webgl",kernelFunc:yle};const wle=Fk;function ble(n){go("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:e,backend:t,attrs:r}=n,{boxes:s,scores:o}=e,{maxOutputSize:i,iouThreshold:a,scoreThreshold:l,softNmsSigma:c}=r,d=t.readSync(s.dataId),p=t.readSync(o.dataId),m=i,y=a,v=l,b=c,{selectedIndices:w,selectedScores:k}=wle(d,p,m,y,v,b);return[t.makeTensorInfo([w.length],"int32",new Int32Array(w)),t.makeTensorInfo([k.length],"float32",new Float32Array(k))]}const Cle={kernelName:qS,backendName:"webgl",kernelFunc:ble};class Sle{constructor(e,t,r,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${s}), float(${r}),
float(index == coords.y)));
}
`}}const kle=n=>{const{inputs:e,backend:t,attrs:r}=n,{indices:s}=e,{dtype:o,depth:i,onValue:a,offValue:l}=r,c=Se(s.shape),d=new Sle(c,i,a,l),p=Ue({inputs:{x:s},backend:t,attrs:{shape:[c]}}),m=t.runWebGLProgram(d,[p],o);t.disposeIntermediateTensorInfo(p);const y=[...s.shape,i],v=Ue({inputs:{x:m},backend:t,attrs:{shape:y}});return t.disposeIntermediateTensorInfo(m),v},Ile={kernelName:tv,backendName:"webgl",kernelFunc:kle};function h1(n){const{inputs:e,backend:t}=n,{x:r}=e;if(r.dtype==="complex64"){const s=vx({inputs:{input:r},backend:t}),o=h1({inputs:{x:s},backend:t}),i=Hv({inputs:{input:r},backend:t}),a=h1({inputs:{x:i},backend:t}),l=ku({inputs:{real:o,imag:a},backend:t});return t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(a),l}else return wx({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:t})}const $le={kernelName:yv,backendName:"webgl",kernelFunc:h1};function XF(n){const{inputs:e,backend:t}=n,{x:r}=e;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){const s=vx({inputs:{input:r},backend:t}),o=XF({inputs:{x:s},backend:t}),i=Hv({inputs:{input:r},backend:t}),a=h1({inputs:{x:i},backend:t}),l=ku({inputs:{real:o,imag:a},backend:t});return t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(a),l}else return wx({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:t})}const Nle={kernelName:ev,backendName:"webgl",kernelFunc:XF};function Tle(n){const{inputs:e,backend:t,attrs:r}=n,{axis:s}=r;if(e.length===1)return BC({inputs:{input:e[0]},backend:t,attrs:{dim:s}});const o=e[0].shape,i=e[0].dtype;e.forEach(d=>{fS(o,d.shape,"All tensors passed to stack must have matching shapes"),U(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});const a=[],l=e.map(d=>{const p=BC({inputs:{input:d},backend:t,attrs:{dim:s}});return a.push(p),p}),c=OF({inputs:l,backend:t,attrs:{axis:s}});return a.forEach(d=>t.disposeIntermediateTensorInfo(d)),c}const Ele={kernelName:nv,backendName:"webgl",kernelFunc:Tle};class Rle{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((c,d)=>c[0]+e[d]+c[1]);const s=e.length,o=on(s),i=t.map(c=>c[0]).join(","),a=t.map((c,d)=>c[0]+e[d]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
int start = ${i};
int end = ${a};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${o} start = ${o}(${i});
${o} end = ${o}(${a});
void main() {
${o} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${o} coords = outC - start;
setOutput(getX(${l}));
}
}
`}}class Ale{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((b,w)=>b[0]+e[w]+b[1]);const s=e.length,o=on(s),i=t.map(b=>b[0]).join(","),a=t.map((b,w)=>b[0]+e[w]).join(","),l=ls("rc",s),c=ls("source",s),d=`${l[s-1]} < ${this.outputShape[s-1]}`,p=s===1?"source":`vec2(${c.slice(-2).join()})`,m=[`${o} rc = outputLoc;`,`${l[s-1]} += 1;
if(${d}) {
`,s===1?"":`}
rc = outputLoc;
${l[s-2]} += 1;
if(${l[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${l[s-1]} += 1;
if(${d}) {`],y=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let v="";for(let b=0,w=s===1?2:4;b<w;b++)v+=`
${m[b]}
if (${y}) {
result[${b}] = float(value);
} else {
${o} source = rc - start;
result[${b}] = getChannel(getX(${c.join()}), ${p});
}
`;v+=s===1?"} ":"}}",this.userCode=`
const ${o} start = ${o}(${i});
const ${o} end = ${o}(${a});
void main() {
${o} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${v}
setOutput(result);
}
`}}const YF=n=>{const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{paddings:o,constantValue:i}=r;if(Se(s.shape)===0){const c=o.map((d,p)=>d[0]+s.shape[p]+d[1]);return wx({backend:t,attrs:{shape:c,value:i,dtype:s.dtype}})}const a=ye().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ale(s.shape,o,i):new Rle(s.shape,o,i),l=[[i]];return t.runWebGLProgram(a,[s],s.dtype,l)},_le={kernelName:rv,backendName:"webgl",kernelFunc:YF};const Dle=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,Ole=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
bvec4 isNaN1 = lessThan(a, vec4(0.0));
bvec4 isNaN2 = lessThan(floor(b), b);
bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
`+mh+`
return result;
`,Fle=Or({opSnippet:Dle,packedOpSnippet:Ole}),Ple={kernelName:_g,backendName:"webgl",kernelFunc:Fle};function Lle(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{axis:o,keepDims:i}=r,a=s.shape.length,l=[],c=Tt(o,s.shape);let d=c;const p=En(d,a);let m=s;p!=null&&(m=cs({inputs:{x:s},backend:t,attrs:{perm:p}}),d=Wn(d.length,a),l.push(m)),Dr("prod",d,a);let y;if(t.shouldExecuteOnCPU([m])){const v=t.texData.get(m.dataId).values,{outVals:b,outShape:w,outDtype:k}=Fte(m.shape,m.dtype,v,d);y=t.makeTensorInfo(w,k,b)}else{const[v,b]=yr(m.shape,d),w=Se(b),k=Ue({inputs:{x:m},backend:t,attrs:{shape:[-1,w]}}),I=ok(s.dtype),N=gh(k,I,"prod",t);y=Ue({inputs:{x:N},backend:t,attrs:{shape:v}}),l.push(k),l.push(N)}if(i){l.push(y);const v=Yn(y.shape,c);y=Ue({inputs:{x:y},backend:t,attrs:{shape:v}})}return l.forEach(v=>t.disposeIntermediateTensorInfo(v)),y}const Mle={kernelName:ov,backendName:"webgl",kernelFunc:Lle};function zle(n){const{inputs:e,backend:t,attrs:r}=n,{paramsNestedSplits:s,paramsDenseValues:o,indices:i}=e,{outputRaggedRank:a}=r,l=s.map(k=>t.readSync(k.dataId)),c=s.map(k=>k.shape),d=t.readSync(o.dataId),p=t.readSync(i.dataId),[m,y,v]=Pte(l,c,d,o.shape,o.dtype,p,i.shape,a),b=m.map(k=>t.makeTensorInfo([k.length],"int32",k)),w=t.makeTensorInfo(v,o.dtype,y);return b.concat([w])}const Ble={kernelName:AE,backendName:"webgl",kernelFunc:zle};function Ule(n){const{inputs:e,backend:t}=n,{starts:r,limits:s,deltas:o}=e,i=t.readSync(r.dataId),a=t.readSync(s.dataId),l=t.readSync(o.dataId),[c,d]=Lte(i,r.shape,r.dtype,a,s.shape,l,o.shape),p=t.makeTensorInfo([c.length],"int32",c),m=t.makeTensorInfo([d.length],r.dtype,d);return[p,m]}const Wle={kernelName:_E,backendName:"webgl",kernelFunc:Ule};function Vle(n){const{inputs:e,backend:t,attrs:r}=n,{shape:s,values:o,defaultValue:i,rowPartitionTensors:a}=e,{rowPartitionTypes:l}=r,c=t.readSync(s.dataId),d=t.readSync(o.dataId),p=t.readSync(i.dataId),m=a.map(w=>t.readSync(w.dataId)),y=a.map(w=>w.shape),[v,b]=Mte(c,s.shape,d,o.shape,o.dtype,p,i.shape,m,y,l);return t.makeTensorInfo(v,o.dtype,b)}const Gle={kernelName:DE,backendName:"webgl",kernelFunc:Vle};const QF=n=>{const{backend:e,attrs:t}=n,{start:r,stop:s,step:o,dtype:i}=t,a=zte(r,s,o,i);return e.makeTensorInfo([a.length],i,a)},jle={kernelName:KS,backendName:"webgl",kernelFunc:QF};const Hle="return 1.0 / x;",qle=zt({opSnippet:Hle}),Kle={kernelName:Dg,backendName:"webgl",kernelFunc:qle};const Xle=Qo+`
return (x < 0.0) ? 0.0 : x;
`,Yle=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,Qle=zt({opSnippet:Xle,packedOpSnippet:Yle}),Jle={kernelName:Og,backendName:"webgl",kernelFunc:Qle};const Zle=Qo+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,eue=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,tue=zt({opSnippet:Zle,packedOpSnippet:eue}),nue={kernelName:Fg,backendName:"webgl",kernelFunc:tue};class rue{constructor(e,t,r,s,o){this.variableNames=["A"],this.outputShape=[];const[i,a,l,c]=e;this.outputShape=[i,t,r,c];const d=[s&&t>1?a-1:a,s&&r>1?l-1:l],p=[s&&t>1?t-1:t,s&&r>1?r-1:r];let m;o?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${d[0]/p[0]},
${d[1]/p[1]});
const vec2 inputShapeRC = vec2(${a}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}}class sue{constructor(e,t,r,s,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,a,l,c]=e;this.outputShape=[i,t,r,c];const d=[s&&t>1?a-1:a,s&&r>1?l-1:l],p=[s&&t>1?t-1:t,s&&r>1?r-1:r];let m;o?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${d[0]/p[0]},
${d[1]/p[1]},
${d[1]/p[1]});
const vec3 inputShapeRC = vec3(${a}.0, ${l}.0,
${l}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${c-1};
bool hasNextRow = coords.z < ${r-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}}function oue(n){const{inputs:e,backend:t,attrs:r}=n,{images:s}=e,{alignCorners:o,halfPixelCenters:i,size:a}=r,[l,c]=a,d=ye().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new sue(s.shape,l,c,o,i):new rue(s.shape,l,c,o,i);return t.runWebGLProgram(d,[s],"float32")}const iue={kernelName:lv,backendName:"webgl",kernelFunc:oue};class aue{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,s,o]=t,[,i,a]=e,l=[r&&i>1?s-1:s,r&&a>1?o-1:o],c=[r&&i>1?i-1:i,r&&a>1?a-1:a],d=l[0]/c[0],p=l[1]/c[1],m=1/d,y=1/p,v=Math.ceil(m)*2+2,b=Math.ceil(y)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${d});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${y});
const int winHeight = int(${v});
const int winWidth = int(${b});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${i}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${a}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${o-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}}function lue(n){const{inputs:e,backend:t,attrs:r}=n,{images:s,dy:o}=e,{alignCorners:i}=r,a=new aue(o.shape,s.shape,i);return t.runWebGLProgram(a,[o],o.dtype)}const uue={kernelName:QS,backendName:"webgl",kernelFunc:lue};class cue{constructor(e,t,r,s,o){this.variableNames=["A"],this.outputShape=[];const[i,a,l,c]=e;this.outputShape=[i,t,r,c];const d=[s&&t>1?a-1:a,s&&r>1?l-1:l],p=[s&&t>1?t-1:t,s&&r>1?r-1:r],m=s?"0.5":"0.0";let y;o?y="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":y="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${d[0]/p[0]},
${d[1]/p[1]});
const vec2 inputShapeRC = vec2(${a}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${y};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}}class hue{constructor(e,t,r,s,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,a,l,c]=e;this.outputShape=[i,t,r,c];const d=[s&&t>1?a-1:a,s&&r>1?l-1:l],p=[s&&t>1?t-1:t,s&&r>1?r-1:r],m=s?"0.5":"0.0";let y;o?y="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":y="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${d[0]/p[0]},
${d[1]/p[1]},
${d[1]/p[1]});
const vec3 inputShapeRC = vec3(${a}.0, ${l}.0,
${l}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${y};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${c-1};
bool hasNextRow = coords.z < ${r-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}}function due(n){const{inputs:e,backend:t,attrs:r}=n,{images:s}=e,{alignCorners:o,halfPixelCenters:i,size:a}=r,[l,c]=a,d=ye().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new hue(s.shape,l,c,o,i):new cue(s.shape,l,c,o,i);return t.runWebGLProgram(d,[s],s.dtype)}const fue={kernelName:av,backendName:"webgl",kernelFunc:due};class pue{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,s,o]=t,[,i,a]=e,l=[r&&i>1?s-1:s,r&&a>1?o-1:o],c=[r&&i>1?i-1:i,r&&a>1?a-1:a],d=l[0]/c[0],p=l[1]/c[1],m=1/d,y=1/p,v=Math.ceil(m)*2+2,b=Math.ceil(y)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${d});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${y});
const int winHeight = int(${v});
const int winWidth = int(${b});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${i}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${a}) {
continue;
}
float sourceFracRow =
float(${l[0]}) *
(float(dyR) / float(${c[0]}));
float sourceFracCol =
float(${l[1]}) *
(float(dyC) / float(${c[1]}));
int sourceNearestRow = int(min(
float(int(${s}) - 1),
${r} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${o}) - 1),
${r} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}}function mue(n){const{inputs:e,backend:t,attrs:r}=n,{images:s,dy:o}=e,{alignCorners:i}=r,a=new pue(o.shape,s.shape,i);return t.runWebGLProgram(a,[o],o.dtype)}const gue={kernelName:YS,backendName:"webgl",kernelFunc:mue};class xue{constructor(e,t){this.variableNames=["x"];const r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}const s=a=>t.indexOf(a)!==-1&&e[a]!==1?`${e[a]} - coords[${a}] - 1`:`coords[${a}]`,o=e.map((a,l)=>s(l)).join(","),i=on(r);this.userCode=`
void main() {
${i} coords = getOutputCoords();
setOutput(getX(${o}));
}
`}}class yue{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=e;const s=ls("rc",r),o=`${s[r-1]} + 1 < ${this.outputShape[r-1]}`,i=`${s[r-2]} + 1 < ${this.outputShape[r-2]}`,a=on(r);r===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${o}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${l(s.slice())};
if(${o}){
result.g = ${c(s.slice())};
}
if(${i}) {
result.b = ${d(s.slice())};
if(${o}) {
result.a = ${p(s.slice())};
}
}
setOutput(result);
}
`;function l(v){return m(v)}function c(v){return v[r-1]="("+v[r-1]+" + 1)",m(v)}function d(v){return v[r-2]="("+v[r-2]+" + 1)",m(v)}function p(v){return v[r-1]="("+v[r-1]+" + 1)",v[r-2]="("+v[r-2]+" + 1)",m(v)}function m(v){const b=e.map((I,N)=>y(N,v)),w=b.join(","),k=b.slice(-2).join(",");return`getChannel(getX(${w}), vec2(${k}))`}function y(v,b){return t.indexOf(v)!==-1&&e[v]!==1?`${e[v]} - ${b[v]} - 1`:`${b[v]}`}}}function vue(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{dims:o}=r,i=s.shape.length,a=Tt(o,s.shape);if(i===0)return Js({inputs:{x:s},backend:t});const l=ye().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yue(s.shape,a):new xue(s.shape,a);return t.runWebGLProgram(l,[s],s.dtype)}const wue={kernelName:uv,backendName:"webgl",kernelFunc:vue};class bue{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];const r=e[1],s=e[2];this.outputShape=e;let o="";typeof t=="number"?o=`float outputValue = ${t.toFixed(2)};`:o=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${o}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${r}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}}const Cue={kernelName:sk,backendName:"webgl",kernelFunc:({inputs:n,attrs:e,backend:t})=>{const{image:r}=n,{radians:s,fillValue:o,center:i}=e,a=t,l=new bue(r.shape,o),[c,d]=qk(i,r.shape[1],r.shape[2]),p=[[c,d,Math.sin(s),Math.cos(s)]];return a.runWebGLProgram(l,[r],r.dtype,p)}};const Sue=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,kue=zt({opSnippet:Sue}),Iue={kernelName:Pg,backendName:"webgl",kernelFunc:kue};const $ue="return inversesqrt(x);",Nue=zt({opSnippet:$ue,cpuKernelImpl:Bte}),Tue={kernelName:Lg,backendName:"webgl",kernelFunc:Nue};class XI{constructor(e,t,r,s,o,i,a=!0,l=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;const c=on(o.length),d=on(i.length);let p="";r===1?p="i":r===2&&(p="i, j");const m=`getIndices(${p})`;let y="";s===1?y="i":s===2&&(y="i, coords[1]");const v=`getUpdates(${y})`;let b="";l&&(b="coords[0], coords[1]");const w=`getDefaultValue(${b})`,k=t>1?"strides[j]":"strides";this.userCode=`
${c} strides = ${c}(${o});
void main() {
${d} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${m});
flattenedIndex += index * ${k};
}
if (flattenedIndex == coords[0]) {
sum += ${v};
found = true;
}
}
setOutput(mix(${w}, sum, float(found)));
}
`}}class Eue{constructor(e,t,r,s,o,i,a=!0,l=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;const c=on(o.length),d=on(i.length);let p="";r===1?p="i":r===2&&(p="i, j");const m=`getIndices(${p})`;let y="";s===1?y="i":s===2&&(y="i, coords[1]");const v=`getUpdates(${y})`;let b="";l&&(b="coords[0], coords[1]");const w=`getDefaultValue(${b})`,k=t>1?"strides[j]":"strides",I=t>1?"strides[j + 1]":"strides";this.userCode=`
${c} strides = ${c}(${o});
void main() {
${d} coords = getOutputCoords();
vec4 sum = vec4(0.);
vec4 found = vec4(0.);
for (int i = 0; i < ${e}; i+=2) {
ivec2 flattenedIndex = ivec2(0);
for (int j = 0; j < ${t}; j+=2) {
ivec4 index = round(${m});
flattenedIndex += index.xz * ${k};
if (j + 1 < ${t}) {
flattenedIndex += index.yw * ${I};
}
}
if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||
flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {
vec4 updVals = ${v};
if (flattenedIndex[0] == coords[0]) {
sum.xy += updVals.xy;
found.xy = vec2(1.);
} else if (flattenedIndex[0] == coords[0] + 1) {
sum.zw += updVals.xy;
found.zw = vec2(1.);
}
if (flattenedIndex[1] == coords[0]) {
sum.xy += updVals.zw;
found.xy = vec2(1.);
} else if (flattenedIndex[1] == coords[0] + 1) {
sum.zw += updVals.zw;
found.zw = vec2(1.);
}
}
}
setOutput(mix(${w}, sum, found));
}
`}}function Rue(n){const{inputs:e,backend:t,attrs:r}=n,{indices:s,updates:o}=e,{shape:i}=r,{sliceRank:a,numUpdates:l,sliceSize:c,strides:d,outputSize:p}=ch(o,s,i),m=[p/c,c];if(p===0)return t.makeTensorInfo(i,s.dtype);const y=Ue({inputs:{x:s},backend:t,attrs:{shape:[l,a]}}),v=Ue({inputs:{x:o},backend:t,attrs:{shape:[l,c]}}),b=t.makeTensorInfo([],"float32",new Float32Array([0]));let w;ye().getBool("WEBGL_PACK")?w=new Eue(l,a,y.shape.length,v.shape.length,d,m):w=new XI(l,a,y.shape.length,v.shape.length,d,m);const k=t.runWebGLProgram(w,[v,y,b],v.dtype),I=Ue({inputs:{x:k},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(y),t.disposeIntermediateTensorInfo(v),t.disposeIntermediateTensorInfo(k),t.disposeIntermediateTensorInfo(b),I}const Aue={kernelName:OE,backendName:"webgl",kernelFunc:Rue};class _ue{constructor(e,t,r,s){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,r];const o="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,a=ye().getNumber("WEBGL_VERSION")===2?o:i,l=s==="left"?"<":"<=";this.userCode=`
int findBound(int batch, float value) {
int left = 0;
int right = numInputs;
int mid;
${a}
mid = (left + right) / 2;
if (getSortedSequence(batch, mid) ${l} value) {
left = mid + 1;
} else {
right = mid;
}
}
return right;
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int valueIndex = coords[1];
float value = getValues(batch, valueIndex);
setOutput(float(findBound(batch, value)));
}
`}}function Due(n){const{inputs:e,backend:t,attrs:r}=n,{sortedSequence:s,values:o}=e,{side:i}=r,a=new _ue(s.shape[0],s.shape[1],o.shape[1],i),l=[[s.shape[1]]];return t.runWebGLProgram(a,[s,o],"int32",l)}const Oue={kernelName:PE,backendName:"webgl",kernelFunc:Due};class Fue{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let s,o;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)o="resRC",s="resRC";else{const a=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],c=[];for(let d=0;d<t.length;d++)c.push(`${a[d]}`),d<e&&l.push(`${a[d]}`);s=l.join(),o=c.join()}const i=on(r);this.userCode=`
void main() {
${i} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${o}));
} else {
setOutput(getB(${o}));
}
}
`}}function Pue(n){const{inputs:e,backend:t}=n,{condition:r,t:s,e:o}=e,i=new Fue(r.shape.length,s.shape,s.shape.length);return t.runWebGLProgram(i,[r,s,o],Ys(s.dtype,o.dtype))}const Lue={kernelName:cv,backendName:"webgl",kernelFunc:Pue};const Mue=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${Tv};
float scale = ${Ev};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,zue=zt({opSnippet:Mue}),Bue={kernelName:Mg,backendName:"webgl",kernelFunc:zue};const Uue=Pf+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,Wue=`
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,Vue=zt({opSnippet:Uue,packedOpSnippet:Wue,cpuKernelImpl:Wte}),Gue={kernelName:Wg,backendName:"webgl",kernelFunc:Vue};const jue=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Hue=zt({opSnippet:jue}),que={kernelName:Ug,backendName:"webgl",kernelFunc:Hue};const Kue=Pf+`
return sin(x);
`,Xue=`
vec4 result = sin(x);
bvec4 isNaN = isnan(x);
${mh}
return result;
`,Yue=zt({opSnippet:Kue,packedOpSnippet:Xue}),Que={kernelName:zg,backendName:"webgl",kernelFunc:Yue};const Jue=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Zue=zt({opSnippet:Jue}),ece={kernelName:Bg,backendName:"webgl",kernelFunc:Zue};const tce=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,nce=zt({opSnippet:tce}),rce={kernelName:Vg,backendName:"webgl",kernelFunc:nce};const sce=n=>{const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{blockShape:o,paddings:i}=r;U(s.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const a=o.reduce((k,I)=>k*I),l=[[0,0]];l.push(...i);for(let k=1+o.length;k<s.shape.length;++k)l.push([0,0]);const c=[],d=YF({inputs:{x:s},backend:t,attrs:{paddings:l,constantValue:0}}),p=sx(d.shape,o,a,!1),m=ox(p.length,o.length,!1),y=ix(d.shape,o,a,!1),v=Ue({inputs:{x:d},backend:t,attrs:{shape:p}}),b=cs({inputs:{x:v},backend:t,attrs:{perm:m}}),w=Ue({inputs:{x:b},backend:t,attrs:{shape:y}});return c.push(d),c.push(v),c.push(b),c.forEach(k=>t.disposeIntermediateTensorInfo(k)),w},oce={kernelName:fv,backendName:"webgl",kernelFunc:sce};function ice(n){const{inputs:e,backend:t}=n,{indices:r,values:s,denseShape:o,defaultValue:i}=e;if(o.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${o.shape}`);if(r.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${r.shape}`);if(s.shape.length!==1)throw new Error(`Values must be a vector, saw:
${s.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);const a=t.readSync(r.dataId),l=t.readSync(s.dataId),c=t.readSync(o.dataId),d=t.readSync(i.dataId)[0],[p,m,y,v,b]=Gte(a,r.shape,r.dtype,l,s.dtype,c,d);return[t.makeTensorInfo(m,r.dtype,p),t.makeTensorInfo([m[0]],s.dtype,y),t.makeTensorInfo([v.length],"bool",new Uint8Array(v.map(w=>Number(w)))),t.makeTensorInfo([b.length],r.dtype,new Int32Array(b))]}const ace={kernelName:LE,backendName:"webgl",kernelFunc:ice};function lce(n){const{inputs:e,backend:t}=n,{inputIndices:r,inputShape:s,newShape:o}=e;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${s.shape}`);if(o.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${o.shape}`);const i=Array.from(t.readSync(s.dataId)),a=t.readSync(r.dataId),l=Array.from(t.readSync(o.dataId)),[c,d,p]=jte(a,r.shape,r.dtype,i,l);return[t.makeTensorInfo(d,r.dtype,c),t.makeTensorInfo([p.length],o.dtype,new Int32Array(p))]}const uce={kernelName:ME,backendName:"webgl",kernelFunc:lce};function cce(n){const{inputs:e,backend:t}=n,{data:r,indices:s,segmentIds:o}=e;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${s.shape}`);if(o.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${o.shape}`);const i=t.readSync(r.dataId),a=t.readSync(s.dataId),l=t.readSync(o.dataId),[c,d]=bF(i,r.shape,r.dtype,a,l,!0);return t.makeTensorInfo(d,r.dtype,c)}const hce={kernelName:zE,backendName:"webgl",kernelFunc:cce};function dce(n){const{inputs:e,backend:t}=n,{data:r,indices:s,segmentIds:o}=e;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${s.shape}`);if(o.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${o.shape}`);const i=t.readSync(r.dataId),a=t.readSync(s.dataId),l=t.readSync(o.dataId),[c,d]=bF(i,r.shape,r.dtype,a,l);return t.makeTensorInfo(d,r.dtype,c)}const fce={kernelName:BE,backendName:"webgl",kernelFunc:dce};function pce(n){const{inputs:e,backend:t,attrs:r}=n,{sparseIndices:s,sparseValues:o,defaultValue:i}=e,{outputShape:a}=r,{sliceRank:l,numUpdates:c,sliceSize:d,strides:p,outputSize:m}=ch(o,s,a),y=!1;if(o.dtype==="string"){const k=t.bufferSync(s),I=t.bufferSync(o),N=au(t.readSync(i.dataId)[0]),T=Ute(k,I,a,m,d,c,l,p,N,y);return t.makeTensorInfo(a,T.dtype,T.values)}const v=new XI(c,l,s.shape.length,o.shape.length,p,[m,1],y),b=t.runWebGLProgram(v,[o,s,i],o.dtype),w=Ue({inputs:{x:b},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(b),w}const mce={kernelName:UE,backendName:"webgl",kernelFunc:pce};function gce(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{numOrSizeSplits:o,axis:i}=r,a=Tt(i,s.shape)[0],l=lI(s,o,a),c=s.shape.length,d=new Array(c).fill(0),p=s.shape.slice();return l.map(m=>{const y=[...p];y[a]=m;const v=Lf({inputs:{x:s},backend:t,attrs:{begin:d,size:y}});return d[a]+=m,v})}const xce={kernelName:pv,backendName:"webgl",kernelFunc:gce};const uT="return sqrt(x);",yce=zt({opSnippet:uT,packedOpSnippet:uT,cpuKernelImpl:Hte}),vce={kernelName:Gg,backendName:"webgl",kernelFunc:yce};const wce="return x * x;",bce=zt({opSnippet:wce}),Cce={kernelName:JS,backendName:"webgl",kernelFunc:bce};const cT="return (a - b) * (a - b);",Sce=Or({opSnippet:cT,packedOpSnippet:cT}),kce={kernelName:jg,backendName:"webgl",kernelFunc:Sce};function Ice(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e;if(s.dtype!=="string")throw new Error("Input must be of datatype string");const o=t.readSync(s.dataId),i=il(o),a=qte(i,"string",r);return t.makeTensorInfo(s.shape,"string",a)}const $ce={kernelName:ZS,backendName:"webgl",kernelFunc:Ice};function Nce({inputs:n,attrs:e,backend:t}){const{x:r}=n,s=Qo+`
return x > 0.0 ? 1.0 : float(${e.alpha});
`,o=new la(r.shape,s);return t.runWebGLProgram(o,[r],r.dtype)}const Tce={kernelName:Yg,backendName:"webgl",kernelFunc:Nce};class Ece{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;const s=r.length,o=on(r.length),i=on(r.length);let a="";if(s===1)a="coords * strides + begin";else{let l=0;a=r.map((c,d)=>(l++,r.length===1?`coords * strides[${d}] + begin[${d}]`:`coords[${l-1}] * strides[${d}] + begin[${d}]`)).join(",")}this.userCode=`
${o} begin = ${o}(${e});
${o} strides = ${o}(${t});
void main() {
${i} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}}function Rce(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{begin:o,end:i,strides:a,beginMask:l,endMask:c,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:m}=r,{finalShapeSparse:y,finalShape:v,isIdentity:b,sliceDim0:w,isSimpleSlice:k,begin:I,end:N,strides:T}=Gk(s.shape,o,i,a,l,c,d,p,m);let A;if(b)A=Ue({inputs:{x:s},backend:t,attrs:{shape:v}});else if(w||k){U(s.shape.length>=1,()=>`Input must have rank at least 1, got: ${s.shape.length}`);const P=Uk(I,N,T),B=Lf({inputs:{x:s},backend:t,attrs:{begin:I,size:P}});A=Ue({inputs:{x:B},backend:t,attrs:{shape:v}}),t.disposeIntermediateTensorInfo(B)}else if(t.shouldExecuteOnCPU([s])){const B=t.readSync(s.dataId),z=Nt(s.shape,s.dtype,B),L=Kte(y,z,T,I);A=t.makeTensorInfo(v,s.dtype,L.values)}else{const B=new Ece(I,T,y);A=t.runWebGLProgram(B,[s],s.dtype)}const O=Ue({inputs:{x:A},backend:t,attrs:{shape:v}});return t.disposeIntermediateTensorInfo(A),O}const Ace={kernelName:ek,backendName:"webgl",kernelFunc:Rce};function _ce(n){const{inputs:e,backend:t,attrs:r}=n,{separator:s,nGramWidths:o,leftPad:i,rightPad:a,padWidth:l,preserveShortSequences:c}=r,{data:d,dataSplits:p}=e,m=t.readSync(d.dataId),y=t.readSync(p.dataId),[v,b]=Xte(m,y,s,o,i,a,l,c);return[t.makeTensorInfo([v.length],"string",v),t.makeTensorInfo(p.shape,"int32",b)]}const Dce={kernelName:WE,backendName:"webgl",kernelFunc:_ce};function Oce(n){const{inputs:e,backend:t,attrs:r}=n,{skipEmpty:s}=r,{input:o,delimiter:i}=e;if(o.dtype!=="string")throw new Error("Input must be of datatype string");if(o.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${o.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);const a=t.readSync(o.dataId),l=t.readSync(i.dataId)[0],[c,d,p]=Yte(a,l,s),m=d.length;return[t.makeTensorInfo([m,2],"int32",c),t.makeTensorInfo([m],"string",d),t.makeTensorInfo([2],"int32",new Int32Array(p))]}const Fce={kernelName:VE,backendName:"webgl",kernelFunc:Oce};function Pce(n){const{inputs:e,backend:t,attrs:r}=n,{numBuckets:s}=r,{input:o}=e;if(o.dtype!=="string")throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");const i=t.readSync(o.dataId),a=Qte(i,s);return t.makeTensorInfo(o.shape,"int32",a)}const Lce={kernelName:GE,backendName:"webgl",kernelFunc:Pce};const Mce="return tan(x);",zce=zt({opSnippet:Mce}),Bce={kernelName:qg,backendName:"webgl",kernelFunc:zce};const Uce=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Wce=zt({opSnippet:Uce}),Vce={kernelName:Kg,backendName:"webgl",kernelFunc:Wce};function Gce(n){const{inputs:e,backend:t,attrs:r}=n,{tensor:s,indices:o,updates:i}=e,{sliceRank:a,numUpdates:l,sliceSize:c,strides:d,outputSize:p}=ch(i,o,s.shape),m=[p/c,c];if(p===0)return t.makeTensorInfo(s.shape,o.dtype);const y=Ue({inputs:{x:o},backend:t,attrs:{shape:[l,a]}}),v=Ue({inputs:{x:i},backend:t,attrs:{shape:[l,c]}}),b=Ue({inputs:{x:s},backend:t,attrs:{shape:m}}),w=new XI(l,a,y.shape.length,v.shape.length,d,m,!1,!0),k=t.runWebGLProgram(w,[v,y,b],b.dtype),I=Ue({inputs:{x:k},backend:t,attrs:{shape:s.shape}});return t.disposeIntermediateTensorInfo(y),t.disposeIntermediateTensorInfo(v),t.disposeIntermediateTensorInfo(b),t.disposeIntermediateTensorInfo(k),I}const jce={kernelName:FE,backendName:"webgl",kernelFunc:Gce};class Hce{constructor(e,t){this.variableNames=["A"];const r=new Array(e.length);for(let i=0;i<r.length;i++)r[i]=e[i]*t[i];this.outputShape=r,this.rank=r.length;const s=on(this.rank),o=qce(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${o}));
}
`}}function qce(n){const e=n.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${n[0]})`;const t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<n.length;s++)r.push(`imod(${t[s]}, ${n[s]})`);return r.join()}function JF(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{reps:o}=r;if(s.dtype==="string"||s.shape.length>5){const l=t.readSync(s.dataId),c=s.dtype==="string"?l.map(m=>au(m)):l,d=Nt(s.shape,s.dtype,c),p=Zte(d,o);return t.makeTensorInfo(p.shape,p.dtype,p.values)}const i=new Hce(s.shape,o);return t.runWebGLProgram(i,[s],s.dtype)}const Kce={kernelName:Xg,backendName:"webgl",kernelFunc:JF};class Xce{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}}class Yce{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}}function Nc(n,e){e!==null&&n.disposeIntermediateTensorInfo(e)}function hT(n){let e=1;for(;e<n;)e*=2;return e}function Qce(n){const{inputs:e,backend:t,attrs:r}=n,{x:s}=e,{k:o,sorted:i}=r,a=ye().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=ye().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=s.shape,d=c[c.length-1];if(t.shouldExecuteOnCPU([s])||d<a||o>l){const L=t.readSync(s.dataId),[_,H]=ene(L,c,s.dtype,o,i);return[t.makeTensorInfo(_.shape,_.dtype,_.values),t.makeTensorInfo(H.shape,H.dtype,H.values)]}if(o===0)return c[c.length-1]=0,[t.makeTensorInfo(c,s.dtype,[]),t.makeTensorInfo(c,"int32",[])];if(d===1)return[s,wx({attrs:{shape:c,dtype:"int32",value:0},backend:t})];const p=t.texData.get(s.dataId),m=p!==null&&p.isPacked,y=m?t.unpackTensor(s):s,b=Se(c)/d,w=Ue({inputs:{x:y},attrs:{shape:[b,d]},backend:t});m&&Nc(t,y);const k=hT(o),I=hT(d);let N=null;const T=()=>N===null?[w,w]:[w,N],A=(L,_,H)=>{const Y=T(),re=new Xce(H),le=[[d],[N===null?1:0],[Number.NEGATIVE_INFINITY],[L],[_]],de=N;N=t.runWebGLProgram(re,Y,"int32",le),Nc(t,de)};for(let L=1;L<k;L*=2){const _=L*2;for(let H=L;H>=1;H/=2)A(_,H,[b,I])}for(let L=I;L>k;L/=2){const _=T(),H=new Yce([b,L/2]),re=[[d],[N===null?1:0],[k]],te=N;N=t.runWebGLProgram(H,_,"int32",re),Nc(t,te);const le=k/2,de=le*2;for(let j=le;j>=1;j/=2)A(de,j,N.shape)}let O=N;N=Lf({inputs:{x:N},backend:t,attrs:{begin:0,size:[b,o]}}),Nc(t,O);let P=GF({inputs:{x:w,indices:N},backend:t,attrs:{axis:1,batchDims:1}});Nc(t,w);const B=c.slice(0,-1);B.push(o),O=N,N=Ue({inputs:{x:N},attrs:{shape:B},backend:t}),Nc(t,O);const z=P;return P=Ue({inputs:{x:P},attrs:{shape:B},backend:t}),Nc(t,z),[P,N]}const Jce={kernelName:tk,backendName:"webgl",kernelFunc:Qce};class Zce{constructor(e,t,r,s,o,i){this.variableNames=["Image","Transforms"],this.outputShape=i;const a=r==="nearest"?1:2;let l;switch(s){case"constant":l=1;break;case"reflect":l=2;break;case"wrap":l=3;break;case"nearest":l=4;break;default:l=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${l} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${l} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${l} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${o});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${o});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${a} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}}function ehe(n){const{inputs:e,backend:t,attrs:r}=n,{image:s,transforms:o}=e,{interpolation:i,fillMode:a,fillValue:l,outputShape:c}=r,[d,p,m,y]=s.shape,[v,b]=c??[p,m],w=[d,v,b,y],k=new Zce(p,m,i,a,l,w);return t.runWebGLProgram(k,[s,o],"float32")}const the={kernelName:nk,backendName:"webgl",kernelFunc:ehe};function nhe(n){const{inputs:e,attrs:t,backend:r}=n,{axis:s}=t,{x:o}=e;xx(o,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const i=r.readSync(o.dataId),{outputValues:a,outputShape:l,indices:c}=tne(i,s,o.shape,o.dtype);return[r.makeTensorInfo(l,o.dtype,a),r.makeTensorInfo([c.length],"int32",c)]}const rhe={kernelName:rk,backendName:"webgl",kernelFunc:nhe};function she(n){const{inputs:e,backend:t,attrs:r}=n,{value:s}=e;let{axis:o}=r;o<0&&(o+=s.shape.length);const i=s,a=i.shape.length,l=s.shape[o],c=new Array(a-1);let d=0;for(let b=0;b<a;b++)b!==o&&(c[d++]=i.shape[b]);const p=[],m=new Array(a).fill(0),y=i.shape.slice();y[o]=1;const v=new Array(l);for(let b=0;b<v.length;b++){m[o]=b;const w=Lf({inputs:{x:i},backend:t,attrs:{begin:m,size:y}}),k=Ue({inputs:{x:w},backend:t,attrs:{shape:c}});v[b]=k,p.push(w)}return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),v}const ohe={kernelName:gv,backendName:"webgl",kernelFunc:she};class ihe{constructor(e,t){this.variableNames=["x","segmentIds"];const r=e.windowSize,s=e.batchSize,o=e.inSize,i=e.numSegments,a=i*Math.ceil(o/r);this.outputShape=[s,a];const l="0.0",c="sumValue",d=Math.floor(r/4)*4,p=r%4,m=`
sumValue += dot(values, segFilter);
`;let y="";o%r>0&&(y=`
if (inIdx < 0 || inIdx >= ${o}) {
return initializationValue;
}
`);let v="";o%r>0&&(v=`
if (inIdx < 0 || inIdx >= ${o}) {
return -1.0;
}
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const float initializationValue = ${l};
float getValue(int batch, int inIdx) {
${y}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${v}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${i})) * float(${r}));
int currentSeg = int(mod(float(outIdx), float(${i})));
float sumValue = 0.0;
for (int i = 0; i < ${d}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${m}
}
int inIdx = inOffset + ${d};
if (${p===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${m}
} else if (${p===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${m}
} else if (${p===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${m}
}
setOutput(${c});
}
`}}function ahe(n){const{inputs:e,backend:t,attrs:r}=n,{x:s,segmentIds:o}=e,{numSegments:i}=r,a=s.shape.length,l=[];let c=0;const d=En([c],a);let p=s;d!=null&&(p=cs({inputs:{x:s},backend:t,attrs:{perm:d}}),l.push(p),c=Wn(1,a)[0]);const m=DA(p.shape,c,i),y=Se([p.shape[c]]),v=Ue({inputs:{x:p},backend:t,attrs:{shape:[-1,y]}});l.push(v);const b=ok(s.dtype),w=(T,A,O,P,B)=>{const z=T.shape[0],L=T.shape[1],_=_A(L,B),H={windowSize:_,inSize:L,batchSize:z,numSegments:B},Y=new ihe(H,A),re=t.compileAndRun(Y,[T,O],P);if(l.push(re),re.shape[1]===B)return re;const te=QF({backend:t,attrs:{start:0,stop:B,step:1,dtype:"float32"}}),le=JF({inputs:{x:te},backend:t,attrs:{reps:[L/_]}});return l.push(te),l.push(le),w(re,A,le,P,B)},k=w(v,"unsortedSegmentSum",o,b,i),I=Ue({inputs:{x:k},backend:t,attrs:{shape:m}});let N=I;if(d!=null){l.push(I);const T=yu(d);N=cs({inputs:{x:N},backend:t,attrs:{perm:T}})}return l.forEach(T=>t.disposeIntermediateTensorInfo(T)),N}const lhe={kernelName:xv,backendName:"webgl",kernelFunc:ahe};const uhe=[Hne,Kne,Qne,ere,nre,ore,are,ure,fre,mre,yre,bre,kre,Tre,Are,Dre,Fre,zre,Ure,Vre,qre,ese,nse,ise,lse,pse,gse,wse,Ene,Sse,Tse,_se,Mse,Use,Vse,jse,qse,Qse,eoe,roe,ooe,aoe,uoe,doe,poe,yoe,woe,Soe,$oe,Toe,_oe,Poe,Boe,Voe,Hoe,qoe,Xoe,Qoe,Zoe,tie,rie,aie,cie,fie,mie,yie,bie,Iie,Eie,Tne,Aie,$se,Oie,Lie,Bie,Ane,Gie,Kie,Yie,eae,rae,aae,cae,pae,yae,bae,Sae,Nae,Eae,Aae,Fae,Lae,zae,Uae,Vae,qae,Qae,tle,ule,One,fle,gle,vle,Cle,cse,Ile,Nle,Ele,_le,Ple,Dne,Mle,Ble,Wle,Gle,jle,hse,ole,Kle,Jle,nue,Pne,iue,uue,fue,gue,wue,Cue,Iue,Tue,Aue,Oue,Lue,Bue,Gue,que,Que,ece,Jre,ale,rce,oce,ace,uce,hce,fce,mce,xce,vce,Cce,kce,$ce,Tce,Ace,Dce,Fce,Lce,ile,Vne,Bce,Vce,jce,Kce,Jce,the,Gne,rhe,ohe,lhe,$le];for(const n of uhe)HE(n);var Im={exports:{}};var che=Im.exports,dT;function hhe(){return dT||(dT=1,(function(n,e){(function(){var t,r="4.17.21",s=200,o="Unsupported core-js use. 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You can also find more social media below, just click the image and it'll bring you to the selected website."]}),ee.jsxs(Za,{title:"CTECH - 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